Neil Ashton

This paper discusses an emerging area of applying machine 1 learning (ML) methods to augment traditional Computational 2 Fluid Dynamics (CFD) simulations of road vehicle aerodynam-3 ics. ML methods have the potential to both reduce the com-4 putational effort to predict a new geometry or car condition 5 and to explore a greater number of design parameters with the 6 same computational budget. Similar to traditional CFD meth-7 ods, there exists a broad range of approaches. In particular, 8 the accuracy and computational efficiency of a CFD simula-9 tion vary greatly depending on the choice of turbulence model 10 (DNS, LES, RANS) and the underlying spatial and temporal nu-11 merical discretizations. Similarly, the end-user must select the 12 correct ML method depending on the use-case, the available in-13 put data, and the trade-off between accuracy and computational 14 cost. In this paper, we showcase several case studies using var-15 ious data-driven ML methods to highlight the promise of these 16 approaches. Whilst these case studies are not comprehensive 17 investigations of the underlying methods and do not include all 18 possible ML approaches (i.e., physics-driven), they highlight 19 the ability of these models to in general predict new designs in 20 near real-time (i.e., less than 5 seconds), after typically less than 21 1 hour of training on a single GPU. There still exists a need for 22 high quality training data from traditional CFD methods and 23 high-fidelity CFD simulations to validate the ML predictions. 24

This paper summarizes the collective efforts of multiple teams that contributed to the hybrid RANS/LES technical focus group for the 4th AIAA CFD High Lift Prediction Workshop (HLPW-4), which took place on January 7, 2022, in San Diego, California. The overall conclusion is that turbulence-resolving methods such as hybrid RANS/LES (HRLES) do offer improved predictions for these high-lift geometries, with respect to the underlying RANS models, but there are nuances, and some unresolved issues remain that should be the focus of future work. In particular, while HRLES methods appear to show clearly improved predictions at higher angles of attack, there is some tendency for HRLES methods to return slightly worse moment predictions at lower angles of attack, suggesting that prediction of the shallow separation from the flaps might need further research. Computing cost also remains a significant issue, with HRLES methods requiring roughly nine times more high-performance computing central processing unit core hours than steady-state RANS methods, indicating that future algorithmic and computational optimization could be beneficial. Finally, there are strong indications that modeling the wind tunnel has a positive impact on correlation with experimental measurements, suggesting that future work might be better focused on in-tunnel simulations.

The 2nd Automotive CFD Prediction workshop (AutoCFD2) was organized to improve the state-of-the-art in automotive aerodynamic prediction. It is the mission of the workshop organizing committee to drive the development and validation of enhanced CFD methods by establishing publicly available standard test cases for which high quality on- and off-body wind tunnel test data is available. This paper reports on the AutoCFD2 workshop for the Ford DrivAer test case. Since its introduction, the DrivAer quickly became the quasi-standard for CFD method development and correlation. The Ford DrivAer has been chosen due to the proven, high-quality experimental data available, which includes integral aerodynamic forces, 209 surface pressures, 11 velocity profiles and 4 flow field planes. For the workshop, the notchback version of the DrivAer in a closed cooling, static floor test condition has been selected. For a better comparability of CFD results, two carefully designed control meshes were provided. Both meshes share identical distributions in the flow field volume but differ in near wall spacing to allow for wall-modelled and wall-resolved solutions. The 65 results, which were submitted by 22 participants, revealed a very significant variability of the aerodynamic force predictions even when using the same turbulence model on the control grids. While individual simulations using scale-resolving hybrid turbulence models correlated very well to the experimental flow field data, other analyses using almost identical simulation approaches resulted in very different predictions. The comparison of transient versus steady state analysis confirmed that transient simulations deliver more accurate flow field predictions. A significant impact of the near wall mesh resolution could not be confirmed by the results submitted for the DrivAer test case.

© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved. This paper presents a detailed analysis of the relative performance and cost of GPU and CPU architectures for a full aircraft RANS simulation using the CFD code zCFD. Using Amazon Web Services as the platform, several generations of NVIDIA GPUs are assessed (T4, V100, and A100) and compared to x86 Intel Broadwell and Skylake CPUs. It is found that for the same computational setup and mesh, one Amazon EC2 p4d.24xlarge instance (containing eight NVIDIA A100 GPUs) provides the same run-time as 2,160 cores (60 nodes) of the Intel Skylake Amazon EC2 c5n.18xlarge. At the maximum number of nodes tested (100 CPU nodes and 10 GPU nodes-each containing 8 GPUs) the GPU-based instance is 2.8x faster than the x86 Intel CPU-based instance (Amazon EC2 c5n.18xlarge) for half the cost. It is hoped these results will provide extra evidence to support the continued focus on CFD code development to support GPUs and will help to make high-fidelity simulations more practical for engineering companies.