PIV analysis and Plotting

Code set using data from PIV experiments on 07-27-17
$U_\infty = 4.5 (400rpm)$
$\today$ Data taken at 500Hz on 10 image loop at 1Hz
Laser Power = 14amps
Last updated: 09-14-17
Code Strucutre:

import libraries
run analysis codes
read in data
plot outer
plot inner



In [1]:

    
import pandas as pd
import numpy as np
import PIV as piv
import time_series as ts
import time
import sys
import h5py
from scipy.signal import medfilt
import matplotlib.pyplot as plt
import hotwire as hw
import imp
%matplotlib inline



In [2]:

    
# import functions to be run
imp.reload(ts)
imp.reload(piv)
imp.reload(hw)
%run 'air_prop.py'
%run 'piv_outer.py'
%run 'piv_readin.py'
%run 'piv_inner.py'



In [ ]:

    
## INITIAL CODE USED FOR READING IN
#Parameter set
date = '072717'
data_delimiter = '\t'
num_images = 10911
sizex = 128
sizey = 128
walloffset = 7.5 #mm
side_error = 5
#determine file name
file_name = dict()
for j in range(1, num_images+1):
    file_name[j] = '/B' + str('{0:05}'.format(j)) + '.txt'
#list name of data set folders
base_name = dict()
#List the base name for each test to be read in and analyzed, names taken directly from folder
base_name[0] = '/home/drummond/Local_files/FPF/test_072717/data/Cam_Date=170727_Time=103432_TR_SeqPIV_MP(2x16x16_50ov_ImgCorr)=unknown'

piv_readin(date, file_name, base_name, num_images, data_delimiter, sizex, sizey, walloffset, side_error)









    



Reading In: |██████████████████████████████████████████████████| 100.0% Complete
Data Saved!



In [3]:

    
##Plot Outer Normalized Data##
date = '072717'
num_tests = 1
piv_outer(date, num_tests)



In [4]:

    
##Plot Inner Normalized Data##
date = '072717'
num_tests = 1
utau = .133
piv_inner(date, num_tests, utau)

conculsions

Mean data matches well with other experimental and DNS datasets
$u_{rms}$ falls on top of hot wire data, yet becomes noisy towards the top of the FOV
$v_{rms}$ has approriate shape and order of magnitude
$u'v'^+$ has appropriate magnitude but off in shape
NEXT STEPS:
Test how increasing # of images taken during each cycle effects rms profiles as laser has a standard time for which it takes to stabalize our and reach an equilibrium. Some of the current rms could be coming from variance in the laser light intensity
Take experiment at 100 images per cycle and 500 images per cycle and 1000 images per cycle



In [ ]: