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Machine Learning for Road Vehicle Aerodynamics
Technical Paper
2024-01-2529
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
This paper discusses an emerging area of applying machine learning (ML) methods to augment traditional Computational Fluid Dynamics (CFD) simulations of road vehicle aerodynamics. ML methods have the potential to both reduce the computational effort to predict a new geometry or car condition and to explore a greater number of design parameters with the same computational budget. Similar to traditional CFD methods, there exists a broad range of approaches. In particular, the accuracy and computational efficiency of a CFD simulation vary greatly depending on the choice of turbulence model (DNS, LES, RANS) and the underlying spatial and temporal numerical discretizations. Similarly, the end-user must select the correct ML method depending on the use-case, the available input data, and the trade-off between accuracy and computational cost. In this paper, we showcase several case studies using various data-driven ML methods to highlight the promise of these approaches. Whilst these case studies are not comprehensive investigations of the underlying methods and do not include all possible ML approaches (i.e., physics-driven), they highlight the ability of these models to in general predict new designs in near real-time (i.e., less than 5 seconds), after typically less than 1 hour of training on a single GPU. There still exists a need for high quality training data from traditional CFD methods and high-fidelity CFD simulations to validate the ML predictions. Thus, ML approaches should be seen as tools to augment traditional CFD methods rather than to replace them. While this work focuses on preliminary studies, future work will look at more comprehensive real-world/industrial-size calculations for the more promising technologies identified here.
Authors
- Vidyasagar Ananthan - Amazon Web Services
- Neil Ashton - Amazon Web Services
- Nate Chadwick - Amazon Web Services
- Mariano Lizarraga - Amazon Web Services
- Danielle Maddix - Amazon Web Services
- Satheesh Maheswaran - Amazon Web Services
- Pablo Hermoso Moreno - Amazon Web Services
- Parisa M. Shabestari - Amazon Web Services
- Sandeep Sovani - Amazon Web Services
- Shreyas Subramanian - Amazon Web Services
- Srinivas Tadepalli - Amazon Web Services
- Peter Yu - Amazon Web Services
Citation
Ananthan, V., Ashton, N., Chadwick, N., Lizarraga, M. et al., "Machine Learning for Road Vehicle Aerodynamics," SAE Technical Paper 2024-01-2529, 2024.Also In
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