Non-Intrusive Reduced-Order Modeling Based on Parametrized Proper Orthogonal Decomposition

Teng Li, Tianyu Pan (), Xiangxin Zhou, Kun Zhang and Jianyao Yao ()
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Teng Li: Research Institute of Aero-Engine, Beihang University, Beijing 100191, China
Tianyu Pan: Research Institute of Aero-Engine, Beihang University, Beijing 100191, China
Xiangxin Zhou: College of Aerospace Engineering, Chongqing University, Chongqing 400044, China
Kun Zhang: College of Aerospace Engineering, Chongqing University, Chongqing 400044, China
Jianyao Yao: College of Aerospace Engineering, Chongqing University, Chongqing 400044, China

Energies, 2023, vol. 17, issue 1, 1-22

Abstract: A new non-intrusive reduced-order modeling method based on space-time parameter decoupling for parametrized time-dependent problems is proposed. This method requires the preparation of a database comprising high-fidelity solutions. The spatial bases are extracted from the database through first-level proper orthogonal decomposition (POD). The algebraic relationship between the time trajectory/parameter positions and the projection coefficient is described by the linear superposition of the second-level POD bases (temporal bases) and the second-level projection coefficients (parameter-dependent coefficients). This decomposition strategy decouples the space-time parameter effects, providing a stable foundation for fast predictions of parametrized time-dependent problems. The mappings between the parameter locations and the parameter-dependent coefficients are approximated as Gaussian process regression (GPR) models. The accuracy and efficiency of the PPOD-ROM are demonstrated through two numerical examples: flows past a cylinder and turbine flows with a clocking effect.

Keywords: non-intrusive reduced-order modeling; parametrized time-dependent problems; proper orthogonal decomposition; Gaussian process regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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