"""Components used mainly for testing Newton and line searches."""
from math import exp
import numpy as np
import openmdao.api as om
[docs]class ImplCompOneState(om.ImplicitComponent):
"""
A Simple Implicit Component
R(x,y) = 0.5y^2 + 2y + exp(-16y^2) + 2exp(-5y) - x
Solution:
x = 1.2278849186466743
y = 0.3968459
"""
[docs] def setup(self):
self.add_input('x', 1.2278849186466743)
self.add_output('y', val=1.0)
[docs] def setup_partials(self):
self.declare_partials(of='*', wrt='*')
[docs] def apply_nonlinear(self, inputs, outputs, resids):
"""
Don't solve; just calculate the residual.
"""
x = inputs['x']
y = outputs['y']
resids['y'] = 0.5*y*y + 2.0*y + exp(-16.0*y*y) + 2.0*exp(-5.0*y) - x
[docs] def linearize(self, inputs, outputs, J):
"""
Analytical derivatives.
"""
y = outputs['y']
# State equation
J[('y', 'x')] = -1.0
J[('y', 'y')] = y + 2.0 - 32.0*y*exp(-16.0*y*y) - 10.0*exp(-5.0*y)
[docs]class ImplCompTwoStates(om.ImplicitComponent):
"""
A Simple Implicit Component with an additional output equation.
f(x,z) = xz + z - 4
y = x + 2z
Sol : when x = 0.5, z = 2.666
Sol : when x = 2.0, z = 1.333
Coupled derivs:
y = x + 8/(x+1)
dy_dx = 1 - 8/(x+1)**2 = -2.5555555555555554
z = 4/(x+1)
dz_dx = -4/(x+1)**2 = -1.7777777777777777
"""
[docs] def setup(self):
self.add_input('x', 0.5)
self.add_output('y', 0.0)
self.add_output('z', 2.0, lower=1.5, upper=2.5)
self.maxiter = 10
self.atol = 1.0e-12
[docs] def setup_partials(self):
self.declare_partials(of='*', wrt='*')
[docs] def apply_nonlinear(self, inputs, outputs, residuals):
"""
Don't solve; just calculate the residual.
"""
x = inputs['x']
y = outputs['y']
z = outputs['z']
residuals['y'] = y - x - 2.0*z
residuals['z'] = x*z + z - 4.0
[docs] def linearize(self, inputs, outputs, jac):
"""
Analytical derivatives.
"""
# Output equation
jac[('y', 'x')] = -1.0
jac[('y', 'y')] = 1.0
jac[('y', 'z')] = -2.0
# State equation
jac[('z', 'z')] = inputs['x'] + 1.0
jac[('z', 'x')] = outputs['z']
[docs]class ImplCompTwoStatesArrays(om.ImplicitComponent):
"""
A Simple Implicit Component with an additional output equation.
f(x,z) = xz + z - 4
y = x + 2z
Sol : when x = 0.5, z = 2.666
Sol : when x = 2.0, z = 1.333
Coupled derivs:
y = x + 8/(x+1)
dy_dx = 1 - 8/(x+1)**2 = -2.5555555555555554
z = 4/(x+1)
dz_dx = -4/(x+1)**2 = -1.7777777777777777
"""
[docs] def setup(self):
self.add_input('x', np.zeros((3, 1)))
self.add_output('y', np.zeros((3, 1)))
self.add_output('z', 2.0*np.ones((3, 1)), lower=1.5,
upper=np.array([2.6, 2.5, 2.65]).reshape((3,1)))
self.maxiter = 10
self.atol = 1.0e-12
[docs] def setup_partials(self):
self.declare_partials(of='*', wrt='*')
[docs] def apply_nonlinear(self, inputs, outputs, residuals):
"""
Don't solve; just calculate the residual.
"""
x = inputs['x']
y = outputs['y']
z = outputs['z']
residuals['y'] = y - x - 2.0*z
residuals['z'] = x*z + z - 4.0
[docs] def linearize(self, inputs, outputs, jac):
"""
Analytical derivatives.
"""
# Output equation
jac[('y', 'x')] = -np.diag(np.array([1.0, 1.0, 1.0]))
jac[('y', 'y')] = np.diag(np.array([1.0, 1.0, 1.0]))
jac[('y', 'z')] = -np.diag(np.array([2.0, 2.0, 2.0]))
# State equation
jac[('z', 'z')] = (inputs['x'] + 1.0) * np.eye(3)
jac[('z', 'x')] = outputs['z'] * np.eye(3)
