for k0, kend in k_range:
for u0, uend in u_range:
for x0, xend in x_range:
for u0typei in u0type:
for sigu0i in sigu0:
for ai, bi in zip(a, b):
for k_disti in k_dist:
if k_disti == 'gaussian':
kparam1, kparam2 = muk, sigk
elif k_disti == 'uniform':
kparam1, kparam2 = mink, maxk
else:
print("wrong k distribution option")
for kparam1i, kparam2i in zip(kparam1, kparam2):
gridvars = {'u': [u0, uend, du], 'k': [k0, kend, dk], 't': [t_range[0], t_range[1], dt], 'x':[x0, xend, dx]}
ICparams = {'u0': u0typei,
'u0param': [ai, bi],
'fu0': 'gaussian',
'fu0param': sigu0i,
'fk': k_disti,
'fkparam': [kparam1i, kparam2i]}
grid = PdfGrid(gridvars)
S = PdfSolver(grid, ICparams, save=True, case=case)
S.solve()
def runsimulation():
dt = 0.03
t0 = 0.0
tend = 4
nt = int((tend - t0) / dt)
dx = 0.03
x0 = -1.5
xend = 1.5
nx = int((xend - x0) / dx)
dk = 0.05
k0 = -1.0
kend = 1.0
nk = int((kend - k0) / dk)
du = 0.04
u0 = -2.5
uend = 2.5
nu = int((uend - u0) / du)
muk = 0.0
sigk = 0.5
sigu = 1.0
mink = -0.5
maxk = 0.5
a = 1.0
b = 0.0
# Second set of data
muk_2 = 0.2
sigk_2 = 1
sigu_2 = 1.1
mink_2 = 0.0
maxk_2 = 1.0
a_2 = 1.0
b_2 = 0.0
runsimulation = 1
IC_opt = 1
solvopt = 'RandomKadvection'
for i in range(1, 5):
print(i)
grid = PdfGrid(x0=x0,
xend=xend,
k0=k0,
kend=kend,
t0=t0,
tend=tend,
u0=u0,
uend=uend,
nx=nx,
nt=nt,
nk=nk,
nu=nu)
grid.printDetails()
S = PdfSolver(grid, save=True)
S.setIC(option=i,
a=a,
b=b,
mink=mink,
maxk=maxk,
muk=muk,
sigk=sigk,
sigu=sigu)
time0 = time.time()
fuk, fu, kmean, uu, kk, xx, tt = S.solve(solver_opt=solvopt)
print('Compute time = ', time.time() - time0)
gridvars = {
'u': [u0, uend, du],
'k': [k0, kend, dk],
't': [t0, tend, dt],
'x': [x0, xend, dx]
}
ICparams = {
'fu0': 'compact_gaussian',
'fu0param': [mux, sigx, muU, sigU, rho],
'fk': 'uniform',
'fkparam': [mink, maxk]
}
grid = PdfGrid(gridvars)
S = PdfSolver(grid, ICparams, save=True, case=case)
S.solve() # no need to return anything
def reaction():
case = 'reaction_linear'
dt = 0.01
t0 = 0
tend = 5
du = 0.005
u0 = -7
uend = 7
umean = 0.2
sigu0 = 1.1