297 lines
8.3 KiB
Python
297 lines
8.3 KiB
Python
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import scipy.io as sio
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1.inset_locator import inset_axes
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from IPython import display
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plt.rcParams.update({'font.size': 22})
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plt.rcParams.update({'figure.max_open_warning': 0})
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plt.interactive(True)
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viscos=3.57E-5
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# makes sure figures are updated when using ipython
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display.clear_output(wait=True)
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datax= np.loadtxt("x2d.dat")
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x=datax[0:-1]
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ni=int(datax[-1])
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datay= np.loadtxt("y2d.dat")
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y=datay[0:-1]
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nj=int(datay[-1])
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x2d=np.zeros((ni+1,nj+1))
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y2d=np.zeros((ni+1,nj+1))
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x2d=np.reshape(x,(ni+1,nj+1))
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y2d=np.reshape(y,(ni+1,nj+1))
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# compute cell centers
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xp2d=0.25*(x2d[0:-1,0:-1]+x2d[0:-1,1:]+x2d[1:,0:-1]+x2d[1:,1:])
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yp2d=0.25*(y2d[0:-1,0:-1]+y2d[0:-1,1:]+y2d[1:,0:-1]+y2d[1:,1:])
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p2d=np.load('p2d_saved.npy')
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u2d=np.load('u2d_saved.npy')
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k2d=np.load('k2d_saved.npy')
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vis2d=np.load('vis2d_saved.npy')
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eps2d = np.load('eps2d_saved.npy')
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k_model2d=np.load('k2d_saved.npy')
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vis2d=vis2d/viscos
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ustar=(viscos*u2d[:,0]/yp2d[:,0])**0.5
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# compute re_theta for boundary layer flow
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dx=x[3]-x[2]
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re_mom_bl=np.zeros(ni)
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for i in range (0,ni-1):
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d_mom=0
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for j in range (1,nj-1):
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up=u2d[i,j]/u2d[i,-1]
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dy=y2d[i,j]-y2d[i,j-1]
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d_mom=d_mom+up*(1.-min(up,1.))*dy
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re_mom_bl[i]=d_mom*u2d[i,-1]/viscos
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re_mom_bl[-1]=re_mom_bl[-1-1]
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cf_exp_retheta=2*(1./0.384*np.log(re_mom_bl)+4.127)**(-2)
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# compute cf
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cf=np.zeros(ni)
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yplus2d=np.zeros((ni,nj))
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for i in range (0,ni-1):
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uwall=u2d[i,0]
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yp=yp2d[i,0]
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ustars=(uwall*viscos/yp)**0.5
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cf[i]=ustars**2/(0.5*u2d[i,-1]**2)
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yplus2d[i,:]=ustar[i]*yp2d[i,:]/viscos
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cf[-1]=cf[-2]
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# find boundary layer thickness
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delta=np.zeros(ni)
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for i in range(1,ni-1):
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for j in range(0,nj-2):
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up=u2d[i,j]/u2d[i,-1]
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up1=u2d[i,j+1]/u2d[i,-1]
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if up < 0.99 and up1 > 0.99:
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jj = j
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break
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up=u2d[i,jj]/u2d[i,-1]
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up1=u2d[i,jj+1]/u2d[i,-1]
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delta[i]=y[jj]+(0.99-up)*(y[jj+1]-y[jj])/(up1-up)
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# delta[i]=y[jj]+(0.99-u2d[i,jj])*(y[jj+1]-y[jj])/(u2d[i,jj+1]-u2d[i,jj])
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# delta[i]=y[jj]
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delta_inlet=delta[1]
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delta[0]=delta[1]
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x_delta=np.zeros(ni)
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x_delta=x/delta[0]
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vel_DNS=np.genfromtxt("vel_2540_dns.prof", dtype=None,comments="%")
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#y/\delta_{99} y+ U+ urms+ vrms+ wrms+ uv+ prms+ pu+ pv+ S(u) F(u) dU+/dy+ V+
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y_DNS=vel_DNS[:,0]
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u_DNS=vel_DNS[:,2]
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yplus_DNS=vel_DNS[:,1]
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uu_DNS=vel_DNS[:,3]**2
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vv_DNS=vel_DNS[:,4]**2
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ww_DNS=vel_DNS[:,5]**2
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uv_DNS=vel_DNS[:,6]
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# find equi.distant DNS cells in log-scale
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xx=0.
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jDNS=[1]*20
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for i in range (0,20):
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i1 = (np.abs(10.**xx-yplus_DNS)).argmin()
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jDNS[i]=int(i1)
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xx=xx+0.2
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u_time= np.loadtxt("u-time-history.dat")
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u5=u_time[:,1]
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u10=u_time[:,2]
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u20=u_time[:,3]
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u30=u_time[:,4]
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u40=u_time[:,5]
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u50=u_time[:,6]
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u60=u_time[:,7]
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########################################## Ustar
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.25,bottom=0.20)
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plt.plot(xp2d[:,1]/delta_inlet,ustar,'b-')
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plt.xlabel(r'$x/\delta$')
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plt.ylabel(r"$u_\tau$")
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plt.savefig('ustar-vs-x.png')
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########################################## U
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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i1 = 1
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plt.semilogx(yplus2d[i1,:],u2d[i1,:]/ustar[i1],'b-',label="$x=0$")
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plt.semilogx(yplus_DNS,u_DNS,'o',label="DNS")
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.semilogx(yplus2d[i1,:],u2d[i1,:]/ustar[i1],'r-',label="$x=30$")
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.semilogx(yplus2d[i1,:],u2d[i1,:]/ustar[i1],'k-.',label="$x=50$")
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plt.legend(loc="upper left",prop=dict(size=18))
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plt.ylabel("$U$")
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plt.xlabel("$y^+$")
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plt.axis([1, 1000, 0, 28])
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plt.savefig('u_log_python.png',bbox_inches='tight')
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########################################## vis
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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i1 = 0
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plt.plot(yplus2d[i1,:],vis2d[i1,:],'b-',label="$x=0$")
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(yplus2d[i1,:],vis2d[i1,:],'r--',label="$x=30$")
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(yplus2d[i1,:],vis2d[i1,:],'k-.',label="$x=50$")
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plt.legend(loc="upper right",prop=dict(size=18))
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plt.ylabel(r"$\nu_t/\nu$")
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plt.xlabel("$y^+$")
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plt.axis([1, 1000, 0,130])
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plt.savefig('vis_python.png',bbox_inches='tight')
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########################################## vis vs y
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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i1 = 0
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plt.plot(yp2d[i1,:],vis2d[i1,:],'b-',label="$x=0$")
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(yp2d[i1,:],vis2d[i1,:],'r--',label="$x=30$")
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(yp2d[i1,:],vis2d[i1,:],'k-.',label="$x=50$")
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plt.legend(loc="upper right",prop=dict(size=18))
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plt.ylabel(r"$\nu_t/\nu$")
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plt.xlabel("$y$")
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plt.axis([0, 3, 0,130])
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plt.savefig('vis_vs_y_python.png',bbox_inches='tight')
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########################################## epsilon vs y
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fig1, ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20, bottom=0.20)
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i1 = 0
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plt.plot(yp2d[i1,:], eps2d[i1,:], 'b-', label="$x=0$")
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yp2d[i1,:], eps2d[i1,:], 'r--', label="$x=30$")
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yp2d[i1,:], eps2d[i1,:], 'k-.', label="$x=50$")
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plt.legend(loc="upper right", prop=dict(size=18))
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plt.ylabel(r"$\varepsilon$")
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plt.xlabel("$y$")
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plt.savefig('eps_vs_y_python.png', bbox_inches='tight')
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########################################## k
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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i1 = 0
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plt.plot(yplus2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'b-',label="$x=0$") # ← i1 not i
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yplus2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'r--',label="$x=30$") # ← i1
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yplus2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'k-.',label="$x=50$") # ← i1
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plt.legend(loc="upper right",prop=dict(size=18))
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plt.ylabel(r"$k$")
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plt.xlabel("$y^+$")
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plt.axis([1, 1000, 0,2.5])
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plt.savefig('k_model_python.png',bbox_inches='tight')
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########################################## k vs y
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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i1 = 0
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plt.plot(yp2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'b-',label="$x=0$") # ← i1
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yp2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'r--',label="$x=30$") # ← i1
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin()
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plt.plot(yp2d[i1,:],k_model2d[i1,:]/ustar[i1]**2,'k-.',label="$x=50$") # ← i1
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plt.legend(loc="upper right",prop=dict(size=18))
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plt.ylabel(r"$k$")
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plt.xlabel("$y$")
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plt.axis([0, 3, 0,2.5])
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plt.savefig('k_vs_y_python.png',bbox_inches='tight')
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########################################## cf
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fig = plt.figure()
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ax1 = fig.add_subplot(111)
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ax2 = ax1.twiny()
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plt.subplots_adjust(left=0.25, bottom=0.2, right=0.92, top=0.82, wspace=0, hspace=0.0)
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ax1.plot(re_mom_bl,cf,'b-',label="$x=0$")
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ax1.plot(re_mom_bl[0::5],cf_exp_retheta[0::5],'bo',label="$x=0$")
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ax1.set_ylabel(r"$C_f$")
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ax1.set_xlabel(r"$Re_\theta$")
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ax1.axis([10,3000,0.003,0.01])
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ax2.axis([0,x_delta[-1], 0.003,0.01])
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ax2.set_xlabel(r"$x/\delta_{in}$")
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plt.savefig('cf_vs_re_mom.png',bbox_inches='tight')
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sys.exit()
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########################################## u time
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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plt.plot(u5,'b-',label="$j=5$")
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plt.plot(u10,'r-',label="$j=10$")
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plt.plot(u20,'k-',label="$j=20$")
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plt.plot(u30,'b--',label="$j=30$")
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plt.plot(u40,'r--',label="$j=40$")
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plt.plot(u50,'k--',label="$j=50$")
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plt.plot(u60,'g-',label="$j=60$")
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#plt.legend()
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plt.xlabel('time step')
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plt.ylabel('$U$')
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plt.savefig('u_vs_time.png',bbox_inches='tight')
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########################################## u
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fig1,ax1 = plt.subplots()
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plt.subplots_adjust(left=0.20,bottom=0.20)
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# inlet
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i1 = 0
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plt.plot(u2d[i1,:],yp2d[i1,:],'b-',label="$x=0$")
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xx=30
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(u2d[i1,:],yp2d[i1,:],'r--',label="$x=30$")
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xx=50
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i1 = (np.abs(xx-xp2d[:,1])).argmin() # find index which closest fits xx
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plt.plot(u2d[i1,:],yp2d[i1,:],'k-.',label="$x=50$")
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plt.legend(loc="upper left",prop=dict(size=18))
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plt.ylabel("$y$")
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plt.xlabel("$U$")
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plt.axis([0, 1.1, 0,2.5])
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plt.savefig('u_python.png',bbox_inches='tight')
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