gemseo / problems / sobieski

# base module¶

## SSBJ base class¶

Classes:

 SobieskiBase(dtype) Class defining Sobieski problem and related method to the problem such as disciplines computation, constraints, reference optimum.
class gemseo.problems.sobieski.base.SobieskiBase(dtype)[source]

Bases: object

Class defining Sobieski problem and related method to the problem such as disciplines computation, constraints, reference optimum.

Constructor.

Parameters

dtype (str) – data type

Attributes:

Methods:

 Computes the aerodynamic center. compute_half_span(x_shared) Compute half-span from surface and aspect ratio. compute_thickness(x_shared) Compute a wing thickness. Definition of constants vector constants for Sobieski problem. derive_normalize_s(s_ref, s_new) Derivation of normalization of input variables for use of polynomial approximation. derive_poly_approx(s_ref, s_new, flag, s_bound) Compute the polynomial coefficients to characterize the behavior of certain synthetic variables and function modifiers. get_bounds_by_name(variables_names) Return bounds of design variables and coupling variables. Return a default initial value for design variables. Get initial values used by polynomial functions. Set the input design bounds and return them as 2 numpy arrays. Set the input design bounds and return them as a tuple of tuples. poly_approx(s_ref, s_new, flag, s_bound) This function calculates polynomial coefficients to characterize the behavior of certain synthetic variables and function modifiers.
DTYPE_COMPLEX = 'complex128'
DTYPE_DEFAULT = 'complex128'
DTYPE_DOUBLE = 'float64'
static compute_aero_center(x_1)[source]

Computes the aerodynamic center.

Parameters

x_1 (numpy array) – local design variables (structure)

Returns

aerodynamic center

Return type

numpy array

compute_half_span(x_shared)[source]

Compute half-span from surface and aspect ratio.

Parameters

x_shared (numpy array) – global design variables

Returns

half-span

Return type

numpy array

compute_thickness(x_shared)[source]

Compute a wing thickness.

Parameters

x_shared (numpy array) – global design variables

Returns

thickness

Return type

numpy array

default_constants()[source]

Definition of constants vector constants for Sobieski problem.

Returns

constant vector

Return type

numpy array

derive_normalize_s(s_ref, s_new)[source]

Derivation of normalization of input variables for use of polynomial approximation.

Parameters
• s_ref (numpy array) – vector of initial values of independent variables (5 variables at max)

• s_new (numpy array) – vector of current values of independent variables

Returns

derivatives of normalized value and normalized+centered value

Return type

numpy array

derive_poly_approx(s_ref, s_new, flag, s_bound)[source]

Compute the polynomial coefficients to characterize the behavior of certain synthetic variables and function modifiers. Compared to poly_approx, also returns polynomial coeff for linearization.

Parameters
• s_ref (numpy array) – vector of initial values of independent variables (5 variables at max)

• s_new (numpy array) – vector of current values of independent variables

• flag (int) –

indicates functional relationship between variables:

• flag = 1: linear >0

• flag = 2: nonlinear >0

• flag = 3: linear < 0

• flag = 4: nonlinear <0

• flag = 5: parabolic

• s_bound (numpy array) – offset value for normalization

Returns

poly_value: value of synthetic variable or modifier

Return type

numpy array

classmethod get_bounds_by_name(variables_names)[source]

Return bounds of design variables and coupling variables.

Parameters

variables_names (str or list(str)) – names of variables

Returns

lower bound and upper bound

get_default_x0()[source]

Return a default initial value for design variables.

Returns

initial design variables

Return type

numpy array

warning: ##DO NOT CHANGE VALUE: THEY ARE USED FOR POLYNOMIAL APPROXIMATION ##

get_initial_values()[source]

Get initial values used by polynomial functions.

Returns

initial values

Return type

tuple(ndarray)

classmethod get_sobieski_bounds()[source]

Set the input design bounds and return them as 2 numpy arrays.

Returns

upper and lower bounds

Return type

numpy array, numpy array

static get_sobieski_bounds_tuple()[source]

Set the input design bounds and return them as a tuple of tuples.

Returns

Subtuple is build with lower bound and upper bound.

Return type

tuple(tuple)

poly_approx(s_ref, s_new, flag, s_bound)[source]

This function calculates polynomial coefficients to characterize the behavior of certain synthetic variables and function modifiers.

Parameters
• s_ref (numpy array) – vector of initial values of independent variables (5 variables at max)

• s_new (numpy array) – vector of current values of independent variables

• flag (int) –

indicates functional relationship between variables:

• flag = 1: linear >0

• flag = 2: nonlinear >0

• flag = 3: linear < 0

• flag = 4: nonlinear <0

• flag = 5: parabolic

• s_bound (numpy array) – offset value for normalization

Returns

poly_value: value of synthetic variable or modifier

Return type

numpy array