Unsteady physics-based reduced order modeling for large-scale compressible aerodynamic applications

Garbo, Andrea; Bekemeyer, Philipp

doi:10.1016/j.compfluid.2022.105385

Cited by 10 publications

(1 citation statement)

References 44 publications

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“…For example, by fixing POD bases of mass and stiffness matrices in the motion equations, the corresponding coefficients of these bases are updated by employing the first-order optimality condition on an error estimator (Dorosti, 2017). Additionally, in several aerodynamic applications, the updating of base coefficients is treated as an optimization problem solved by the Levenberg-Marquardt optimization method (Garbo and Bekemeyer, 2022;Mifsud et al, 2015) or the SNOPT toolbox (Vetrano et al, 2015). Further, to update the POD bases in reduced-order PDEs, a closed-form quadratic equation is formulated to determine the optimal scalar value in the updating formula, where the POD bases are revised by adding a scaled prior matrix (Griffiths et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Online Update Method for Reduced-Order-Model-Based Digital Twin

Tang,

Sajadi,

Dehaghani

et al. 2024

Preprint

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A digital twin (DT) is a model that mirrors a physical system and is continuously updated with real-time data from the physical system. Recent implementations of reduced-order-model-based DT (DT-ROM) have been applied in aerodynamics and structural health monitoring, where partial differential equations (PDEs) are utilized to update reduced bases and coefficients. However, these methods are not directly applicable when the PDEs of the system are unknown. This paper addresses the online update challenge for DT-ROM in scenarios lacking known PDEs of the system. To tackle the challenge, a systematic online update and application method is proposed. During the online update, the projection residual of online data on the reduced bases determines the necessity of updating reduced bases, while the prediction residual of online data obtained by the current DT-ROM is used to decide whether to update the coefficient model. By sequentially evaluating both criteria, the method selectively incorporates essential online data for the online DT model update. During the online application, a criterion defined based on online data is adopted to determine whether the offline DT-ROM or the online one is applied to output final predictions. The capability of the proposed method is tested through three numerical and three engineering problems. Results indicate that the proposed online update method consistently reduces both projection and prediction residuals, thereby progressively enhancing the performance of the online DT-ROM on test data. Meanwhile, the online application method provides a prediction performance better than using offline DT-ROM only. Both demonstrate that the proposed work could be applied to online DT update where the PDEs of the system are unknown.

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