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all fixed Dm balance terms

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This commit is contained in:
ignis 2021-08-24 10:21:22 +09:00
parent 1d161dd900
commit d244ac4a43
2 changed files with 98 additions and 24 deletions
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119
binning_balance_terms_from_raw.py
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@ -37,9 +37,7 @@ nx, ny, nz = case.shape
n_timestep = len(case.data_files)
if params["test"] :
n_timestep = 10
u_idx = 0
y_idx = 4
fdebug = True
x_indices_all = {
'IC1': np.array([272, 285, 294, 302, 309, 315, 321, 329, 339]),
@ -65,10 +63,13 @@ print(" * x index:", x_indices)
class ConditionalMeanBinning:
def __init__ (self, name='', nbins=100, boundary=0.1):
def __init__ (self, var='phi', case='default', loc=0, nbins=100, boundary=0.1):
self.eps = np.finfo(np.float).eps
self.name = name
self.var = var
self.case = case
self.loc = loc
self.name = "cavg_{}_c_{}_{}".format(var, case, loc)
# Point grid containing interval boundaries
self.grid = np.hstack(
@ -87,15 +88,23 @@ class ConditionalMeanBinning:
self.v_sums = np.zeros(self.grid.size)
self.v_counts = np.zeros(self.grid.size, dtype=int)
def feed_samples (self, c, v):
def calculate_conditional_mask (self, c):
''' Feed arrays c, v of samples and conditions '''
bin_indices = np.digitize(c, self.grid, right=True)
# assign bin indices. bin intervals are right inclusive
# i-th bin: c_bin[i-1] < c <= c_bin[i]
return np.vstack([bin_indices == i for i in range(1, self.grid.size)]).reshape((-1, *bin_indices.shape))
def feed_samples (self, c, v, bin_masks=None):
''' Feed arrays c, v of samples and conditions '''
if bin_masks is None:
bin_masks = self.calculate_conditional_mask(c)
for i in range(1, self.grid.size):
binned = v[bin_indices == i]
binned = v[bin_masks[i-1]]
self.v_sums[i] += binned.sum()
self.v_counts[i] += binned.size
@ -111,32 +120,85 @@ class ConditionalMeanBinning:
def sum_count (self):
return {
"bin_grid" : self.grid,
"bin_sum" : self.v_sums,
"bin_grid" : self.grid,
"bin_sum" : self.v_sums,
"bin_count" : self.v_counts
}
def save_to_file (self):
np.savez(self.name, **self.average())
print("saved ", self.name)
cs = CompactScheme(nx, ny, nz, False, True, True, 4, 2, 2)
# c mean at sampling x coordinates (verification purpose)
cmean_verify = np.zeros(len(x_indices))
avg_objs_ddxc = [ConditionalMeanBinning(name=str(xi)) for xi in x_indices]
avg_objs_d2dxc = [ConditionalMeanBinning(name=str(xi)) for xi in x_indices]
# Conditional Average Objects
avg_objs_ddxc = [ConditionalMeanBinning(var='ddxc', case=casename, loc=xi) for xi in x_indices]
avg_objs_ddyc = [ConditionalMeanBinning(var='ddyc', case=casename, loc=xi) for xi in x_indices]
avg_objs_ddzc = [ConditionalMeanBinning(var='ddzc', case=casename, loc=xi) for xi in x_indices]
avg_objs_d2dxc = [ConditionalMeanBinning(var='d2dxc', case=casename, loc=xi) for xi in x_indices]
avg_objs_d2dyc = [ConditionalMeanBinning(var='d2dyc', case=casename, loc=xi) for xi in x_indices]
avg_objs_d2dzc = [ConditionalMeanBinning(var='d2dzc', case=casename, loc=xi) for xi in x_indices]
avg_objs_uddxc = [ConditionalMeanBinning(var='uddxc', case=casename, loc=xi) for xi in x_indices]
avg_objs_vddyc = [ConditionalMeanBinning(var='vddyc', case=casename, loc=xi) for xi in x_indices]
avg_objs_wddzc = [ConditionalMeanBinning(var='wddzc', case=casename, loc=xi) for xi in x_indices]
avg_objs_ddxcddxc = [ConditionalMeanBinning(var='ddxcddxc', case=casename, loc=xi) for xi in x_indices]
targets=[ 'ddxc', 'ddyc', 'ddzc', 'd2dxc', 'd2dyc', 'd2dzc', 'uddxc', 'vddyc', 'wddzc', ]
mean_array = np.zeros((10, nx))
# Loop over dns data files
for i, fname in enumerate(case.data_files[:n_timestep]):
print(datetime.datetime.now(), '{:5d}/{}'.format(i+1, n_timestep), fname)
fpath = os.path.join(case.case_root, fname)
time, y = case.read_single_field(fpath, y_idx)
time, u0, shape, velocity, scalar = case.read_data(fpath)
u, v, w = velocity
u = u+u0
y = scalar[-1]
c = (1. - y)
ddxc = cs.ddx(c)
d2dxc = cs.d2dx(c)
d2dyc = cs.d2dy(c)
d2dzc = cs.d2dz(c)
ddzc = cs.ddz(c)
ddyc = cs.ddy(c)
ddxc = cs.ddx(c)
ddxcddxc = ddxc * ddxc
uddxc = u*ddxc
vddyc = v*ddyc
wddzc = w*ddzc
for idx, field in enumerate([u, v, w, c, ddxc, ddyc, ddzc, d2dxc, d2dyc, d2dzc]):
mean_array[idx] += field.sum(axis=(0,1))
for j, xi in enumerate(x_indices):
avg_objs_ddxc[j].feed_samples(c[:,:,xi], ddxc[:,:,xi])
avg_objs_d2dxc[j].feed_samples(c[:,:,xi], d2dxc[:,:,xi])
mask = avg_objs_ddxc[j].calculate_conditional_mask(c[:,:,xi])
avg_objs_ddxc[j].feed_samples(c[:,:,xi], ddxc[:,:,xi], mask)
avg_objs_ddyc[j].feed_samples(c[:,:,xi], ddyc[:,:,xi], mask)
avg_objs_ddzc[j].feed_samples(c[:,:,xi], ddzc[:,:,xi], mask)
avg_objs_d2dxc[j].feed_samples(c[:,:,xi], d2dxc[:,:,xi], mask)
avg_objs_d2dyc[j].feed_samples(c[:,:,xi], d2dyc[:,:,xi], mask)
avg_objs_d2dzc[j].feed_samples(c[:,:,xi], d2dzc[:,:,xi], mask)
avg_objs_ddxcddxc[j].feed_samples(c[:,:,xi], ddxcddxc[:,:,xi], mask)
avg_objs_uddxc[j].feed_samples(c[:,:,xi], uddxc[:,:,xi], mask)
avg_objs_vddyc[j].feed_samples(c[:,:,xi], vddyc[:,:,xi], mask)
avg_objs_wddzc[j].feed_samples(c[:,:,xi], wddzc[:,:,xi], mask)
cmean_verify[j] += np.sum(c[:,:,xi])
print(datetime.datetime.now(), 'Finished')
print()
@ -147,12 +209,23 @@ for xi_cmean_pair in zip(x_indices, cmean_verify):
print()
print("Save the result")
for j, xi in enumerate(x_indices):
filename = "cavg_ddxc_c_{}_{}".format(casename, avg_objs_ddxc[j].name)
np.savez(filename, **avg_objs_ddxc[j].average())
print("saved ", filename)
for j, xi in enumerate(x_indices):
filename = "cavg_d2dxc_c_{}_{}".format(casename, avg_objs_d2dxc[j].name)
np.savez(filename, **avg_objs_d2dxc[j].average())
print("saved ", filename)
np.save("avg_uvwc_dc_d2c_{}".format(casename), mean_array)
def save_cmean_result (fmt, obj_list):
for j, xi in enumerate(x_indices):
obj_list[j].save_to_file()
save_cmean_result("cavg_ddxc_c_{}_{}", avg_objs_ddxc)
save_cmean_result("cavg_ddyc_c_{}_{}", avg_objs_ddyc)
save_cmean_result("cavg_ddzc_c_{}_{}", avg_objs_ddzc)
save_cmean_result("cavg_uddxc_c_{}_{}", avg_objs_uddxc)
save_cmean_result("cavg_vddyc_c_{}_{}", avg_objs_vddyc)
save_cmean_result("cavg_wddzc_c_{}_{}", avg_objs_wddzc)
save_cmean_result("cavg_d2dxc_c_{}_{}", avg_objs_d2dxc)
save_cmean_result("cavg_d2dyc_c_{}_{}", avg_objs_d2dyc)
save_cmean_result("cavg_d2dzc_c_{}_{}", avg_objs_d2dzc)
save_cmean_result("cavg_ddxcddxc_c_{}_{}", avg_objs_ddxcddxc)

3
dnstool.py
View file

@ -131,6 +131,7 @@ class DnsCase:
nx = info_dict['nx']
ny = info_dict['ny']
nz = info_dict['nz']
u0 = info_dict['u0']
with open(fname, 'rb') as f1 :
f1.seek(offset_data)
@ -140,7 +141,7 @@ class DnsCase:
V = np.fromfile(f1, dtype=np.double, count=(3*count)).reshape((3,nz,ny,nx))
s = np.fromfile(f1, dtype=np.double, count=(2*count)).reshape((2,nz,ny,nx))
return t, (nx, ny, nz), V, s
return t, u0, (nx, ny, nz), V, s
def read_single_field (self, fname, ifield):