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

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.

KEYWORDS:

• Computer experiments
• Kriging
• Rocket injectors
• Sparsity
• Spatio-temporal flow
• Turbulence

Supplementary Materials

This article is accompanied by two supplementary files: the first (“appendix.pdf”) provides further details on algorithmic implementation and technical proofs, and the second (“code.zip”) contains relevant code files.

Acknowledgments

The authors thank the associate editor, two anonymous referees and Dr. Mitat A. Birkan.

Additional information

Funding

This work was sponsored partly by the Air Force Office of Scientific Research under Grant No. FA 9550-10-1-0179, and partly by the William R. T. Oakes Endowment of Georgia Institute of Technology. Wu’s work is partially supported by NSF DMS 1564438.