Analysis and Comparison of Surface Roughness Effects on Pressure Data from SLS Wind Tunnel Test was conducted during Summer 2019 internship at Fluid Mechanics Lab. Student has been requested by academic adviser to present final presentation to aerospace department at university

Douthitt, Autumn N.

Roozeboom, Nettie H.

English

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[Courtesy of NASA]
1
Analysis and Comparison of Surface Roughness 
Effects on Pressure Data from SLS Wind Tunnel Test
Presented By: Autumn N. Douthitt
Mentor: Nettie H. Roozeboom
NASA Ames Summer 2019 Internship Branch Presentation
Acknowledgements: Dr. Rabindra Mehta, Lara Lash, Thomas Steva (MSFC), 
NASA PSP Team, Jie Li, and Nick Garbe
https://ntrs.nasa.gov/search.jsp?R=20200001133 2020-03-28T19:04:15+00:00Z
Mission Objective
2
• Necessary to know flow field characteristics of SLS design configurations
• Does paint application affect signal?
• Does sanding of the paint affect signal?
Shoulder of paint 
around the Kulite
Sanded paint 
around the Kulite
December 2017
[SLS and Kulite images courtesy of NASA PSP Team] [Moon and Mars images courtesy of Google Images]
2024 Future
Timeline of Events
3
Day 0 (Dec. 1st) Day 1 (Dec. 4th) Day 2 (Dec. 5th) Day 3 (Dec. 6th)
• Clean Model
• Kulite Data
• Epoxy, uPSP Base and 
Topcoat
• Test with no sanded 
Kulites
• Refreshed uPSP Topcoat
• Test with 10 Sanded 
Kulites
• 940, 952, 925, 998, 
923, 416, 792, 779, 
996, 738
• No refresh of uPSP
• Test with 5 sanded Kulites
• 414, 773, 763, 419, 
404
Configuration 17
All Days Tested at Mach
0.7
0.8
0.85
0.9
0.95
1.05
1.1
1.2
1.4
[SLS and Kulite images courtesy of NASA PSP Team]
Kulite Locations on SLS
4
Mach
0.7
0.8
0.85
0.9
0.95
1.05
1.1
1.2
1.4
[Run Log data courtesy of NASA PSP Team]
Day 0 Day 1 Day 2 Day 3
0 ͦ
90 ͦ
180 ͦ
270 ͦ
Tools for Mission Success
1. Notes, Pictures, and 
Run Log from Test
2. Reduced Kulite and uPSP
Pressure Time History Data
5. Requirements of SLS Customers
3. MatlabPlot 3D and Modified 
uPSP Example Code
4. Dots
[SLS image courtesy of NASA PSP Team]
6Processing the Kulite Data
Locating Kulites on SLS using Dots
7
• Began with studying documentation taken during Kulite sanding (photos, notes, test run 
log) 
• Using Dots, was able to determine location of these Kulites on SLS model
Kulite names can 
be featured beside 
their location
Picture of one of 
the sanded Kulites
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC] [Kulite images courtesy of NASA PSP 
Flow
Data Processing Method for Kulites
• 15 Kulites, 9 Mach Numbers, 4 Days of Testing 
8
Input working directory
Enter Desired 
Run Number
Enter Desired 
Sequence #
Enter Desired 
Kulite Result is FPL of Kulite at that Mach 
number and Model Orientation
Comparing the Kulite Data
9
• Combined the plots for all four days for each Mach number 
Day 0 Day 1
Day 2 Day 3
10
Results of Sanded Kulite Data
11
11 DDS Results Along SLS at Mach 0.7, α=0 β=0
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
11 DDS Results Along SLS at Mach 0.95, α=0 β=0
12 [Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
11 DDS Results Along SLS at Mach 1.4, α=0 β=0
13 [Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
Results: Mach 0.7 @ α=0, β=0
K998
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA925 KA923 KA952
KA763 KA773 K416 K414 K419
Results: Mach 0.95 @ α=0, β=0
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA925 KA923 KA952
KA763 KA773 K416 K414 K419
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA998
Results: Mach 1.4 @ α=0, β=0
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA925 KA923 KA952
KA763 KA773 K416 K414 K419
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA998
17
Results with Large Differences in FPL, α=0 β=0
414
414
414
763
763
763
773
773
773
779
779
779
Mach 0.7
Mach 0.95
Mach 1.4
18
11 DDS Results Along SLS at Mach 0.7, α=0 β=4
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
19
11 DDS Results Along SLS at Mach 0.95, α=0 β=4
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
20
11 DDS Results Along SLS at Mach 1.4, α=0 β=4
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Flow
Results: Mach 0.7 @ α=0, β=4 
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA923 KA952
KA763 KA773 K416 K414 K419
KA925
K998
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA925
Results: Mach 0.95 @ α=0, β=4 
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA923 KA952
KA763 KA773 K416 K414 K419
KA925
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA998
KA925
Results: Mach 1.4 @ α=0, β=4 
KA738 KA996 KA779 KA998 KA940
K404 KA792 KA925 KA923 KA952
KA763 KA773 K416 K414 K419
KA738 KA996
K404
KA779
KA763
KA773
KA792
K416
K414
K419
KA923
KA925 KA940
KA952
KA998
24
Results with Large Differences in FPL, α=0 β=4
414
414
414
763
763
763
773
773
773
779
779
779
Mach 0.7
Mach 0.95
Mach 1.4
25
Processing the uPSP Data
Locating Nodes on SLS using Dots
26
• SLS is defined by PSP grideach grid has multiple zoneseach zone has nodes
• Selected a 3x3 section of nodes near a sanded Kulite using Dots grid
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Each node has an i and j coordinate
• i coordinate goes around from 1-361 for every line of nodes in the grids for the larger parts of the model
• J=1 at the start of every zone from left to right
i=314 
j=56
i=314 
j=57
i=314 
j=58
Data Processing Method for uPSP
27
• Converted the 3 x 3 section of chosen nodes into a virtual Kulite
Specified h5 file and zone of nodes
Extracting Node Information: 
node_start first node in zone in relation to all nodes
nodes_zone number of nodes in specified zone
node_end last node in zone in relation to all nodes
numOfNodesnodes_zone
Extracted h5 file information and Grid Sizes;
“jmax” is max of “i” and “kmax” is max of “j” in Dots program for 
specified zone
Specified (i,j) location of node to obtain node number in 
specified zone and in relation with all the nodes in Dots program
Data Processing Method for uPSP Cont.
28
Extract the Frame x Node Data from h5 file
Isolate chosen nodes in columns perpendicular to flow for 3 
vectors
Reshape these into 3x1xtime matrices
Combine the matrices into one 3x3xtime matrix
Average matrix to create virtual Kulite, now a 1x1xtime matrix
Reshape into 2D matrix (1xtime)
Processed uPSP data to obtain Power Spectral Density plot
Sampling Rate10kHz
FFT size512
Overlap0.75
Detrended virtual Kulite (although not needed)
Normalized by variance of detrended data
Plotting PSD in semilog fashion
Conclusions Gathered to Help Complete the Mission
• No right or wrong answer
• Changing the roughness of a model will affect the flow
• Positive Note: uPSP not creating tonesnot translating to design change
• uPSP surface roughness does affect flow, magnified at areas of high fluctuating 
pressures (do see offset but consistent across days)
29
Future Work Towards Mission Accomplishment
• Process uPSP data for same runs
• Prepare for September uPSP demonstration
• Only sanding in areas of high fluctuation
• Painting over Kulitesdoes uPSP damage the Kulite?
30
Thank You! Any Questions?
Power of Pink!
31
32 [Steady State PSP image courtesy of NASA PSP Team]
Results
M=0.7
α=0, β=0
33
Results
M=0.7
α=0, β=0
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
140 dB
34
Results
M=0.95
α=0, β=0
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
35
Results
M=1.4
α=0, β=0
[Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Results
M=0.7
α=0, β=4
36 [Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Results
M=0.95
α=0, β=4
37 [Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
Results
M=1.4
α=0, β=4
38 [Dots image courtesy of NASA PSP Team and Thomas Steva of MSFC]
39
K414
K414
K414
K414
K414
K414
KA763
KA763
KA763
KA763
KA763
KA763


Analysis and Comparison of Surface Roughness Effects on Pressure Data from SLS Wind Tunnel Test

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