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

Shuvayan Brahmachary, Ananthakrishnan Bhagyarajan, Hideaki Ogawa

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Article number106110
JournalPhysics of Fluids
Issue number10
Publication statusPublished - Oct 1 2021

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes


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