print("Extracting 2d data in wall-parallel plane at y+ =",
(1 - r[k]) * ReTau)
uz2d = u_z[k, :, :]
uzF2d = u_zF[k, :, :]
omegaZ2d = omegaZ[k, :, :]
pi2d = pi[k, :, :]
# isolate forward/backward flux events in the 2d velocity subset
pip2d = np.where(pi2d > 0, pi2d, 0) # only positive, zero elsewhere
pin2d = np.where(pi2d < 0, pi2d, 0) # only negative, zero elsewhere
# compute correlations and sum up temporal (ensemble) statistics
tcorr = timeit.default_timer()
print('Computing 2d correlations... ', end='', flush=True)
import crossCorrelation as c
acUz = acUz + c.corr2d(uz2d, uz2d) # auto-correlations
acUzF = acUzF + c.corr2d(uzF2d, uzF2d)
acOmegaZ = acOmegaZ + c.corr2d(omegaZ2d, omegaZ2d)
acPi = acPi + c.corr2d(pi2d, pi2d)
ccUzPi = ccUzPi + c.corr2d(uz2d, pi2d) # cross-correlations full flux
ccUzFPi = ccUzFPi + c.corr2d(uzF2d, pi2d)
ccOmegaZPi = ccOmegaZPi + c.corr2d(omegaZ2d, pi2d)
ccUzPip = ccUzPip + c.corr2d(uz2d,
pip2d) # cross-correlations forward flux
ccUzFPip = ccUzFPip + c.corr2d(uzF2d, pip2d)
ccOmegaZPip = ccOmegaZPip + c.corr2d(omegaZ2d, pip2d)
ccUzPin = ccUzPin + c.corr2d(uz2d, pin2d) # cross-correlations full flux
ccUzFPin = ccUzFPin + c.corr2d(uzF2d, pin2d)
ccOmegaZPin = ccOmegaZPin + c.corr2d(omegaZ2d, pin2d)
print('Time elapsed:', '{:3.1f}'.format(timeit.default_timer() - tcorr),
'seconds')
j,
i] # inward interaction: low-speed fluid towards wall
if (uz2d[j, i] > 0) and (ur2d[j, i] > 0):
q4[j, i] = ur2d[j, i] * uz2d[
j,
i] # sweep event: high-speed fluid towards wall
ioi = q1 - q3 # unify inward interactions (Q3 being negativ) and outward interactions (Q1 being positive) in one array
see = q2 - q4 # unify sweep events (Q4 being negativ) and ejection events (Q2 being positiv) in one array
print('Time elapsed:', '{:3.1f}'.format(timeit.default_timer() - tqs),
'seconds')
# compute correlations and sum up temporal (ensemble) statistics
tcorr = timeit.default_timer()
print('Computing 2d correlations... ', end='', flush=True)
import crossCorrelation as c
acQ1 = acQ1 + c.corr2d(q1, q1) # auto-correlations
acQ2 = acQ2 + c.corr2d(q2, q2)
acQ3 = acQ3 + c.corr2d(q3, q3)
acQ4 = acQ4 + c.corr2d(q4, q4)
acPi = acPi + c.corr2d(pi2d, pi2d)
ccQ1Pi = ccQ1Pi + c.corr2d(q1, pi2d) # cross-correlations
ccQ2Pi = ccQ2Pi + c.corr2d(q2, pi2d)
ccQ3Pi = ccQ3Pi + c.corr2d(q3, pi2d)
ccQ4Pi = ccQ4Pi + c.corr2d(q4, pi2d)
print('Time elapsed:', '{:3.1f}'.format(timeit.default_timer() - tcorr),
'seconds')
# sum up first and second statistical moments in time and (homogeneous) theta and z direction for normalisation
q11 = q11 + np.sum(np.sum(q1, axis=1),
axis=0) # sum over all elements of 2d data sub set
q12 = q12 + np.sum(np.sum(q1**2, axis=1), axis=0)