# 6. User Guide¶

## 6.1. Importing DAE Tools modules¶

pyDAE modules can be imported in the following way:

from daetools.pyDAE import *


This will set the python sys.path for importing the platform dependent c++ extension modules (i.e. .../daetools/pyDAE/Windows_win32_py34 and .../daetools/solvers/Windows_win32_py34 in Windows, .../daetools/pyDAE/Linux_x86_64_py34 and .../daetools/solvers/Linux_x86_64_py34 in GNU/Linux), import all symbols from all pyDAE modules: pyCore, pyActivity, pyDataReporting, pyIDAS, pyUnits and import some platform independent modules: logs, variable_types, hr_upwind_scheme, simulator, simulation_explorer, simulation_inspector, thermo_packages.

Alternatively, only the top-level daetools module can be imported and classes from the pyDAE extension modules accessed using fully qualified names. For instance:

import daetools

model = daetools.pyDAE.pyCore.daeModel("name")


Once the pyDAE module is imported, the other modules (such as third party linear solvers, optimisation solvers etc.) can be imported in the following way:

# Import SuperLU linear solver:
from daetools.solvers.superlu import pySuperLU


Since domains, parameters and variables in DAE Tools have a numerical value in terms of a unit of measurement (quantity) the modules containing definitions of units and variable types must be imported. They can be imported in the following way:

from daetools.pyDAE.variable_types import length_t, area_t, volume_t
from daetools.pyDAE.pyUnits import m, kg, s, K, Pa, J, W


The complete list of units and variable types can be found in variable_types and units modules.

## 6.2. Developing models¶

In DAE Tools models are developed by deriving a new class from the base daeModel class. An empty model definition is presented below:

class myModel(daeModel):
def __init__(self, name, parent = None, description = ""):
daeModel.__init__(self, name, parent, description)

# Declaration/instantiation of domains, parameters, variables, ports, etc:
...

def DeclareEquations(self):
# Declaration of equations, state transition networks etc.:
...


The process consists of the following steps:

1. Calling the base class constructor:

daeModel.__init__(self, name, parent, description)

2. Declaring the model structure (domains, parameters, variables, ports, components etc.) in the __init__() function:

One of the fundamental ideas in DAE Tools is separation of the model specification from the activities that can be performed on that model: this way, different simulation scenarios can be developed based on a single model definition. Thus, all objects are defined in two stages:

Therefore, parameters, domains and variables are only declared here, while their initialisation (setting the parameter value, setting up the domain, assigning or setting an initial condition etc.) is postponed and will be done in the simulation class.

All objects must be declared as data members of the model since the base daeModel class keeps only week references and does not own them:

def __init__(self, name, parent = None, description = ""):
self.domain    = daeDomain(...)
self.parameter = daeParameter(...)
self.variable  = daeVariable(...)
... etc.


and not:

def __init__(self, name, parent = None, description = ""):
domain    = daeDomain(...)
parameter = daeParameter(...)
variable  = daeVariable(...)
... etc.


because at the exit from the __init__() function the objects will go out of scope and get destroyed. However, the underlying c++ model object still holds references to them which will eventually result in the segmentation fault.

3. Specification of the model functionality (equations, state transition networks, and OnEvent and OnCondition actions) in the DeclareEquations() function.

Nota bene: This function is never called directly by the user and will be called automatically by the framework.

Initialisation of the simulation object is done in several phases. At the point when this function is called by the framework the model parameters, domains, variables etc. are fully initialised. Therefore, it is safe to obtain the values of parameters or domain points and use them to create equations at the run-time.

Nota bene: However, the variable values are obviously not available at this moment (they get initialised at the later stage) and using the variable values during the model specification phase is not allowed.

A simplest DAE Tools model with a description of all steps/tasks necessary to develop a model can be found in the What’s the time? (AKA: Hello world!) tutorial (whats_the_time.py).

### 6.2.1. Parameters¶

Parameters are time invariant quantities that do not change during a simulation. Usually a good choice what should be a parameter is a physical constant, number of discretisation points in a domain etc.

There are two types of parameters in DAE Tools:

• Ordinary
• Distributed

The process of defining parameters is again carried out in two phases:

#### 6.2.1.1. Declaring parameters¶

Parameters are declared in the __init__() function. An ordinary parameter can be declared in the following way:

self.myParam = daeParameter("myParam", units, parentModel, "description")


Parameters can be distributed on domains. A distributed parameter can be declared in the following way:

self.myParam = daeParameter("myParam", units, parentModel, "description")
self.myParam.DistributeOnDomain(myDomain)

# Or simply:
self.myParam = daeParameter("myParam", units, parentModel, "description", [myDomain])


#### 6.2.1.2. Initialising parameters¶

Parameters are initialised in the SetUpParametersAndDomains() function. To set a value of an ordinary parameter the following can be used:

myParam.SetValue(value)


where value can be a floating point value or the quantity object, while to set a value of distributed parameters (one-dimensional for example):

for i in range(myDomain.NumberOfPoints):
myParam.SetValue(i, value)


where the value can be either a float (i.e. 1.34) or the quantity object (i.e. 1.34 * W/(m*K)). If the simple floats are used it is assumed that they represent values with the same units as in the parameter definition.

Nota bene

DAE Tools (as it is the case in C/C++ and Python) use zero-based arrays in which the initial element of a sequence is assigned the index 0, rather than 1.

In addition, all values can be set at once using:

myParam.SetValues(values)


where values is a numpy array of floats/quantity objects.

#### 6.2.1.3. Using parameters¶

The most commonly used functions are:

Notate bene

The functions __call__() and array() return adouble and adouble_array objects, respectively and does not contain values. They are only used to specify equations’ residual expressions which are stored in their Node / Node properties.

Other functions (such as npyValues and GetValue()) can be used to access the values data during the simulation.

All above stands for similar functions in daeDomain and daeVariable classes.

1. To get a value of the ordinary parameter the __call__() function (operator ()) can be used. For instance, if the variable myVar has to be equal to the sum of the parameter myParam and 15:

$myVar = myParam + 15$

in DAE Tools it is specified in the following acausal way:

# Notation:
#  - eq is a daeEquation object created using the model.CreateEquation(...) function
#  - myParam is an ordinary daeParameter object (not distributed)
#  - myVar is an ordinary daeVariable (not distributed)

eq.Residual = myVar() - (myParam() + 15)

2. To get a value of a distributed parameter the __call__() function (operator ()) can be used again. For instance, if the distributed variable myVar has to be equal to the sum of the parameter myParam and 15 at each point of the domain myDomain:

$myVar(i) = myParam(i) + 15; \forall i \in [0, n_d - 1]$

in DAE Tools it is specified in the following acausal way:

# Notation:
#  - myDomain is daeDomain object
#  - eq is a daeEquation object distributed on the myDomain
#  - i is daeDistributedEquationDomainInfo object (used to iterate through the domain points)
#  - myParam is daeParameter object distributed on the myDomain
#  - myVar is daeVariable object distributed on the myDomain
i = eq.DistributeOnDomain(myDomain, eClosedClosed)
eq.Residual = myVar(i) - (myParam(i) + 15)


This code translates into a set of n algebraic equations.

Obviously, a parameter can be distributed on more than one domain. In the case of two domains:

$myVar(d_1,d_2) = myParam(d_1,d_2) + 15; \forall d_1 \in [0, n_{d1} - 1], \forall d_2 \in [0, n_{d2} - 1]$

the following can be used:

# Notation:
#  - myDomain1, myDomain2 are daeDomain objects
#  - eq is a daeEquation object distributed on the domains myDomain1 and myDomain2
#  - i1, i2 are daeDistributedEquationDomainInfo objects (used to iterate through the domain points)
#  - myParam is daeParameter object distributed on the myDomain1 and myDomain2
#  - myVar is daeVariable object distributed on the myDomaina and myDomain2
i1 = eq.DistributeOnDomain(myDomain1, eClosedClosed)
i2 = eq.DistributeOnDomain(myDomain2, eClosedClosed)
eq.Residual = myVar(i1,i2) - (myParam(i1,i2) + 15)

3. To get an array of parameter values the function array() can be used, which returns the adouble_array object. The ordinary mathematical functions can be used with the adouble_array objects: Sqrt(), Sin(), Cos(), Min(), Max(), Log(), Log10(), etc. In addition, some additional functions are available such as Sum() and Product().

For instance, if the variable myVar has to be equal to the sum of values of the parameter myParam for all points in the domain myDomain, the function Sum() can be used.

The array() function accepts the arguments of the following type:

• plain integer (to select a single index from a domain); a special case: index -1 returns the last point in the domain)
• python list (to select a list of indexes from a domain)
• python slice (to select a portion of indexes from a domain: startIndex, endIindex, step)
• character * (to select all points from a domain)
• empty python list [] (to select all points from a domain)

Basically all arguments listed above are internally used to create the daeIndexRange object. daeIndexRange constructor has three variants:

1. The first one accepts a single argument: daeDomain object. In this case the returned adouble_array object will contain the parameter values at all points in the specified domain.
2. The second one accepts two arguments: daeDomain object and a list of integer that represent indexes within the specified domain. In this case the returned adouble_array object will contain the parameter values at the selected points in the specified domain.
3. The third one accepts four arguments: daeDomain object, and three integers: startIndex, endIndex and step (which is basically a slice, that is a portion of a list of indexes: start through end-1, by the increment step). More info about slices can be found in the Python documentation. In this case the returned adouble_array object will contain the parameter values at the points in the specified domain defined by the slice object.

Suppose that the variable myVar has to be equal to the sum of values in the array values that holds values from the parameter myParam at the specified indexes in the domains myDomain1 and myDomain2:

$myVar = \sum values$

There are several different scenarios for creating the array values from the parameter myParam distributed on two domains:

# Notation:
#  - myDomain1, myDomain2 are daeDomain objects
#  - n1, n2 are the number of points in the myDomain1 and myDomain2 domains
#  - eq1, eq2 are daeEquation objects
#  - mySum is daeVariable object
#  - myParam is daeParameter object distributed on myDomain1 and myDomain2 domains
#  - values is the adouble_array object

# Case 1. An array contains the following values from myParam:
#  - the first point in the domain myDomain1
#  - all points from the domain myDomain2
# All expressions below are equivalent:
values = myParam.array(0, '*')
values = myParam.array(0, [])

eq1.Residual = mySum() - Sum(values)

# Case 2. An array contains the following values from myParam:
#  - the first three points in the domain myDomain1
#  - all even points from the domain myDomain2
values = myParam.array([0,1,2], slice(0, myDomain2.NumberOfPoints, 2))

eq2.Residual = mySum() - Sum(values)


The case 1. translates into:

$mySum = myParam(0,0) + myParam(0,1) + ... + myParam(0,n_2 - 1)$

where n2 is the number of points in the domain myDomain2.

The case 2. translates into:

$\begin{split}mySum = & myParam(0,0) + myParam(0,2) + myParam(0,4) + ... + myParam(0, n_2 - 1) + \\ & myParam(1,0) + myParam(1,2) + myParam(1,4) + ... + myParam(1, n_2 - 1) + \\ & myParam(2,0) + myParam(2,2) + myParam(2,4) + ... + myParam(2, n_2 - 1)\end{split}$

More information about parameters can be found in the API reference daeParameter and in Tutorials.

### 6.2.2. Variable types¶

Variable types are used in DAE Tools to describe variables and they contain the following information:

• Name: string
• Units: unit object
• LowerBound: float
• UpperBound: float
• InitialGuess: float
• AbsoluteTolerance: float

Declaration of variable types is commonly done outside of the model definition (in the module scope).

#### 6.2.2.1. Declaring variable types¶

A variable type can be declared in the following way:

# Temperature type with units Kelvin, limits 100-1000K, the default value 273K and the absolute tolerance 1E-5
typeTemperature = daeVariableType("Temperature", K, 100, 1000, 273, 1E-5)


### 6.2.3. Distribution domains¶

There are two types of domains in DAE Tools:

• Simple arrays
• Distributed domains (used to distribute variables, parameters, and equations in space)

Distributed domains can form uniform grids (the default) or non-uniform grids (user-specified). In DAE Tools many objects can be distributed on domains: parameters, variables, equations, even models and ports. Distributing a model on a domain (that is in space) can be useful for modelling of complex multi-scale systems where each point in the domain have a corresponding model instance. In addition, domain points values can be obtained as a numpy one-dimensional array; this way DAE Tools can be easily used in conjunction with other scientific python libraries: NumPy, SciPy and many other.

Again, the domains are defined in two phases:

• Declaring a domain in the model
• Initialising it in the simulation

#### 6.2.3.1. Declaring domains¶

Domains are declared in the __init__() function:

self.myDomain = daeDomain("myDomain", parentModel, units, "description")


#### 6.2.3.2. Initialising domains¶

Domains are initialised in the SetUpParametersAndDomains() function. To set up a domain as a simple array the function CreateArray() can be used:

# Array of N elements
myDomain.CreateArray(N)


while to set up a domain distributed on a structured grid the function CreateStructuredGrid():

# Uniform structured grid with N elements and bounds [lowerBound, upperBound]
myDomain.CreateStructuredGrid(N, lowerBound, upperBound)


where the lower and upper bounds can be simple floats or quantity objects. If the simple floats are used it is assumed that they represent values with the same units as in the domain definition. Typically, it is better to use quantities to avoid mistakes with wrong units:

# Uniform structured grid with 10 elements and bounds [0,1] in centimeters:
myDomain.CreateStructuredGrid(10, 0.0 * cm, 1.0 * cm)


Nota bene

Domains with N elements consists of N+1 points.

It is also possible to create an unstructured grid (for use in Finite Element models). However, creation and setup of such domains is an implementation detail of corresponding modules (i.e. pyDealII).

In certain situations it is not desired to have a uniform distribution of the points within the given interval, defined by the lower and upper bounds. In these cases, a non-uniform structured grid can be specified using the attribute Points which contains the list of the points and that can be manipulated by the user:

# First create a structured grid domain
myDomain.CreateStructuredGrid(10, 0.0, 1.0)

# The original 11 points are: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
# If the system is stiff at the beginning of the domain more points can be placed there
myDomain.Points = [0.0, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.60, 1.00]


The effect of uniform and non-uniform grids is given in Fig. 6.1 (a simple heat conduction problem from the Tutorial 3 has been served as a basis for comparison). Here, there are three cases:

• Black line: the analytic solution
• Blue line (10 intervals): uniform grid - a very rough prediction
• Red line (10 intervals): non-uniform grid - more points at the beginning of the domain

Fig. 6.1 Effect of uniform and non-uniform grids on numerical solution (zoomed to the first 5 points)

It can be clearly observed that in this problem the more precise results are obtained by using denser grid at the beginning of the interval.

#### 6.2.3.3. Using domains¶

The most commonly used functions are:

Nota bene

The functions __call__(), __getitem__() and array() can only be used to build equations’ residual expressions. On the other hand, the attribute Points can be used at any point.

The arguments of the array() function are the same as explained in Using parameters.

1. To get a point at the specified index within the domain the __getitem__() function (operator []) can be used. For instance, if the variable myVar has to be equal to the sixth point in the domain myDomain:

$myVar = myDomain[5]$

the following can be used:

# Notation:
#  - eq is a daeEquation object
#  - myDomain is daeDomain object
#  - myVar is daeVariable object
eq.Residual = myVar() - myDomain[5]


More information about domains can be found in the API reference daeDomain and in Tutorials.

### 6.2.4. Variables¶

Variables define time varying quantities that change during a simulation. Variables in DAE Tools can be:

• Ordinary
• Distributed

and:

• Algebraic
• Differential
• Constant (that is their value is assigned by fixing the number of degrees of freedom - DOF)

Again, variables are defined in two phases:

• Declaring a variable in the model
• Initialising it, if required (by assigning its value or setting an initial condition) in the simulation

#### 6.2.4.1. Declaring variables¶

Variables are declared in the __init__() function. An ordinary variable can be declared in the following way:

self.myVar = daeVariable("myVar", variableType, parentModel, "description")


Variables can also be distributed on domains. A distributed variable can be declared in the following way:

self.myVar = daeVariable("myVar", variableType, parentModel, "description")
self.myVar.DistributeOnDomain(myDomain)

# Or simply:
self.myVar = daeVariable("myVar", variableType, parentModel, "description", [myDomain])


#### 6.2.4.2. Initialising variables¶

Variables are initialised in the SetUpVariables() function:

• To assign the variable value/fix the degrees of freedom the following can be used:

myVar.AssignValue(value)


or, if the variable is distributed:

for i in range(myDomain.NumberOfPoints):
myVar.AssignValue(i, value)

# or using a numpy array of values
myVar.AssignValues(values)


where value can be either a float (i.e. 1.34) or the quantity object (i.e. 1.34 * W/(m*K)), and values is a numpy array of floats or quantity objects. If the simple floats are used it is assumed that they represent values with the same units as in the variable type definition.

• To set an initial condition use the following:

myVar.SetInitialCondition(value)


or, if the variable is distributed:

for i in range(myDomain.NumberOfPoints):
myVar.SetInitialCondition(i, value)

# or using a numpy array of values
myVar.SetInitialConditions(values)


where the value can again be either a float or the quantity object, and values is a numpy array of floats or quantity objects. If the simple floats are used it is assumed that they represent values with the same units as in the variable type definition.

• To set an absolute tolerance the following can be used:

myVar.SetAbsoluteTolerances(1E-5)

• To set an initial guess use the following:

myVar.SetInitialGuess(value)


or, if the variable is distributed:

for i in range(0, myDomain.NumberOfPoints):
myVar.SetInitialGuess(i, value)

# or using a numpy array of values
myVar.SetInitialGuesses(values)


where the value can again be either a float or the quantity object and values is a numpy array of floats or quantity objects.

#### 6.2.4.3. Using variables¶

The most commonly used functions are:

Nota bene

The functions __call__(), dt(), d(), d2(), array(), dt_array(), d_array() and d2_array() can only be used to build equations’ residual expressions. On the other hand, the functions GetValue, SetValue and npyValues can be used to access the variable data at any point.

The above mentioned functions accept the same arguments as explained in Using parameters. More information will be given here on getting time and partial derivatives.

1. To get a time derivative of the ordinary variable the function dt() can be used. For instance, if a time derivative of the variable myVar has to be equal to some constant, let’s say 1.0:

${ d(myVar) \over {d}{t} } = 1$

the following can be used:

# Notation:
#  - eq is a daeEquation object
#  - myVar is an ordinary daeVariable
eq.Residual = dt(myVar()) - 1.0

2. To get a time derivative of a distributed variable the dt() function can be used again. For instance, if a time derivative of the distributed variable myVar has to be equal to some constant at each point of the domain myDomain:

${\partial myVar(i) \over \partial t} = 1; \forall i \in [0, n]$

the following can be used:

# Notation:
#  - myDomain is daeDomain object
#  - n is the number of points in the myDomain
#  - eq is a daeEquation object distributed on the myDomain
#  - d is daeDEDI object (used to iterate through the domain points)
#  - myVar is daeVariable object distributed on the myDomain
d = eq.DistributeOnDomain(myDomain, eClosedClosed)
eq.Residual = dt(myVar(d)) - 1.0


This code translates into a set of n equations.

Obviously, a variable can be distributed on more than one domain. To write a similar equation for a two-dimensional variable:

${d(myVar(d_1, d_2)) \over dt} = 1; \forall d_1 \in [0, n_1], \forall d_2 \in [0, n_2]$

the following can be used:

# Notation:
#  - myDomain1, myDomain2 are daeDomain objects
#  - n1 is the number of points in the myDomain1
#  - n2 is the number of points in the myDomain2
#  - eq is a daeEquation object distributed on the domains myDomain1 and myDomain2
#  - d is daeDEDI object (used to iterate through the domain points)
#  - myVar is daeVariable object distributed on the myDomaina and myDomain2
d1 = eq.DistributeOnDomain(myDomain1, eClosedClosed)
d2 = eq.DistributeOnDomain(myDomain2, eClosedClosed)
eq.Residual = dt(myVar(d1,d2)) - 1.0


This code translates into a set of n1 * n2 equations.

3. To get a partial derivative of a distributed variable the functions d() and d2() can be used. For instance, if a partial derivative of the distributed variable myVar has to be equal to 1.0 at each point of the domain myDomain:

${\partial myVar(d) \over \partial myDomain} = 1.0; \forall d \in [0, n]$

we can write:

# Notation:
#  - myDomain is daeDomain object
#  - n is the number of points in the myDomain
#  - eq is a daeEquation object distributed on the myDomain
#  - d is daeDEDI object (used to iterate through the domain points)
#  - myVar is daeVariable object distributed on the myDomain
d = eq.DistributeOnDomain(myDomain, eClosedClosed)
eq.Residual = d(myVar(d), myDomain, discretizationMethod=eCFDM, options={}) - 1.0

# since the defaults are eCFDM and an empty options dictionary the above is equivalent to:
eq.Residual = d(myVar(d), myDomain) - 1.0


Again, this code translates into a set of n equations.

The default discretisation method is center finite difference method (eCFDM) and the default discretisation order is 2 and can be specified in the options dictionary: options["DiscretizationOrder"] = integer. At the moment, only the finite difference discretisation methods are supported by default (but the finite volume and finite elements implementations exist through the third party libraries):

• Center finite difference method (eCFDM)
• Backward finite difference method (eBFDM)
• Forward finite difference method (eFFDM)

More information about variables can be found in the API reference daeVariable and in Tutorials.

### 6.2.5. Ports¶

Ports define connection points between models instances for exchange of continuous quantities. In other words, ports can be used to provide the model inputs and outputs. Like models, ports can contain domains, parameters and variables. Ports can be inlet or outlet depending on whether they represent model inputs or model outputs.

In DAE Tools ports are defined by deriving a new class from the base daePort. An empty port definition is presented below:

class myPort(daePort):
def __init__(self, name, parent = None, description = ""):
daePort.__init__(self, name, type, parent, description)

# Declaration/instantiation of domains, parameters and variables
...


The process consists of the following steps:

1. Calling the base class constructor:

daePort.__init__(self, name, type, parent, description)

2. Declaring domains, parameters and variables in the __init__() function

The same rules apply as described in the Developing models section.

Two ports can be connected by using the ConnectPorts() function.

#### 6.2.5.1. Instantiating ports¶

Ports are instantiated in the __init__() function:

self.myPort = daePort("myPort", eInletPort, parentModel, "description")


### 6.2.6. Event ports¶

Event ports define connection points between models instances for exchange of discrete messages/events. Events can be triggered manually (using the SendEvent() function) or when a specified condition is satisfied. The main difference between event and ordinary ports is that the former allow a discrete communication between models while the latter allow a continuous exchange of information.

Messages contain a floating point value that can be used by a recipient. Upon a reception of an event certain actions can be executed. The actions are specified in the ON_EVENT() function. The events received by an event port can be recorded by setting the boolean RecordEvents property to true and retrieved using the Events property.

Two event ports can be connected by using the ConnectEventPorts() function. A single outlet event port can be connected to unlimited number of inlet event ports.

#### 6.2.6.1. Instantiating event ports¶

Event ports are instantiated in the __init__() function:

self.myEventPort = daeEventPort("myEventPort", eOutletPort, parentModel, "description")


### 6.2.7. Equations¶

Model equations in DAETools are given in an implicit/acausal form. There are four types of equations in DAE Tools:

• Ordinary or distributed
• Continuous or discontinuous

Distributed equations are equations which are distributed on one or more domains and valid on the selected points within those domains. Equations can be distributed on a whole domain, on a portion of it or even on a single point (useful for specifying boundary conditions).

#### 6.2.7.1. Declaring equations¶

Equations are declared in the DeclareEquations() function. To declare an ordinary equation the CreateEquation() function can be used:

eq = model.CreateEquation("MyEquation", "description")


while to declare a distributed equation:

eq = model.CreateEquation("MyEquation")
d = eq.DistributeOnDomain(myDomain, eClosedClosed)


Equations can be distributed on a whole domain or on a portion of it. Currently there are 7 options:

• Distribute on a closed (whole) domain - analogous to: $$x \in [x_0, x_n]$$
• Distribute on a left open domain - analogous to: $$x \in (x_0, x_n]$$
• Distribute on a right open domain - analogous to: $$x \in [x_0, x_n)$$
• Distribute on a domain open on both sides - analogous to: $$x \in (x_0, x_n)$$
• Distribute on the lower bound - only one point: $$x \in \{ x_0 \}$$
• Distribute on the upper bound - only one point: $$x \in \{ x_n \}$$
• Custom array of points within a domain: i.e. $$x \in \{ x_0, x_3, x_7, x_8 \}$$

where $$x_0$$ stands for the LowerBound and $$x_n$$ stands for the UpperBound of the domain.

An overview of various bounds is given in the table below. Assume that we have an equation which is distributed on two domains: x and y. The table below shows various options while distributing an equation. Green squares represent portions of a domain included in the distributed equation, while white squares represent excluded portions.

 x = eClosedClosed; y = eClosedClosed $$x \in [x_0, x_n], y \in [y_0, y_n]$$ x = eOpenOpen; y = eOpenOpen $$x \in ( x_0, x_n ), y \in ( y_0, y_n )$$ x = eClosedClosed; y = eOpenOpen $$x \in [x_0, x_n], y \in ( y_0, y_n )$$ x = eClosedClosed; y = eOpenClosed $$x \in [x_0, x_n], y \in ( y_0, y_n ]$$ x = eLowerBound; y = eClosedOpen $$x = x_0, y \in [ y_0, y_n )$$ x = eLowerBound; y = eClosedClosed $$x = x_0, y \in [y_0, y_n]$$ x = eUpperBound; y = eClosedClosed $$x = x_n, y \in [y_0, y_n]$$ x = eLowerBound; y = eUpperBound $$x = x_0, y = y_n$$

#### 6.2.7.2. Defining equations (equation residual expressions)¶

Equations in DAE Tools are given in implicit (acausal) form and specified as residual expressions. For instance, to define a residual expression of an ordinary equation:

${\partial V_{14} \over \partial t} + {V_1 \over V_{14} + 2.5} + sin(3.14 \cdot V_3) = 0$

the following can be used:

# Notation:
#  - V1, V3, V14 are ordinary variables
eq.Residal = dt(V14()) + V1() / (V14() + 2.5) + sin(3.14 * V3())


To define a residual expression of a distributed equation:

${\partial V_{14}(x,y)) \over \partial t} + {V_1 \over V_{14}(x,y) + 2.5} + sin(3.14 \cdot V_3(x,y)) = 0; \forall x \in [0, nx], \forall y \in (0, ny)$

the following can be used:

# Notation:
#  - V1 is an ordinary variable
#  - V3 and V14 are variables distributed on domains x and y
eq = model.CreateEquation("MyEquation")
dx = eq.DistributeOnDomain(x, eClosedClosed)
dy = eq.DistributeOnDomain(y, eOpenOpen)
eq.Residal = dt(V14(dx,dy)) + V1() / ( V14(dx,dy) + 2.5) + sin(3.14 * V3(dx,dy) )


where dx and dy are daeDEDI (which is short for daeDistributedEquationDomainInfo) objects. These objects are used internally by the framework to iterate over the domain points when generating a set of equations from a distributed equation. If a daeDEDI object is used as an argument of the operator (), dt, d, d2, array, dt_array, d_array, or d2_array functions, it represents a current index in the domain which is being iterated. Hence, the equation above is equivalent to writing:

# Notation:
#  - V1 is an ordinary variable
#  - V3 and V14 are variables distributed on domains x and y
for dx in range(0, x.NumberOfPoints): # x: [x0, xn]
for dy in range(1, y.NumberOfPoints-1): # y: (y0, yn)
eq = model.CreateEquation("MyEquation_%d_%d" % (dx, dy) )
eq.Residal = dt(V14(dx,dy)) + V1() / ( V14(dx,dy) + 2.5) + sin(3.14 * V3(dx,dy) )


The second way can be used for writing equations that are different for different points within domains.

daeDEDI class has the __call__ (operator ()) function defined which returns the current index as the adouble object. In addition, the class provides operators + and - which can be used to return the current index offset by the specified integer. For instance, to define the equation below:

$V_1(x) = V_2(x) + V_2(x+1); \forall x \in [0, nx)$

the following can be used:

# Notation:
#  - V1 and V2 are variables distributed on the x domain
eq = model.CreateEquation("MyEquation")
dx = eq.DistributeOnDomain(x, eClosedOpen)
eq.Residal = V1(dx) - ( V2(dx) + V2(dx+1) )


Units consistency for all equations is checked by default. This can be changed for individual equations using the CheckUnitsConsistency boolean property.

Scaling of equations’ residuals could be very important for the convergence of the numerical integration. Large condition numbers produce ill-conditioned Jacobian matrices and a solution of a linear system of equations is prone to large numerical errors. The equation scaling is 1.0 by default and can be changed using the Scaling property.

Evaluation of derivatives of very large equations can be very costly since they contain a large number of variables. For instance, taking an average value of all points in a large 2D or 3D domain can produce an equation residual with tens of thousands of terms. To determine all Jacobian items for such equations a calculation of tens of thousands of terms per every Jacobian item is required while in reality only a single term has to be calculated. Building of Jacobian expressions ahead of time can significantly improve the numerical performance (at the cost of somewhat larger memory requirements). Pre-building of Jacobian expressions can be set using the BuildJacobianExpressions boolean property (default is False).

#### 6.2.7.3. Supported mathematical operations and functions¶

DAE Tools support five basic mathematical operations (+, -, *, /, **) and the following standard mathematical functions: Sqrt(), Pow(), Log(), Log10(), Exp(), Min(), Max(), Floor(), Ceil(), Abs(), Sin(), Cos(), Tan(), ASin(), ACos(), ATan(), Sinh(), Cosh(), Tanh(), ASinh(), ACosh(), ATanh(), ATan2(), Erf(). All the above-mentioned operators and functions operate on adouble and adouble_array objects. In addition, functions such as Sum(), Product(), Average(), Min() and Max() operate only on adouble_array objects.

To define conditions the following comparison operators: < (less than), <= (less than or equal), == (equal), != (not equal), > (greater), >= (greater than or equal) and the following logical operators: & (logical AND), | (logical OR), ~ (logical NOT) can be used.

Nota bene

Since it is not allowed to overload Python’s operators and, or and not they cannot be used to define logical conditions; therefore, the custom operators &, | and ~ are defined and should be used instead.

#### 6.2.7.4. Interoperability with NumPy¶

The adouble and adouble_array classes are designed with the support for numpy library in mind. They implement most of the standard mathematical functions available in numpy() (i.e. numpy.sqrt(), numpy.pow(), numpy.log(), numpy.log10(), numpy.exp(), numpy.min(), numpy.max(), numpy.floor(), numpy.ceil(), numpy.abs(), numpy.sin(), numpy.cos(), numpy.tan(), numpy.asin(), numpy.acos(), numpy.atan(), numpy.sinh(), numpy.cosh(), numpy.tanh(), numpy.asinh(), numpy.acosh(), numpy.atanh(), numpy.atan2(), and numpy.erf()) so that the numpy functions also operate on the adouble and adouble_array objects. Therefore, these classes can be used as native data types in numpy. In addition, numpy and DAE Tools mathematical functions are interchangeable. In the example given below, the Exp() and numpy.exp() function calls produce identical results:

# Notation:
#  - Var is an ordinary variable
#  - result is an ordinary variable
eq = self.CreateEquation("...")
eq.Residual = result() - numpy.exp( Var() )

# The above is identical to:
eq.Residual = result() - Exp( Var() )


Often, it is desired to apply numpy/scipy numerical functions on arrays of adouble objects. In those cases the functions such as array(), d_array(), dt_array(), Array() etc. are NOT applicable since they return adouble_array objects. However, numpy arrays can be created and populated with adouble objects and numpy functions applied on them. In addition, an adouble_array object can be created from resulting numpy arrays of adouble objects, if necessary.

For instance, to define the equation below:

$sum = \sum\limits_{i=0}^{N_x-1} \left( V_1(i) + 2 \cdot V_2(i)^2 \right)$

the following code can be used:

# Notation:
#  - x is a continuous domain
#  - V1 is a variable distributed on the x domain
#  - V2 is a variable distributed on the x domain
#  - sum is an ordinary variable
#  - ndarr_V1 is one dimensional numpy array with dtype=object
#  - ndarr_V2 is one dimensional numpy array with dtype=object
#  - Nx is the number of points in the domain x

# 1.Create empty numpy arrays as a container for daetools adouble objects
ndarr_V1 = numpy.empty(Nx, dtype=object)
ndarr_V2 = numpy.empty(Nx, dtype=object)

# 2. Fill the created numpy arrays with adouble objects
ndarr_V1[:] = [V1(x) for x in range(Nx)]
ndarr_V2[:] = [V2(x) for x in range(Nx)]

# Now, ndarr_V1 and ndarr_V2 represent arrays of Nx adouble objects each:
#  ndarr_V1 := [V1(0), V1(1), V1(2), ..., V1(Nx-1)]
#  ndarr_V2 := [V2(0), V2(1), V2(2), ..., V2(Nx-1)]

# 3. Create an equation using the common numpy/scipy functions/operators
eq = self.CreateEquation("sum")
eq.Residual = sum() - numpy.sum(ndarr_V1 + 2*ndarr_V2**2)

# If adouble_array is needed after operations on a numpy array, the following two functions can be used:
# Both return an adouble_array object.


#### 6.2.7.5. Details on autodifferentiation support¶

To calculate a residual and its gradients (which represent a single row in the Jacobian matrix) DAE Tools combine the operator overloading technique for automatic differentiation (adopted from ADOL-C library) using the concept of representing equations as evaluation trees. Evaluation trees consist of binary or unary nodes, each node representing a basic mathematical operation or the standard mathematical function. The basic mathematical operations and functions are re-defined to operate on a heavily modified ADOL-C class adouble (which has been extended to contain information about domains/parameters/variables etc). In addition, a new adouble_array class has been introduced to support all above-mentioned operations on arrays. What is different here is that adouble/adouble_array classes and mathematical operators/functions work in two modes; they can either build-up an evaluation tree or calculate a value/derivative of an expression. Once built, the evaluation trees can be used to calculate equation residuals or derivatives to fill a Jacobian matrix necessary for a Newton-type iteration. A typical evaluation tree is presented in the Fig. 6.2 below.

Fig. 6.2 Equation evaluation tree in DAE Tools

The equation F in Fig. 6.2 is a result of the following DAE Tools equation:

eq = model.CreateEquation("F", "F description")
eq.Residal = dt(x1()) + x2() / (x3() + 2.5) + Sin(x4())


As it has been described in the previous sections, domains, parameters, and variables contain functions that return adouble/adouble_array objects used to construct the evaluation trees. These functions include functions to get a value of a domain/parameter/variable (operator ()), to get a time or a partial derivative of a variable (functions dt(), d(), or d2()) or functions to obtain an array of values, time or partial derivatives (array(), dt_array(), d_array(), and d2_array()).

Another useful feature of DAE Tools equations is that they can be exported into MathML or Latex format and easily visualised.

#### 6.2.7.6. Defining boundary conditions¶

Assume that a simple heat transfer needs to be modelled: heat conduction through a very thin rectangular plate. At one side (at y = 0) we have a constant temperature (500 K) while at the opposite end we have a constant flux (1E6 W/m2). The problem can be described by a single distributed equation:

# Notation:
#  - T is a variable distributed on x and y domains
#  - rho, k, and cp are parameters
eq = model.CreateEquation("MyEquation")
dx = eq.DistributeOnDomain(x, eClosedClosed)
dy = eq.DistributeOnDomain(y, eOpenOpen)
eq.Residual = rho() * cp() * dt(T(dx,dy)) - k() * ( d2(T(dx,dy), x) + d2(T(dx,dy), y) )


The equation is defined on the y domain open on both ends; thus, the additional equations (boundary conditions at y = 0 and y = ny points) need to be specified to make the system well posed:

$\begin{split}T(x,y) &= 500; \forall x \in [0, nx], y = 0 \\ -k \cdot {\partial T(x,y) \over \partial y} &= 1E6; \forall x \in [0, nx], y = ny\end{split}$

To do so, the following equations can be used:

# "Bottom edge" boundary conditions:
bceq = model.CreateEquation("Bottom_BC")
dx = bceq.DistributeOnDomain(x, eClosedClosed)
dy = bceq.DistributeOnDomain(y, eLowerBound)
bceq.Residal = T(dx,dy) - Constant(500 * K)  # Constant temperature (500 K)

# "Top edge" boundary conditions:
bceq = model.CreateEquation("Top_BC")
dx = bceq.DistributeOnDomain(x, eClosedClosed)
dy = bceq.DistributeOnDomain(y, eUpperBound)
bceq.Residal = - k() * d(T(dx,dy), y) - Constant(1E6 * W/m**2)  # Constant flux (1E6 W/m2)


#### 6.2.7.7. Making equations more readable¶

Equations residuals can be made more readable by defining some auxiliary functions (as illustrated in Tutorial 2):

def DeclareEquations(self):
daeModel.DeclareEquations(self)

# Create some auxiliary functions to make equations more readable
rho     = self.rho()
Q       = lambda i:      self.Q(i)
cp      = lambda x,y:    self.cp(x,y)
k       = lambda x,y:    self.k(x,y)
T       = lambda x,y:    self.T(x,y)
dT_dt   = lambda x,y: dt(self.T(x,y))
dT_dx   = lambda x,y:  d(self.T(x,y), self.x, eCFDM)
dT_dy   = lambda x,y:  d(self.T(x,y), self.y, eCFDM)
d2T_dx2 = lambda x,y: d2(self.T(x,y), self.x, eCFDM)
d2T_dy2 = lambda x,y: d2(self.T(x,y), self.y, eCFDM)

# Now the equations expressions are more readable
eq = self.CreateEquation("HeatBalance", "Heat balance equation valid on the open x and y domains")
x = eq.DistributeOnDomain(self.x, eOpenOpen)
y = eq.DistributeOnDomain(self.y, eOpenOpen)
eq.Residual = rho * cp(x,y) * dT_dt(x,y) - k(x,y) * (d2T_dx2(x,y) + d2T_dy2(x,y))

eq = self.CreateEquation("BC_bottom", "Neumann boundary conditions at the bottom edge (constant flux)")
x = eq.DistributeOnDomain(self.x, eOpenOpen)
y = eq.DistributeOnDomain(self.y, eLowerBound)
# Now we use Q(0) as the heat flux into the bottom edge
eq.Residual = -k(x,y) * dT_dy(x,y) - Q(0)

eq = self.CreateEquation("BC_top", "Neumann boundary conditions at the top edge (constant flux)")
x = eq.DistributeOnDomain(self.x, eOpenOpen)
y = eq.DistributeOnDomain(self.y, eUpperBound)
# Now we use Q(1) as the heat flux at the top edge
eq.Residual = -k(x,y) * dT_dy(x,y) - Q(1)

eq = self.CreateEquation("BC_left", "Neumann boundary conditions at the left edge (insulated)")
x = eq.DistributeOnDomain(self.x, eLowerBound)
y = eq.DistributeOnDomain(self.y, eClosedClosed)
eq.Residual = dT_dx(x,y)

eq = self.CreateEquation("BC_right", " Neumann boundary conditions at the right edge (insulated)")
x = eq.DistributeOnDomain(self.x, eUpperBound)
y = eq.DistributeOnDomain(self.y, eClosedClosed)
eq.Residual = dT_dx(x,y)


Obviously, the heat conduction equation from Tutorial 2:

...

eq.Residual = rho * cp(x,y) * dT_dt(x,y) - k(x,y) * (d2T_dx2(x,y) + d2T_dy2(x,y))


is much more readable than the same equation from Tutorial 1:

...

eq.Residual = self.rho() * self.cp() * dt(self.T(x,y)) - \
self.k() * (d2(self.T(x,y), self.x, eCFDM) + d2(self.T(x,y), self.y, eCFDM))


### 6.2.8. State Transition Networks¶

Discontinuous equations are equations that take different forms subject to certain conditions. For example, to model a flow through a pipe one can observe three different flow regimes:

• Laminar: if Reynolds number is less than 2,100
• Transient: if Reynolds number is greater than 2,100 and less than 10,000
• Turbulent: if Reynolds number is greater than 10,000

From any of these three states the system can go to any other state. This type of discontinuities is called a reversible discontinuity and can be described using IF(), ELSE_IF(), ELSE() and END_IF() functions:

IF(Re() <= 2100)                    # (Laminar flow)
#... (equations go here)

ELSE_IF(Re() > 2100 & Re() < 10000) # (Transient flow)
#... (equations go here)

ELSE()                              # (Turbulent flow)
#... (equations go here)

END_IF()


The comparison operators operate on adouble objects and Float values. Units consistency is strictly checked and expressions including Float values are allowed only if a variable or parameter is dimensionless. The following expressions are valid:

# Notation:
#  - T is a variable with units: K
#  - m is a variable with units: kg
#  - p is a dimensionless parameter

# T < 0.5 K
T() < Constant(0.5 * K)

# (T >= 300 K) or (m < 1 kg)
(T() >= Constant(300 * K)) | (m < Constant(0.5 * kg))

# p <= 25.3 (use of the Constant function not necessary)
p() <= 25.3


Reversible discontinuities can be symmetrical and non-symmetrical. The above example is symmetrical. However, to model a CPU and its power dissipation one can observe three operating modes with the following state transitions:

• Normal mode
• switch to Power saving mode if CPU load is below 5%
• switch to Fried mode if the temperature is above 110 degrees
• Power saving mode
• switch to Normal mode if CPU load is above 5%
• switch to Fried mode if the temperature is above 110 degrees
• Fried mode
• Damn, no escape from here... go to the nearest shop and buy a new one! Or, donate some money to DAE Tools project :-)

What can be seen is that from the Normal mode the system can either go to the Power saving mode or to the Fried mode. The same stands for the Power saving mode: the system can either go to the Normal mode or to the Fried mode. However, once the temperature exceeds 110 degrees the CPU dies (let’s say it is heavily overclocked) and there is no return. This type of discontinuities is called an irreversible discontinuity and can be described using STN(), STATE(), END_STN() functions:

STN("CPU")

STATE("Normal")
#... (equations go here)
ON_CONDITION( CPULoad() < 0.05,       switchToStates = [ ("CPU", "PowerSaving") ] )
ON_CONDITION( T() > Constant(110*K),  switchToStates = [ ("CPU", "Fried") ] )

STATE("PowerSaving")
#... (equations go here)
ON_CONDITION( CPULoad() >= 0.05,      switchToStates = [ ("CPU", "Normal") ] )
ON_CONDITION( T() > Constant(110*K),  switchToStates = [ ("CPU", "Fried") ] )

STATE("Fried")
#... (equations go here)

END_STN()


The function ON_CONDITION() is used to define actions to be performed when the specified condition is satisfied. In addition, the function ON_EVENT() can be used to define actions to be performed when an event is triggered on a specified event port. Details on how to use ON_CONDITION() and ON_EVENT() functions can be found in the OnCondition actions and OnEvent actions sections, respectively.

More information about state transition networks can be found in daeSTN, daeIF and in Tutorials.

### 6.2.9. OnCondition actions¶

The function ON_CONDITION() can be used to define actions to be performed when a specified condition is satisfied. The available actions include:

• Changing the active state in specified State Transition Networks (argument switchToStates)
• Re-assigning or re-ininitialising specified variables (argument setVariableValues)
• Triggering an event on the specified event ports (argument triggerEvents)
• Executing user-defined actions (argument userDefinedActions)

Nota bene

OnCondition actions can be added to models or to states in State Transition Networks (daeSTN or daeIF):

• When added to a model they will be active throughout the simulation
• When added to a state they will be active only when that state is active

Nota bene

switchToStates, setVariableValues, triggerEvents and userDefinedActions are empty by default. The user has to specify at least one action.

For instance, to execute some actions when the temperature becomes greater than 340 K the following can be used:

def DeclareEquations(self):
...

self.ON_CONDITION( T() > Constant(340*K), switchToStates     = [ ('STN', 'State'), ... ],
setVariableValues  = [ (variable, newValue), ... ],
triggerEvents      = [ (eventPort, eventMessage), ... ],
userDefinedActions = [ userDefinedAction, ... ] )


where the first argument of the ON_CONDITION() function is a condition specifying when the actions will be executed and:

For more details on how to use ON_CONDITION() function have a look on Tutorial 13.

### 6.2.10. OnEvent actions¶

The function ON_EVENT() can be used to define actions to be performed when an event is triggered on the specified event port. The available actions are the same as in the ON_CONDITION() function.

Nota bene

OnEvent actions can be added to models or to states in State Transition Networks (daeSTN or daeIF):

• When added to a model they will be active throughout the simulation
• When added to a state they will be active only when that state is active

Nota bene

switchToStates, setVariableValues, triggerEvents and userDefinedActions are empty by default. The user has to specify at least one action.

For instance, to execute some actions when an event is triggered on an event port the following can be used:

def DeclareEquations(self):
...

self.ON_EVENT( eventPort, switchToStates     = [ ('STN', 'State'), ... ],
setVariableValues  = [ (variable, newValue), ... ],
triggerEvents      = [ (eventPort, eventMessage), ... ],
userDefinedActions = [ userDefinedAction, ... ] )


where the first argument of the ON_EVENT() function is the daeEventPort object to be monitored for events, while the rest of the arguments is the same as in the ON_CONDITION() function.

For more details on how to use ON_EVENT() function have a look on Tutorial 13.

### 6.2.11. User-defined actions¶

User-defined actions can be executed in a response to specified conditions in OnCondition handlers or in a response to triggered events in OnEvent handlers.

They are created by deriving a class from the daeAction base and implementing the Execute() function. The Execute() function takes no arguments. If some information from the model is required they should be specified in the constructor.

User-defined actions do not return a value and should not change the values of variables (other types of actions must be used for that purpose), but perform some user-defined operations. The source code for a simple action that prints a message with the data sent to a specified event port is given below:

# User-defined action executed when an event is triggered on a specified event port.
class simpleUserAction(daeAction):
def __init__(self, eventPort):
daeAction.__init__(self)

# Store the daeEventPort object for later use.
self.eventPort = eventPort

def Execute(self):
# The floating point value of the data sent when the event is triggered
# can be retrieved using the daeEventPort.EventData property.
msg = 'simpleUserAction executed; input data = %f' % self.eventPort.EventData

print('********************************************************')
print(msg)
print('********************************************************')


Notate bene

User-defined action objects should be instantiated in the DeclareEquations() function if they access parameters’ and variables’ symbolic representations (available only there).

User-defined action objects must be stored in the model, otherwise they will get destroyed when they go out of scope.

def DeclareEquations(self):
...

# User-defined action objects should be stored in the model, otherwise
# they will get destroyed when they go out of scope.
self.action = simpleUserAction(self.eventPort)

# The actions executed when the event on the inlet 'eventPort' event port is received.
# daeEventPort defines the operator() which returns adouble object that can be used
# at the moment when the action is executed to get the value of the event data.
self.ON_EVENT(self.eventPort, userDefinedActions = [self.action])


For more details on user-defined actions have a look on the Tutorial 13.

### 6.2.12. External functions¶

The external functions concept in DAE Tools is used to handle and calculate user-defined functions or to call functions from external libraries. External functions can return scalar (daeScalarExternalFunction) or vector (daeVectorExternalFunction) values.

Nota bene

The vector external functions are not implemented at the moment.

External functions are created by deriving a class from the daeScalarExternalFunction base, specifying its arguments in the constructor and implementing the Calculate() function. The source code for a simple $$F(x) = x ^ 2$$ external function is given below:

class F(daeScalarExternalFunction):
def __init__(self, Name, parentModel, units, x):
# Instantiate the scalar external function by specifying
# the arguments dictionary {'name' : adouble-object}
arguments = {}
arguments["x"]  = x

daeScalarExternalFunction.__init__(self, Name, parentModel, units, arguments)

def Calculate(self, values):
# Calculate function is used to calculate a value and a derivative of the external
# function per given argument (if requested). Here, a simple function is given by:
#    F(x) = x**2

# Procedure:
# 1. Get the arguments from the dictionary values: {'arg-name' : adouble-object}.
#    Every adouble object has two properties: Value and Derivative that can be
#    used to evaluate function or its partial derivatives per arguments
#    (partial derivatives are used to fill in a Jacobian matrix necessary to solve
#    a system of non-linear equations using the Newton method).
x = values["x"]

# 2. Always calculate the value of a function (derivative part is zero by default).

# 3. If a function derivative per one of its arguments is requested,
#    the derivative part of that argument will be non-zero.
#    In that case, investigate which derivative is requested and calculate it
#    using the chain rule: f'(x) = x' * df(x)/dx
if x.Derivative != 0:
# A derivative per 'x' was requested; its value is: x' * 2x
res.Derivative = x.Derivative * (2 * x.Value)

# 4. Return the result as a adouble object (contains both a value and a derivative)
return res


Notate bene

External function objects must be instantiated in the DeclareEquations() function since they access parameters’ and variables’ symbolic representations (available only there).

External function objects must be stored in the model, otherwise they will get destroyed when they go out of scope.

def DeclareEquations(self):
...

# Create external function (it has to be created in DeclareEquations!),
# specify its units (here for simplicity dimensionless) and
# arguments (here only a single argument: x)
# External function objects should be stored in the model, otherwise
# they will get destroyed when they go out of scope.
self.F = F("F", self, unit(), self.x())

# External function can now be used in daetools equations.
# Its value can be obtained using the operator() (python special function __call__)
eq = self.CreateEquation("...", "...")
eq.Residual = ... self.F() ...


A more complex example is given in the Tutorial 14. There, the external function concept is used to interpolate a set of values using the scipy.interpolate.interp1d object.

class extfn_interp1d(daeScalarExternalFunction):
def __init__(self, Name, parentModel, units, times, values, Time):
arguments = {}
arguments["t"] = Time

# Instantiate interp1d object and initialise interpolation using supplied (time,y) values.
self.interp = scipy.interpolate.interp1d(times, values)

# During the solver iterations, the function is called very often with the same arguments.
# Therefore, cache the last interpolated value to speed up a simulation.
self.cache = None

daeScalarExternalFunction.__init__(self, Name, parentModel, units, arguments)

def Calculate(self, values):
# Get the argument from the dictionary of arguments' values.
time = values["t"].Value

# Here we do not need to return a derivative for it is not a function of variables.

# First check if an interpolated value was already calculated during the previous call.
# If it was, return the cached value (the derivative part is always equal to zero in this case).
if self.cache:
if self.cache[0] == time:

# The time received is not in the cache and has to be interpolated.
# Convert the result to float datatype since daetools can't accept
# numpy.float64 types as arguments at the moment.
interp_value = float(self.interp(time))

# Save it in the cache for later use.
self.cache = (time, res.Value)

return res


The extfn_interp1d class is used here to approximate some function f:

$y = f(t) = 2t$

using its t ad y values:

def DeclareEquations(self):
...

# Create scipy.interp1d interpolation external function.
# Create 'times' and 'values' arrays to be used for interpolation:
times  = numpy.arange(0.0, 1000.0)
values = 2*times
# The external function accepts only a single argument: the current time in the simulation
# that can be obtained using the Time() daetools function.
# The external function units are seconds.
self.interp1d = extfn_interp1d("interp1d", self, s, times, values, Time())


Alternatively, DAE Tools can utilise functions defined in shared libraries via daeCTypesExternalFunction class. As an argument it accepts a function pointer from libraries loaded using Python ctypes. Sample usage can be found in the Tutorial 14:

def DeclareEquations(self):
...

self.ext_lib = ctypes.CDLL("libheat_function.so")

# Function arguments:
arguments = {}
arguments['m']     = self.m()
arguments['cp']    = self.cp()
arguments['dT/dt'] = dt(self.T())

# Function pointer ('calculate' function is used):
function_ptr = self.ext_lib.calculate

self.exfnHeat2 = daeCTypesExternalFunction("heat_function", self, W, function_ptr, arguments)


The calculate function is defined in the heat_function c shared library:

#include <string.h>

typedef struct
{
double Value;
double Derivative;
}

#if defined(_WIN32) || defined(WIN32) || defined(WIN64) || defined(_WIN64)
#define DLLEXPORT  extern "C" __declspec(dllexport)
#else
#define DLLEXPORT
#endif

{

/* Get the arguments' values. */
for(int i = 0; i < no_arguments; i++)
{
if(strcmp(names[i], "m") == 0)
m = values[i];
else if(strcmp(names[i], "cp") == 0)
cp = values[i];
else if(strcmp(names[i], "dT/dt") == 0)
dT_dt = values[i];
}

/* Calculate the value. */
result.Value = m.Value * cp.Value * dT_dt.Value;

/* Calculate the derivative. */
if(m.Derivative != 0) /* A derivative per 'm' was requested */
result.Derivative = m.Derivative * (cp.Value * dT_dt.Value);
else if(cp.Derivative != 0) /* A derivative per 'cp' was requested */
result.Derivative = cp.Derivative * (m.Value * dT_dt.Value);
else if(dT_dt.Derivative != 0) /* A derivative per 'dT_dt' was requested */
result.Derivative = dT_dt.Derivative * (m.Value * cp.Value);

return result;
}


The library can be compiled using the following commands:

# GNU/Linux gcc:
gcc -fPIC -shared -o libheat_function.so tutorial4_heat_function.c

# macOS gcc:
gcc -fPIC -dynamiclib -o libheat_function.dylib tutorial14_heat_function.c

# Windows vc++:
cl /LD tutorial14_heat_function.c /link /dll /out:heat_function.dll


## 6.3. Numerical Methods for Partial Differential Equations¶

### 6.3.1. The Finite Difference Method¶

DAE Tools support numerical simulation of partial differential equations on structured grids using the Finite Difference Method. Three different methods are provided:

• Backward Finite Difference method (eBFDM)
• Forward Finite Difference method (eFFDM)
• Center Finite Difference method (eCFDM)

The partial derivatives of the first and second order can be specified using the functions d() and d2().

As an illustration, the 1D convection-diffusion-reaction equation:

$\begin{split}{\partial c \over \partial t} + u {\partial c \over \partial x} - D {\partial^2 c \over \partial x^2} &= s(x), \forall x \in \left( 0,L \right] \\ c(0) &= 0.0\end{split}$

can be specified in the following way (using the Center Finite Difference Method):

class modTutorial(daeModel):
...
def DeclareEquations(self):
daeModel.DeclareEquations(self)

# Notation:
#  - c is a state variable
#  - x is a domain object
#  - u is velocity

# Declare some auxiliary functions to make equations more readable
c       = lambda i: self.c(i)
dc_dt   = lambda i: dt(c(x))
dc_dx   = lambda i: d (c(x), self.x, eCFDM)
d2c_dx2 = lambda i: d2(c(x), self.x, eCFDM)
s       = lambda i: c(i)**2

# Declare the Convection-Diffusion-Reaction equation distributed on (0, L]:
eq = self.CreateEquation("c")
eq.DistributeOnDomain(self.x, eOpenClosed)
eq.Residual = dc_dt(x) + u * dc_dx(x) - D * d2c_dx2(x) - s(x)

# Boundary conditions at x = 0:
eq = self.CreateEquation("c(0)")
eq.Residual = c(0) - 0.0


### 6.3.2. The Finite Volume Method¶

DAE Tools support numerical simulation of partial differential equations on 1D structured grids using the Finite Volume Method (high-resolution upwind scheme with flux limiter).

Consider the 1D convection-diffusion-reaction equation:

${\partial c \over \partial t} + u {\partial c \over \partial x} - D {\partial^2 c \over \partial x^2} = s(x)$

A cell-centered finite-volume discretisation yields the semi-discrete equation [1] [2]:

$\int_{\Omega_i} {\partial c_i \over \partial t} dx + u \left[ c_{i + {1 \over 2}} - c_{i - {1 \over 2}} \right] - D \left[ \left( \partial c \over \partial x \right)_{i + {1 \over 2}} - \left( \partial c \over \partial x \right)_{i - {1 \over 2}} \right] = \int_{\Omega_i} s_i dx$

where the half-integer indices refer to cell faces $$\delta\Omega_{i-{1 \over 2}}$$ and $$\delta\Omega_{i+{1 \over 2}}$$ between cell centers $$\Omega_{i-1}$$ and $$\Omega_i$$ as presented in the figure below:

The accuracy of the above finite volume discretisation is determined by the way in which the cell-face fluxes are computed. Applying the high-resolution upwind scheme with flux limiter [1] [2] for the cell-face state $$c_{i+{1 \over 2}}$$ results in the following equation:

${c}_{i + {1 \over 2}} = c_i + \phi \left( r_{i + {1 \over 2}} \right) \left( c_i - c_{i-1} \right)$

where $$\phi$$ is the flux limiter function and $$r_{i + {1 \over 2}}$$ the upwind ratio of consecutive solution gradients:

$r_{i + {1 \over 2}} = {{c_{i+1} - c_{i} + \epsilon} \over {c_{i} - c_{i-1} + \epsilon}}$

There is a large number of flux limiters [3] implemented in DAE Tools:

• CHARM [not 2nd order TVD] (Zhou, 1995):

$$\phi(r)= \begin{cases} \frac{r\left(3r+1\right)}{\left(r+1\right)^{2}} & r>0, \lim_{r \rightarrow \infty} \phi(r)=3 \\ 0 & otherwise \end{cases}$$

• HCUS (not 2nd order TVD) (Waterson and Deconinck, 1995):

$$\phi(r) = \frac{ 1.5 \left(r+\left| r \right| \right)}{ \left(r+2 \right)} , \lim_{r \rightarrow \infty}\phi_{hc}(r) = 3$$

• HQUICK (not 2nd order TVD) (Waterson and Deconinck, 1995):

$$\phi(r) = \frac{2 \left(r + \left|r \right| \right)}{ \left(r+3 \right)}, \lim_{r \rightarrow \infty}\phi_{hq}(r) = 4$$

• Koren (Koren, 1993):

$$\phi(r) = \max \left[ 0, \min \left(2 r, \left(2 + r \right)/3, 2 \right) \right], \lim_{r \rightarrow \infty}\phi(r) = 2$$

• minmod - symmetric (Philip and Roe, 1986):

$$\phi (r) = \max \left[ 0 , \min \left( 1 , r \right) \right], \lim_{r \rightarrow \infty}\phi(r) = 1$$

• monotonized central (MC) – symmetric (van Leer, 1977):

$$\phi (r) = \max \left[ 0 , \min \left( 2 r, 0.5 (1+r), 2 \right) \right] , \lim_{r \rightarrow \infty}\phi(r) = 2$$

• Osher (Chatkravathy and Osher, 1983):

$$\phi (r) = \max \left[ 0 , \min \left( r, \beta \right) \right], \left(1 \leq \beta \leq 2 \right), \lim_{r \rightarrow \infty}\phi (r) = \beta$$

• ospre - symmetric (Waterson and Deconinck, 1995):

$$\phi (r) = \frac{1.5 \left(r^2 + r \right) }{\left(r^2 + r +1 \right)} , \lim_{r \rightarrow \infty}\phi (r) = 1.5$$

• smart (not 2nd order TVD) (Gaskell and Lau, 1988):

$$\phi(r) = \max \left[ 0, \min \left(2 r, \left(0.25 + 0.75 r \right), 4 \right) \right], \lim_{r \rightarrow \infty}\phi(r) = 4$$

• superbee – symmetric (Roe, 1986):

$$\phi (r) = \max \left[ 0, \min \left( 2 r , 1 \right), \min \left( r, 2 \right) \right] , \lim_{r \rightarrow \infty}\phi (r) = 2$$

• Sweby – symmetric (Sweby, 1984):

$$\phi (r) = \max \left[ 0 , \min \left( \beta r, 1 \right), \min \left( r, \beta \right) \right], \left(1 \leq \beta \leq 2 \right), \lim_{r \rightarrow \infty}\phi (r) = \beta$$

• UMIST (Lien and Leschziner, 1994):

$$\phi(r) = \max \left[ 0, \min \left(2 r, \left(0.25 + 0.75 r \right), \left(0.75 + 0.25 r \right), 2 \right) \right] , \lim_{r \rightarrow \infty}\phi(r) = 2$$

$$\phi (r) = \frac{r^2 + r}{r^2 + 1 } , \lim_{r \rightarrow \infty}\phi (r) = 1$$

• van Albada 2 : alternative form (not 2nd order TVD; Kermani, 2003)

$$\phi (r) = \frac{2 r}{r^2 + 1}, \lim_{r \rightarrow \infty}\phi(r) = 0$$

• van Leer - symmetric (van Leer, 1974):

$$\phi (r) = \frac{r + \left| r \right| }{1 + \left| r \right| } , \lim_{r \rightarrow \infty}\phi (r) = 2$$

For the diffusive flux, the gradient $$\left( \partial c \over \partial x \right)_{i + {1 \over 2}}$$ is evaluated using the standard second-order accurate central difference formula:

$\left( \partial c \over \partial x \right)_{i + {1 \over 2}} = {{c_{i+1} - c_i} \over h}$

except at the inflow and outflow boundaries where:

$\begin{split}\left( \partial c \over \partial x \right)_{{1 \over 2}} &= {{-8c_{1 \over 2} + 9c_1 - c_2} \over 3h} \\ \left( \partial c \over \partial x \right)_{n + {1 \over 2}} &= {{8c_{n + {1 \over 2}} - 9c_n + c_{n-1}} \over 3h}\end{split}$

The above convection-diffusion-reaction equation can be specified using the daeHRUpwindSchemeEquation class with the following functions:

• Accumulation term in the cell-centered finite-volume discretisation: dc_dt():

$$dc\_dt(i) = \int_{\Omega_i} {\partial c_i \over \partial t} dx$$

• Convection term in the cell-centered finite-volume discretisation: dc_dx() (may contain the $$\mathbf{S} = {1 \over u} \int_{\Omega_i} s(x) dx$$ integral for the consistent discretisation of the convection and the source terms):

$$dc\_dx(i) = c_{i + {1 \over 2}} - c_{i - {1 \over 2}}$$

or (if the source integral $$\mathbf{S}$$ has been specified):

$$dc\_dx(i) = \left( c_{i + {1 \over 2}} - \mathbf{S}_{i + {1 \over 2}} \right) - \left( c_{i - {1 \over 2}} - \mathbf{S}_{i - {1 \over 2}} \right)$$

• Diffusion term in the cell-centered finite-volume discretisation: d2c_dx2():

$$d2c\_dx2(i) = \left( \partial c \over \partial x \right)_{i + {1 \over 2}} - \left( \partial c \over \partial x \right)_{i - {1 \over 2}}$$

• Source term in the cell-centered finite-volume discretisation: source():

$$source(i) = \int_{\Omega_i} s_i dx$$

as given in the example below:

class modTutorial(daeModel):
def __init__(self, Name, Parent = None, Description = ""):
daeModel.__init__(self, Name, Parent, Description)

def DeclareEquations(self):
daeModel.DeclareEquations(self)

xp = self.x.Points
Nx = self.x.NumberOfPoints

c = lambda i: self.c(i)

# 1. Declare the HR upwind scheme object:
#     - c is a state variable
#     - x is a domain object
#     - Phi_Koren is a flux limiter function
hr = daeHRUpwindSchemeEquation(self.c, self.x, daeHRUpwindSchemeEquation.Phi_Koren, 1e-10)

# 2. Define the source term function
def s(i):
return self.c(i)**2

# 3. Declare the Convection-Diffusion-Reaction equation distributed on (0, L]:
for i in range(1, Nx):
eq = self.CreateEquation("c(%d)" % i)
eq.Residual = hr.dc_dt(i) + u * hr.dc_dx(i) - D * hr.d2c_dx2(i) - hr.source(s,i)

# Boundary conditions at x=0:
eq = self.CreateEquation("c(0)")
eq.Residual = c(0) - ...


It is desired that the discretisation of the source term should be consistent with that of the advection operator. For this purpose, if the source term integral: $$S(x) = {1 \over u} \int_{\Omega_i} s(x) dx$$ can be calculated analytically, the convection term can be rewritten as:

${\partial c \over \partial t} + u {\partial (c-\mathbf{S}) \over \partial x} - D {\partial^2 c \over \partial x^2} = 0$

and the semi-discrete equation becomes:

$\int_{\Omega_i} {\partial c_i \over \partial t} dx + u \left[ \left( c_{i + {1 \over 2}} - \mathbf{S}_{i + {1 \over 2}} \right) - \left( c_{i - {1 \over 2}} - \mathbf{S}_{i - {1 \over 2}} \right) \right] - D \left[ \left( \partial c_i \over \partial x \right)_{i + {1 \over 2}} - \left( \partial c_i \over \partial x \right)_{i - {1 \over 2}} \right] = 0$

The example above now becomes:

class modTutorial(daeModel):
def __init__(self, Name, Parent = None, Description = ""):
daeModel.__init__(self, Name, Parent, Description)

def DeclareEquations(self):
daeModel.DeclareEquations(self)

xp = self.x.Points
Nx = self.x.NumberOfPoints

c = lambda i: self.c(i)

# 1. Declare the HR upwind scheme object:
#     - c is a state variable
#     - x is a domain object
#     - u is velocity
#     - Phi_Koren is a flux limiter function
hr = daeHRUpwindSchemeEquation(self.c, self.x, daeHRUpwindSchemeEquation.Phi_Koren, 1e-10)

# 2. Consistent discretisation of convection and source terms:
#    Calculate an analytical integral of the source term S = 1/u * Integral(s(x)*dx)
def S(i):
C1 = 0.0
return c(i)**2 / 3 + C1

# 3. Declare the Convection-Diffusion-Reaction equation distributed on (0, L]
#    (with the S integral in the convection term):
for i in range(1, Nx):
eq = self.CreateEquation("c(%d)" % i)
eq.Residual = hr.dc_dt(i) + u * hr.dc_dx(i, S) - D * hr.d2c_dx2(i)

# Boundary Conditions:
eq = self.CreateEquation("c(0)")
eq.Residual = c(0) - ...


Footnotes

 [1] (1, 2) B. Koren. A robust upwind discretisation method for advection, diffusion and source terms. In: Vreugdenhil CB, Koren B, editors. Numerical Methods for Advection–Diffusion Problems. Braunschweig: Vieweg; 1993. ISBN-10:3528076453.
 [2] (1, 2) B. Koren. A robust upwind discretization method for advection, diffusion and source terms. Department of Numerical Mathematics. Report NM-R9308 (1993). PDF
 [3] Flux-limiter functions on Wikipedia

### 6.3.3. The Finite Element Method¶

DAE Tools support numerical simulation of partial differential equations on unstructured grids using the Finite Element method. The main idea is to utilise available state-of-the-art Finite Element libraries (i.e. deal.II) for low-level tasks such as mesh loading, management of finite element spaces, degrees of freedom, assembly of the system stiffness and mass matrices and the system load vector, setting the boundary conditions etc.. After the assembly phase in an external library the matrices are used to generate a set of equations in the following form:

$\left[ M_{ij} \right] \left\{ {dx_j} \over {dt} \right\} + \left[ A_{ij} \right] \left\{ x_j \right\} = \left\{ F_i \right\}$

where $$x_j$$ is a state variable, $$M_{ij}$$ and $$A_{ij}$$ are mass and stiffness matrices and $$F_i$$ is the load vector. This system is in a general case a DAE system, although it can also be a linear or a non-linear (if the mass matrix is zero). The generated set of equations are solved together with the rest of equations in the model.

The unique feature of this approach is a capability to use DAE Tools objects (parameters and variables) as a native data type in deal.II functions to specify boundary conditions, time varying coefficients and source terms. This way, the non-linear finite element systems are automatically supported and the equations resulting from the finite element discretisation are fully integrated with the rest of the model equations. Moreover, multiple FE systems can be created and coupled together.

The information required (from the modeller’s perspective):

• Mesh file with the specified boundary indicators (integers)
• Variables (degrees of freedom in deal.II) and their Finite Element spaces
• Quadrature formulas for elements and their faces
• The weak form of the problem which contains expressions for the cells’ and boundary faces’ contributions to the system mass and stiffness matrix and the load vector.

The weak form expressions are specified using the DAE Tools API that wraps deal.II concepts used to assembly the matrices/vectors. The weak forms in daetools contain expressions as they would appear in typical nested for loops. In deal.II a typical cell assembly loop (in C++) would look like (i.e. a very simple example given in step-7):

FEValues<dim>             fe_values(...);
std::vector<unsigned int> local_dof_indices;

typename DoFHandler<dim>::active_cell_iterator cell = dof_handler.begin_active(),
endc = dof_handler.end();
for(; cell != endc; ++cell)
{
fe_values.reinit(cell);
cell->get_dof_indices(local_dof_indices);

for(unsigned int q_point = 0; q_point < n_q_points; ++q_point)
{
for(unsigned int i = 0; i < dofs_per_cell; ++i)
{
for(unsigned int j = 0; j < dofs_per_cell; ++j)
{
+
fe_values.shape_value(i,q_point) *
fe_values.shape_value(j,q_point)) *
fe_values.JxW(q_point));
}

cell_rhs(i) += (fe_values.shape_value(i,q_point) *
rhs_values [q_point] * fe_values.JxW(q_point));
}
}
}


An equivalent problem in DAE Tools creates an execution context used in a generic loop to evaluate the cell/face contributions:

FEValues<dim>             fe_values(...);
std::vector<unsigned int> local_dof_indices;

// Create evaluation context objects where the expressions will be evaluated:
feCellContext<dim> cellContext(fe_values, local_dof_indices, ...);

typename DoFHandler<dim>::active_cell_iterator cell = dof_handler.begin_active(),
endc = dof_handler.end();
for(; cell != endc; ++cell)
{
// Update the evaluation context with the current cell.
// It will call fe_values.reinit(cell), cell->get_dof_indices(local_dof_indices) etc.
update(cellContext, cell);

for(unsigned int q_point = 0; q_point < n_q_points; ++q_point)
{
// update cellContext with the current quadrature point index

for(unsigned int i = 0; i < dofs_per_cell; ++i)
{
// update cellContext with the current i loop dof index

for(unsigned int j = 0; j < dofs_per_cell; ++j)
{
// update cellContext with the current j loop dof index

cell_matrix(i,j) += evaluate(cell_contribution_to_stiffness_matrix, cellContext);
}

// update cellContext with the current i, j, q indices

}
}
}


Apart from specifying the weak formulation using the single expressions evaluated in a generic loop as shown above, tuples of expressions representing independent terms evaluated in the q, i and j loops can be also used. This is useful for complex weak form expressions involving non-linear terms (i.e. dof approximations) and results in simpler expressions and a faster evaluation. The usage of separate items for different loop iterations is illustrated below:

FEValues<dim>             fe_values(...);
std::vector<unsigned int> local_dof_indices;

// Create evaluation context objects where the expressions will be evaluated:
feCellContext<dim> cellContext(fe_values, local_dof_indices, ...);

typename DoFHandler<dim>::active_cell_iterator cell = dof_handler.begin_active(),
endc = dof_handler.end();
for(; cell != endc; ++cell)
{
// Update the evaluation context with the current cell.
// It will call fe_values.reinit(cell), cell->get_dof_indices(local_dof_indices) etc.
update(cellContext, cell);

for(unsigned int q_point = 0; q_point < n_q_points; ++q_point)
{
// Temporary storage

// update cellContext with the current quadrature point index

q_loop_term = evaluate(q_loop_expression, cellContext);

for(unsigned int i = 0; i < dofs_per_cell; ++i)
{
// update cellContext with the current i loop dof index

i_loop_term = evaluate(i_loop_expression, cellContext);

for(unsigned int j = 0; j < dofs_per_cell; ++j)
{
// update cellContext with the current j loop dof index

j_loop_term = evaluate(j_loop_expression, cellContext);

cell_matrix_temp(i,j) += i_loop_term * j_loop_term;
}

cell_rhs_temp(i) += i_loop_term;
}

cell_rhs_temp    *= q_loop_term;
cell_matrix_temp *= q_loop_term;
}

// Add the temporary data to the cell matrix/vector
cell_rhs    += cell_rhs_temp;
cell_matrix += cell_matrix_temp;
}


Obviously, the generic loops can be used to solve many FE problems but not all. However, they can support a large number of problems at the moment. In the future they will be expanded to support a broader class of problems.

Equations produced by the Finite Element matrix assembly can be extremely long/complex. Under the hood, they will be simplified as much as possible. Some examples are given below.

DAE Tools provide four main classes to support the deal.II library:

1. In deal.II it represents a degree of freedom distributed on a finite element domain. In DAE Tools it represents a variable distributed on a finite element domain.

2. dealiiFiniteElementSystem_1D, dealiiFiniteElementSystem_2D and dealiiFiniteElementSystem_3D (implements daeFiniteElementObject)

It is a wrapper around deal.II FESystem<dim> class and handles all finite element related details. It uses information about the mesh, quadrature and face quadrature formulas, degrees of freedom and the FE weak formulation to assemble the system’s mass matrix (Mij), stiffness matrix (Aij) and the load vector (Fi).

3. Contains weak form expressions for the contribution of FE cells to the system/stiffness matrices, the load vector, boundary conditions and (optionally) surface/volume integrals (as an output).

4. daeFiniteElementModel

daeModel-derived class that use system matrices/vectors from the dealiiFiniteElementSystem_nD object to generate a system of equations.

A typical use-case scenario consists of the following steps:

1. The starting point is a definition of the dealiiFiniteElementSystem_nD class (where nD can be 1D, 2D or 3D). That includes specification of:

• Mesh file in one of the formats supported by deal.II (GridIn)
• Degrees of freedom (as a python list of dealiiFiniteElementDOF_nD objects). Every dof has a name which will be also used to declare DAE Tools variable with the same name, description, finite element space FE (deal.II FiniteElement<dim> instance) and the multiplicity
• Quadrature formulas for elements and their faces
2. Creation of daeFiniteElementModel object (similarly to the ordinary DAE Tools model) with the finite element system object as the last argument.

3. Definition of the weak form of the problem using the following functions (a version exist for 1D, 2D and 3D):

and constants: fe_i, fe_j and fe_q used to access the current indexes in the DOF and quadrature points loops.

The weak form contains the following contributions:

• Aij - cell contribution to the system stiffness matrix
• Mij - cell contribution to the system mass matrix
• Fi - cell contribution to the system load vector
• boundaryFaceAij - boundary face contribution to the system stiffness matrix
• boundaryFaceFi - boundary face contribution to the load vector
• innerCellFaceAij - inner cell face contribution to the system stiffness matrix
• innerCellFaceFi - inner cell face contribution to the load vector
• functionsDirichletBC - Dirichlet boundary conditions
• surfaceIntegrals - surface integrals
• volumeIntegrals - volume integrals

Example for the Helmholtz problem (step-7 tutorial from deal.II). The strong form of the Helmholtz equation is given by (with Neumann boundary conditions):

$\begin{split}-\Delta u + u &= f \; in \; \Omega \\ {\mathbf n}\cdot \nabla u &= g \; on \; \delta \Omega\end{split}$

The weak form of the above equation is:

${(\nabla u, \nabla v)}_\Omega + {(u,v)}_\Omega = {(f,v)}_\Omega + {(g,v)}_{\delta \Omega}$

Cell contributions to the stiffness matrix and the load vector are:

$\begin{split}A_{ij} &= \left(\nabla \varphi_i, \nabla \varphi_j\right) +\left(\varphi_i, \varphi_j\right) \\ F_i &= \left(f,\varphi_i\right) + \left(g, \varphi_i\right)_{\delta \Omega}\end{split}$

The c++ implementation in deal.II is given in the step-7 example and in the previous c++ listing. In DAE Tools the above equation is specified in the following way ($$\delta \Omega$$ boundary is marked with id = 0 and for simplicity, $$f$$ and $$g$$ are assumed constant):

class modTutorial(daeModel):
def __init__(self, Name, Parent = None, Description = ""):
daeModel.__init__(self, Name, Parent, Description)

dofs = [dealiiFiniteElementDOF_2D(name='u',
description='u description',
fe = FE_Q_2D(1),
multiplicity=1)]

self.fe_system = dealiiFiniteElementSystem_2D(meshFilename    = '...',        # path to the .msh file
dofs            = dofs)         # degrees of freedom

self.fe_model = daeFiniteElementModel('Helmholtz', self, 'Helholtz problem', self.fe_system)

def DeclareEquations(self):
daeModel.DeclareEquations(self)

# Create some auxiliary objects for readability
phi_i  = phi_2D ('u', fe_i, fe_q)
phi_j  = phi_2D ('u', fe_j, fe_q)
dphi_i = dphi_2D('u', fe_i, fe_q)
dphi_j = dphi_2D('u', fe_j, fe_q)
JxW    = JxW_2D(fe_q)
f      = 1.0
g      = 1.0

weakForm = dealiiFiniteElementWeakForm_2D(Aij = (dphi_i*dphi_j + phi_i*phi_j) * JxW,
Mij = 0.0,
Fi  = phi_i * f * JxW,
boundaryFaceFi = {0 : phi_i * g * JxW} )

self.fe_system.WeakForm = weakForm


In DAE Tools v1.7.1 an additional way of specifying the weak formulation has been introduced. Now, weak form contributions can also be python lists containing feExpression objects or tuples of feExpression objects. Tuples can contain two or three items. Matrix contributions contain three items representing q-loop, i-loop and j-loop expressions in the deal.ii matrix assembly. Load vector contributions contain two items representing q-loop and i-loop expressions in the deal.ii vector assembly. This way the expressions produced by the finite element system assembly can be much simpler and the simulations faster. The new way of specifying weak formulations is useful mostly for specification of non-linear terms (i.e. those including scalar or vector DOF approximations).

For instance, the convection term in the heat convection-diffusion equation (tutorial_dealii_7.py) can be specified in a more efficient way:

class modTutorial(daeModel):
...
def DeclareEquations(self):
...

# Contributions from the Stokes equation:
# Using the new way (q-loop, i-loop tuple for the load vector contributions):
Fi_buoyancy        = (T_dof, -rho * beta * (gravity * phi_vector_u_i) * JxW)

# Contributions from the continuity equation:
Aij_continuity     = ...

# Contributions from the heat convection-diffusion equation:
Mij_T_accumulation = ...
# Using the new way (q-loop, i-loop, j-loop tuple for matrix contributions):
Aij_T_convection   = (u_dof, phi_T_i, dphi_T_j * JxW)
Aij_T_diffusion    = ...
Fi_T_source        = ...

# Total contributions (using the new way - python lists of expressions or tuples):
Mij = [Mij_T_accumulation]
Fi  = [Fi_T_source, Fi_buoyancy]

weakForm = dealiiFiniteElementWeakForm_2D(Aij = Aij,
Mij = Mij,
Fi  = Fi)


Using the old way the vector DOF approximation ($$u_{dof}$$) is evaluated $$N_{quadrature\,points} \cdot N_{dofs\,per\,cell} \cdot N_{dofs\,per\,cell}$$ times while only $$N_{quadrature\,points}$$ evaluations are required. A vector dof approximation is an expensive operation calculated in the following way:

$u_{dof} = \sum\limits_{j=1}^{N_{dofs\,per\,cell}} \phi^u(j,q) \cdot u(j)$

where $$\phi^u(j,q)$$ is a rank=1 tensor. Reduction in the number of evaluations by $$N_{dofs\,per\,cell}^2$$ leads to the huge amounts of memory and computation time saved.

More information about the finite element method in DAE Tools can be found in the API reference and in Finite Element Tutorials (in particular Tutorial deal.II 1).

## 6.4. Configuration¶

Various options related to DAE Tools simulation can be set in the daetools.cfg configuration file (in JSON format). The configuration file can be obtained using the global function daeGetConfig():

cfg = daeGetConfig()


which returns the daeConfig object. The configuration file is first searched in the HOME directory, the application folder and finally in the default location. It also can be specified manually using the function daeSetConfigFile(). However, this has to be done before the DAE Tools objects are created. The current configuration file name can be retrieved using the ConfigFileName attribute. The options can also be programmatically changed using the Get/Set functions i.e. GetBoolean()/SetBoolean():

cfg = daeGetConfig()
checkUnitsConsistency = cfg.GetBoolean("daetools.core.checkUnitsConsistency")
cfg.SetBoolean("daetools.core.checkUnitsConsistency", True)


Supported data types are: Boolean, Integer, Float and String. The whole configuration file with all options can be printed using:

cfg = daeGetConfig()
print(cfg)


The sample configuration file is given below:

{
"daetools":
{
"core":
{
"checkForInfiniteNumbers"         : false,
"eventTolerance"                  : 1E-7,
"pythonIndent"                    : "    ",
"checkUnitsConsistency"           : true,
"resetLAMatrixAfterDiscontinuity" : true,
"printInfo"                       : false,
"deepCopyClonedNodes"             : true,
"equations":
{
"simplifyExpressions" : false,
"parallelEvaluation"  : true,
}
},
"activity":
{
"timeHorizon"                       : 100.0,
"reportingInterval"                 : 1.0,
"reportTimeDerivatives"             : false,
"reportSensitivities"               : false,
"objFunctionAbsoluteTolerance"      : 1E-8,
"constraintsAbsoluteTolerance"      : 1E-8,
"measuredVariableAbsoluteTolerance" : 1E-8
},
"datareporting":
{
"tcpipNumberOfRetries"      : 10,
"tcpipRetryAfterMilliSecs"  : 1000
},
"logging":
{
"tcpipLogPort"    : 51000
},
"minlpsolver":
{
"printInfo": false
},
"IDAS":
{
"relativeTolerance"             : 1E-5,
"nextTimeAfterReinitialization" : 1E-7,
"printInfo"                     : false,
"numberOfSTNRebuildsDuringInitialization": 1000,
"SensitivitySolutionMethod"     : "Staggered",
"SensErrCon"                    : false,
"maxNonlinIters"                : 3,
"sensRelativeTolerance"         : 1E-5,
"sensAbsoluteTolerance"         : 1E-5,
"MaxOrd"            : 5,
"MaxNumSteps"       : 1000,
"InitStep"          : 0.0,
"MaxStep"           : 0.0,
"MaxErrTestFails"   : 10,
"MaxNonlinIters"    : 4,
"MaxConvFails"      : 10,
"NonlinConvCoef"    : 0.33,
"SuppressAlg"       : false,
"NoInactiveRootWarn": false,
"NonlinConvCoefIC"  : 0.0033,
"MaxNumStepsIC"     : 5,
"MaxNumJacsIC"      : 4,
"MaxNumItersIC"     : 10,
"LineSearchOffIC"   : false
},
"superlu":
{
"factorizationMethod"      : "SamePattern_SameRowPerm",
"useUserSuppliedWorkSpace" : false,
"workspaceSizeMultiplier"  : 3.0,
"workspaceMemoryIncrement" : 1.5
},
"BONMIN":
{
"IPOPT":
{
"print_level"          : 0,
"tol"                  : 1E-5,
"linear_solver"        : "mumps",
"hessianApproximation" : "limited-memory",
}
},
"NLOPT":
{
"printInfo"  : false,
"xtol_rel"   : 1E-6,
"xtol_abs"   : 1E-6,
"ftol_rel"   : 1E-6,
"ftol_abs"   : 1E-6,
"constr_tol" : 1E-6
},
"deal_II":
{
"printInfo": false,
"assembly":
{
"parallelAssembly" : "OpenMP",
"queueSize"        : 32
}
}
}
}


## 6.5. Units and quantities¶

There are three classes in the framework: base_unit, unit and quantity. The base_unit class handles seven SI base dimensions: length, mass, time, electric current, temperature, amount of substance, and luminous intensity (m, kg, s, A, K, mol, cd). The unit class operates on base units defined using the base seven dimensions. The quantity class defines a numerical value in terms of a unit of measurement (it contains the value and its units).

There is a large pool of base units and units defined (all base and derived SI units) in the pyUnits module:

• m
• s
• cd
• A
• mol
• kg
• g
• t
• K
• sr
• min
• hour
• day
• l
• dl
• ml
• N
• J
• W
• V
• C
• F
• Ohm
• T
• H
• S
• Wb
• Pa
• P
• St
• Bq
• Gy
• Sv
• lx
• lm
• kat
• knot
• bar
• b
• Ci
• R
• rd
• rem

and all above with 20 SI prefixes:

• yotta = 1E+24 (symbol Y)
• zetta = 1E+21 (symbol Z)
• exa = 1E+18 (symbol E)
• peta = 1E+15 (symbol P)
• tera = 1E+12 (symbol T)
• giga = 1E+9 (symbol G)
• mega = 1E+6 (symbol M)
• kilo = 1E+3 (symbol k)
• hecto = 1E+2 (symbol h)
• deka = 1E+1 (symbol da)
• deci = 1E-1 (symbol d)
• centi = 1E-2 (symbol c)
• milli = 1E-3 (symbol m)
• micro = 1E-6 (symbol u)
• nano = 1E-9 (symbol n)
• pico = 1E-12 (symbol p)
• femto = 1E-15 (symbol f)
• atto = 1E-18 (symbol a)
• zepto = 1E-21 (symbol z)
• yocto = 1E-24 (symbol y)

for instance: kmol (kilo mol), MW (mega Watt), ug (micro gram) etc.

New units can be defined in the following way:

rho = unit({"kg":1, "dm":-3})


The constructor accepts a dictionary of base_unit : exponent items as its argument. The above defines a new density unit $$\frac{kg}{dm^3}$$.

The unit class defines mathematical operators *, / and ** to allow creation of derived units. Thus, the density unit can be also defined in the following way:

mass   = unit({"kg" : 1})
volume = unit({"dm" : 3})
rho = mass / volume


Quantities are created by multiplying a value with unit objects:

heat = 1.5 * J


The quantity class defines all mathematical operators (+, -, *, / and **) and mathematical functions.

heat = 1.5 * J
time = 12 * s
power = heat / time


Units-consistency of equations and logical conditions is strictly enforced (although it can be switched off, if required). For instance, the operation below is not allowed:

power = heat + time


since their units are not consistent (J + s).

## 6.6. Logging¶

Log objects define an API for sending messages from a simulation to the user. Currently three implementations exist: daeStdOutLog (prints messages to the standard c++ output), daePythonStdOutLog (prints messages using Python print() function), daeFileLog (stores messages into a specified text file), and daeTCPIPLog (sends messages via TCP/IP protocol to the daeTCPIPLogServer).

The messages are sent using Message() function.

## 6.7. DAE Solvers¶

Currently only Sundials IDAS solver is supported for the numerical solution of DAE systems and for calculation of sensitivities.

DAE solvers have some user-tunable options such as relative and absolute tolerances used to control the accuracy of the integration process. In DAE Tools, scalar relative and a vector of absolute tolerances are used. Every variable has the default absolute tolerances set in the associated variable type. These default values can be changed during the initialisation of the system (in SetUpVariables()) using the SetAbsoluteTolerances() function. On the other hand, the scalar relative tolerance can be set using the RelativeTolerance attribute or in the daetools.cfg configuration file. The default value is $$10^{-5}$$.

The DAE solver statistic can be obtained after every call to one of Integrate() functions using the IntegratorStats attribute. It contains statistics returned by IDAGetIntegratorStats() and IDAGetNonlinSolvStats() Sundials IDAS functions. IntegratorStats attribute is a dictionary with the following data:

stats = {'ActualInitStep': 0.00104,
'CurrentOrder': 5.0,
'CurrentStep': 89.01075,
'CurrentTime': 1000.0,
'LastOrder': 5.0,
'LastStep': 44.50537,
'NumErrTestFails': 4.0,
'NumLinSolvSetups': 22.0,
'NumNonlinSolvConvFails': 0.0,
'NumNonlinSolvIters': 128.0,
'NumResEvals': 130.0,
'NumSteps': 91.0}


daeIDAS class contains the following functions:

The current iteration variable values, time derivatives, residuals, Jacobian matrix and sensitivity residuals can be accessed using the following properties:

User-defined functions can be attached to daeIDAS objects. For instance:

log          = daePythonStdOutLog()
daesolver    = daeIDAS()
datareporter = daeTCPIPDataReporter()
simulation   = simTutorial()

def OnCalculateResiduals():
# In general, this function is a member of daeIDAS class.
# However, non-member functions can also be attached and the global daesolver object used.
y   = daesolver.Values
yt  = daesolver.TimeDerivatives
res = daesolver.Residuals
print('y: % s' % y.Values)
print('yt: % s' % yt.Values)
print('residuals: % s' % res.Values)

daesolver.OnCalculateResiduals = OnCalculateResiduals


## 6.8. Linear Equation Solvers¶

DAE Tools support direct dense and sparse matrix linear equation (LA) solvers (sequential and multi-threaded versions). In addition to the built-in Sundials dense linear solver, several third party libraries are interfaced: SuperLU/SuperLU_MT, Pardiso, Intel Pardiso, Trilinos Amesos (KLU, Umfpack, SuperLU, Lapack), and Trilinos AztecOO (with built-in, Ifpack or ML preconditioners):

• Trilinos (Amesos, AztecOO): pyTrilinos class from daetools.solvers.trilinos module.
• SuperLU: pySuperLU class from daetools.solvers.superlu module.
• SuperLU_MT: pySuperLU_MT class from daetools.solvers.superlu_mt module.
• Pardiso: pyPardiso class from daetools.solvers.pardiso module.
• Intel Pardiso: pyIntelPardiso class from daetools.solvers.intel_pardiso module.

Modules can be imported in the following way:

# Import Trilinos (Amesos, AztecOO):
from daetools.solvers.trilinos import pyTrilinos

# Import SuperLU solver:
from daetools.solvers.superlu import pySuperLU

# Import SuperLU_MT solver:
from daetools.solvers.superlu_mt import pySuperLU_MT

# Import Pardiso solver:
from daetools.solvers.pardiso import pyPardiso

# Import Intel Pardiso solver:
from daetools.solvers.intel_pardiso import pyIntelPardiso


The incidence matrix of the DAE system can be saved in the text-only .xpm format:

lasolver.SaveAsXPM('path to .xpm')


## 6.9. Data Reporting¶

Simulation results are obtained using the data reporter and data receiver concepts. Data reporter defines a functionality used by a simulation object to report the simulation results. Data receiver defines a functionality/data structures for accessing the simulation results.

By default, parameters and variables are not reported to a data reporter. Individual parameters/variables can be selected/deselected using the ReportingOn boolean property, while all variables from a model and all child-models can be reported using the SetReportingOn() function. In addition, time derivatives for all enabled variables can be reported using the ReportTimeDerivatives property. If sensitivity analysis is enabled, the sensitivities for all enabled variables can be reported using the ReportSensitivities property.

# Enable reporting of all variables
simulation.m.SetReportingOn(True)

# Enable reporting of time derivatives for all reported variables
simulation.ReportTimeDerivatives = True

# Enable reporting of sensitivities for all reported variables
simulation.ReportSensitivities = True


There is a large number of available data reporters in DAE Tools:

The best starting point in creating custom data reporters is daeDataReporterLocal class. It internally does all the processing and offers to users the Process property (daeDataReceiverProcess instance) which contains all domains, parameters and variables in the simulation.

The following functions have to be implemented (overloaded):

• Connect(): Connects the data reporter. In the case when the local data reporter is used it may contain a file name, for instance.
• Disconnect(): Disconnects the data reporter.
• IsConnected(): Checks if the data reporter is connected or not.

All functions must return True if successful or False otherwise.

An empty custom data reporter is presented below:

class MyDataReporter(daeDataReporterLocal):
def __init__(self):
daeDataReporterLocal.__init__(self)

def Connect(self, ConnectString, ProcessName):
...
return True

def Disconnect(self):
...
return True

def IsConnected(self):
...
return True


To write the results into a file the daeDataReporterFile base class can be used. It writes the data into a file in the WriteDataToFile() function called in the Disconnect() function. The only function that needs to be overloaded is WriteDataToFile() while the base class handles all other operations.

daeDataReceiverProcess class contains the following properties that can be used to obtain the results data from a data reporter:

The example below shows how to save the results to the Matlab .mat file:

class MyDataReporter(daeDataReporterFile):
def __init__(self):
daeDataReporterFile.__init__(self)

def WriteDataToFile(self):
mdict = {}
for var in self.Process.Variables:
mdict[var.Name] = var.Values

import scipy.io
scipy.io.savemat(self.ConnectString,
mdict,
appendmat=False,
format='5',
long_field_names=False,
do_compression=False,
oned_as='row')


The filename is provided as the first argument (connectString) of the Connect() function.

Only one data receiver is implemented: daeTCPIPDataReceiver. It is used by the DAE Plotter application to receive the results.

### 6.9.1. DAE Plotter Application¶

The simulation results can be plotted using the DAE Tools Plotter application. It can be started using the following commands:

# GNU/Linux:
daeplotter

# Windows:
daeplotter.bat

# Platform independent:
python -m daetools.dae_plotter.plotter
# or
python -c "from daetools.dae_plotter.plotter import daeStartPlotter; daeStartPlotter()"


It supports the following types of plots:

• 2D plot (using matplotlib)
• 3D plot (Mayavi)
• Animated 2D plot (using matplotlib)
• Auto-update 2D plot (using matplotlib)
• User-defined 2D plot (using the user-supplied source code)
• Variable 1 vs. Variable 2 2D plot (using matplotlib)
• 2D plot of user-provided data (using matplotlib)
• 2D plot using the .vtk file

After choosing a desired type, a Choose variable dialog appears where a variable to be plotted can be selected and information about domains specified - some domains should be fixed while leaving another free by selecting * from the list (to create a 2D plot one domain must remain free, while for a 3D plot two domains):

2D plots can be saved as templates (.pt files) which store the information in JSON format.

{
"curves": [
[
"tutorial4.T",
[
-1
],
[
"*"
],
"tutorial4.T(*)",
{
"color": "black",
"linestyle": "-",
"linewidth": 0.5,
"marker": "o",
"markeredgecolor": "black",
"markerfacecolor": "black",
"markersize": 6
}
]
],
"gridOn": true,
"legendOn": true,
"plotTitle": "",
"updateInterval": 0,
"windowTitle": "tutorial4.T(*)",
"xlabel": "Time (s)",
"xmax": 525.0,
"xmax_policy": 0,
"xmin": -25.0,
"xmin_policy": 0,
"xscale": "linear",
"xtransform": 1.0,
"ylabel": "T (K)",
"ymax": 361.74772465755922,
"ymax_policy": 1,
"ymin": 279.2499308975365,
"ymin_policy": 1,
"yscale": "linear",
"ytransform": 1.0,
}


## 6.10. Executing simulations¶

### 6.10.1. Initialising domains and parameters¶

Domains and parameters are initialised in the SetUpParametersAndDomains() function. In the case of deep model hierarchies domains and parameters having identical names can be initialised using the propagation technique and PropagateDomain() and PropagateParameter() functions. These functions traverse down all models in the hierarchy (starting with the calling model) and copy the properties of the given domain/parameter to all domains/properties with the same name.

### 6.10.2. Initialising variables (initial conditions, degrees of freedom)¶

All information related to variables are set in the SetUpVariables() function. Here, operations such as setting initial conditions, initial guesses, absolute tolerances and fixing degrees of freedom can be performed.

There are two initial condition modes supported by the Sundials IDAS solver (daeeInitialConditionMode):

The initial condition mode can be set using the InitialConditionMode property (during the initialisation of the system in the SetUpVariables() function).

The initial conditions for Finite Element models are set in a different way (due to a nature of the block matrices used). Here, the function setFEInitialConditions() is used. It accepts the following arguments:

def SetUpVariables(self):
setFEInitialConditions(self.m.fe_model, self.m.fe_system, 'T', 300.0)


### 6.10.3. Developing schedules (operating procedures)¶

The model specified in the simulation constructor is integrated in time in the Run() function. The default implementation iterates over the specified time horizon (TimeHorizon property), integrates for time intervals specified using the ReportingInterval property and reports the data. If a discontinuity occurs during the current integration interval, the precise discontinuity time is determined, model integrated until the discontinuity time point, data reported, the DAE system reinitialised and integration resumed.

The user-defined schedule can be implemented by overloading the Run() function. Here, a custom schedule can be specified using the following functions from the daeSimulation class:

• Integrate() integrates the system until the given time horizon is reached and reports the data after every reporting interval. If the stopCriterion argument is set to eStopAtModelDiscontinuity the simulation will be stopped at every discontinuity occurrence and if the reportDataAroundDiscontinuities argument is set to True the data data reported at the discontinuity and after the system is reinitialised. The default value for reportDataAroundDiscontinuities is True. The condition that triggered the discontinuity can be retrieved using the LastSatisfiedCondition property which returns the daeCondition object.
• IntegrateForTimeInterval() integrates the system for the given time interval (in seconds). Its behaviour can be controlled using the stopCriterion and reportDataAroundDiscontinuities arguments in the same way as in the Integrate() function.
• IntegrateUntilTime() integrates the system until the specified time is reached (in seconds). Its behaviour can be controlled using the stopCriterion and reportDataAroundDiscontinuities arguments in the same way as in the Integrate() function.
• ReportData() reports the data at the current time in simulation.
• The functions ReSetInitialCondition() and ReAssignValue() can be used to re-set the initial conditions and re-assign the values of degrees of freedom. These calls must be followed by a call to the ReInitialize() function to reinitialise the system after the variable values have been modified.

The functionality of the default Run() function is roughly identical to the following python code:

def Run(self):
# Python implementation of daeSimulation::Run() C++ function.

import math
while self.CurrentTime < self.TimeHorizon:
# Get the time step (based on the TimeHorizon and the ReportingInterval).
# Do not allow to get past the TimeHorizon.
t = self.NextReportingTime
if t > self.TimeHorizon:
t = self.TimeHorizon

# If the flag is set - a user tries to pause the simulation, therefore return.
if self.ActivityAction == ePauseActivity:
self.Log.Message("Activity paused by the user", 0)
return

# If a discontinuity is found, loop until the end of the integration period.
# The data will be reported around discontinuities!
while t > self.CurrentTime:
self.Log.Message("Integrating from [%f] to [%f] ..." % (self.CurrentTime, t), 0)
self.IntegrateUntilTime(t, eStopAtModelDiscontinuity, True)

# After the integration period, report the data.
self.ReportData(self.CurrentTime)

# Set the simulation progress.
newProgress = math.ceil(100.0 * self.CurrentTime / self.TimeHorizon)
if newProgress > self.Log.Progress:
self.Log.Progress = newProgress


A very simple schedule can be found in the Tutorial 7:

# The schedule:
#  1. Run the simulation for 100s using the function daeSimulation.IntegrateForTimeInterval()
#     and report the data using the function daeSimulation.ReportData().
#  2. Re-assign the value of Qin to 2000W. After re-assigning DOFs or re-setting initial conditions
#     the function daeSimulation.Reinitialize() has to be called to reinitialise the DAE system.
#     Use the function daeSimulation.IntegrateUntilTime() to run until the time reaches 200s
#     and report the data.
#  3. Re-assign the variable Qin to a new value 1500W, re-initialise the temperature again to 300K
#     re-initialise the system, run the simulation until the TimeHorizon is reached using the function
#     daeSimulation.Integrate() and report the data.
def Run(self):
# 1. Integrate for 100s
self.Log.Message("OP: Integrating for 100 seconds ... ", 0)
time = self.IntegrateForTimeInterval(100, eDoNotStopAtDiscontinuity)
self.ReportData(self.CurrentTime)
self.Log.SetProgress(int(100.0 * self.CurrentTime/self.TimeHorizon));

# 2. Set Qin=2000W and integrate until t=200s
self.m.Qin.ReAssignValue(750 * W)
self.Reinitialize()
self.ReportData(self.CurrentTime)
self.Log.Message("OP: Integrating until time = 200 seconds ... ", 0)
time = self.IntegrateUntilTime(200, eDoNotStopAtDiscontinuity)
self.ReportData(self.CurrentTime)
self.Log.SetProgress(int(100.0 * self.CurrentTime/self.TimeHorizon));

# 3. Set Qin=1.5E6 and integrate until the specified TimeHorizon
self.m.Qin.ReAssignValue(1000 * W)
self.m.T.ReSetInitialCondition(300 * K)
self.Reinitialize()
self.ReportData(self.CurrentTime)
self.Log.SetProgress(int(100.0 * self.CurrentTime/self.TimeHorizon))
self.Log.Message("OP: Integrating from " + str(time) + " to the time horizon (" + str(self.TimeHorizon) + ") ... ", 0)
time = self.Integrate(eDoNotStopAtDiscontinuity)
self.ReportData(self.CurrentTime)
self.Log.SetProgress(int(100.0 * self.CurrentTime/self.TimeHorizon));
self.Log.Message("OP: Finished", 0)


In addition, the Advanced Tutorial 1 illustrates interactive schedules where the pyQt graphical user interface (GUI) is utilised to manipulate the model variables and integrate the system in time:

### 6.10.4. Exploring models using the GUI (Simulation Explorer)¶

The model structure and the domains/parameters/variables data can be interactively explored and updated using the Simulation Explorer GUI. The Simulation Explorer can be started using the following code:

...
# Create and initialize the simulation and the auxiliary objects and
# determine the consistent initial conditions using the SolveInitial function.
...
simulation.SolveInitial()

# Show the simulation explorer GUI
app = daeCreateQtApplication(sys.argv)
se = daeSimulationExplorer(app, simulation)
se.exec_()


or from the DAE Toools Simulator Start/Show simulation explorer ad run... button:

Screenshots of the running Simulation Explorer:

Fig. 6.3 Runtime simulation options

Fig. 6.4 Parameters values

Fig. 6.5 Initial conditions

Fig. 6.6 Degrees of freedom

### 6.10.5. Parallel computation¶

Parallel computation is supported using the shared-memory parallel programming model at the moment. The following part of the code support parallelisation:

• Evaluation of equations, Jacobian matrix and sensitivity residuals using the OpenMP interface.

This can be controlled in the daetools.cfg config file, section daetools.core.equations. The parallel evaluation can be specified using the boolean parallelEvaluation option while the number of threads using the numThreads option. If numThreads is 0 the default number of threads will be used (typically the number of cores in the system).

• Assembly of Finite Element systems using the OpenMP interface or Intel Thread Building Blocks (TBB)

This can be also controlled in the daetools.cfg config file, section daetools.deal_II.assembly. The parallel assembly can be specified using the string parallelAssembly option while the number of threads using the numThreads option. parallelAssembly can be one of: Sequential, OpenMP or TBB (multithreaded in GNU/Linux only). If numThreads is 0 the default number of threads will be used (typically the number of cores in the system). The queueSize specifies the size of the internal queue; when this size is reached the local data are copied to the global matrices.

• Solution of systems of linear equations (SuperLU_MT, Pardiso and Intel Pardiso solvers)

• Global Sensitivity Analysis (using multiprocessing.Pool available in Python)

In addition, there is an experimental code generator that generates C++ source code with the support for MPI interface.

## 6.11. DAE Tools web services¶

Simulations can be loaded and executed using the Representational state transfer (REST) web service. RESTful API is provided for almost complete daeSimulation functionality (daetools_ws) and for all FMI v2 for co-simulation functions (daetools_fmi_ws). The RESTful API is language-independent and can be used from any language (i.e. JavaScript, Python, C++ ...).

Fig. 6.7 REST web service

Web services can be started using the following commands:

# DAE Tools simulations web service.
# By default, starts the web service on the "localhost" (http://127.0.0.1:8001)
python -m daetools.dae_simulator.daetools_ws

# Individual simulations as a web service.
# Add the simulation names to the 'availableSimulations' dictionary and start the service.
# The loaderFunction is a Python callable object that returns an initialised simulation object
# and will be called when a client requests a simulation by name.
availableSimulations = {}
daeSimulationWebService.runSimulationsAsWebService(availableSimulations)

# FMI v2 web service.
# By default, starts the web service on the "localhost" (http://127.0.0.1:8002)
python -m daetools.dae_simulator.daetools_fmi_ws


JavaScript client classes are developed for both types of web services. Classes are located in the daetools/dae_simulator folder. web_service.js contains the web service client, daetools_ws.js contains daeSimulation interface and daetools_fmi_ws.js contains FMI interface.

JavaScript client interface for daetools_ws simulations web service:

class daeSimulation
{
AvailableSimulations();
Finalize();
get ModelInfo();
get Name();
get DataReporter();
get DAESolver();
get CurrentTime();
get TimeHorizon();
set TimeHorizon(timeHorizon);
get ReportingInterval();
set ReportingInterval(reportingInterval);
Run();
SolveInitial();
Reinitialize();
Reset();
ReportData();
Integrate(stopAtDiscontinuity, reportDataAroundDiscontinuities);
IntegrateForTimeInterval(timeInterval, stopAtDiscontinuity, reportDataAroundDiscontinuities);
IntegrateUntilTime(time, stopAtDiscontinuity, reportDataAroundDiscontinuities);
GetParameterValue(name);
GetVariableValue(name);
GetActiveState(stnName);
SetParameterValue(name, value);
ReAssignValue(name, value);
ReSetInitialCondition(name, value);
SetActiveState(stnName, activeState);
}


JavaScript client interface for daetools_fmi_ws FMI web service:

class daeFMI2Simulation
{
fmi2Instantiate(instanceName, guid, resourceLocation);
fmi2Terminate();
fmi2FreeInstance();
fmi2SetupExperiment(toleranceDefined, tolerance, startTime, stopTimeDefined, stopTime);
fmi2EnterInitializationMode();
fmi2ExitInitializationMode();
fmi2Reset();
fmi2DoStep(currentCommunicationPoint, communicationStepSize, noSetFMUStatePriorToCurrentPoint);
fmi2CancelStep();
fmi2GetReal(valReferences);
fmi2SetReal(valReferences, values);
fmi2GetString(valReferences);
fmi2SetString(valReferences, values);
fmi2GetBoolean(valReferences);
fmi2SetBoolean(valReferences, values);
fmi2GetInteger(valReferences);
fmi2SetInteger(valReferences, values);
}


A sample simulation in JavaScript is given in the following listing:

// Create the web service client.
var webService = new daeWebService('127.0.0.1', 8001, 'daetools_ws');

// Create the simulation object.
var simulation = new daeSimulation(webService);

// Load simulation by name (if it has been started as a web service):
// or load one of tutorials:

var reportingInterval = simulation.ReportingInterval;
var timeHorizon       = simulation.TimeHorizon;
var currentTime       = simulation.CurrentTime;

// Get the consistent initial conditions.
simulation.SolveInitial();

// Integrate the system in a loop until the time horizon is reached.
while(currentTime < timeHorizon)
{
// Get the next time (based on the TimeHorizon and the ReportingInterval).
// Do not allow to get past the TimeHorizon.
var t = currentTime + reportingInterval;
if(t > timeHorizon)
t = timeHorizon;

// If a discontinuity is found, loop until the end of the integration period.
// The data will be reported around discontinuities!
while(t > currentTime)
{
log('Integrating from ' + currentTime.toFixed(2) + ' to ' + t.toFixed(2) + ' ...');
currentTime = simulation.IntegrateUntilTime(t, true, true);
}

// After the integration period, report the data.
simulation.ReportData()

// Set the simulation progress.
var newProgress = Math.ceil(100.0 * currentTime / timeHorizon)
setProgress(newProgress);
}

// Clean up.
simulation.Finalize();


Calls to the above functions translate to HTTP requests. For instance, a call to simulation.IntegrateUntilTime(100.0, true, true) results in a HTTP request sent to the daetools_ws web service: http://127.0.0.1:8001/daetools_ws?simulationID=ba2a694a-b591-11e7-9f58-680715e7b846&function=IntegrateUntilTime&time=100.0&stopAtDiscontinuity=true&reportDataAroundDiscontinuities=true. The response is returned in JSON format.

Sample html pages with the Graphical User Interface (GUI) using JavaScript and Plotly.js library are provided for both types of web services and presented in figure below. Web pages are located in the daetools/dae_simulator folder: daetools_ws_test.html and daetools_fmi_ws_test.html.

Fig. 6.8 HTML+JavaScript GUI for daetools_ws web service client

Fig. 6.9 Plots produced by HTML GUI for daetools_ws web service client using Plotly.js library

Fig. 6.10 HTML+JavaScript GUI for daetools_fmi_ws web service client

Fig. 6.11 Plots produced by daetools_fmi_ws web service client using Plotly.js library

## 6.12. Generating code for other modelling languages¶

DAE Tools can generate code for several modelling or programming languages (Modelica, gPROMS, c99, c++ with MPI). The code generation should be performed after the simulation is initialised (ideally after a call to SolveInitial() so that the consistent initial conditions are determined and available).

# Generate Modelica source code:
from daetools.code_generators.modelica import daeCodeGenerator_Modelica
cg = daeCodeGenerator_Modelica()
cg.generateSimulation(simulation, tmp_folder)

# Generate gPROMS source code:
from daetools.code_generators.gproms import daeCodeGenerator_gPROMS
cg = daeCodeGenerator_gPROMS()
cg.generateSimulation(simulation, tmp_folder)

# Generate c++ MPI code for 4 nodes:
from daetools.code_generators.cxx_mpi import daeCodeGenerator_cxx_mpi
cg = daeCodeGenerator_cxx_mpi()
cg.generateSimulation(simulation, tmp_folder, 4)


The generateSimulation() function requires an initialised simulation object and the directory where the code will be generated.

The code can be also generated from the Generate code... option in the Simulation Explorer GUI:

Fig. 6.12 Code generation from the Simulation Explorer GUI

For more details on how to use code-generation have a look on Advanced Tutorial 3 and Advanced Tutorial 4.

## 6.13. Simulating models in other simulators (co-simulation)¶

DAE Tools can also wrap the models developed in python for use in other software/simulators: FMI (Functional Mock-up Interface for co-simulation), Matlab, Scilab and GNU Octave MEX functions, and Simulink S-functions.

### 6.13.1. Functional Mock-up Interface for co-simulation (FMI)¶

DAE Tools models can be used to generate .fmu files for FMI for co-simulation.

Nota bene

The model inputs are defined by the inlet ports while the model outputs by the outlet ports. In addition, assigned variables (degrees of freedom) from the top-level model are added to the list of inputs and the rest of variables from the top-level model can be added as either local FMI variables or as outputs.

Generating FMU files for use in FMI capable simulators is similar to the code-generation procedure. The generateSimulation() function generates a simulation name.fmu file which is basically a zip file with the files required by the FMI standard. Here, the generateSimulation() function requires the following arguments:

• simulation: initialised daeSimulation object
• directory: directory where the .fmu file will be generated
• py_simulation_file: a path to the python file with the simulation source code
• create_simulation_callable_object: the name of python callable object that returns an initialized daeSimulation object
• arguments: arguments for the above callable object; can be anything that python accepts
• additional_files: paths to the additional files to pack into a .fmu file (empty list by default)
• localsAsOutputs: if True all variables from the top-level model will be treated as model outputs, otherwise as locals
• add_xml_stylesheet: if True the xsl file daetools-fmi.xsl will be added to the modelDescription.xml file
• useWebService: if True the web service version (fmi_ws) shared library will be packed into the fmu file. DAE Tools FMI web service can be started using: python -m daetools.dae_simulator.daetools_fmi_ws. The fmu will attempt to start it automatically and connect during the initialisation phase.

All tutorials provide run(**kwargs) function that can be used to run a simulation (using the console or Qt GUI) and for co-simulation to return an initialised simulation object (if the initializeAndReturn argument is True). This way the same setup can be used for different activities.

from daetools.code_generators.fmi import daeCodeGenerator_FMI
cg = daeCodeGenerator_FMI()
cg.generateSimulation(simulation,
directory            = tmp_folder,
py_simulation_file   = __file__,
callable_object_name = 'run',
arguments            = 'initializeAndReturn = True',
localsAsOutputs      = True,


In addition, the create_simulation_callable_object argument can be any other user-defined function (as specified in the Advanced Tutorial 3 example):

# This function is used by daetools_mex, daetools_s and daetools_fmi_cs to load a simulation.
# It can have any number of arguments, but must return an initialized daeSimulation object.
def create_simulation():
# Create Log, Solver, DataReporter and Simulation object
log          = daePythonStdOutLog()
daesolver    = daeIDAS()
datareporter = daeNoOpDataReporter()
simulation   = simTutorial()

# Enable reporting of all variables
simulation.m.SetReportingOn(True)

# Set the time horizon and the reporting interval
simulation.ReportingInterval = 1
simulation.TimeHorizon       = 100

# Initialize the simulation
simulation.Initialize(daesolver, datareporter, log)

return simulation


### 6.13.2. MEX functions¶

In order to simulate DAE Tools models from Matlab/Scilab/GNU Octave the wrapper code first needs to be compiled from source. The wrapper code is located in the daetools/trunk/mex directory. The compilation procedure requires a compiled cdaeSimulationLoader-py[Major][(Minor] library (which is a part of DAE Tools installation) located in the daetools/solibs/Platform_Architecture directory (i.e. daetools/solibs/Windows_win32). That directory has to be made available to the mex compiler and Matlab during runtime:

1. Set environmental variable LD_LIBRARY_PATH in Matlab (using the setenv and getenv commands)
2. Set RPATH in the mex file during the linking procedure

For instance, setting the LD_LIBRARY_PATH environmental variable can be done with the following code:

% Note the path separator (; or :)
% Windows
setenv('LD_LIBRARY_PATH', [getenv('LD_LIBRARY_PATH') ';path to daetools/solibs directory']);
% GNU/Linux
setenv('LD_LIBRARY_PATH', [getenv('LD_LIBRARY_PATH') ':path to daetools/solibs directory']);


Compilation from Matlab (for python 2.7):

cd mex


Compilation from GNU Octave (for python 2.7):

cd mex


Compilation from Scilab (for python 2.7):

cd mex
# check the include flag: cflags = -I... and set the correct full path for simulation loader
exec('builder.sce')


Now the compiled daetools_mex executable can be used to run DAE Tools simulations from Matlab:

res = daetools_mex('.../daetools/examples/tutorial_adv_3.py', 'create_simulation', ' ', 100.0, 10.0)


daetools_mex accepts the following arguments:

1. Path to the python file with daetools simulation (char array)
2. The name of python callable object that returns an initialized daeSimulation object (char array)
3. Arguments for the above callable object; can be anything that python accepts (char array)
4. Time horizon (double scalar)
5. Reporting interval (double scalar)

The outputs from the daetools_mex MEX-function are:

1. Cell array (pairs: {‘variable_name’, double matrix}). The variables values are put into the caller’s workspace as well.

## 6.14. Performing sensitivity analysis¶

Sensitivity analysis (SA) is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs [4]. Sensitivity analysis is conducted to [4] [5]:

1. Identify the most important parameters (those that contribute the most to output variability)
2. Identify insignificant parameters (can be eliminated from the model)
3. Determine if and which parameters interact with each other
4. Simplify the model (by fixing model inputs that have no effect on the output)
5. Test the robustness of a model in the presence of uncertainty
6. Detect errors in the model (by encountering unexpected relationships between inputs and outputs)
7. Reduce uncertainty through the identification of model inputs that cause significant uncertainty in the output
8. Determine the optimal regions within the parameters space (for use in optimisation studies)

There is a large number of SA methods, but in general they can be grouped into the local and global methods. The local methods involve calculation of the partial derivatives of the output with respect to an input factor. The most important local (derivative-based) methods are:

• Forward sensitivity method

The global methods can be grouped into:

• The screening methods (coarse sorting of the most influential inputs among a large number, such as Morris Elementary Effects method)
• The measures of importance (quantitative sensitivity indices)
• The variance-based methods for deep exploration of the model behaviour (measuring the effects of inputs on their all variation range, such as FAST, Sobol)

Other methods include One-at-a-time (changing one-factor-at-a-time to determine the effect on the output), Regression analysis and Scatter plots (scatter plots of the output variable against individual input variables, after sampling the model over its input distributions).

DAE Tools support both local and global sensitivity analysis methods. The local derivative-based method utilises Sundials IDAS sensitivity analysis capabilities to determine the sensitivity of the results with respect to the model parameters. At the moment, only the forward sensitivity method is available. The global SA can be performed using the external libraries (i.e. SALib).

### 6.14.1. Local (derivative-based) SA methods¶

To enable integration of sensitivity equations the function Initialize() must be called with the calculateSensitivities argument set to true:

simulation.Initialize(daesolver, datareporter, log, calculateSensitivities = True)


and the model parameters specified in the SetUpSensitivityAnalysis() function using the SetSensitivityParameter() method:

def SetUpSensitivityAnalysis(self):
self.SetSensitivityParameter(variable_1)
...
self.SetSensitivityParameter(variable_n)


Nota bene

The model parameters must be variables with the assigned value (that is to be degrees of freedom)!

Sensitivities can be obtained from a simulation in three different ways:

1. Sensitivities can be reported to a data reporter like any ordinary variable by setting the boolean property ReportSensitivities to True. In this case, the sensitivities for the variable var per parameter param are reported as sensitivities.d(var)_d(param).

2. Raw sensitivity matrices can be saved into a specified directory using the SensitivityDataDirectory property (before a call to Initialize). The sensitivity matrices are saved in .mmx coordinate format where the first dimensions is Nparameters and second Nvariables: SensitivityMatrix[Np, Nvars].

3. Directly from the DAE solver after every call to Integrate() functions using the SensitivityMatrix property. This property returns a dense matrix as a daeDenseMatrix object. The numpy array object can be obtained from the daeDenseMatrix using the npyValues property.

For more details on sensitivity analysis have a look on the Sensitivity Analysis Example 1, Sensitivity Analysis Example 2 and Chem. Eng. Example 9 examples.

### 6.14.2. Global SA methods¶

Typically, the global sensitivity analysis is conducted by [4] [6]:

1. Defining the model and its input parameters and output variables
2. Assigning probability density functions to each input parameter
3. Generating an input matrix through an appropriate random sampling method, evaluating the output
4. Assessing the influences or relative importance of each input parameter on the output variable

An example of the global sensitivity analysis in DAE Tools is given in Sensitivity Analysis Example 3. This tutorial illustrates the global variance-based sensitivity analysis methods available in SALib python library. Three SA methods were applied on a thermal analysis of a batch reactor with exothermic reaction $$A \rightarrow B$$ [6]: Morris (Elementary Effect method), FAST and Sobol (Variance-based methods).

In DAE Tools the procedure includes the following steps:

1. Selection of the global sensitivity method

2. Definition of the model inputs:

SA_problem = {
'num_vars': 5,
'names': ['B', 'gamma', 'psi', 'theta_a', 'theta_0'],
'bounds': [[10, 30],    # B
[15, 25],    # gamma
[0.4, 0.6],  # psi
[-0.2, 0.2], # theta_a
[-0.2, 0.2]  # theta_0
]
}

3. Generation of samples:

from SALib.sample.saltelli import sample

# Generate samples using a Saltelli's extension of the Sobol sequence.
# Sample size n=512, no. params k=5 => N=(2k+2)*n=6144 (as in the referenced article).
param_values = sample(problem, 512, calc_second_order=True)

4. Generation of outputs for a given input matrix (by repeated simulations of a DAE Tools model):

# theta_max is the analysed output from simulations
# The function simulate() performs daetools simulation for a given set of input parameters
theta_max  = numpy.zeros(N)
for i in range(N):
theta_max[i] = simulate(run_no  = i,
n       = 1,
B       = B[i],
gamma   = gamma[i],
psi     = psi[i],
theta_a = theta_a[i],
x_0     = 0.0,
theta_0 = theta_0[i])


Calculations can also be performed in parallel:

from multiprocessing import Pool
# Create a pool of workers to calculate N outputs
# Don't forget to disable OpenMP for evaluation of residuals and derivatives!!
pool = Pool()
args = [(i, 1, B[i], gamma[i], psi[i], theta_a[i], 0.0, theta_0[i]) for i in range(N)]
theta_max[:] = pool.map(simulate_p, args, chunksize=1)


Whether the simulations will be performed in parallel depends on model complexity and size. Running several large simulations that require a lot of memory at the same time may actually slow down the overall progress. Those cases will benefit more from parallel evaluation of equations / use of multithreaded linear equations solvers.

5. Performing sensitivity analysis

from SALib.analyze.sobol import analyze

res = analyze(problem, theta_max, print_to_console=False)

# 1st-order variance-based sensitivities and the confidence intervals
# (individual parameters contributions)
S1      = res['S1']
S1_conf = res['S1_conf']

# 2nd-order variance-based sensitivities and the confidence intervals
# (interactions between parameters)
S2      = res['S2']
S2_conf = res['S2_conf']

# Total sensitivity indices and the confidence intervals
ST      = res['ST']
ST_conf = res['ST_conf']


1st order and total sensitivity indices (for the Sobol method):

-------------------------------------------------------
Param          S1    S1_conf         ST    ST_conf
-------------------------------------------------------
B    0.094110   0.089475   0.581946   0.150334
gamma   -0.002416   0.011938   0.044354   0.028461
psi    0.171040   0.097859   0.524576   0.140252
theta_a    0.072511   0.043878   0.523382   0.177241
theta_0    0.002343   0.004746   0.008174   0.007650


2nd order sensitivities (for the Sobol method):

-------------------------------------------------------
Parameter pairs          S2    S2_conf
-------------------------------------------------------
B/gamma    0.180434   0.153318
B/psi    0.260698   0.172012
B/theta_a    0.143292   0.145452
B/theta_0    0.177137   0.150218
gamma/psi    0.000981   0.024855
gamma/theta_a    0.004953   0.040380
gamma/theta_0   -0.009390   0.027726
psi/theta_a    0.166102   0.173568
psi/theta_0   -0.016474   0.132210
theta_a/theta_0    0.109086   0.112104

6. Generation of scatter plots (using matplotlib.pyplot.scatter() function):

Footnotes

 [4] (1, 2, 3) A. Saltelli et al. Global sensitivity analysis. The Primer. Wiley-Interscience (2008). ISBN-10: 0470059974
 [5] D.J. Pannell. Sensitivity Analysis of Normative Economic Models: Theoretical Framework and Practical Strategies. Agricultural Economics 1997; 16:139–152. doi:10.1016/S0169-5150(96)01217-0
 [6] (1, 2) A. Saltelli, M. Ratto, S. Tarantola, F. Campolongo. Sensitivity Analysis for Chemical Models. Chem. Rev. (2005), 105(7):2811-2828. doi:10.1021/cr040659d

## 6.15. Executing optimisations¶

The goal of mathematical optimisation is to select a best element (with regard to some criterion) from some set of available alternatives [7].

The following types of optimisation problems are supported in DAE Tools:

• Nonlinear optimisation problems
• Mixed-integer nonlinear programming
• Constrained and unconstrained problems
• Continuous and discrete problems

### 6.15.1. Optimisation setup¶

In general, the optimisation activities use the same model specification and the same simulation setup as in the simulation runs. However, some additional information are required:

1. The objective function
2. Optimisation variables
3. Optimisation constraints

These information are specified in the SetUpOptimization() function.

#### 6.15.1.1. Specifying the objective function¶

By default, a single objective function object is declared and the objective function can be accessed using the ObjectiveFunction property. Its residual can be set as in ordinary equations. It is assumed that the optimisation solver tries to perform a minimisation. If a maximisation is required the objective function should be specified as $$-F_{obj}$$.

def SetUpOptimization(self):
# x1, x2 and x3 are optimisation variables (assigned variables, that is degrees of freedom).

# The ObjectiveFunction object is automatically created by the framework
# and only its residual needs to be specified.
self.ObjectiveFunction.Residual = self.m.x1() + self.m.x2() + self.m.x3()


Scaling of the objective function can be left to be done by optimisation solvers or set manually. The default scaling is 1.0 and can be changed using the Scaling property. In addition, the objective function absolute tolerance can be specified using the AbsTolerance property.

#### 6.15.1.2. Specifying optimisation variables¶

There are three types of optimisation variables in DAE Tools:

1. Continuous (the function SetContinuousOptimizationVariable())
2. Integer (the function SetIntegerOptimizationVariable())
3. Binary (value 0 or 1) (the function SetBinaryOptimizationVariable())
def SetUpOptimization(self):
# x1, x2 and x3 are optimisation variables (assigned variables, that is degrees of freedom).

# Continuous optimisation variable: (lower bound, upper bound and the starting point as floats)
self.ov1 = self.SetContinuousOptimizationVariable(self.m.x1, 0.0, 10.0, 1.5);

# Integer optimisation variable: (lower bound, upper bound and the starting point as integers)
self.ov2 = self.SetIntegerOptimizationVariable(self.m.x2, 1, 5, 2)

# Binary optimisation variable: (the starting point)
self.ov3 = self.SetBinaryOptimizationVariable(self.m.x3, 0)


#### 6.15.1.3. Specifying optimisation constraints¶

There are three types of optimisation constraints in DAE Tools:

1. Inequality (the function CreateInequalityConstraint())
2. Equality (the function CreateEqualityConstraint())

Constraints in DAE Tools are similar to ordinary equations.

def SetUpOptimization(self):
# x1, x2 and x3 are optimisation variables (assigned variables, that is degrees of freedom).

# Inequality constraint: g(i) <= 0)
# The constraint: x1 >= 25 in daetools becomes: 25 - x1 <= 0
self.c1 = self.CreateInequalityConstraint("Constraint 1")
self.c1.Residual = 25 - self.m.x1()

# Equality constraint: h(i) = 0
# The constraint: x2 + x3 = 10 in daetools becomes: x2 + x3 - 10 = 0
self.c2 = self.CreateEqualityConstraint("Constraint 2")
self.c2.Residual = self.m.x1() + self.m.x2() - 10


Scaling of constraints can be left to be done by optimisation solvers or set manually. The default scaling is 1.0 and can be changed using the Scaling property. In addition, the absolute tolerance for constraints can be specified using the AbsTolerance property.

### 6.15.2. Optimisation Solvers¶

The following optimisation solvers are interfaced BONMIN, IPOPT, and NLOPT :

• IPOPT NLP solver: pyIPOPT class from daetools.solvers.ipopt module.
• BONMIN MINLP solver: pyBONMIN class from daetools.solvers.bonmin module.
• NLOPT set of local/global optimisation solvers: pyNLOPT class from daetools.solvers.nlopt module.

Solvers can be imported in the following way:

# Import IPOPT NLP solver:
from daetools.solvers.ipopt import pyIPOPT

# Import BONMIN MINLP solver:
from daetools.solvers.bonmin import pyBONMIN

# Import NLOPT set of optimisation solvers:
from daetools.solvers.nlopt import pyNLOPT


### 6.15.3. Running an optimisation¶

Optimisations are run in a very similar fashion as simulations. The only additional information required are the daeOptimization and the NLP/MINLP solver objects (depending on the problem type):

# Create Log, NLPSolver, DAESolver, DataReporter, Simulation and Optimization objects
log          = daePythonStdOutLog()
daesolver    = daeIDAS()
nlpsolver    = daeIPOPT()
datareporter = daeTCPIPDataReporter()
simulation   = mySimulation()
optimization = daeOptimization()

# Enable reporting of all variables
simulation.m.SetReportingOn(True)

# Set the time horizon and the reporting interval
simulation.ReportingInterval = ...
simulation.TimeHorizon = ...

# Connect data reporter
simName = simulation.m.Name + strftime(" [m.%Y %H:%M:%S]", localtime())
if(datareporter.Connect("", simName) == False):
sys.exit()

# Initialise the optimisation
optimization.Initialize(simulation, nlpsolver, daesolver, datareporter, log)

# Run
optimization.Run()

# Clean up
optimization.Finalize()
`

Footnotes