flow360.UnsteadyTimeStepping

pydantic model UnsteadyTimeStepping

Unsteady time stepping component

Parameters:

max_pseudo_steps (Optional[ConstrainedIntValue] = 2000) – order_of_accuracy : Optional[Literal[1, 2]] = 2 CFL : Union[RampCFL, AdaptiveCFL, NoneType] = AdaptiveCFL(type=’adaptive’, min=0.1, max=1000000.0, max_relative_change=50.0, convergence_limiting_factor=1.0, _type=’AdaptiveCFL’) model_type : Literal[‘Unsteady’] = Unsteady physical_steps : PositiveInt time_step_size : _Constrained

Fields:

• CFL (flow360.component.flow360_params.time_stepping.RampCFL | flow360.component.flow360_params.time_stepping.AdaptiveCFL | None)

• max_pseudo_steps (flow360.component.flow360_params.time_stepping.ConstrainedIntValue | None)

• model_type (Literal['Unsteady'])

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

• physical_steps (pydantic.types.PositiveInt)

• time_step_size (flow360.component.flow360_params.unit_system._Constrained)

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

Constraints:

• const = Unsteady

field physical_steps [Required] (alias 'physicalSteps')

Constraints:

• exclusiveMinimum = 0

field time_step_size [Required] (alias 'timeStepSize')

Constraints:

• properties = {‘value’: {‘type’: ‘number’, ‘exclusiveMinimum’: 0}, ‘units’: {‘type’: ‘string’, ‘dimension’: ‘time’, ‘enum’: [‘s’, ‘flow360_time_unit’]}}

field CFL = AdaptiveCFL(type='adaptive', min=0.1, max=1000000.0, max_relative_change=50.0, convergence_limiting_factor=1.0, _type='AdaptiveCFL')

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_solver(params, **kwargs)

returns configuration object in flow360 units system

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 max_pseudo_steps = 2000 (alias 'maxPseudoSteps')

Constraints:

• exclusiveMinimum = 0

• maximum = 100000

field order_of_accuracy = 2 (alias 'orderOfAccuracy')