PanelLengthConstraint

Author:

  • Alasdair Christison Gray

This class implements a constraint which enforces the panel length design variable values passed to elements using the BladeStiffenedShell constitutive model to be consistent with the true length of the panel they are a part of.

Note

This class should be created using the pyTACS.createPanelLengthConstraint method.

API Reference

class tacs.constraints.PanelLengthConstraint(name, assembler, comm, outputViewer=None, meshLoader=None, options=None)[source]

NOTE: This class should not be initialized directly by the user. Use pyTACS.createPanelLengthConstraint instead.

Parameters:

  • name (str) -- Name of this tacs problem

  • assembler (TACS.Assembler) -- Cython object responsible for creating and setting tacs objects used to solve problem

  • comm (mpi4py.MPI.Intracomm) -- The comm object on which to create the pyTACS object.

  • outputViewer (TACS.TACSToFH5) -- Cython object used to write out f5 files that can be converted and used for postprocessing.

  • meshLoader (pymeshloader.pyMeshLoader) -- pyMeshLoader object used to create the assembler.

  • options (dict) -- Dictionary holding problem-specific option parameters (case-insensitive).

setDesignVars(x)[source]

Update the design variables used by tacs.

Parameters:

x (numpy.ndarray or dict or tacs.TACS.Vec) -- The variables (typically from the optimizer) to set. It looks for variable in the self.varName attribute if in dict.

externalClearUpToDate()[source]

clear UpToDate by FUNtoFEM which sets variables into TACS through a different interface

setNodes(Xpts)[source]

Set the mesh coordinates of the structure.

Parameters:

coords (numpy.ndarray) -- Structural coordinate in array of size (N * 3) where N is the number of structural nodes on this processor.

computeRefAxis(refAxis: ndarray, comp_bndry_node_coords: ndarray)[source]

remove the panelNormal from the refAxis

Parameters:

  • refAxis (numpy.ndarray) -- an array of size (3,) for the xyz coordinates of the original refAxis from transform object

  • comp_bndry_node_coords (numpy.ndarray) -- an array of size (3*N,) for the boundary nodal coordinates on the current panel / current TACS component

Returns:

an array of size (3,) for xyz coords of the panel length axis after the panelNormal component has been removed

Return type:

numpy.ndarray

addConstraint(conName, compIDs=None, lower=None, upper=None, dvIndex=0)[source]

Generic method to adding a new constraint set for TACS.

Parameters:

  • conName (str) -- The user-supplied name for the constraint set. This will typically be a string that is meaningful to the user

  • compIDs (list[int] or None) -- List of compIDs to apply constraints to. If None, all compIDs will be used. Defaults to None.

  • lower (float or complex) -- lower bound for constraint. Not used.

  • upper (float or complex) -- upper bound for constraint. Not used.

  • dvIndex (int) -- Index number of the panel length DV's. Defaults to 0.

getConstraintBounds(bounds, evalCons=None)[source]

Get bounds for constraints. The constraints corresponding to the strings in evalCons are evaluated and updated into the provided dictionary.

The panel length constraints are equality constraints so both the upper and lower bounds are zero

Parameters:

  • bounds (dict) -- Dictionary into which the constraint bounds are saved. Bounds will be saved as a tuple: (lower, upper)

  • evalCons (iterable object containing strings.) -- If not none, use these constraints to evaluate.

Examples

>>> conBounds = {}
>>> tacsConstraint.getConstraintBounds(conBounds, 'LE_SPAR')
>>> conBounds
>>> # Result will look like (if TACSConstraint has name of 'c1'):
>>> # {'c1_LE_SPAR': (array([-1e20]), array([1e20]))}
getConstraintSizes(sizes, evalCons=None)[source]

Get number for constraint equations in each set. The constraints corresponding to the strings in evalCons are evaluated and updated into the provided dictionary.

Parameters:

  • sizes (dict) -- Dictionary into which the constraint sizes are saved.

  • evalCons (iterable object containing strings.) -- If not none, use these constraints to evaluate.

Examples

>>> conSizes = {}
>>> tacsConstraint.getConstraintSizes(conSizes, 'LE_SPAR')
>>> funconSizescs
>>> # Result will look like (if TACSConstraint has name of 'c1'):
>>> # {'c1_LE_SPAR': 10}
evalConstraints(funcs, evalCons=None, ignoreMissing=False)[source]

Evaluate values for constraints. The constraints corresponding to the strings in evalCons are evaluated and updated into the provided dictionary.

The same constraint arrays are returned on every proc

Parameters:

  • funcs (dict) -- Dictionary into which the constraints are saved.

  • evalCons (iterable object containing strings.) -- If not none, use these constraints to evaluate.

  • ignoreMissing (bool) -- Flag to supress checking for a valid constraint. Please use this option with caution.

Examples

>>> funcs = {}
>>> adjConstraint.evalConstraints(funcs, 'LE_SPAR')
>>> funcs
>>> # Result will look like (if PanelLengthConstraint has name of 'c1'):
>>> # {'c1_LE_SPAR': array([1.325, 2.1983645, 3.1415926, ...])}
evalConstraintsSens(funcsSens, evalCons=None)[source]

This is the main routine for returning useful (sensitivity) information from constraint. The derivatives of the constraints corresponding to the strings in evalCons are evaluated and updated into the provided dictionary. The derivitives with respect to all design variables and node locations are computed.

The sensitivities returned on each proc are a sparse m x n matrix where m is the number of constraints and n is the number of design variables or 3x the number of nodes on this proc. The matrix contains the sensitivities of all constraints w.r.t only the design variables/node coordinates on this proc.

Parameters:

  • funcsSens (dict) -- Dictionary into which the derivatives are saved.

  • evalCons (iterable object containing strings) -- The constraints the user wants returned

Examples

>>> funcsSens = {}
>>> adjConstraint.evalConstraintsSens(funcsSens, 'LE_SPAR')
>>> funcsSens
>>> # Result will look like (if AdjacencyConstraint has name of 'c1'):
>>> # {'c1_LE_SPAR':{'struct':<50x242 sparse matrix of type '<class 'numpy.float64'>' with 100 stored elements in Compressed Sparse Row format>}}
dtype

alias of float64

getConstraintKeys()

Return a list of the current constraint key names

Returns:

conNames -- List containing user-defined names for constraint groups added so far.

Return type:

list[str]

getDesignVarRange()

get the lower/upper bounds for the design variables.

Returns:

  • xlb (numpy.ndarray) -- The design variable lower bound.

  • xub (numpy.ndarray) -- The design variable upper bound.

getDesignVars()

Get the current set of design variables for this problem.

Returns:

x -- The current design variable vector set in tacs.

Return type:

numpy.ndarray

getNodes()

Return the mesh coordinates of this problem.

Returns:

coords -- Structural coordinate in array of size (N * 3) where N is the number of structural nodes on this processor.

Return type:

numpy.ndarray

getNumCoordinates()

Return the number of mesh coordinates on this processor.

Returns:

ncoords -- Number of mesh coordinates on this processor.

Return type:

int

getNumDesignVars()

Return the number of design variables on this processor.

Returns:

ndvs -- Number of design variables on this processor.

Return type:

int

getNumOwnedNodes()

Get the number of nodes owned by this processor.

Returns:

nnodes -- Number of nodes on this processor.

Return type:

int

getNumVariables()

Return the number of degrees of freedom (states) that are on this processor

Returns:

nstate -- number of states.

Return type:

int

getOption(name)

Get a solver option value. The name is not case sensitive.

Parameters:

name (str) -- Name of option to get

getVarsPerNode()

Get the number of variables per node for the model.

Returns:

vpn -- Number of variables per node.

Return type:

int

classmethod printDefaultOptions()

Prints a nicely formatted dictionary of all the default solver options to the stdout

printModifiedOptions()

Prints a nicely formatted table of all the options that have been modified from their defaults

printOptions()

Prints a nicely formatted dictionary of all the current solver options to the stdout on the root processor

setOption(name, value)

Set a solver option value. The name is not case sensitive.

Parameters:

  • name (str) -- Name of option to modify

  • value (depends on option) -- New option value to set

setOptions(options)

Set multiple solver options at once. The names are not case sensitive.

Parameters:

options (dict) -- Dictionary of option names and values to set

setVarName(varName)

Set a name for the structural variables in pyOpt. Only needs to be changed if more than 1 pytacs object is used in an optimization

Parameters:

varName (str) -- Name of the structural variable used in addVarGroup().