Machine learning–based reduced-order modeling of hydrodynamic forces using pressure mode decomposition. (December 2021)

• Record Type: Journal Article
• Authors: Ahmed, Hassan F; Farooq, Hamayun; Akhtar, Imran; Bangash, Zafar
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 16(2021)
• Page Start: 2517
• Page End: 2528
• Publication Date: 2021-12
• Subjects: Machine learning -- reduced-order modeling -- proper orthogonal decomposition -- long short-term memory network -- lift and drag coefficients; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410021999864
• Languages: English
• ISSNs: 0954-4100

Abstract

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients.