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flow360.NavierStokesSolver

flow360.NavierStokesSolver#

pydantic model NavierStokesSolver[source]#

NavierStokesSolver class for setting up compressible Navier-Stokes solver

Parameters:

  • absolute_tolerance (Optional[PositiveFloat] = 1e-10) – relative_tolerance : Optional[NonNegativeFloat] = 0 order_of_accuracy : Optional[Literal[1, 2]] = 2 CFL_multiplier : Optional[PositiveFloat] = 1.0 update_jacobian_frequency : Optional[PositiveInt] = 4 max_force_jac_update_physical_steps : Optional[NonNegativeInt] = 0 kappa_MUSCL : Optional[ConstrainedFloatValue] = -1 equation_eval_frequency : Optional[PositiveInt] = 1 numerical_dissipation_factor : Optional[ConstrainedFloatValue] = 1 limit_velocity : Optional[bool] = False limit_pressure_density : Optional[bool] = False linear_solver : Optional[LinearSolver] = LinearSolver(max_iterations=30, absolute_tolerance=None, relative_tolerance=None, _type=’LinearSolver’) model_type : Literal[‘Compressible’] = Compressible low_mach_preconditioner : Optional[bool] = False low_mach_preconditioner_threshold : Optional[NonNegativeFloat] = None

  • absolute_tolerance – Tolerance for the NS residual, below which the solver goes to the next physical step

  • relative_tolerance – Tolerance to the relative residual, below which the solver goes to the next physical step. Relative residual is defined as the ratio of the current pseudoStep’s residual to the maximum residual present in the first 10 pseudoSteps within the current physicalStep. NOTE: relativeTolerance is ignored in steady simulations and only absoluteTolerance is used as the convergence criterion

  • CFL_multiplier – Factor to the CFL definitions defined in “timeStepping” section

  • kappa_MUSCL – Kappa for the MUSCL scheme, range from [-1, 1], with 1 being unstable. The default value of -1 leads to a 2nd order upwind scheme and is the most stable. A value of 0.33 leads to a blended upwind/central scheme and is recommended for low subsonic flows leading to reduced dissipation

  • update_jacobian_frequency – Frequency at which the jacobian is updated.

  • equation_eval_frequency – Frequency at which to update the compressible NS equation in loosely-coupled simulations

  • max_force_jac_update_physical_steps – When which physical steps, the jacobian matrix is updated every pseudo step

  • order_of_accuracy – Order of accuracy in space

  • limit_velocity – Limiter for velocity

  • limit_pressure_density – Limiter for pressure and density

  • numerical_dissipation_factor – A factor in the range [0.01, 1.0] which exponentially reduces the dissipation of the numerical flux. The recommended starting value for most low-dissipation runs is 0.2

  • linear_solver – Linear solver settings

  • low_mach_preconditioner – Uses preconditioning for accelerating low Mach number flows.

  • low_mach_preconditioner_threshold – For flow regions with Mach numbers smaller than threshold, the input Mach number to the preconditioner is assumed to be the threshold value if it is smaller than the threshold. The default value for the threshold is the freestream Mach number.

Returns:

An instance of the component class NavierStokesSolver.

Return type:

NavierStokesSolver

Example

>>> ns = NavierStokesSolver(absolute_tolerance=1e-10)
Fields:

  • CFL_multiplier (pydantic.types.PositiveFloat | None)

  • absolute_tolerance (pydantic.types.PositiveFloat | None)

  • equation_eval_frequency (pydantic.types.PositiveInt | None)

  • kappa_MUSCL (flow360.component.flow360_params.solvers.ConstrainedFloatValue | None)

  • limit_pressure_density (bool | None)

  • limit_velocity (bool | None)

  • linear_solver (flow360.component.flow360_params.solvers.LinearSolver | None)

  • low_mach_preconditioner (bool | None)

  • low_mach_preconditioner_threshold (pydantic.types.NonNegativeFloat | None)

  • max_force_jac_update_physical_steps (pydantic.types.NonNegativeInt | None)

  • model_type (Literal['Compressible'])

  • numerical_dissipation_factor (flow360.component.flow360_params.solvers.ConstrainedFloatValue | None)

  • order_of_accuracy (Literal[1, 2] | None)

  • relative_tolerance (pydantic.types.NonNegativeFloat | None)

  • update_jacobian_frequency (pydantic.types.PositiveInt | None)

field CFL_multiplier = 1.0 (alias 'CFLMultiplier')#
Constraints:

  • exclusiveMinimum = 0

field kappa_MUSCL = -1 (alias 'kappaMUSCL')#
Constraints:

  • minimum = -1

  • maximum = 1

field equation_eval_frequency = 1 (alias 'equationEvalFrequency')#
Constraints:

  • exclusiveMinimum = 0

field numerical_dissipation_factor = 1 (alias 'numericalDissipationFactor')#
Constraints:

  • minimum = 0.01

  • maximum = 1

field limit_velocity = False (alias 'limitVelocity')#
field limit_pressure_density = False (alias 'limitPressureDensity')#
field linear_solver = LinearSolver(max_iterations=30, absolute_tolerance=None, relative_tolerance=None, _type='LinearSolver') (alias 'linearSolver')#
Constraints:

  • title = LinearSolver

  • description = LinearSolver class for setting up linear solver for heat equation Parameters ———- max_iterations : Optional[PositiveInt] = 50 absolute_tolerance : Optional[PositiveFloat] = None relative_tolerance : Optional[PositiveFloat] = None Parameters ———- max_iterations : PositiveInt, optional Maximum number of linear solver iterations, by default 50 absolute_tolerance : PositiveFloat, optional The linear solver converges when the final residual of the pseudo steps below this value. Either absolute tolerance or relative tolerance can be used to determine convergence, by default 1e-10 relative_tolerance : The linear solver converges when the ratio of the final residual and the initial residual of the pseudo step is below this value. Returns ——- LinearSolver An instance of the component class LinearSolver. Example ——- >>> ls = LinearSolver( max_iterations=50, absoluteTolerance=1e-10 )

  • type = object

  • properties = {‘maxIterations’: {‘title’: ‘Maxiterations’, ‘default’: 50, ‘exclusiveMinimum’: 0, ‘type’: ‘integer’}, ‘absoluteTolerance’: {‘title’: ‘Absolutetolerance’, ‘exclusiveMinimum’: 0, ‘type’: ‘number’}, ‘relativeTolerance’: {‘title’: ‘Relativetolerance’, ‘exclusiveMinimum’: 0, ‘type’: ‘number’}, ‘_type’: {‘title’: ‘ Type’, ‘default’: ‘LinearSolver’, ‘enum’: [‘LinearSolver’], ‘type’: ‘string’}}

  • additionalProperties = False

field model_type = 'Compressible' (alias 'modelType')#
Constraints:

  • const = Compressible

field low_mach_preconditioner = False (alias 'lowMachPreconditioner')#
field low_mach_preconditioner_threshold = None (alias 'lowMachPreconditionerThreshold')#
Constraints:

  • minimum = 0

to_solver(params, **kwargs)[source]#

Set preconditioner threshold to freestream Mach number

classmethod add_type_field()#

Automatically place “type” field with model name in the model field dictionary.

append(params, overwrite=False)#

append parametrs to the model

Parameters:

  • params (Flow360BaseModel) – Flow360BaseModel parameters to be appended

  • overwrite (bool, optional) – Whether to overwrite if key exists, by default False

classmethod construct(_fields_set=None, **values)#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(update=None, **kwargs)#

Copy a Flow360BaseModel. With deep=True as default.

dict(*args, exclude=None, **kwargs)#

Returns dict representation of the model.

Parameters:

  • *args – Arguments passed to pydantic’s dict method.

  • **kwargs – Keyword arguments passed to pydantic’s dict method.

Returns:

A formatted dict.

Return type:

dict

Example

>>> params.dict()
classmethod dict_from_file(filename)#

Loads a dictionary containing the model from a .json or .yaml file.

Parameters:

filename (str) – Full path to the .yaml or .json file to load the Flow360BaseModel from.

Returns:

A dictionary containing the model.

Return type:

dict

Example

>>> params = Flow360Params.from_file(filename='folder/flow360.json')
classmethod flow360_schema()#

Generate a schema json string for the flow360 model

classmethod flow360_ui_schema()#

Generate a UI schema json string for the flow360 model

classmethod from_file(filename)#

Loads a Flow360BaseModel from .json, or .yaml file.

Parameters:

filename (str) – Full path to the .yaml or .json file to load the Flow360BaseModel from.

Returns:

An instance of the component class calling load.

Return type:

Flow360BaseModel

Example

>>> simulation = Simulation.from_file(filename='folder/sim.json')
classmethod from_json(filename, **parse_obj_kwargs)#

Load a Flow360BaseModel from .json file.

Parameters:

filename (str) – Full path to the .json file to load the Flow360BaseModel from.

Returns:

  • Flow360BaseModel – An instance of the component class calling load.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Example

>>> params = Flow360Params.from_json(filename='folder/flow360.json')
classmethod from_orm(obj)#
classmethod from_yaml(filename, **parse_obj_kwargs)#

Loads Flow360BaseModel from .yaml file.

Parameters:

  • filename (str) – Full path to the .yaml file to load the Flow360BaseModel from.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Returns:

An instance of the component class calling from_yaml.

Return type:

Flow360BaseModel

Example

>>> params = Flow360Params.from_yaml(filename='folder/flow360.yaml')
classmethod generate_docstring()#

Generates a docstring for a Flow360 model and saves it to the __doc__ of the class.

help(methods=False)#

Prints message describing the fields and methods of a Flow360BaseModel.

Parameters:

methods (bool = False) – Whether to also print out information about object’s methods.

Example

>>> solver_params.help(methods=True)
json(*args, exclude=None, **kwargs)#

Returns json representation of the model.

Parameters:

  • *args – Arguments passed to pydantic’s json method.

  • **kwargs – Keyword arguments passed to pydantic’s json method.

Returns:

A formatted json. Sets default vaules by_alias=True, exclude_none=True

Return type:

json

Example

>>> params.json()
classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
classmethod parse_obj(obj)#
classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
classmethod schema(by_alias=True, ref_template='#/definitions/{model}')#
classmethod schema_json(*, by_alias=True, ref_template='#/definitions/{model}', **dumps_kwargs)#
set_will_export_to_flow360(flag)#

Recursivly sets flag will_export_to_flow360

Parameters:

flag (bool) – set to true before exporting to flow360 json

to_file(filename)#

Exports Flow360BaseModel instance to .json or .yaml file

Parameters:

filename (str) – Full path to the .json or .yaml or file to save the Flow360BaseModel to.

Example

>>> params.to_file(filename='folder/flow360.json')
to_json(filename)#

Exports Flow360BaseModel instance to .json file

Parameters:

filename (str) – Full path to the .json file to save the Flow360BaseModel to.

Example

>>> params.to_json(filename='folder/flow360.json')
to_yaml(filename)#

Exports Flow360BaseModel instance to .yaml file.

Parameters:

filename (str) – Full path to the .yaml file to save the Flow360BaseModel to.

Example

>>> params.to_yaml(filename='folder/flow360.yaml')
classmethod update_forward_refs(**localns)#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value)#
field absolute_tolerance = 1e-10 (alias 'absoluteTolerance')#
Constraints:

  • exclusiveMinimum = 0

field relative_tolerance = 0 (alias 'relativeTolerance')#
Constraints:

  • minimum = 0

field order_of_accuracy = 2 (alias 'orderOfAccuracy')#
field update_jacobian_frequency = 4 (alias 'updateJacobianFrequency')#
Constraints:

  • exclusiveMinimum = 0

field max_force_jac_update_physical_steps = 0 (alias 'maxForceJacUpdatePhysicalSteps')#
Constraints:

  • minimum = 0

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