»ã±¨±êÌâ (Title):Reduced-order modeling for uncertainty quantification of parameterized fluid-structure interaction problems

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»ã±¨ÈË (Speaker)£ºËïÏé ¸±½ÌÊÚ£¨Öйúº£Ñó´óѧ£©

»ã±¨¹¦·ò (Time)£º2025Äê6ÔÂ13ÈÕ (ÖÜÎå) 10:15

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿GJ303

Ô¼ÇëÈË(Inviter)£º¼ÍÀö½à

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ÌáÒª£ºA non-intrusive reduced-order modeling (ROM) method, based on tensor-train decomposition (TTD) and polynomial chaos expansion (PCE), is proposed for parameterized fluid-structure interaction problems. TTD is used to extract the spatial, temporal, and parametric modes into TT-cores to reduce the degrees of freedom. PCE is used to approximate the parameter-dependent TT-cores by utilizing a finite set of polynomials. To validate the proposed TTD-PCE, we considered 1D Burgers¡¯ and diffusion-reaction equations with random force terms. Compared to POD-PCE, TTD-PCE demonstrated superior performance, with eight times faster construction and two times faster prediction for a single sample. Moreover, a TTD-PCE-based uncertainty quantification (UQ) framework involving uncertainty estimation and sensitivity analysis is constructed. Subsequently, flow over a circular cylinder validated the effectiveness of the proposed method for FSI problems. Finally, a flexible filament with various conditions demonstrated the efficacy of the proposed method for UQ analysis. The results indicated a higher level of uncertainty at the free end of the self-propelled filament. Global sensitivity analysis revealed that the impact factor has different effects depending on the computational configurations. The unknown parameter of the filament was identified using TTD-PCE-based Bayesian inference, demonstrating TTD-PCE as a robust UQ framework for both calibration and parameter identification. The great potential of TTD-PCE in UQ analysis demonstrates it as a reliable tool for managing uncertainty in complex dynamical systems, providing valuable insight for inverse problems related to FSI problems.