An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

Simon Mak, Chih Li Sung, Xingjian Wang, Shiang Ting Yeh, Yu Hung Chang, V. Roshan Joseph, Vigor Yang, C. F.Jeff Wu

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1443-1456
Number of pages14
JournalJournal of the American Statistical Association
Volume113
Issue number524
DOIs
Publication statusPublished - 2018 Oct 2

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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