fastoad.model_base.propulsion module

Base classes for propulsion components.

class fastoad.model_base.propulsion.IPropulsion[source]

Bases: ABC

Interface that should be implemented by propulsion models.

Using this class allows to delegate to the propulsion model the management of propulsion-related data when computing performances.

The performance model calls compute_flight_points() by providing one or several flight points. The method will feed these flight points with results of the model (e.g. thrust, SFC, ..).

The performance model will then be able to call get_consumed_mass() to know the mass consumption for each flight point.

Note:

If the propulsion model needs fields that are not among defined fields
of the :class`FlightPoint class`, these fields can be made authorized by
:class`FlightPoint class`. Please see part about extensibility in
:class`FlightPoint class` documentation.

abstract compute_flight_points(flight_points: FlightPoint | DataFrame)[source]

Computes Specific Fuel Consumption according to provided conditions.

See FlightPoint for available fields that may be used for computation. If a DataFrame instance is provided, it is expected that its columns match field names of FlightPoint (actually, the DataFrame instance should be generated from a list of FlightPoint instances).

Note

About thrust_is_regulated, thrust_rate and thrust

thrust_is_regulated tells if a flight point should be computed using thrust_rate (when False) or thrust (when True) as input. This way, the method can be used in a vectorized mode, where each point can be set to respect a thrust order or a thrust rate order.

if thrust_is_regulated is not defined, the considered input will be the defined one between thrust_rate and thrust (if both are provided, thrust_rate will be used)
if thrust_is_regulated is True or False (i.e., not a sequence), the considered input will be taken accordingly, and should of course be defined.
if there are several flight points, thrust_is_regulated is a sequence or array, thrust_rate and thrust should be provided and have the same shape as thrust_is_regulated:code:. The method will consider for each element which input will be used according to thrust_is_regulated.

Parameters:: flight_points – FlightPoint or DataFrame instance
Returns:: None (inputs are updated in-place)

abstract get_consumed_mass(flight_point: FlightPoint, time_step: float) → float[source]

Computes consumed mass for provided flight point and time step.

This method should rely on FlightPoint fields that are generated by :meth: compute_flight_points.

Parameters:

flight_point –
time_step –

Returns:

the consumed mass in kg

class fastoad.model_base.propulsion.IOMPropulsionWrapper[source]

Bases: object

Interface for wrapping a IPropulsion subclass in OpenMDAO.

The implementation class defines the needed input variables for instantiating the IPropulsion subclass in setup() and use them for instantiation in get_model()

See OMRubberEngineWrapper for an example of implementation.

abstract setup(component: Component)[source]

Defines the needed OpenMDAO inputs for propulsion instantiation as done in get_model()

Use add_inputs and declare_partials methods of the provided component

Parameters:: component –

abstract static get_model(inputs) → IPropulsion[source]

This method defines the used IPropulsion subclass instance.

Parameters:: inputs – OpenMDAO input vector where the parameters that define the propulsion model are
Returns:: the propulsion model instance

class fastoad.model_base.propulsion.BaseOMPropulsionComponent(**kwargs)[source]

Bases: ExplicitComponent, ABC

Base class for creating an OpenMDAO component from subclasses of IOMPropulsionWrapper.

Classes that implements this interface should add their own inputs in setup() and implement get_wrapper().

Store some bound methods so we can detect runtime overrides.

setup()[source]

Declare inputs and outputs.

Available attributes:: name pathname comm options

setup_partials()[source]

Declare partials.

This is meant to be overridden by component classes. All partials should be declared here since this is called after all size/shape information is known for all variables.

compute(inputs, outputs, discrete_inputs=None, discrete_outputs=None)[source]

Compute outputs given inputs. The model is assumed to be in an unscaled state.

Parameters:

inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].
outputs (Vector) – Unscaled, dimensional output variables read via outputs[key].
discrete_inputs (dict or None) – If not None, dict containing discrete input values.
discrete_outputs (dict or None) – If not None, dict containing discrete output values.

abstract static get_wrapper() → IOMPropulsionWrapper[source]

This method defines the used IOMPropulsionWrapper instance.

Returns:: an instance of OpenMDAO wrapper for propulsion model

abs_name_iter(iotype, local=True, cont=True, discrete=False)

Iterate over absolute variable names for this System.

By setting appropriate values for ‘cont’ and ‘discrete’, yielded variable names can be continuous only, discrete only, or both.

Parameters:

iotype (str) – Either ‘input’ or ‘output’.
local (bool) – If True, include only names of local variables. Default is True.
cont (bool) – If True, include names of continuous variables. Default is True.
discrete (bool) – If True, include names of discrete variables. Default is False.

Yields:

str

add_constraint(name, lower=None, upper=None, equals=None, ref=None, ref0=None, adder=None, scaler=None, units=None, indices=None, linear=False, parallel_deriv_color=None, cache_linear_solution=False, flat_indices=False, alias=None)

Add a constraint variable to this system.

Parameters:

name (str) – Name of the response variable in the system.
lower (float or ndarray, optional) – Lower boundary for the variable.
upper (float or ndarray, optional) – Upper boundary for the variable.
equals (float or ndarray, optional) – Equality constraint value for the variable.
ref (float or ndarray, optional) – Value of response variable that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of response variable that scales to 0.0 in the driver.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
units (str, optional) – Units to convert to before applying scaling.
indices (sequence of int, optional) – If variable is an array, these indicate which entries are of interest for this particular response. These may be positive or negative integers.
linear (bool) – Set to True if constraint is linear. Default is False.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
cache_linear_solution (bool) – If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1. The arguments (lower, upper, equals) can not be strings or variable names.

add_design_var(name, lower=None, upper=None, ref=None, ref0=None, indices=None, adder=None, scaler=None, units=None, parallel_deriv_color=None, cache_linear_solution=False, flat_indices=False)

Add a design variable to this system.

Parameters:

name (str) – Promoted name of the design variable in the system.
lower (float or ndarray, optional) – Lower boundary for the input.
upper (upper or ndarray, optional) – Upper boundary for the input.
ref (float or ndarray, optional) – Value of design var that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of design var that scales to 0.0 in the driver.
indices (iter of int, optional) – If an input is an array, these indicate which entries are of interest for this particular design variable. These may be positive or negative integers.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
units (str, optional) – Units to convert to before applying scaling.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
cache_linear_solution (bool) – If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.

Notes

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

add_discrete_input(name, val, desc='', tags=None)

Add a discrete input variable to the component.

Parameters:

name (str) – Name of the variable in this component’s namespace.
val (a picklable object) – The initial value of the variable being added.
desc (str) – Description of the variable.
tags (str or list of strs) – User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs.

Returns:

Metadata for added variable.

Return type:

dict

add_discrete_output(name, val, desc='', tags=None)

Add an output variable to the component.

Parameters:

name (str) – Name of the variable in this component’s namespace.
val (a picklable object) – The initial value of the variable being added.
desc (str) – Description of the variable.
tags (str or list of strs or set of strs) – User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs.

Returns:

Metadata for added variable.

Return type:

dict

add_input(name, val=1.0, shape=None, units=None, desc='', tags=None, shape_by_conn=False, copy_shape=None, distributed=None)

Add an input variable to the component.

Parameters:

name (str) – Name of the variable in this component’s namespace.
val (float or list or tuple or ndarray or Iterable) – The initial value of the variable being added in user-defined units. Default is 1.0.
shape (int or tuple or list or None) – Shape of this variable, only required if val is not an array. Default is None.
units (str or None) – Units in which this input variable will be provided to the component during execution. Default is None, which means it is unitless.
desc (str) – Description of the variable.
tags (str or list of strs) – User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs.
shape_by_conn (bool) – If True, shape this input to match its connected output.
copy_shape (str or None) – If a str, that str is the name of a variable. Shape this input to match that of the named variable.
distributed (bool) – If True, this variable is a distributed variable, so it can have different sizes/values across MPI processes.

Returns:

Metadata for added variable.

Return type:

dict

add_objective(name, ref=None, ref0=None, index=None, units=None, adder=None, scaler=None, parallel_deriv_color=None, cache_linear_solution=False, flat_indices=False, alias=None)

Add a response variable to this system.

Parameters:

name (str) – Name of the response variable in the system.
ref (float or ndarray, optional) – Value of response variable that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of response variable that scales to 0.0 in the driver.
index (int, optional) – If variable is an array, this indicates which entry is of interest for this particular response. This may be a positive or negative integer.
units (str, optional) – Units to convert to before applying scaling.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
cache_linear_solution (bool) – If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

Notes

The objective can be scaled using scaler and adder, where

\[x_{scaled} = scaler(x + adder)\]

or through the use of ref/ref0, which map to scaler and adder through the equations:

\[ \begin{align}\begin{aligned}0 = scaler(ref_0 + adder)\\1 = scaler(ref + adder)\end{aligned}\end{align} \]

which results in:

\[ \begin{align}\begin{aligned}adder = -ref_0\\scaler = \frac{1}{ref + adder}\end{aligned}\end{align} \]

add_output(name, val=1.0, shape=None, units=None, res_units=None, desc='', lower=None, upper=None, ref=1.0, ref0=0.0, res_ref=None, tags=None, shape_by_conn=False, copy_shape=None, distributed=None)

Add an output variable to the component.

For ExplicitComponent, res_ref defaults to the value in res unless otherwise specified.

Parameters:

name (str) – Name of the variable in this component’s namespace.
val (float or list or tuple or ndarray) – The initial value of the variable being added in user-defined units. Default is 1.0.
shape (int or tuple or list or None) – Shape of this variable, only required if val is not an array. Default is None.
units (str or None) – Units in which the output variables will be provided to the component during execution. Default is None, which means it has no units.
res_units (str or None) – Units in which the residuals of this output will be given to the user when requested. Default is None, which means it has no units.
desc (str) – Description of the variable.
lower (float or list or tuple or ndarray or None) – Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upper (float or list or tuple or ndarray or None) – Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
ref (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0 (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_ref (float) – Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.
tags (str or list of strs) – User defined tags that can be used to filter what gets listed when calling list_inputs and list_outputs and also when listing results from case recorders.
shape_by_conn (bool) – If True, shape this output to match its connected input(s).
copy_shape (str or None) – If a str, that str is the name of a variable. Shape this output to match that of the named variable.
distributed (bool) – If True, this variable is a distributed variable, so it can have different sizes/values across MPI processes.

Returns:

Metadata for added variable.

Return type:

dict

add_recorder(recorder, recurse=False)

Add a recorder to the system.

Parameters:

recorder (<CaseRecorder>) – A recorder instance.
recurse (bool) – Flag indicating if the recorder should be added to all the subsystems.

add_response(name, type_, lower=None, upper=None, equals=None, ref=None, ref0=None, indices=None, index=None, units=None, adder=None, scaler=None, linear=False, parallel_deriv_color=None, cache_linear_solution=False, flat_indices=None, alias=None)

Add a response variable to this system.

The response can be scaled using ref and ref0. The argument ref0 represents the physical value when the scaled value is 0. The argument ref represents the physical value when the scaled value is 1.

Parameters:

name (str) – Promoted name of the response variable in the system.
type (str) – The type of response. Supported values are ‘con’ and ‘obj’.
lower (float or ndarray, optional) – Lower boundary for the variable.
upper (upper or ndarray, optional) – Upper boundary for the variable.
equals (equals or ndarray, optional) – Equality constraint value for the variable.
ref (float or ndarray, optional) – Value of response variable that scales to 1.0 in the driver.
ref0 (upper or ndarray, optional) – Value of response variable that scales to 0.0 in the driver.
indices (sequence of int, optional) – If variable is an array, these indicate which entries are of interest for this particular response.
index (int, optional) – If variable is an array, this indicates which entry is of interest for this particular response.
units (str, optional) – Units to convert to before applying scaling.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
linear (bool) – Set to True if constraint is linear. Default is False.
parallel_deriv_color (str) – If specified, this design var will be grouped for parallel derivative calculations with other variables sharing the same parallel_deriv_color.
cache_linear_solution (bool) – If True, store the linear solution vectors for this variable so they can be used to start the next linear solution with an initial guess equal to the solution from the previous linear solve.
flat_indices (bool) – If True, interpret specified indices as being indices into a flat source array.
alias (str or None) – Alias for this response. Necessary when adding multiple responses on different indices of the same variable.

check_config(logger)

Perform optional error checks.

Parameters:: logger (object) – The object that manages logging output.

property checking

Return True if check_partials or check_totals is executing.

Returns:: True if we’re running within check_partials or check_totals.
Return type:: bool

cleanup(): Clean up resources prior to exit.

compute_jacvec_product(inputs, d_inputs, d_outputs, mode, discrete_inputs=None)

Compute jac-vector product. The model is assumed to be in an unscaled state.

If mode is:

‘fwd’: d_inputs |-> d_outputs

‘rev’: d_outputs |-> d_inputs

Parameters:

inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].
d_inputs (Vector) – See inputs; product must be computed only if var_name in d_inputs.
d_outputs (Vector) – See outputs; product must be computed only if var_name in d_outputs.
mode (str) – Either ‘fwd’ or ‘rev’.
discrete_inputs (dict or None) – If not None, dict containing discrete input values.

compute_partials(inputs, partials, discrete_inputs=None)

Compute sub-jacobian parts. The model is assumed to be in an unscaled state.

Parameters:

inputs (Vector) – Unscaled, dimensional input variables read via inputs[key].
partials (Jacobian) – Sub-jac components written to partials[output_name, input_name]..
discrete_inputs (dict or None) – If not None, dict containing discrete input values.

convert2units(name, val, units)

Convert the given value to the specified units.

Parameters:

name (str) – Name of the variable.
val (float or ndarray of float) – The value of the variable.
units (str) – The units to convert to.

Returns:

The value converted to the specified units.

Return type:

float or ndarray of float

convert_from_units(name, val, units)

Convert the given value from the specified units to those of the named variable.

Parameters:

name (str) – Name of the variable.
val (float or ndarray of float) – The value of the variable.
units (str) – The units to convert to.

Returns:

The value converted to the specified units.

Return type:

float or ndarray of float

convert_units(name, val, units_from, units_to)

Wrap the utility convert_units and give a good error message.

Parameters:

name (str) – Name of the variable.
val (float or ndarray of float) – The value of the variable.
units_from (str) – The units to convert from.
units_to (str) – The units to convert to.

Returns:

The value converted to the specified units.

Return type:

float or ndarray of float

declare_coloring(wrt=('*',), method='fd', form=None, step=None, per_instance=True, num_full_jacs=3, tol=1e-25, orders=None, perturb_size=1e-09, min_improve_pct=5.0, show_summary=True, show_sparsity=False)

Set options for deriv coloring of a set of wrt vars matching the given pattern(s).

Parameters:

wrt (str or list of str) – The name or names of the variables that derivatives are taken with respect to. This can contain input names, output names, or glob patterns.
method (str) – Method used to compute derivative: “fd” for finite difference, “cs” for complex step.
form (str) – Finite difference form, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
step (float) – Step size for finite difference. Leave undeclared to keep unchanged from previous or default value.
per_instance (bool) – If True, a separate coloring will be generated for each instance of a given class. Otherwise, only one coloring for a given class will be generated and all instances of that class will use it.
num_full_jacs (int) – Number of times to repeat partial jacobian computation when computing sparsity.
tol (float) – Tolerance used to determine if an array entry is nonzero during sparsity determination.
orders (int) – Number of orders above and below the tolerance to check during the tolerance sweep.
perturb_size (float) – Size of input/output perturbation during generation of sparsity.
min_improve_pct (float) – If coloring does not improve (decrease) the number of solves more than the given percentage, coloring will not be used.
show_summary (bool) – If True, display summary information after generating coloring.
show_sparsity (bool) – If True, display sparsity with coloring info after generating coloring.

declare_partials(of, wrt, dependent=True, rows=None, cols=None, val=None, method='exact', step=None, form=None, step_calc=None, minimum_step=None)

Declare information about this component’s subjacobians.

Parameters:

of (str or list of str) – The name of the residual(s) that derivatives are being computed for. May also contain a glob pattern.
wrt (str or list of str) – The name of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
dependent (bool(True)) – If False, specifies no dependence between the output(s) and the input(s). This is only necessary in the case of a sparse global jacobian, because if ‘dependent=False’ is not specified and declare_partials is not called for a given pair, then a dense matrix of zeros will be allocated in the sparse global jacobian for that pair. In the case of a dense global jacobian it doesn’t matter because the space for a dense subjac will always be allocated for every pair.
rows (ndarray of int or None) – Row indices for each nonzero entry. For sparse subjacobians only.
cols (ndarray of int or None) – Column indices for each nonzero entry. For sparse subjacobians only.
val (float or ndarray of float or scipy.sparse) – Value of subjacobian. If rows and cols are not None, this will contain the values found at each (row, col) location in the subjac.
method (str) – The type of approximation that should be used. Valid options include: ‘fd’: Finite Difference, ‘cs’: Complex Step, ‘exact’: use the component defined analytic derivatives. Default is ‘exact’.
step (float) – Step size for approximation. Defaults to None, in which case the approximation method provides its default value.
form (str) – Form for finite difference, can be ‘forward’, ‘backward’, or ‘central’. Defaults to None, in which case the approximation method provides its default value.
step_calc (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. Defaults to None, in which case the approximation method provides its default value.
minimum_step (float) – Minimum step size allowed when using one of the relative step_calc options.

Returns:

Metadata dict for the specified partial(s).

Return type:

dict

get_coloring_fname()

Return the full pathname to a coloring file.

Returns:: Full pathname of the coloring file.
Return type:: str

get_constraints(recurse=True, get_sizes=True, use_prom_ivc=False)

Get the Constraint settings from this system.

Retrieve the constraint settings for the current system as a dict, keyed by variable name.

Parameters:

recurse (bool, optional) – If True, recurse through the subsystems and return the path of all constraints relative to the this system.
get_sizes (bool, optional) – If True, compute the size of each constraint.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The constraints defined in the current system.

Return type:

dict

get_design_vars(recurse=True, get_sizes=True, use_prom_ivc=True)

Get the DesignVariable settings from this system.

Retrieve all design variable settings from the system and, if recurse is True, all of its subsystems.

Parameters:

recurse (bool) – If True, recurse through the subsystems and return the path of all design vars relative to the this system.
get_sizes (bool, optional) – If True, compute the size of each design variable.
use_prom_ivc (bool) – Translate auto_ivc_names to their promoted input names.

Returns:

The design variables defined in the current system and, if recurse=True, its subsystems.

Return type:

dict

get_io_metadata(iotypes=('input', 'output'), metadata_keys=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, tags=(), get_remote=False, rank=None, return_rel_names=True)

Retrieve metadata for a filtered list of variables.

Parameters:

iotypes (str or iter of str) – Will contain either ‘input’, ‘output’, or both. Defaults to both.
metadata_keys (iter of str or None) – Names of metadata entries to be retrieved or None, meaning retrieve all available ‘allprocs’ metadata. If ‘val’ or ‘src_indices’ are required, their keys must be provided explicitly since they are not found in the ‘allprocs’ metadata and must be retrieved from local metadata located in each process.
includes (str, iter of str or None) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all variables.
excludes (str, iter of str or None) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
tags (str or iter of strs) – User defined tags that can be used to filter what gets listed. Only inputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
get_remote (bool) – If True, retrieve variables from other MPI processes as well.
rank (int or None) – If None, and get_remote is True, retrieve values from all MPI process to all other MPI processes. Otherwise, if get_remote is True, retrieve values from all MPI processes only to the specified rank.
return_rel_names (bool) – If True, the names returned will be relative to the scope of this System. Otherwise they will be absolute names.

Returns:

A dict of metadata keyed on name, where name is either absolute or relative based on the value of the return_rel_names arg, and metadata is a dict containing entries based on the value of the metadata_keys arg. Every metadata dict will always contain two entries, ‘promoted_name’ and ‘discrete’, to indicate a given variable’s promoted name and whether or not it is discrete.

Return type:

dict

get_linear_vectors()

Return the linear inputs, outputs, and residuals vectors.

Returns:: (inputs, outputs, residuals) – Yields the linear inputs, outputs, and residuals vectors.
Return type:: tuple of <Vector> instances

get_nonlinear_vectors()

Return the inputs, outputs, and residuals vectors.

Returns:: Yields the inputs, outputs, and residuals nonlinear vectors.
Return type:: (inputs, outputs, residuals)

get_objectives(recurse=True, get_sizes=True, use_prom_ivc=False)

Get the Objective settings from this system.

Retrieve all objectives settings from the system as a dict, keyed by variable name.

Parameters:

recurse (bool, optional) – If True, recurse through the subsystems and return the path of all objective relative to the this system.
get_sizes (bool, optional) – If True, compute the size of each objective.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The objectives defined in the current system.

Return type:

dict

get_promotions(inprom=None, outprom=None)

Return all promotions for the given promoted variable(s).

In other words, how and where did promotions occur to convert absolute variable names into the given promoted name(s) at the current System level.

Parameters:

inprom (str or None) – The promoted input variable name.
outprom (str or None) – The promoted output variable name.

Returns:

Dictionary keyed on system pathname containing input and/or output promotion lists for each System where promotions occurred to produce the given promoted variable(s).

Return type:

dict

get_reports_dir()

Get the path to the directory where the report files should go.

If it doesn’t exist, it will be created.

Returns:: The path to the directory where reports should be written.
Return type:: str

get_responses(recurse=True, get_sizes=True, use_prom_ivc=False)

Get the response variable settings from this system.

Retrieve all response variable settings from the system as a dict, keyed by either absolute variable name, promoted name, or alias name, depending on the value of use_prom_ivc and whether the original key was a promoted output, promoted input, or an alias.

Parameters:

recurse (bool, optional) – If True, recurse through the subsystems and return the path of all responses relative to the this system.
get_sizes (bool, optional) – If True, compute the size of each response.
use_prom_ivc (bool) – Translate ivc names to their promoted input names.

Returns:

The responses defined in the current system and, if recurse=True, its subsystems.

Return type:

dict

get_source(name)

Return the source variable connected to the given named variable.

The name can be a promoted name or an absolute name. If the given variable is an input, the absolute name of the connected source will be returned. If the given variable itself is a source, its own absolute name will be returned.

Parameters:: name (str) – Absolute or promoted name of the variable.
Returns:: The absolute name of the source variable.
Return type:: str

get_val(name, units=None, indices=None, get_remote=False, rank=None, vec_name='nonlinear', kind=None, flat=False, from_src=True)

Get an output/input/residual variable.

Function is used if you want to specify display units.

Parameters:

name (str) – Promoted or relative variable name in the root system’s namespace.
units (str, optional) – Units to convert to before return.
indices (int or list of ints or tuple of ints or int ndarray or Iterable or None, optional) – Indices or slice to return.
get_remote (bool or None) – If True, retrieve the value even if it is on a remote process. Note that if the variable is remote on ANY process, this function must be called on EVERY process in the Problem’s MPI communicator. If False, only retrieve the value if it is on the current process, or only the part of the value that’s on the current process for a distributed variable. If None and the variable is remote or distributed, a RuntimeError will be raised.
rank (int or None) – If not None, only gather the value to this rank.
vec_name (str) – Name of the vector to use. Defaults to ‘nonlinear’.
kind (str or None) – Kind of variable (‘input’, ‘output’, or ‘residual’). If None, returned value will be either an input or output.
flat (bool) – If True, return the flattened version of the value.
from_src (bool) – If True, retrieve value of an input variable from its connected source.

Returns:

The value of the requested output/input variable.

Return type:

object

initialize(): Perform any one-time initialization run at instantiation.

is_explicit()

Return True if this is an explicit component.

Returns:: True if this is an explicit component.
Return type:: bool

property linear_solver: Get the linear solver for this system.

list_inputs(val=True, prom_name=True, units=False, shape=False, global_shape=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

Write a list of input names and other optional information to a specified stream.

Parameters:

val (bool, optional) – When True, display/return input values. Default is True.
prom_name (bool, optional) – When True, display/return the promoted name of the variable. Default is True.
units (bool, optional) – When True, display/return units. Default is False.
shape (bool, optional) – When True, display/return the shape of the value. Default is False.
global_shape (bool, optional) – When True, display/return the global shape of the value. Default is False.
desc (bool, optional) – When True, display/return description. Default is False.
hierarchical (bool, optional) – When True, human readable output shows variables in hierarchical format.
print_arrays (bool, optional) – When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
tags (str or list of strs) – User defined tags that can be used to filter what gets listed. Only inputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
includes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all input variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to an output tagged openmdao:indep_var. If False, list only inputs _not_ connected to outputs tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
all_procs (bool, optional) – When True, display output on all ranks. Default is False, which will display output only from rank 0.
out_stream (file-like object) – Where to send human readable output. Default is sys.stdout. Set to None to suppress.
print_min (bool) – When true, if the input value is an array, print its smallest value.
print_max (bool) – When true, if the input value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of input names and other optional information about those inputs.

Return type:

metadata}

list_outputs(explicit=True, implicit=True, val=True, prom_name=True, residuals=False, residuals_tol=None, units=False, shape=False, global_shape=False, bounds=False, scaling=False, desc=False, hierarchical=True, print_arrays=False, tags=None, includes=None, excludes=None, is_indep_var=None, is_design_var=None, all_procs=False, list_autoivcs=False, out_stream=DEFAULT_OUT_STREAM, print_min=False, print_max=False, return_format='list')

Write a list of output names and other optional information to a specified stream.

Parameters:

explicit (bool, optional) – Include outputs from explicit components. Default is True.
implicit (bool, optional) – Include outputs from implicit components. Default is True.
val (bool, optional) – When True, display output values. Default is True.
prom_name (bool, optional) – When True, display the promoted name of the variable. Default is True.
residuals (bool, optional) – When True, display residual values. Default is False.
residuals_tol (float, optional) – If set, limits the output of list_outputs to only variables where the norm of the resids array is greater than the given ‘residuals_tol’. Default is None.
units (bool, optional) – When True, display units. Default is False.
shape (bool, optional) – When True, display/return the shape of the value. Default is False.
global_shape (bool, optional) – When True, display/return the global shape of the value. Default is False.
bounds (bool, optional) – When True, display/return bounds (lower and upper). Default is False.
scaling (bool, optional) – When True, display/return scaling (ref, ref0, and res_ref). Default is False.
desc (bool, optional) – When True, display/return description. Default is False.
hierarchical (bool, optional) – When True, human readable output shows variables in hierarchical format.
print_arrays (bool, optional) – When False, in the columnar display, just display norm of any ndarrays with size > 1. The norm is surrounded by vertical bars to indicate that it is a norm. When True, also display full values of the ndarray below the row. Format is affected by the values set with numpy.set_printoptions Default is False.
tags (str or list of strs) – User defined tags that can be used to filter what gets listed. Only outputs with the given tags will be listed. Default is None, which means there will be no filtering based on tags.
includes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to include. Default is None, which includes all output variables.
excludes (None, str, or iter of str) – Collection of glob patterns for pathnames of variables to exclude. Default is None.
is_indep_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only outputs tagged openmdao:indep_var. If False, list only outputs that are _not_ tagged openmdao:indep_var.
is_design_var (bool or None) – If None (the default), do no additional filtering of the inputs. If True, list only inputs connected to outputs that are driver design variables. If False, list only inputs _not_ connected to outputs that are driver design variables.
all_procs (bool, optional) – When True, display output on all processors. Default is False.
list_autoivcs (bool) – If True, include auto_ivc outputs in the listing. Defaults to False.
out_stream (file-like) – Where to send human readable output. Default is sys.stdout. Set to None to suppress.
print_min (bool) – When true, if the output value is an array, print its smallest value.
print_max (bool) – When true, if the output value is an array, print its largest value.
return_format (str) – Indicates the desired format of the return value. Can have value of ‘list’ or ‘dict’. If ‘list’, the return value is a list of (name, metadata) tuples. if ‘dict’, the return value is a dictionary mapping {name: metadata}.

Returns:

list of (name, metadata) or dict of {name – List or dict of output names and other optional information about those outputs.

Return type:

metadata}

load_model_options()

Load the relevant model options from Problem._metadata[‘model_options’].

This method examines each path filter and corresponding options in self._problem_meta[‘model_options’]. If this System’s pathname matches the given path filter, it will assume the value for each given option which it possesses.

property msginfo

Our instance pathname, if available, or our class name. For use in error messages.

Returns:: Either our instance pathname or class name.
Return type:: str

property nonlinear_solver: Get the nonlinear solver for this system.

record_iteration(): Record an iteration of the current System.

run_apply_linear(mode, scope_out=None, scope_in=None)

Compute jac-vec product.

This calls _apply_linear, but with the model assumed to be in an unscaled state.

Parameters:

mode (str) – ‘fwd’ or ‘rev’.
scope_out (set or None) – Set of absolute output names in the scope of this mat-vec product. If None, all are in the scope.
scope_in (set or None) – Set of absolute input names in the scope of this mat-vec product. If None, all are in the scope.

run_apply_nonlinear()

Compute residuals.

This calls _apply_nonlinear, but with the model assumed to be in an unscaled state.

run_linearize(sub_do_ln=True)

Compute jacobian / factorization.

This calls _linearize, but with the model assumed to be in an unscaled state.

Parameters:: sub_do_ln (bool) – Flag indicating if the children should call linearize on their linear solvers.

run_solve_linear(mode)

Apply inverse jac product.

This calls _solve_linear, but with the model assumed to be in an unscaled state.

Parameters:: mode (str) – ‘fwd’ or ‘rev’.

run_solve_nonlinear()

Compute outputs.

This calls _solve_nonlinear, but with the model assumed to be in an unscaled state.

set_check_partial_options(wrt, method='fd', form=None, step=None, step_calc=None, minimum_step=None, directional=False)

Set options that will be used for checking partial derivatives.

Parameters:

wrt (str or list of str) – The name or names of the variables that derivatives are taken with respect to. This can contain the name of any input or output variable. May also contain a glob pattern.
method (str) – Method for check: “fd” for finite difference, “cs” for complex step.
form (str) – Finite difference form for check, can be “forward”, “central”, or “backward”. Leave undeclared to keep unchanged from previous or default value.
step (float) – Step size for finite difference check. Leave undeclared to keep unchanged from previous or default value.
step_calc (str) – Step type for computing the size of the finite difference step. It can be ‘abs’ for absolute, ‘rel_avg’ for a size relative to the absolute value of the vector input, or ‘rel_element’ for a size relative to each value in the vector input. In addition, it can be ‘rel_legacy’ for a size relative to the norm of the vector. For backwards compatibilty, it can be ‘rel’, which is now equivalent to ‘rel_avg’. Defaults to None, in which case the approximation method provides its default value..
minimum_step (float) – Minimum step size allowed when using one of the relative step_calc options.
directional (bool) – Set to True to perform a single directional derivative for each vector variable in the pattern named in wrt.

set_constraint_options(name, ref=UNDEFINED, ref0=UNDEFINED, equals=UNDEFINED, lower=UNDEFINED, upper=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options that were set using add_constraint.

Parameters:

name (str) – Name of the response variable in the system.
ref (float or ndarray, optional) – Value of response variable that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of response variable that scales to 0.0 in the driver.
equals (float or ndarray, optional) – Equality constraint value for the variable.
lower (float or ndarray, optional) – Lower boundary for the variable.
upper (float or ndarray, optional) – Upper boundary for the variable.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
alias (str, optional) – Alias for this response. Necessary when adding multiple constraints on different indices or slices of a single variable.

set_design_var_options(name, lower=UNDEFINED, upper=UNDEFINED, scaler=UNDEFINED, adder=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED)

Set options for design vars in the model.

Can be used to set the options outside of setting them when calling add_design_var

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or ndarray, optional) – Lower boundary for the input.
upper (upper or ndarray, optional) – Upper boundary for the input.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
ref (float or ndarray, optional) – Value of design var that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of design var that scales to 0.0 in the driver.

set_objective_options(name, ref=UNDEFINED, ref0=UNDEFINED, adder=UNDEFINED, scaler=UNDEFINED, alias=UNDEFINED)

Set options for objectives in the model.

Can be used to set options after they have been set by add_objective.

Parameters:

name (str) – Name of the response variable in the system.
ref (float or ndarray, optional) – Value of response variable that scales to 1.0 in the driver.
ref0 (float or ndarray, optional) – Value of response variable that scales to 0.0 in the driver.
adder (float or ndarray, optional) – Value to add to the model value to get the scaled value for the driver. adder is first in precedence. adder and scaler are an alterantive to using ref and ref0.
scaler (float or ndarray, optional) – Value to multiply the model value to get the scaled value for the driver. scaler is second in precedence. adder and scaler are an alterantive to using ref and ref0.
alias (str) – Alias for this response. Necessary when adding multiple objectives on different indices or slices of a single variable.

set_output_solver_options(name, lower=UNDEFINED, upper=UNDEFINED, ref=UNDEFINED, ref0=UNDEFINED, res_ref=UNDEFINED)

Set solver output options.

Allows the user to set output solver options after the output has been defined and metadata set using the add_ouput method.

Parameters:

name (str) – Name of the variable in this system’s namespace.
lower (float or list or tuple or ndarray or None) – Lower bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no lower bound. Default is None.
upper (float or list or tuple or ndarray or None) – Upper bound(s) in user-defined units. It can be (1) a float, (2) an array_like consistent with the shape arg (if given), or (3) an array_like matching the shape of val, if val is array_like. A value of None means this output has no upper bound. Default is None.
ref (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 1. Default is 1.
ref0 (float) – Scaling parameter. The value in the user-defined units of this output variable when the scaled value is 0. Default is 0.
res_ref (float) – Scaling parameter. The value in the user-defined res_units of this output’s residual when the scaled value is 1. Default is None, which means residual scaling matches output scaling.

set_solver_print(level=2, depth=1e+99, type_='all')

Control printing for solvers and subsolvers in the model.

Parameters:

level (int) – Iprint level. Set to 2 to print residuals each iteration; set to 1 to print just the iteration totals; set to 0 to disable all printing except for failures, and set to -1 to disable all printing including failures.
depth (int) – How deep to recurse. For example, you can set this to 0 if you only want to print the top level linear and nonlinear solver messages. Default prints everything.
type (str) – Type of solver to set: ‘LN’ for linear, ‘NL’ for nonlinear, or ‘all’ for all.

set_val(name, val, units=None, indices=None)

Set an input or output variable.

Parameters:

name (str) – Promoted or relative variable name in the system’s namespace.
val (object) – Value to assign to this variable.
units (str, optional) – Units of the value.
indices (int or list of ints or tuple of ints or int ndarray or Iterable or None, optional) – Indices or slice to set.

system_iter(include_self=False, recurse=True, typ=None)

Yield a generator of local subsystems of this system.

Parameters:

include_self (bool) – If True, include this system in the iteration.
recurse (bool) – If True, iterate over the whole tree under this system.
typ (type) – If not None, only yield Systems that match that are instances of the given type.

Yields:

type or None

property under_approx

Return True if under complex step or finite difference.

Returns:: True if under CS or FD.
Return type:: bool

use_fixed_coloring(coloring=<object object>, recurse=True)

Use a precomputed coloring for this System.

Parameters:

coloring (str) – A coloring filename. If no arg is passed, filename will be determined automatically.
recurse (bool) – If True, set fixed coloring in all subsystems that declare a coloring. Ignored if a specific coloring is passed in.

class fastoad.model_base.propulsion.AbstractFuelPropulsion[source]

Bases: IPropulsion, ABC

Propulsion model that consume any fuel should inherit from this one.

In inheritors, compute_flight_points() is expected to define “sfc” and “thrust” in computed FlightPoint instances.

get_consumed_mass(flight_point: FlightPoint, time_step: float) → float[source]

Computes consumed mass for provided flight point and time step.

This method should rely on FlightPoint fields that are generated by :meth: compute_flight_points.

Parameters:

flight_point –
time_step –

Returns:

the consumed mass in kg

abstract compute_flight_points(flight_points: FlightPoint | DataFrame)

Computes Specific Fuel Consumption according to provided conditions.

See FlightPoint for available fields that may be used for computation. If a DataFrame instance is provided, it is expected that its columns match field names of FlightPoint (actually, the DataFrame instance should be generated from a list of FlightPoint instances).

Note

About thrust_is_regulated, thrust_rate and thrust

thrust_is_regulated tells if a flight point should be computed using thrust_rate (when False) or thrust (when True) as input. This way, the method can be used in a vectorized mode, where each point can be set to respect a thrust order or a thrust rate order.

if thrust_is_regulated is not defined, the considered input will be the defined one between thrust_rate and thrust (if both are provided, thrust_rate will be used)
if thrust_is_regulated is True or False (i.e., not a sequence), the considered input will be taken accordingly, and should of course be defined.
if there are several flight points, thrust_is_regulated is a sequence or array, thrust_rate and thrust should be provided and have the same shape as thrust_is_regulated:code:. The method will consider for each element which input will be used according to thrust_is_regulated.

Parameters:: flight_points – FlightPoint or DataFrame instance
Returns:: None (inputs are updated in-place)

class fastoad.model_base.propulsion.FuelEngineSet(engine: IPropulsion, engine_count)[source]

Bases: AbstractFuelPropulsion

Class for modelling an assembly of identical fuel engines.

Thrust is supposed equally distributed among them.

Parameters:

engine – the engine model
engine_count –

compute_flight_points(flight_points: FlightPoint | DataFrame)[source]

Computes Specific Fuel Consumption according to provided conditions.

See FlightPoint for available fields that may be used for computation. If a DataFrame instance is provided, it is expected that its columns match field names of FlightPoint (actually, the DataFrame instance should be generated from a list of FlightPoint instances).

Note

About thrust_is_regulated, thrust_rate and thrust

thrust_is_regulated tells if a flight point should be computed using thrust_rate (when False) or thrust (when True) as input. This way, the method can be used in a vectorized mode, where each point can be set to respect a thrust order or a thrust rate order.

if thrust_is_regulated is not defined, the considered input will be the defined one between thrust_rate and thrust (if both are provided, thrust_rate will be used)
if thrust_is_regulated is True or False (i.e., not a sequence), the considered input will be taken accordingly, and should of course be defined.
if there are several flight points, thrust_is_regulated is a sequence or array, thrust_rate and thrust should be provided and have the same shape as thrust_is_regulated:code:. The method will consider for each element which input will be used according to thrust_is_regulated.

Parameters:: flight_points – FlightPoint or DataFrame instance
Returns:: None (inputs are updated in-place)

get_consumed_mass(flight_point: FlightPoint, time_step: float) → float

Computes consumed mass for provided flight point and time step.

This method should rely on FlightPoint fields that are generated by :meth: compute_flight_points.

Parameters:

flight_point –
time_step –

Returns:

the consumed mass in kg