Fast estimation of internal flowfields in scramjet intakes via reduced-order modeling and machine learning

Shuvayan Brahmachary, Ananthakrishnan Bhagyarajan, Hideaki Ogawa

研究成果: Contribution to journalArticle査読

抄録

The interface between fluid mechanics and machine learning has ushered in a new avenue of scientific inquiry for complex fluid flow problems. This paper presents the development of a reduced-order predictive framework for the fast and accurate estimation of internal flowfields in two classes of scramjet intakes for hypersonic airbreathing propulsion. Proper orthogonal decomposition is employed as a reduced-order model while the moving least squares-based regression model and the multilayer perceptron-based neural network technique are employed. The samples required for the training process are generated using a sampling strategy, such as Latin hypercube sampling, or obtained as an outcome of multi-objective optimization. The study explores the flowfield estimation capability of this framework for the two test cases, each representing a unique type of scramjet intake. The importance of tuning the user-defined parameters as well as the use of multiple reduced-order bases instead of a global basis are highlighted. It is also demonstrated that the bias involved in the generation of input samples in an optimization problem can potentially be utilized to build a reduced-order predictive framework while using only a moderate number of training samples. This offers the potential to significantly reduce the computational time involved in expensive optimization problems, especially those relying on a population-based approach to identify global optimal solutions.

本文言語英語
論文番号106110
ジャーナルPhysics of Fluids
33
10
DOI
出版ステータス出版済み - 10 1 2021

All Science Journal Classification (ASJC) codes

  • 計算力学
  • 凝縮系物理学
  • 材料力学
  • 機械工学
  • 流体および伝熱

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