2:15 PM - 3:05 PM
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特別講演・計算工学大賞授賞式
[SL1] 特別講演 (1)
From low to high-order approximations in surrogate models for parametric flows
Thu. May 27, 2021 2:15 PM - 3:05 PM メインホール (Web会議室)
Antonio Huerta
Professor of Applied Mathematics, Laboratori de Càlcul Numèric (LaCàN), Universitat Politècnica de Catalunya, Barcelona, Spain
Chair: Junji Kato (Nagoya University)
Daily industrial practice requires fast and robust strategies to solve computationally-demanding problems. Moreover, Computational Fluid Dynamics (CFD) simulations in industrial design and optimization procedures involve the exploration of large sets of admissible configurations. Parameters of interest may include boundary conditions, physical properties of the fluid and geometric configurations. Hence, efficient tools to solve multiple queries of the same flow problem are required. Surrogate models based on reduced order methods are commonly employed to ease the computational burden of such high-dimensional problems, allowing to efficiently perform parametric studies and to evaluate quantities of interest in real-time.
Two major issues will be discussed. On the one hand, the development of robust computational technologies to accurately reproduce complex flows. Novel advances for robust high-order and low order strategies in compressible and incompressible flows will be described. Special emphasis will be given to a unified framework for the treatment of Riemann solvers in high-order strategies and to the face-centered finite volume (FCFV) method that secures first-order accuracy for the stress tensor without the need for gradient reconstruction, thus being insensitive to cell distortion and stretching.
On the other hand, this talk reviews some recent contributions involving a priori and a posteriori surrogate models for the solution of parameterized incompressible flow problems, from microfluidics to viscous laminar and turbulent flows. The Proper Generalized Decomposition (PGD) will be used because it provides separated solutions, explicitly depending on the parameters of interest for the problem. The performance of a priori PGD and sampling-based a posteriori PGD is compared in terms of accuracy and computational cost for the simulation of Stokes flows in geometrically parameterized domains. This problem is particularly challenging as the geometric parameters affect both the solution manifold and the computational spatial domain. Moreover, in order to make the a priori PGD strategy appealing for industrial applications, a non-intrusive approach exploiting OpenFOAM native solver for incompressible flows is devised for viscous laminar and turbulent Navier-Stokes equations coupled with the Spalart-Allmaras model.