Physics based compressive sensing approach applied to airfoil data collection and analysis

Zhe Bai, Thakshila Wimalajeewa, Zachary P. Berger, Guannan Wang, Mark Glauser, Pramod K. Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In this study, Compressive Sensing (CS), a recently developed low dimensional signal acquisition scheme, was used to reconstruct a high Reynolds number turbulent flow PIV velocity field over a NACA-4412 airfoil. The 2D PIV velocity data was obtained in the Syracuse University subsonic wind tunnel. In the CS framework, a small collection of linear random projections of a sparse signal contains sufficient information for signal recovery. The principle of CS and its feasibility has been demonstrated using the Discrete Cosine Transform (DCT) and the Proper Orthogonal Decompositon (POD)/ Principal Component Analysis (PCA) to obtain the sparsity. The reconstruction performance of CS taking different basis in which the data is sparse is compared to the performance with the traditional snapshot POD/PCA based reconstruction. When DCT is used as the saprsifying basis, acceptable performance with CS is achieved. The reconstruction performance with CS is further improved by taking the POD/PCA basis as the spasifying basis resulting in a much faster and efficient reconstruction process. Finally we demonstrate with some success, a modified snapshot POD/PCA approach that computes the correlation matrices after CS compression so as to decrease the complexity of doing the eigenvalue problem for the snapshot POD/PCA basis.

Original languageEnglish (US)
Title of host publication51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013
StatePublished - Aug 19 2013
Event51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013 - Grapevine, TX, United States
Duration: Jan 7 2013Jan 10 2013

Publication series

Name51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013

Other

Other51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013
CountryUnited States
CityGrapevine, TX
Period1/7/131/10/13

Fingerprint

airfoils
Airfoils
Principal component analysis
principal component analysis
physics
Physics
principal components analysis
Discrete cosine transforms
discrete cosine transform
transform
particle image velocimetry
subsonic wind tunnels
eigenvalue
turbulent flow
wind tunnel
Reynolds number
Flow velocity
flow velocity
Turbulent flow
Wind tunnels

All Science Journal Classification (ASJC) codes

  • Space and Planetary Science
  • Aerospace Engineering

Cite this

Bai, Z., Wimalajeewa, T., Berger, Z. P., Wang, G., Glauser, M., & Varshney, P. K. (2013). Physics based compressive sensing approach applied to airfoil data collection and analysis. In 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013 (51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013).
Bai, Zhe ; Wimalajeewa, Thakshila ; Berger, Zachary P. ; Wang, Guannan ; Glauser, Mark ; Varshney, Pramod K. / Physics based compressive sensing approach applied to airfoil data collection and analysis. 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013. 2013. (51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013).
@inproceedings{0d1f0a5caf1548778141d69f8c09ff63,
title = "Physics based compressive sensing approach applied to airfoil data collection and analysis",
abstract = "In this study, Compressive Sensing (CS), a recently developed low dimensional signal acquisition scheme, was used to reconstruct a high Reynolds number turbulent flow PIV velocity field over a NACA-4412 airfoil. The 2D PIV velocity data was obtained in the Syracuse University subsonic wind tunnel. In the CS framework, a small collection of linear random projections of a sparse signal contains sufficient information for signal recovery. The principle of CS and its feasibility has been demonstrated using the Discrete Cosine Transform (DCT) and the Proper Orthogonal Decompositon (POD)/ Principal Component Analysis (PCA) to obtain the sparsity. The reconstruction performance of CS taking different basis in which the data is sparse is compared to the performance with the traditional snapshot POD/PCA based reconstruction. When DCT is used as the saprsifying basis, acceptable performance with CS is achieved. The reconstruction performance with CS is further improved by taking the POD/PCA basis as the spasifying basis resulting in a much faster and efficient reconstruction process. Finally we demonstrate with some success, a modified snapshot POD/PCA approach that computes the correlation matrices after CS compression so as to decrease the complexity of doing the eigenvalue problem for the snapshot POD/PCA basis.",
author = "Zhe Bai and Thakshila Wimalajeewa and Berger, {Zachary P.} and Guannan Wang and Mark Glauser and Varshney, {Pramod K.}",
year = "2013",
month = "8",
day = "19",
language = "English (US)",
isbn = "9781624101816",
series = "51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013",
booktitle = "51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013",

}

Bai, Z, Wimalajeewa, T, Berger, ZP, Wang, G, Glauser, M & Varshney, PK 2013, Physics based compressive sensing approach applied to airfoil data collection and analysis. in 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013. 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013, 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013, Grapevine, TX, United States, 1/7/13.

Physics based compressive sensing approach applied to airfoil data collection and analysis. / Bai, Zhe; Wimalajeewa, Thakshila; Berger, Zachary P.; Wang, Guannan; Glauser, Mark; Varshney, Pramod K.

51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013. 2013. (51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Physics based compressive sensing approach applied to airfoil data collection and analysis

AU - Bai, Zhe

AU - Wimalajeewa, Thakshila

AU - Berger, Zachary P.

AU - Wang, Guannan

AU - Glauser, Mark

AU - Varshney, Pramod K.

PY - 2013/8/19

Y1 - 2013/8/19

N2 - In this study, Compressive Sensing (CS), a recently developed low dimensional signal acquisition scheme, was used to reconstruct a high Reynolds number turbulent flow PIV velocity field over a NACA-4412 airfoil. The 2D PIV velocity data was obtained in the Syracuse University subsonic wind tunnel. In the CS framework, a small collection of linear random projections of a sparse signal contains sufficient information for signal recovery. The principle of CS and its feasibility has been demonstrated using the Discrete Cosine Transform (DCT) and the Proper Orthogonal Decompositon (POD)/ Principal Component Analysis (PCA) to obtain the sparsity. The reconstruction performance of CS taking different basis in which the data is sparse is compared to the performance with the traditional snapshot POD/PCA based reconstruction. When DCT is used as the saprsifying basis, acceptable performance with CS is achieved. The reconstruction performance with CS is further improved by taking the POD/PCA basis as the spasifying basis resulting in a much faster and efficient reconstruction process. Finally we demonstrate with some success, a modified snapshot POD/PCA approach that computes the correlation matrices after CS compression so as to decrease the complexity of doing the eigenvalue problem for the snapshot POD/PCA basis.

AB - In this study, Compressive Sensing (CS), a recently developed low dimensional signal acquisition scheme, was used to reconstruct a high Reynolds number turbulent flow PIV velocity field over a NACA-4412 airfoil. The 2D PIV velocity data was obtained in the Syracuse University subsonic wind tunnel. In the CS framework, a small collection of linear random projections of a sparse signal contains sufficient information for signal recovery. The principle of CS and its feasibility has been demonstrated using the Discrete Cosine Transform (DCT) and the Proper Orthogonal Decompositon (POD)/ Principal Component Analysis (PCA) to obtain the sparsity. The reconstruction performance of CS taking different basis in which the data is sparse is compared to the performance with the traditional snapshot POD/PCA based reconstruction. When DCT is used as the saprsifying basis, acceptable performance with CS is achieved. The reconstruction performance with CS is further improved by taking the POD/PCA basis as the spasifying basis resulting in a much faster and efficient reconstruction process. Finally we demonstrate with some success, a modified snapshot POD/PCA approach that computes the correlation matrices after CS compression so as to decrease the complexity of doing the eigenvalue problem for the snapshot POD/PCA basis.

UR - http://www.scopus.com/inward/record.url?scp=84881468666&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84881468666&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84881468666

SN - 9781624101816

T3 - 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013

BT - 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013

ER -

Bai Z, Wimalajeewa T, Berger ZP, Wang G, Glauser M, Varshney PK. Physics based compressive sensing approach applied to airfoil data collection and analysis. In 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013. 2013. (51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 2013).