flow360.TransitionModelSolver

pydantic model TransitionModelSolver

TransitionModelSolver class for setting up transition model solver

Parameters:

• absolute_tolerance (Optional[PositiveFloat] = 1e-07) – relative_tolerance : Optional[NonNegativeFloat] = 0 order_of_accuracy : Optional[Literal[1, 2]] = 2 CFL_multiplier : Optional[PositiveFloat] = 2.0 update_jacobian_frequency : Optional[PositiveInt] = 4 max_force_jac_update_physical_steps : Optional[NonNegativeInt] = 0 model_type : Literal[‘AmplificationFactorTransport’] = AmplificationFactorTransport equation_eval_frequency : Optional[PositiveInt] = 4 turbulence_intensity_percent : Optional[ConstrainedFloatValue] = None N_crit : Optional[ConstrainedFloatValue] = None reconstruction_gradient_limiter : Optional[ConstrainedFloatValue] = 1.0 linear_solver : Optional[LinearSolver] = LinearSolver(max_iterations=20, absolute_tolerance=None, relative_tolerance=None, _type=’LinearSolver’)

• (...) –

Returns:

An instance of the component class TransitionModelSolver.

Return type:

TransitionModelSolver

Example

>>> ts = TransitionModelSolver(absolute_tolerance=1e-10)

Fields:

• CFL_multiplier (pydantic.types.PositiveFloat | None)

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

• absolute_tolerance (pydantic.types.PositiveFloat | None)

• equation_eval_frequency (pydantic.types.PositiveInt | None)

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

• max_force_jac_update_physical_steps (pydantic.types.NonNegativeInt | None)

• model_type (Literal['AmplificationFactorTransport'])

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

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

• relative_tolerance (pydantic.types.NonNegativeFloat | None)

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

• update_jacobian_frequency (pydantic.types.PositiveInt | None)

field model_type = 'AmplificationFactorTransport' (alias 'modelType')

Constraints:

• const = AmplificationFactorTransport

field absolute_tolerance = 1e-07 (alias 'absoluteTolerance')

Constraints:

• exclusiveMinimum = 0

field equation_eval_frequency = 4 (alias 'equationEvalFrequency')

Constraints:

• exclusiveMinimum = 0

field turbulence_intensity_percent = None (alias 'turbulenceIntensityPercent')

Constraints:

• minimum = 0.03

• maximum = 2.5

field N_crit = None (alias 'Ncrit')

Constraints:

• minimum = 1

• maximum = 11

field reconstruction_gradient_limiter = 1.0 (alias 'reconstructionGradientLimiter')

Constraints:

• minimum = 0

• maximum = 2

field linear_solver = LinearSolver(max_iterations=20, 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

to_solver(params, **kwargs)

Convert turbulenceIntensityPercent to Ncrit

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 relative_tolerance = 0 (alias 'relativeTolerance')

Constraints:

• minimum = 0

field order_of_accuracy = 2 (alias 'orderOfAccuracy')

field CFL_multiplier = 2.0 (alias 'CFLMultiplier')

Constraints:

• exclusiveMinimum = 0

field update_jacobian_frequency = 4 (alias 'updateJacobianFrequency')

Constraints:

• exclusiveMinimum = 0

field max_force_jac_update_physical_steps = 0 (alias 'maxForceJacUpdatePhysicalSteps')

Constraints:

• minimum = 0