def termwise_Rsquared(self):
keepers = []
values = []
print('%s terms at start' % len(self.N0))
for i in range(len(self.N0)):
n = helmholtz.vectord([float(1)])
d = helmholtz.vectord([self.D0[i]])
t = helmholtz.vectord([self.T0[i]])
l = helmholtz.vectord([self.L0[i]])
self.phir = helmholtz.phir_power(n, d, t, l, 0, 0)
PPF = self.evaluate_EOS(np.array(list(n)))
R2 = rsquared(PPF.p, self.phir.dDeltaV(self.tauV, self.deltaV))
values.append((R2, i))
if R2 > 0.9:
keepers.append(i)
values, indices = zip(*reversed(sorted(values)))
keepers = list(indices[0:30])
self.N0 = self.N0[keepers]
self.T0 = self.T0[keepers]
self.D0 = self.D0[keepers]
self.L0 = self.L0[keepers]
print('%s terms at end' % len(self.N0))
def fit(self):
# Kill off some not as good terms
# self.termwise_Rsquared()
# Load up the residual Helmholtz term with parameters
n = helmholtz.vectord(self.N0)
d = helmholtz.vectord(self.D0)
t = helmholtz.vectord(self.T0)
l = helmholtz.vectord(self.L0)
self.phir = helmholtz.phir_power(n, d, t, l, 1, len(self.N0) - 1)
# Solve for the coefficients
Nbounds = [(-10, 10) for _ in range(len(self.N0))]
tbounds = [(-1, 30) for _ in range(len(self.T0))]
print(self.OBJECTIVE(np.array(list(self.N0))))
#self.N = self.N0
#self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), bounds = Nbounds, options = dict(maxiter = 5)).x
self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), method='L-BFGS-B', bounds=Nbounds, options=dict(maxiter=100)).x
# Write the coefficients to HDF5 file
h = h5py.File('fit_coeffs.h5', 'w')
grp = h.create_group(self.Ref)
grp.create_dataset("n", data=np.array(self.N), compression="gzip")
print(self.N)
#grp.create_dataset("t", data = np.array(self.N[len(self.N)//2::]), compression = "gzip")
h.close()
def fit(self):
# Kill off some not as good terms
# self.termwise_Rsquared()
# Load up the residual Helmholtz term with parameters
n = helmholtz.vectord(self.N0)
d = helmholtz.vectord(self.D0)
t = helmholtz.vectord(self.T0)
l = helmholtz.vectord(self.L0)
self.phir = helmholtz.phir_power(n, d, t, l, 1, len(self.N0) - 1)
# Solve for the coefficients
Nbounds = [(-10, 10) for _ in range(len(self.N0))]
tbounds = [(-1, 30) for _ in range(len(self.T0))]
print(self.OBJECTIVE(np.array(list(self.N0))))
#self.N = self.N0
#self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), bounds = Nbounds, options = dict(maxiter = 5)).x
self.N = scipy.optimize.minimize(self.OBJECTIVE,
np.array(list(self.N0)),
method='L-BFGS-B',
bounds=Nbounds,
options=dict(maxiter=100)).x
# Write the coefficients to HDF5 file
h = h5py.File('fit_coeffs.h5', 'w')
grp = h.create_group(self.Ref)
grp.create_dataset("n", data=np.array(self.N), compression="gzip")
print(self.N)
#grp.create_dataset("t", data = np.array(self.N[len(self.N)//2::]), compression = "gzip")
h.close()
def termwise_Rsquared(self):
keepers = []
values = []
print('%s terms at start' % len(self.N0))
for i in range(len(self.N0)):
n = helmholtz.vectord([float(1)])
d = helmholtz.vectord([self.D0[i]])
t = helmholtz.vectord([self.T0[i]])
l = helmholtz.vectord([self.L0[i]])
self.phir = helmholtz.phir_power(n, d, t, l, 0, 0)
PPF = self.evaluate_EOS(np.array(list(n)))
R2 = rsquared(PPF.p, self.phir.dDeltaV(self.tauV, self.deltaV))
values.append((R2, i))
if R2 > 0.9:
keepers.append(i)
values, indices = zip(*reversed(sorted(values)))
keepers = list(indices[0:30])
self.N0 = self.N0[keepers]
self.T0 = self.T0[keepers]
self.D0 = self.D0[keepers]
self.L0 = self.L0[keepers]
print('%s terms at end' % len(self.N0))
def evaluate_EOS(self, N):
self.phir.n = helmholtz.vectord(N)
dDelta = self.phir.dDeltaV(self.tauV, self.deltaV)
dTau2 = self.phir.dTau2V(self.tauV, self.deltaV)
dDelta2 = self.phir.dDelta2V(self.tauV, self.deltaV)
dDelta_dTau = self.phir.dDelta_dTauV(self.tauV, self.deltaV)
# Evaluate the pressure
p = (self.rho * self.R * self.T) * (1 + self.delta * dDelta)
# Evaluate the specific heat at constant volume
cv_over_R = -self.tau**2 * (self.d2phi0_dTau2 + dTau2)
cv = cv_over_R * self.R
# Evaluate the specific heat at constant pressure
cp_over_R = cv_over_R + (1.0 + self.delta * dDelta - self.delta * self.tau * dDelta_dTau)**2 / (1 + 2 * self.delta * dDelta + self.delta**2 * dDelta2)
cp = cp_over_R * self.R
# Evaluate the speed of sound
w = np.sqrt(1000 * self.R * self.T * cp_over_R / cv_over_R * (1 + 2 * self.delta * dDelta + self.delta**2 * dDelta2))
class stub: pass
PPF = stub()
PPF.p = np.array(p, ndmin=1).T
PPF.cp = np.array(cp, ndmin=1).T
PPF.cv = np.array(cv, ndmin=1).T
PPF.w = np.array(w, ndmin=1).T
return PPF
def load_data(self):
h = h5py.File('T_rho_p.h5', 'r')
self.T = h.get(self.Ref + '/T').value
self.rho = h.get(self.Ref + '/rho').value
self.p = h.get(self.Ref + '/p').value
self.cp = h.get(self.Ref + '/cp').value
self.cv = h.get(self.Ref + '/cv').value
self.speed_sound = h.get(self.Ref + '/speed_sound').value
self.tau = self.Tc / self.T
self.delta = self.rho / self.rhoc
self.tauV = helmholtz.vectord(self.tau)
self.deltaV = helmholtz.vectord(self.delta)
# Get the derivative d2phi0_dTau2 from the ideal part fitter
self.d2phi0_dTau2 = self.IPF.d2phi0_dTau2(self.tau)
def evaluate_EOS(self, N):
self.phir.n = helmholtz.vectord(N)
dDelta = self.phir.dDeltaV(self.tauV, self.deltaV)
dTau2 = self.phir.dTau2V(self.tauV, self.deltaV)
dDelta2 = self.phir.dDelta2V(self.tauV, self.deltaV)
dDelta_dTau = self.phir.dDelta_dTauV(self.tauV, self.deltaV)
# Evaluate the pressure
p = (self.rho * self.R * self.T) * (1 + self.delta * dDelta)
# Evaluate the specific heat at constant volume
cv_over_R = -self.tau**2 * (self.d2phi0_dTau2 + dTau2)
cv = cv_over_R * self.R
# Evaluate the specific heat at constant pressure
cp_over_R = cv_over_R + (
1.0 + self.delta * dDelta - self.delta * self.tau * dDelta_dTau
)**2 / (1 + 2 * self.delta * dDelta + self.delta**2 * dDelta2)
cp = cp_over_R * self.R
# Evaluate the speed of sound
w = np.sqrt(1000 * self.R * self.T * cp_over_R / cv_over_R *
(1 + 2 * self.delta * dDelta + self.delta**2 * dDelta2))
class stub:
pass
PPF = stub()
PPF.p = np.array(p, ndmin=1).T
PPF.cp = np.array(cp, ndmin=1).T
PPF.cv = np.array(cv, ndmin=1).T
PPF.w = np.array(w, ndmin=1).T
return PPF
def load_data(self):
h = h5py.File('T_rho_p.h5', 'r')
self.T = h.get(self.Ref + '/T').value
self.rho = h.get(self.Ref + '/rho').value
self.p = h.get(self.Ref + '/p').value
self.cp = h.get(self.Ref + '/cp').value
self.cv = h.get(self.Ref + '/cv').value
self.speed_sound = h.get(self.Ref + '/speed_sound').value
self.tau = self.Tc / self.T
self.delta = self.rho / self.rhoc
self.tauV = helmholtz.vectord(self.tau)
self.deltaV = helmholtz.vectord(self.delta)
# Get the derivative d2phi0_dTau2 from the ideal part fitter
self.d2phi0_dTau2 = self.IPF.d2phi0_dTau2(self.tau)
def load_data(self):
h = h5py.File("T_rho_p.h5", "r")
self.T = h.get(self.Ref + "/T").value
self.rho = h.get(self.Ref + "/rho").value
self.p = h.get(self.Ref + "/p").value
self.cp = h.get(self.Ref + "/cp").value
self.cv = h.get(self.Ref + "/cv").value
self.speed_sound = h.get(self.Ref + "/speed_sound").value
self.tau = self.Tc / self.T
self.delta = self.rho / self.rhoc
self.tauV = helmholtz.vectord(self.tau)
self.deltaV = helmholtz.vectord(self.delta)
# Get the derivative d2phi0_dTau2 from the ideal part fitter
self.d2phi0_dTau2 = self.IPF.d2phi0_dTau2(self.tau)
