Free Form Deformation, mesh morphing and reduced order methods: enablers for efficient aerodynamic shape optimization

2018/03/13 by Filippo Salmoiraghi, Salmoiraghi, F., Angela Scardigli +5 · 2 citations
Decision Sciences · Engineering · Physics and Astronomy · #Computational Fluid Dynamics and Aerodynamics #FOS: Mathematics #Model Reduction and Neural Networks #Numerical Analysis (math.NA) #Probabilistic and Robust Engineering Design

paper · pdf · doi:10.48550/arxiv.1803.04688

openalex publication_date 2018/03/13 · openalex created_date 2018/03/29 · openalex updated_date 2026/07/28

Abstract

The work provides an integrated pipeline for the model order reduction of turbulent flows around parametrised geometries in aerodynamics. In particular, Free-Form Deformation is applied for geometry parametrisation, whereas two different reduced-order models based on Proper Orthogonal Decomposition (POD) are employed in order to speed-up the full-order simulations: the first method exploits POD with interpolation, while the second one is based on domain decomposition. For the sampling of the parameter space, we adopt a Greedy strategy coupled with Constrained Centroidal Voronoi Tessellations, in order to guarantee a good compromise between space exploration and exploitation. The proposed framework is tested on an industrially relevant application, i.e. the front-bumper morphing of the DrivAer car model, using the finite-volume method for the full-order resolution of the Reynolds-Averaged Navier-Stokes equations.

Citations

• Online Adaptive Model Reduction for Nonlinear Systems via Low-Rank Updates
• Centroidal Voronoi Tessellations: Applications and Algorithms
• A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

Cited by

• Sectional Kolmogorov N-widths for parameter-dependent function spaces: A general framework with application to parametrized Friedrichs' systems
• Three-dimensional Deep Shape Optimization with a Limited Dataset

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