import scipy.optimize as opt
file_root = 'flow_'
# Linear flow profile fit
print("[densmap] Fitting LINEAR flow profile")
FP = dm.fitting_parameters(par_file='parameters_shear.txt')
# FP = dm.fitting_parameters( par_file='parameters_viscosity.txt' )
folder_name = FP.folder_name
Lx = FP.lenght_x
Lz = FP.lenght_z
vel_x, vel_z = dm.read_velocity_file(folder_name + '/' + file_root +
'00001.dat')
Nx = vel_x.shape[0]
Nz = vel_x.shape[1]
hx = Lx / Nx
hz = Lz / Nz
x = hx * np.arange(0.0, Nx, 1.0, dtype=float) + 0.5 * hx
z = hz * np.arange(0.0, Nz, 1.0, dtype=float) + 0.5 * hz
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
profile_velocity_x = np.zeros(len(z), dtype=float)
profile_velocity_z = np.zeros(len(z), dtype=float)
profile_kinetic_energy = np.zeros(len(z), dtype=float)
spin_up_steps = 0
n_init = FP.first_stamp + spin_up_steps
n_fin = FP.last_stamp
file_name = 'flow_00150.dat'
base_name = 'flow_'
# folder_name = '100nm/third_run'
# file_name = 'flow_00900.dat'
# folder_name = 'RawFlowData'
# file_name = 'flow_SOL_00100.dat'
# PARAMETERS TO TUNE
Lx = 60.00000
Lz = 35.37240
# Lx = 300.00000
# Lz = 200.44360
# Lx = 75.60000
# Lz = 28.00000
vel_x, vel_z = dm.read_velocity_file(folder_name + '/' + file_name)
rho = dm.read_density_file(folder_name + '/' + file_name, bin='y')
# p_x = np.multiply(rho, vel_x)
# p_z = np.multiply(rho, vel_z)
Nx = vel_x.shape[0]
Nz = vel_x.shape[1]
hx = Lx / Nx # [nm]
hz = Lz / Nz # [nm]
x = hx * np.arange(0.0, Nx, 1.0, dtype=float)
z = hz * np.arange(0.0, Nz, 1.0, dtype=float)
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
kin_ener = 0.5 * np.multiply(
rho,
np.multiply(vel_x, vel_x) + np.multiply(vel_z, vel_z))
import densmap as dm
import numpy as np
FP = dm.fitting_parameters(par_file='parameters_shear.txt')
folder_name = FP.folder_name
file_root = 'flow_'
Lx = FP.lenght_x
Lz = FP.lenght_z
# CREATING MESHGRID
print("Creating meshgrid")
dummy_dens = dm.read_density_file(folder_name + '/' + file_root + '00500.dat',
bin='y')
dummy_vel_x, dummy_vel_z = dm.read_velocity_file(folder_name + '/' +
file_root + '00500.dat')
Nx = dummy_dens.shape[0]
Nz = dummy_dens.shape[1]
hx = Lx / Nx
hz = Lz / Nz
x = hx * np.arange(0.0, Nx, 1.0, dtype=float)
z = hz * np.arange(0.0, Nz, 1.0, dtype=float)
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
# Manually tune, crop window [nm]
x0_crop = 87.00
x1_crop = 100.0
z0_crop = 0.000
z1_crop = 6.000
# Ny need to be strictly larger than 1
Ny = 2
from matplotlib import cm import pandas as pd ## ## ### # ## ### # INITIALIZATION # # #### ## # # ## # FP = dm.fitting_parameters( par_file='parameters_shear.txt' ) folder_name = FP.folder_name file_root = 'flow_' Lx = FP.lenght_x Lz = FP.lenght_z vel_x, vel_z = dm.read_velocity_file(folder_name+'/'+file_root+'00001.dat') Nx = vel_x.shape[0] Nz = vel_x.shape[1] hx = Lx/Nx hz = Lz/Nz x = hx*np.arange(0.0,Nx,1.0, dtype=float)+0.5*hx z = hz*np.arange(0.0,Nz,1.0, dtype=float)+0.5*hz X, Z = np.meshgrid(x, z, sparse=False, indexing='ij') n_init = FP.first_stamp n_fin = FP.last_stamp dt = FP.time_step delta_th = 2.0 z0 = 0.80 i0 = np.abs(z-z0).argmin() n_transient = int(max(1, n_fin-1000)) n_dump = 10
import densmap as dm import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib import cm folder_name = 'RawFlowData' file_name = 'combined_00100.dat' # PARAMETERS TO TUNE Lx = 75.60000 Lz = 28.00000 vel_x, vel_z = dm.read_velocity_file(folder_name + '/' + file_name) kin_ener = 0.5 * (np.multiply(vel_x, vel_x) + np.multiply(vel_z, vel_z)) Nx = vel_x.shape[0] Nz = vel_x.shape[1] hx = Lx / Nx # [nm] hz = Lz / Nz # [nm] x = hx * np.arange(0.0, Nx, 1.0, dtype=float) z = hz * np.arange(0.0, Nz, 1.0, dtype=float) X, Z = np.meshgrid(x, z, sparse=False, indexing='ij') # Try to average with values at previous steps n_hist = 10 n_step = 100 n_hist = min(n_hist, n_step) w = np.exp(-np.linspace(0.0, 5.0, n_hist))
import matplotlib.pyplot as plt
import scipy.optimize as opt
file_root = 'flow_'
FP = dm.fitting_parameters(par_file='parameters_test.txt')
# folder_poiseuille = FP.folder_name+'LJPoiseuille/Flow_epsilon03_f6'
# folder_couette = FP.folder_name+'LJCouette/Flow_epsilon03'
folder_poiseuille = FP.folder_name + 'ConfinedPoiseuille_Q1_match/Flow'
folder_couette = FP.folder_name + 'ConfinedCouette_Q1/Flow'
Lx = FP.lenght_x
Lz = FP.lenght_z
vel_x, vel_z = dm.read_velocity_file(folder_poiseuille + '/' + file_root +
'00001.dat')
Nx = vel_x.shape[0]
Nz = vel_x.shape[1]
hx = Lx / Nx
hz = Lz / Nz
x = hx * np.arange(0.0, Nx, 1.0, dtype=float) + 0.5 * hx
z = (hz * np.arange(0.0, Nz, 1.0, dtype=float) + 0.5 * hz)
z_s = 1.2
z_f = 1.8
n_exclude = np.argmin(np.abs(z - z_f))
n_data = len(z) - n_exclude
print("# exclude = " + str(n_exclude))
z = (hz * np.arange(0.0, Nz, 1.0, dtype=float) + 0.5 * hz) - 0.5 * Lz
X, Z = np.meshgrid(x, z, sparse=False, indexing='ij')
n_init = FP.first_stamp
