Modeling-based hyper reduction of multidimensional cfd data

Application to ship airwake data

X. Q. Wang, D. Sarhaddi, Z. Wang, Marc Mignolet, P. C. Chen

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

Abstract

This investigation focuses on hyper reduction of CFD data that depends on a series of coordinates (space, time) and/or parameters (flow speed, flow direction, etc.) into a series of basis vectors and generalized coordinates occupying much less storage. The data, first mapped on a multidimensional rectangular grid, can be “shuffled” into two-dimensional arrays on which Proper Orthogonal Decomposition (POD) is applied. This shuffling can be done in various ways but also repeated leading to a succession of POD-based data reduction, termed Progressive Proper Orthogonal Decomposition (p-POD). The possibility of downsampling is also explored using autoregressive (AR) modeling. These techniques are demonstrated on ship airwake data from two ships which depends on the three spatial coordinates, time, the Wind-Over-Deck angles, and wind speed. The p-POD approach leads to a data reduction by a factor 265 while maintaining 99% of the energy. Alone, the AR based approach allows a data reduction by a factor of 12.5 on its own and 297 with the p-POD. The stated reduction factors are computed with respect to the data on the Cartesian extraction grid. If the mapping on this grid is also included, the data reduction achieved from the CFD mesh maybe even much larger.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
DOIs
StatePublished - Jan 1 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum

Conference

ConferenceAIAA Scitech Forum, 2019
CountryUnited States
CitySan Diego
Period1/7/191/11/19

Fingerprint

Data reduction
Decomposition
Computational fluid dynamics
Ships

ASJC Scopus subject areas

  • Aerospace Engineering

Cite this

Wang, X. Q., Sarhaddi, D., Wang, Z., Mignolet, M., & Chen, P. C. (2019). Modeling-based hyper reduction of multidimensional cfd data: Application to ship airwake data. In AIAA Scitech 2019 Forum (AIAA Scitech 2019 Forum). American Institute of Aeronautics and Astronautics Inc, AIAA. https://doi.org/10.2514/6.2019-1850

Modeling-based hyper reduction of multidimensional cfd data : Application to ship airwake data. / Wang, X. Q.; Sarhaddi, D.; Wang, Z.; Mignolet, Marc; Chen, P. C.

AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA, 2019. (AIAA Scitech 2019 Forum).

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

Wang, XQ, Sarhaddi, D, Wang, Z, Mignolet, M & Chen, PC 2019, Modeling-based hyper reduction of multidimensional cfd data: Application to ship airwake data. in AIAA Scitech 2019 Forum. AIAA Scitech 2019 Forum, American Institute of Aeronautics and Astronautics Inc, AIAA, AIAA Scitech Forum, 2019, San Diego, United States, 1/7/19. https://doi.org/10.2514/6.2019-1850
Wang XQ, Sarhaddi D, Wang Z, Mignolet M, Chen PC. Modeling-based hyper reduction of multidimensional cfd data: Application to ship airwake data. In AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA. 2019. (AIAA Scitech 2019 Forum). https://doi.org/10.2514/6.2019-1850
Wang, X. Q. ; Sarhaddi, D. ; Wang, Z. ; Mignolet, Marc ; Chen, P. C. / Modeling-based hyper reduction of multidimensional cfd data : Application to ship airwake data. AIAA Scitech 2019 Forum. American Institute of Aeronautics and Astronautics Inc, AIAA, 2019. (AIAA Scitech 2019 Forum).
@inproceedings{dc983613e8504372b6ada3e301f422aa,
title = "Modeling-based hyper reduction of multidimensional cfd data: Application to ship airwake data",
abstract = "This investigation focuses on hyper reduction of CFD data that depends on a series of coordinates (space, time) and/or parameters (flow speed, flow direction, etc.) into a series of basis vectors and generalized coordinates occupying much less storage. The data, first mapped on a multidimensional rectangular grid, can be “shuffled” into two-dimensional arrays on which Proper Orthogonal Decomposition (POD) is applied. This shuffling can be done in various ways but also repeated leading to a succession of POD-based data reduction, termed Progressive Proper Orthogonal Decomposition (p-POD). The possibility of downsampling is also explored using autoregressive (AR) modeling. These techniques are demonstrated on ship airwake data from two ships which depends on the three spatial coordinates, time, the Wind-Over-Deck angles, and wind speed. The p-POD approach leads to a data reduction by a factor 265 while maintaining 99{\%} of the energy. Alone, the AR based approach allows a data reduction by a factor of 12.5 on its own and 297 with the p-POD. The stated reduction factors are computed with respect to the data on the Cartesian extraction grid. If the mapping on this grid is also included, the data reduction achieved from the CFD mesh maybe even much larger.",
author = "Wang, {X. Q.} and D. Sarhaddi and Z. Wang and Marc Mignolet and Chen, {P. C.}",
year = "2019",
month = "1",
day = "1",
doi = "10.2514/6.2019-1850",
language = "English (US)",
isbn = "9781624105784",
series = "AIAA Scitech 2019 Forum",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Scitech 2019 Forum",

}

TY - GEN

T1 - Modeling-based hyper reduction of multidimensional cfd data

T2 - Application to ship airwake data

AU - Wang, X. Q.

AU - Sarhaddi, D.

AU - Wang, Z.

AU - Mignolet, Marc

AU - Chen, P. C.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This investigation focuses on hyper reduction of CFD data that depends on a series of coordinates (space, time) and/or parameters (flow speed, flow direction, etc.) into a series of basis vectors and generalized coordinates occupying much less storage. The data, first mapped on a multidimensional rectangular grid, can be “shuffled” into two-dimensional arrays on which Proper Orthogonal Decomposition (POD) is applied. This shuffling can be done in various ways but also repeated leading to a succession of POD-based data reduction, termed Progressive Proper Orthogonal Decomposition (p-POD). The possibility of downsampling is also explored using autoregressive (AR) modeling. These techniques are demonstrated on ship airwake data from two ships which depends on the three spatial coordinates, time, the Wind-Over-Deck angles, and wind speed. The p-POD approach leads to a data reduction by a factor 265 while maintaining 99% of the energy. Alone, the AR based approach allows a data reduction by a factor of 12.5 on its own and 297 with the p-POD. The stated reduction factors are computed with respect to the data on the Cartesian extraction grid. If the mapping on this grid is also included, the data reduction achieved from the CFD mesh maybe even much larger.

AB - This investigation focuses on hyper reduction of CFD data that depends on a series of coordinates (space, time) and/or parameters (flow speed, flow direction, etc.) into a series of basis vectors and generalized coordinates occupying much less storage. The data, first mapped on a multidimensional rectangular grid, can be “shuffled” into two-dimensional arrays on which Proper Orthogonal Decomposition (POD) is applied. This shuffling can be done in various ways but also repeated leading to a succession of POD-based data reduction, termed Progressive Proper Orthogonal Decomposition (p-POD). The possibility of downsampling is also explored using autoregressive (AR) modeling. These techniques are demonstrated on ship airwake data from two ships which depends on the three spatial coordinates, time, the Wind-Over-Deck angles, and wind speed. The p-POD approach leads to a data reduction by a factor 265 while maintaining 99% of the energy. Alone, the AR based approach allows a data reduction by a factor of 12.5 on its own and 297 with the p-POD. The stated reduction factors are computed with respect to the data on the Cartesian extraction grid. If the mapping on this grid is also included, the data reduction achieved from the CFD mesh maybe even much larger.

UR - http://www.scopus.com/inward/record.url?scp=85068916884&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068916884&partnerID=8YFLogxK

U2 - 10.2514/6.2019-1850

DO - 10.2514/6.2019-1850

M3 - Conference contribution

SN - 9781624105784

T3 - AIAA Scitech 2019 Forum

BT - AIAA Scitech 2019 Forum

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

ER -