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Artificial Neural Network augmented hyper-reduced order model for transient flow optimization problems
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DOI：10.1016/j.cma.2026.119090.png)
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Abstract
En 中文
This paper introduces an Artificial Neural Network (ANN) augmented, Variational Multiscale (VMS) stabilized, hyper-reduced order model (HROM) for time-dependent fluid flow shape optimization applications. The method extends the authors VMS-based ROM with Discrete Empirical Interpolation Method (DEIM) hyper-reduction to achieve further computational efficiency. A fully discrete level nonlinear ANN correction term is applied to account for errors associated with both ROM projection and hyper-reduction that improves the stability, as well as the accuracy required for unsteady flows. The HROM approach we obtain here is implemented into a Sequential Least Squares Quadratic Programming (SLSQP) based gradient-based aerodynamic shape optimization problem. NACA airfoils at high angles of attack are chosen to get the transient effects like separation and vortex-shedding. The ANN correction is trained on snapshots from full-order simulations and used directly in the optimization loop at no additional cost. Numerical results show that the HROM with ANN-correction successfully captures the full order model accuracy for both transient prediction and optimization and achieves more than 95% reduction in computational cost. The results validate the effectiveness of the developed ANN-augmented HROM as an accurate and efficient counterpart for transient-CFD-based optimization problems.
Keywords:
Reduced Order Modeling (ROM)
Variational Multiscale Method (VMS)
Discrete Empirical Interpolation Method (DEIM)
Artificial Neural Network (ANN)
Transient flow optimization
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7.3
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