An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations. Issue 524 (2nd October 2018)
- Record Type:
- Journal Article
- Title:
- An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations. Issue 524 (2nd October 2018)
- Main Title:
- An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations
- Authors:
- Mak, Simon
Sung, Chih-Li
Wang, Xingjian
Yeh, Shiang-Ting
Chang, Yu-Hung
Joseph, V. Roshan
Yang, Vigor
Wu, C. F. Jeff - Abstract:
- ABSTRACT: In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplifications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
- Is Part Of:
- Journal of the American Statistical Association. Volume 113:Issue 524(2018)
- Journal:
- Journal of the American Statistical Association
- Issue:
- Volume 113:Issue 524(2018)
- Issue Display:
- Volume 113, Issue 524 (2018)
- Year:
- 2018
- Volume:
- 113
- Issue:
- 524
- Issue Sort Value:
- 2018-0113-0524-0000
- Page Start:
- 1443
- Page End:
- 1456
- Publication Date:
- 2018-10-02
- Subjects:
- Computer experiments -- Kriging -- Rocket injectors -- Sparsity -- Spatio-temporal flow -- Turbulence
Statistics -- Periodicals
Statistics -- Periodicals
Statistiques -- Périodiques
États-Unis -- Statistiques -- Périodiques
519.5 - Journal URLs:
- http://www.jstor.org/journals/01621459.html ↗
http://www.ingentaconnect.com/content/asa/jasa ↗
http://www.tandfonline.com/loi/uasa20 ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/01621459.2017.1409123 ↗
- Languages:
- English
- ISSNs:
- 0162-1459
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4694.000000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 9422.xml