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fix for complex warnings. DO NOT merge this until the openmdao complex vector refactor is merged! #32

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Merged
eshendricks merged 3 commits into from
Dec 1, 2020
Merged

fix for complex warnings. DO NOT merge this until the openmdao complex vector refactor is merged! #32

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2c25024
fix for complex warnings
naylor-b Nov 18, 2020
96f7b49
modified Bret's fix to remove local allocation altogether.
Nov 25, 2020
c8698fa
Merge pull request #1 from Kenneth-T-Moore/dtype
naylor-b Nov 25, 2020
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31 changes: 11 additions & 20 deletions pycycle/thermo/cea/props_calcs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,12 +173,9 @@ def compute_partials(self, inputs, J):
dCp_dnj -= R_UNIVERSAL_ENG * H0_T * result_T_last
J['Cp', 'n'] = dCp_dnj

dCp_dresultT = np.zeros(num_element+1, dtype=inputs._data.dtype)
# for i in range(num_element):
# self.dCp_dresultT[i] = -R_UNIVERSAL_ENG*np.sum(aij[i]*nj_H0)
dCp_dresultT[:num_element] = -R_UNIVERSAL_ENG*np.sum(thermo.aij*nj_H0, axis=1)
dCp_dresultT[num_element] = - R_UNIVERSAL_ENG*np.sum(nj_H0)
J['Cp', 'result_T'] = dCp_dresultT
J['Cp', 'result_T'][0, :num_element] = -R_UNIVERSAL_ENG*np.sum(thermo.aij*nj_H0, axis=1)
J['Cp', 'result_T'][0, num_element] = - R_UNIVERSAL_ENG*np.sum(nj_H0)
dCp_dresultT = J['Cp', 'result_T'][0]

dCp_dT = (dCpe_dT + dCpf_dT)*R_UNIVERSAL_ENG
J['Cp', 'T'] = dCp_dT
Expand All @@ -189,27 +186,21 @@ def compute_partials(self, inputs, J):
J['Cv', 'n_moles'] = dCv_dnmoles
J['Cv', 'T'] = dCp_dT

dCv_dresultP = np.zeros((1, num_element+1), dtype=inputs._data.dtype)
dCv_dresultP[0, -1] = -R_UNIVERSAL_ENG*n_moles*(dlnVqdlnT/dlnVqdlnP)**2
J['Cv', 'result_P'] = dCv_dresultP
dCv_dresultP = -R_UNIVERSAL_ENG*n_moles*(dlnVqdlnT/dlnVqdlnP)**2
J['Cv', 'result_P'] [0, -1] = dCv_dresultP

dCv_dresultT = dCp_dresultT.copy()
dCv_dresultT[-1] -= n_moles*R_UNIVERSAL_ENG/dlnVqdlnP*(2*dlnVqdlnT)
dCv_dresultT_last = dCv_dresultT[-1]
J['Cv', 'result_T'] = dCv_dresultT
J['Cv', 'result_T'] = dCp_dresultT
J['Cv', 'result_T'][0, -1] -= n_moles*R_UNIVERSAL_ENG/dlnVqdlnP*(2*dlnVqdlnT)
dCv_dresultT_last = J['Cv', 'result_T'][0, -1]

J['gamma', 'n'] = dCp_dnj*(Cp/Cv-1)/(dlnVqdlnP*Cv)
J['gamma', 'n_moles'] = Cp/dlnVqdlnP/Cv**2*dCv_dnmoles
J['gamma', 'T'] = dCp_dT/dlnVqdlnP/Cv*(Cp/Cv-1)

dgamma_dresultT = np.zeros((1, num_element+1), dtype=inputs._data.dtype)
dgamma_dresultT[0, :num_element] = 1/Cv/dlnVqdlnP*dCp_dresultT[:num_element]*(Cp/Cv-1)
dgamma_dresultT[0, -1] = (-dCp_dresultT[-1]/Cv+Cp/Cv**2*dCv_dresultT_last)/dlnVqdlnP
J['gamma', 'result_T'] = dgamma_dresultT
J['gamma', 'result_T'][0, :num_element] = 1/Cv/dlnVqdlnP*dCp_dresultT[:num_element]*(Cp/Cv-1)
J['gamma', 'result_T'][0, -1] = (-dCp_dresultT[-1]/Cv+Cp/Cv**2*dCv_dresultT_last)/dlnVqdlnP

gamma_dresultP = np.zeros((1, num_element+1), dtype=inputs._data.dtype)
gamma_dresultP[0, num_element] = Cp/Cv/dlnVqdlnP*(dCv_dresultP[0, -1]/Cv + 1/dlnVqdlnP)
J['gamma', 'result_P'] = gamma_dresultP
J['gamma', 'result_P'][0, num_element] = Cp/Cv/dlnVqdlnP*(dCv_dresultP/Cv + 1/dlnVqdlnP)


if __name__ == "__main__":
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