def setUp(self):
self.nn = 5
self.p = Problem(model=Group())
ivc = IndepVarComp()
ivc.add_output(name='a', shape=(self.nn, 3), units='ft')
ivc.add_output(name='b', shape=(self.nn, 3), units='lbf')
self.p.model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['a', 'b'])
self.p.model.add_subsystem(name='cross_prod_comp',
subsys=CrossProductComp(vec_size=self.nn,
a_units='m', b_units='N',
c_units='N*m'))
self.p.model.connect('a', 'cross_prod_comp.a')
self.p.model.connect('b', 'cross_prod_comp.b')
self.p.setup()
self.p['a'] = np.random.rand(self.nn, 3)
self.p['b'] = np.random.rand(self.nn, 3)
self.p.run_model()
def setUp(self):
self.nn = 5
self.p = Problem(model=Group())
ivc = IndepVarComp()
ivc.add_output(name='a', shape=(self.nn, 3, 1))
ivc.add_output(name='b', shape=(self.nn, 3, 1))
self.p.model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['a', 'b'])
self.p.model.add_subsystem(name='cross_prod_comp',
subsys=CrossProductComp(vec_size=self.nn))
self.p.model.connect('a', 'cross_prod_comp.a')
self.p.model.connect('b', 'cross_prod_comp.b')
self.p.setup(force_alloc_complex=True)
self.p['a'] = np.random.rand(self.nn, 3, 1)
self.p['b'] = np.random.rand(self.nn, 3, 1)
self.p['a'][:, 0, 0] = 2.0
self.p['a'][:, 1, 0] = 3.0
self.p['a'][:, 2, 0] = 4.0
self.p['b'][:, 0, 0] = 5.0
self.p['b'][:, 1, 0] = 6.0
self.p['b'][:, 2, 0] = 7.0
self.p.run_model()
def test(self):
import numpy as np
from openmdao.api import Problem, Group, IndepVarComp, CrossProductComp
from openmdao.utils.assert_utils import assert_rel_error
n = 100
p = Problem(model=Group())
ivc = IndepVarComp()
ivc.add_output(name='r', shape=(n, 3))
ivc.add_output(name='F', shape=(n, 3))
p.model.add_subsystem(name='ivc',
subsys=ivc,
promotes_outputs=['r', 'F'])
p.model.add_subsystem(name='cross_prod_comp',
subsys=CrossProductComp(vec_size=n,
a_name='r',
b_name='F',
c_name='torque',
a_units='m',
b_units='N',
c_units='N*m'))
p.model.connect('r', 'cross_prod_comp.r')
p.model.connect('F', 'cross_prod_comp.F')
p.setup()
p['r'] = np.random.rand(n, 3)
p['F'] = np.random.rand(n, 3)
p.run_model()
# Check the output in units of ft*lbf to ensure that our units work as expected.
for i in range(n):
a_i = p['r'][i, :]
b_i = p['F'][i, :]
expected_i = np.cross(a_i, b_i) * 0.73756215
assert_rel_error(self,
p.get_val('cross_prod_comp.torque',
units='ft*lbf')[i, :],
expected_i,
tolerance=1.0E-8)