def test_partials(self):
n = 10
p = om.Problem(model=om.Group())
ivc = om.IndepVarComp()
ivc.add_output(name='mach', units=None, val=np.zeros(n))
p.model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['*'])
p.model.add_subsystem(name='kappa_comp',
subsys=KappaComp(num_nodes=n))
p.model.connect('mach', 'kappa_comp.mach')
p.setup()
p['mach'] = np.linspace(0, 1.8, n)
p.run_model()
cpd = p.check_partials(compact_print=False, out_stream=None)
assert_check_partials(cpd, atol=1.0E-5, rtol=1.0E-4)
def test_value(self):
n = 500
p = om.Problem(model=om.Group())
ivc = om.IndepVarComp()
ivc.add_output(name='mach', units=None, val=np.zeros(n))
p.model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['*'])
p.model.add_subsystem(name='kappa_comp', subsys=KappaComp(num_nodes=n))
p.model.connect('mach', 'kappa_comp.mach')
p.setup()
p['mach'] = np.linspace(0, 1.8, n)
p.run_model()
M = p.get_val('mach')
kappa = p.get_val('kappa_comp.kappa')
idxs_0 = np.where(M <= 1.15)[0]
idxs_1 = np.where(M > 1.15)[0]
kappa_analtic_0 = 0.54 + 0.15 * (1.0 + np.tanh(
(M[idxs_0] - 0.9) / 0.06))
kappa_analtic_1 = 0.54 + 0.15 * (1.0 + np.tanh(0.25 / 0.06)) + 0.14 * (
M[idxs_1] - 1.15)
assert_near_equal(kappa[idxs_0], kappa_analtic_0)
assert_near_equal(kappa[idxs_1], kappa_analtic_1)
def test_partials(self):
n = 10
p = om.Problem(model=om.Group())
p.model.add_subsystem(name='kappa_comp', subsys=KappaComp(num_nodes=n))
p.setup()
p.set_val('kappa_comp.mach', np.linspace(0, 1.8, n))
p.run_model()
cpd = p.check_partials(compact_print=False, out_stream=None)
assert_check_partials(cpd, atol=1.0E-5, rtol=1.0E-4)
def test_value(self):
n = 500
p = om.Problem()
p.model.add_subsystem(name='kappa_comp', subsys=KappaComp(num_nodes=n))
p.setup()
p.set_val('kappa_comp.mach', np.linspace(0, 1.8, n))
p.run_model()
M = p.get_val('kappa_comp.mach')
kappa = p.get_val('kappa_comp.kappa')
idxs_0 = np.where(M <= 1.15)[0]
idxs_1 = np.where(M > 1.15)[0]
kappa_analtic_0 = 0.54 + 0.15 * (1.0 + np.tanh(
(M[idxs_0] - 0.9) / 0.06))
kappa_analtic_1 = 0.54 + 0.15 * (1.0 + np.tanh(0.25 / 0.06)) + 0.14 * (
M[idxs_1] - 1.15)
assert_near_equal(kappa[idxs_0], kappa_analtic_0)
assert_near_equal(kappa[idxs_1], kappa_analtic_1)
def test_visual_inspection(self):
n = 500
p = Problem(model=Group())
ivc = IndepVarComp()
ivc.add_output(name='mach', units=None, val=np.zeros(n))
p.model.add_subsystem(name='ivc', subsys=ivc, promotes_outputs=['*'])
p.model.add_subsystem(name='kappa_comp',
subsys=KappaComp(num_nodes=n))
p.model.connect('mach', 'kappa_comp.mach')
p.setup()
p['mach'] = np.linspace(0, 1.8, n)
p.run_model()
import matplotlib.pyplot as plt
plt.plot(p['mach'], p['kappa_comp.kappa'], 'ro', ms=2)
plt.show()