def main():
## parameters
dir_in = '/usr/local/home/yl8bc/duannas/'
filename_in_grid = 'nozzle_grid_test.h5'
filename_outlet = 'Test_Incompact3d/HLB_Nozzle/AcousticZgrid_k500.dat'
## output
dir_out = '../../data/nozzle/'
filename_out = 'nozzle_grid_test_try1'
## BELOW NO PARAMS ARE DEFINED
tstart = time.time()
## Read grid(uniform)
grid, vars_name = IO_util.read_hdf5(dir_out + filename_in_grid)
u = grid['x']
v = grid['z']
dset, zones_name, vars_name = util_tp.read_tp(dir_in + filename_outlet)
data_outlet, nodemap = util_tp.read_tp_zone(dset, 'SubZone', vars_name)
## intersections
nx, ny = u.shape
v[-1, :] = v[-1, -1] * normalized_flipped(data_outlet['z'], ny)
u, v = gridgen.cm_gridgen.gridgen_checker(u, v)
print 'Checker done. Elapsed time: %f' % (time.time() - tstart)
## Write grid
grid['x'] = u
grid['z'] = v
IO_util.write_hdf5(dir_out + filename_out, grid)
def main():
###---parameters----###
shape = (500, 100)
# Files required to start interpolation
dir_in = '/usr/local/home/yl8bc/duannas/'
filename_fluent = 'jhyt7/Acoustics/HLB/Interp_Fluent_DNS/nozzle2d_fluent.dat'
filename_outlet = 'Test_Incompact3d/HLB_Nozzle/AcousticZgrid_k500.dat'
# Files generated by the program
dir_out = '../../data/nozzle/'
filename_out = 'nozzle_grid_test'
###---no parameters below---#
#----read fluent grid & data-----!
dset, zones_name, vars_name = util_tp.read_tp(dir_in + filename_fluent)
data_wall, nodemap = util_tp.read_tp_zone(dset, 'nozzlewall', vars_name)
## define boundary
x0 = data_wall['X'][0]
xm = data_wall['X'][-1]
y0 = data_wall['Y'][0]
ym = data_wall['Y'][-1]
choice_left = np.stack(
[np.ones((2**16, )) * x0,
np.linspace(0., y0, 2**16)], axis=1)
# y0*normalized_flipped(grid_outlet['SubZone']['z'], ny)], axis=1)
choice_right = np.stack(
[np.ones((2**16, )) * xm,
np.linspace(0., ym, 2**16)], axis=1)
# ym*normalized_flipped(grid_outlet['SubZone']['z'], ny)], axis=1)
choice_bottom = np.stack([np.linspace(x0, xm, 2**16),
np.zeros((2**16, ))],
axis=1)
choice_top = np.stack([data_wall['X'], data_wall['Y']], axis=1)
choice_top = gridgen.cm_gridgen.refine_boundary(
choice_top, 2**16 / choice_top.shape[0])
## compute
u, v = util_grid.gridgen_orth(shape,
choice_left,
choice_right,
choice_bottom,
choice_top,
dir_out + filename_out,
varname=['x', 'z'],
tol_in=1.e-8,
tol_bdry=0,
nsave=64,
fixed_boundary=[0, 0, 0, 0])
def main():
###params###
mrange = range(1, 99, 1)
mmax = len(mrange)
nmax = 90
dir_in = '/usr/local/home/yl8bc/duannas/jhyt7/Acoustics/HIFiRE_Cone/FluentData/' #'/usr/local/home/yl8bc/duannas/jhyt7/Acoustics/HLB/Interp_Fluent_DNS/'
filename_in_data = 'CircularCone2D.dat' # 'nozzle2d_fluent.dat'
filename_in_case = 'CircularCone2D.cas' # 'nozzle2d_fluent.dat'
## names of the zones below
interior = 'unspecified'
wall_right = 'wall'
## files output:
dir_out = '/usr/local/home/yl8bc/yl8bc/data/cone/FLUENT/'
filename_out_data = 'data_fluent'
filename_out_profiles = 'profiles_fluent'
filename_out_integrals = 'integrals_fluent'
##--below no param is defined--##
tstart = time.time()
keys_change = {
'X': 'x',
'Y': 'z',
'X Velocity': 'u',
'Y Velocity': 'w',
'Pressure': 'p',
'Density': 'rho',
'Temperature': 'T'
}
grid_keys = ['x', 'z']
## read grid and data
data = util_tp.read_tp(dir_in + filename_in_data,
file_type='fluent',
case_filenames=dir_in + filename_in_case)
print('Data read. Time elapsed: %f secs' % (time.time() - tstart))
## recover structured
print('Rebuilding...')
data_int = util_data.change_dict(data[interior], keys_change)
data_int = util_data.recover_structured_data(data_int, grid_keys=grid_keys)
#data_int = recovery.recover_interior(data_int, grid_keys)
R = util_flow.get_R(data_int['p'], data_int['rho'], data_int['T'])
print(R)
## boundary
data_right = util_data.change_dict(data[wall_right], keys_change)
data_right = util_data.recover_structured_data(data_right,
grid_keys=grid_keys,
standard=False)
data_right['rho'] = data_right['p'] / (R * data_right['T'])
data = {
name: np.concatenate((data_int[name], data_right[name][np.newaxis, :]),
axis=0)
for name in data_int.keys()
}
print('Rebuilt. Time elapsed: %f secs' % (time.time() - tstart))
## wall normal profiles
sdata = util_data.structured_data(data, grid_keys)
profiles_out = sdata.get_wall_normal_profiles('right', mrange, nmax)
## integral
delta = np.zeros(mmax)
delta_star = np.zeros(mmax)
theta = np.zeros(mmax)
for n in range(mmax):
wd = profiles_out['wd'][n, :]
x = profiles_out['x'][n, :]
z = profiles_out['z'][n, :]
u = profiles_out['u'][n, :]
w = profiles_out['w'][n, :]
rho = profiles_out['rho'][n, :]
up = util_flow.get_up_2d(u, w, x, z)
delta[n] = util_flow.get_delta(wd, up)
delta_star[n] = util_flow.get_delta_star(wd, up, rho)
theta[n] = util_flow.get_theta(wd, up, rho)
int_out = {}
int_out['x'] = profiles_out['x'][:, 0]
int_out['z'] = profiles_out['z'][:, 0]
int_out['delta'] = delta
int_out['delta*'] = delta_star
int_out['theta'] = theta
## out
IO_util.write_hdf5(dir_out + filename_out_data, data)
IO_util.write_hdf5(dir_out + filename_out_profiles, profiles_out)
IO_util.write_hdf5(dir_out + filename_out_integrals, int_out)
def main():
'''
This script converts unstructured cell-centered fluent data to structured data. Tecplot needs to be used to load fluent case and data files and write the data in Tecplot ASCII (*.dat) or binary format (*.plt,*szplt), which is used for variable "filename_in_data".
run this script: python3 nozzle_fluent.py
I've added one script that builds structured data from a Fluent/PLT file under /DNSMST_utility/src/pypost/grid_interpolation/nozzle_fluent.py. It has been tested to work with data located at duannas/duanl/Acoustics/TestFluentConversion which is a nozzle with wall on the top.
If you're trying this on some other dataset, please pay attention to the following potential issues.
Input data file_type. The data could be of PLT, SZPLT, DAT type, so please specify for the function read_tp()
Data variable. Those variables could be named differently. By default the x-axis goes by the name 'X' and y-axis 'Y' and all other variables will be reconstructred to structured data according to these too coordinate variables.
Wall direction. By default the wall is on the top.
Broken connectivity. If the boundary points are not arranged in a ordered manner(eg. not following descending order with respect to 'X'), those points might need to be reordered. But this is not common anyway.
The runtime for my test(around 200k datapoints) is ~20mins and the code is not optimized to its best state yet.
ATTENTION!!! ------------------------------
POTENTIAL FAILURE:
1. Unmatached variable names. please go to line 37 keys_change and re-define!
2. Wrong direction. Please go to line to chooose one of those boundaries!
3. Input file format. Caution with tecplot files, for the format is unclear(ordered/unordered).
-----------------------------------------------'''
###params###
dir_in = './'
filename_in_data = 'nozzle_cartesian_231x101_kwsst_refine1_2nd_UnstrTecplotCellCentered.plt'
## names of the zones below
interior = 'unspecified'
wall_top = 'wall'
## files output:
dir_out = './'
filename_out_data = 'nozzle_cartesian_460x200_kwsst_2nd_structured'
##--below no param is defined--##
tstart = time.time()
keys_change = {'X':'x', 'Y':'z', 'X Velocity':'u', 'Y Velocity':'w', 'Pressure':'p', 'Density':'rho', 'Temperature':'T'}
grid_keys = ['x','z']
## read grid and data
data = util_tp.read_tp(dir_in+filename_in_data)
print('Data read. Time elapsed: %f secs'%(time.time()-tstart))
## recover structured
print('Rebuilding...')
data_int = util_data.change_dict(data[interior], keys_change)
data_int = util_data.recover_structured_data(data_int, grid_keys=grid_keys)
R = util_flow.get_R(data_int['p'], data_int['rho'], data_int['T'])
print(R)
## boundary
data_top = util_data.change_dict(data[wall_top], keys_change)
data_top = util_data.recover_structured_data(data_top, grid_keys=grid_keys, standard=False)
data_top['rho'] = data_top['p'] / (R*data_top['T'])
data_top = sort_top(data_top, grid_keys[0])
## one of these boundries below, need to choose!!
## top
data = {name:np.concatenate((data_int[name], data_top[name][:,np.newaxis]), axis=1) for name in data_int.keys()}
## bot
#data = {name:np.concatenate((data_top[name][:,np.newaxis], data_int[name]), axis=1) for name in data_int.keys()}
## right
#data = {name:np.concatenate((data_int[name], data_top[name][np.newaxis,:]), axis=0) for name in data_int.keys()}
## left
#data = {name:np.concatenate((data_top[name][np.newaxis,:], data_int[name]), axis=0) for name in data_int.keys()}
print('Rebuilt. Time elapsed: %f secs'%(time.time()-tstart))
## out
# Put gridkeys in first two
def sortkey(x):
if x[0]==grid_keys[0]:
return 0
elif x[0]==grid_keys[1]:
return 1
else:
return 2
data =OrderedDict( sorted(data.items(), key=sortkey))
print( data.keys())
IO_util.write_hdf5(dir_out+filename_out_data, data)
util_tp.write_tp(dir_out+filename_out_data, data, old=True, order='F')
def main():
'''
Note:
The boundary thickness calculation relies on a clear profile that converges. current code cannot intelligently tell which wall normal profile is "perfect". Thus boundary layer thickness might yield to poor quality if the profile is badly extracted.
'''
###params##
mrange = range(100, 300, 100) ## along the wall at which index to extract
nmax = 75 ## along wall normal direction how many nodes to extract
fluid_type = 'nitrogen'
dir_in = './'
filename_in_data = 'structured_tec.plt'
is_in_tecplot = True # Input is of tecplot format ??? False to be HDF5, True to be Tecplot format
## files output---------------------------------!
dir_out = './'
filename_out_profiles = 'profiles'
filename_out_integrals = 'integrals'
## names of variables---------------------------!
name_x = 'X' # name of these variables!
name_z = 'Y'
name_u = 'u'
name_w = 'w'
name_p = 'p'
name_T = 'T'
## --------------------------------------
keys_change = {name_x: 'x', name_z: 'z'}
grid_keys = ['x', 'z']
## end params
tstart = time.time()
## read grid and data
if is_in_tecplot:
data = util_tp.read_tp(dir_in + filename_in_data, order='F')
data = next(iter(data.values()))
else:
data = IO_util.read_hdf5(dir_in + filename_in_data)
print('Data read. Time elapsed: %f secs' % (time.time() - tstart))
## slice
data = util_data.change_dict(data, keys_change=keys_change)
sdata = util_data.structured_data(data, grid_keys)
## wall normal profiles
profiles_out = sdata.get_wall_normal_profiles('top', mrange, nmax)
## integral
mmax = len(mrange)
delta = np.zeros(mmax)
delta_star = np.zeros(mmax)
theta = np.zeros(mmax)
tauw = np.zeros(mmax)
utau = np.zeros(mmax)
ztau = np.zeros(mmax)
for n in range(mmax):
wd = profiles_out['wd'][n, :]
x = profiles_out['x'][n, :]
z = profiles_out['z'][n, :]
u = profiles_out[name_u][n, :]
w = profiles_out[name_w][n, :]
p = profiles_out[name_p][n, :]
T = profiles_out[name_T][n, :]
rho = util_flow.get_rho(profiles_out[name_p][n, :],
profiles_out[name_T][n, :],
fluid_type=fluid_type)
up = util_flow.get_up_2d(u, w, x, z)
delta[n] = util_flow.get_delta(wd, up)
delta_star[n] = util_flow.get_delta_star(wd, up, rho)
theta[n] = util_flow.get_theta(wd, up, rho)
mu = util_flow.get_mu_Sutherland(T[0], fluid_type=fluid_type)
tauw[n] = np.abs(mu * up[1] / wd[1])
utau[n] = np.sqrt(tauw[n] / rho[0])
ztau[n] = mu / rho[0] / utau[n]
int_out = {}
int_out['x'] = profiles_out['x'][:, 0]
int_out['z'] = profiles_out['z'][:, 0]
int_out['delta'] = delta
int_out['delta*'] = delta_star
int_out['theta'] = theta
int_out['tauw'] = tauw
int_out['utau'] = utau
int_out['ztau'] = ztau
## out
IO_util.write_hdf5(dir_out + filename_out_profiles, profiles_out)
IO_util.write_hdf5(dir_out + filename_out_integrals, int_out)
IO_util.write_ascii_point(dir_out + filename_out_profiles, profiles_out)
IO_util.write_ascii_point(dir_out + filename_out_integrals, int_out)
filename = dir_in + filename_stat_h5[0] + n.__str__().zfill(
8) + filename_stat_h5[1]
filename_list.append(filename)
stat_in, varsname = read_hdf5_group_series(filename_list, 'Stat2d',
['pave', 'p2'])
stat_bot, varsname = read_hdf5_group_series(filename_list,
'Int_BotWall',
['tauw', 'delta'])
stat_top, varsname = read_hdf5_group_series(filename_list,
'Int_TopWall',
['tauw', 'delta'])
tauw = 0.5 * (stat_bot['tauw'] + stat_top['tauw'])
delta = 0.5 * (stat_bot['delta'] + stat_top['delta'])
if in_plt:
from util import util_tp
dset, zones_name, vars_name = util_tp.read_tp(dir_in +
filename_stat_plt)
stat_in = util_tp.read_tp_zone(dset,
zones_name[0],
['pave', 'p2', 'tauw', 'delta'],
order='F')
tauw = 0.5 * (stat_in['tauw'][:, 0] + stat_in['tauw'][:, -1])
delta = 0.5 * (stat_in['delta'][:, 0] + stat_in['delta'][:, -1])
## calculation
stat_out = {}
stat_out['prms'] = np.sqrt(np.abs(stat_in['p2'] - stat_in['pave']**2))
prms_tauw_vs_x = stat_out['prms'][:, index_k_vs_x] / tauw
#
prms_tauw_vs_z = np.mean(stat_out['prms'][ibe:ien, :] /
tauw[ibe:ien, np.newaxis],
axis=0)