def derivative(y, z):
T, lnP = y
P = np.exp(lnP)
# print z, T, P
# return np.array([ T*TDeriv(T, P, z)/H(T, P, z), 1/H(T, P, z) ])
return np.array(
[T * TDeriv(T, P, z) / H(T, P, z),
rho(T, P, z) * g(z) / P])
def Fconv(T, P, z, alpha):
u = U(T, P, z)
logG = TDeriv(T, P, z)
logadG = adG(T, P) #definovano v temp_derivative
#commaG = logadG-2*u**2+2*u*np.sqrt(logG-logadG+u**2)
return rho(T, P, z) * cp(T, P, z) * T * np.sqrt(
g(z) * delta(T, P, z)) * alpha**2 * np.sqrt(H(T, P, z)) * 8 * U(
T, P, z) * (radG(T, P, z) - logG) / (4 * 9 * np.sqrt(2.))
def mass(P0, y, z, T): P = integrate.odeint(PDeriv, P0, np.linspace(1.25e7, 0, 500), args = (T)) y = comp_mag(y, P) B = y*y rhoArr = np.zeros(500) for i in range(500): rhoArr[i] = rho(T[i], P[i], z[i]) mmbrs = np.zeros(499) for i in range(499): mmbrs[i] = (rhoArr[i]/B[i]+rhoArr[i+1]/B[i+1])*(z[i+1]-z[i])/2 integral = mmbrs.sum() return phi*integral
def mass(P0, y, z, T):
P = integrate.odeint(PDeriv, P0, np.linspace(1.25e7, 0, 500), args=(T))
y = comp_mag(y, P)
B = y * y
rhoArr = np.zeros(500)
for i in range(500):
rhoArr[i] = rho(T[i], P[i], z[i])
mmbrs = np.zeros(499)
for i in range(499):
mmbrs[i] = (rhoArr[i] / B[i] + rhoArr[i + 1] / B[i + 1]) * (z[i + 1] -
z[i]) / 2
integral = mmbrs.sum()
return phi * integral
def flux(T, dTdz, P, dPdz, z, alpha):
G = (P / T) * (dTdz / dPdz)
rG = radG(T, P, z)
# rho = dPdz/g(z)
r = rho(T, P, z)
o = opac(T, r)
u = U(T, P, z)
aG = adG(T, P)
# frad = 16*sigma*T**3*g(z)/(3*opac(T, P, z)*r*P)*dTdz/dPdz
frad = (16. / 3.) * sigma * g(z) * T**4 * G / (o * P)
# if frad>sigma*Teff**4:
# return np.array([ sigma*Teff**4, 0, sigma*Teff**4 ])
# else:
eG = aG - 2 * u**2 + 2 * u * np.sqrt(G - aG + u**2)
fconv = r * cp(
T, P, z) * T * np.sqrt(g(z) * delta(T, P, z)) * alpha**2 * np.sqrt(
H(T, P, z)) * (G - eG)**1.5 / (4 * np.sqrt(2.))
# fconv = r*cp(T, P, z)*T*np.sqrt(g(z)*delta(T,P,z))*alpha**2*np.sqrt(H(T,P,z))*(abs(G-eG))**(3./2.)/(4*np.sqrt(2.))
# return np.array([ frad, fconv ])
# ftot = c*3.846e26*g(z)/(4*M(z)*G*np.pi)
ftot = (16. / 3.) * sigma * g(z) * T**4 * rG / (o * P)
return np.array([frad, fconv, ftot])
# -*- coding: utf-8 -*-
import os.path
import sys
sys.path.append('/home/tom/Documents/skola/bakalarka/python/')
from derivatives import Frad, Fconv
from scipy.interpolate import UnivariateSpline
from EOS import rho, cp
from const import *
from ext_data import opac
for i in range(0, z.size):
FconvArr[i] = Fconv(T[i], P[i], z[i])
FradArr[i] = Frad(T[i], P[i], z[i])
rhoArr[i] = rho(T[i], P[i], z[i])
cpArr[i] = cp(T[i], P[i])
opacArr[i] = opac(T[i], rhoArr[i])
F = FconvArr + FradArr
F_spline = UnivariateSpline(z, F, s=0)
dFdz = F_spline.derivative()
dFdz = dFdz(z)
#dFdz = np.diff(F)/h
#dFdz = np.append(dFdz, dFdz[dFdz.size-1])
# semi-implicitni metoda pro casovy prirustek
#d_main = np.empty(500)
#d_sub = np.zeros(500)
#d_sup = np.zeros(500)
from derivatives import Frad, Fconv from EOS import rho, cp from const import dt def disconnection_check(y): B = y*y return False while disconnection_check(y) == False: for i in range (0, z.size): FconvArr[i] = Fconv(T[i], P[i], z[i]) FradArr[i] = Frad(T[i], P[i], z[i]) rhoArr[i] = rho(T[i], P[i], z[i]) cpArr[i] = cp(T[i], P[i]) F = FconvArr+FradArr dFdz = np.diff(F)/h dFdz = np.append(dFdz, dFdz[dFdz.size-1]) dTdt = -dFdz/(cpArr*rhoArr) T = T+dt*dTdt P0 = adjust_baseP(y, z, T, rho(T[499], P[499], z[499])) P = integrate.odeint(lambda P, z: PDeriv(T_func(z), P, z), P0, z_inv)
# -*- coding: utf-8 -*-
import os.path
import sys
sys.path.append('/home/tom/Documents/skola/bakalarka/python/')
from derivatives import Frad, Fconv
from scipy.interpolate import UnivariateSpline
from EOS import rho, cp
from const import *
from ext_data import opac
for i in range (0, z.size):
FconvArr[i] = Fconv(T[i], P[i], z[i])
FradArr[i] = Frad(T[i], P[i], z[i])
rhoArr[i] = rho(T[i], P[i], z[i])
cpArr[i] = cp(T[i], P[i])
opacArr[i] = opac(T[i],rhoArr[i])
F = FconvArr+FradArr
F_spline = UnivariateSpline(z, F, s=0)
dFdz = F_spline.derivative()
dFdz = dFdz(z)
#dFdz = np.diff(F)/h
#dFdz = np.append(dFdz, dFdz[dFdz.size-1])
# semi-implicitni metoda pro casovy prirustek
#d_main = np.empty(500)
#d_sub = np.zeros(500)
def PDeriv(P, z, T):
r = rho(T, P, z)
D = r * g(z)
return D
def Frad(T, P, z):
dTdz = T * TDeriv(T, P, z) / H(T, P, z)
FR = -(16 * sigma * T**3 / (3 * opac(T, P, z) * rho(T, P, z))) * dTdz
return FR
