Large Airfoil Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929760864567296 |
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| author | Lee, Howon Save, Aanchal Seshadri, Pranay Rauleder, Juergen |
| author_facet | Lee, Howon Save, Aanchal Seshadri, Pranay Rauleder, Juergen |
| contents | The development of a Large Airfoil Model (LAM), a transformative approach for answering technical questions on airfoil aerodynamics, requires a vast dataset and a model to leverage it. To build this foundation, a novel probabilistic machine learning approach, A Deep Airfoil Prediction Tool (ADAPT), has been developed. ADAPT makes uncertainty-aware predictions of airfoil pressure coefficient ($C_p$) distributions by harnessing experimental data and incorporating measurement uncertainties. By employing deep kernel learning, performing Gaussian Process Regression in a ten-dimensional latent space learned by a neural network, ADAPT effectively handles unstructured experimental datasets. In tandem, Airfoil Surface Pressure Information Repository of Experiments (ASPIRE), the first large-scale, open-source repository of airfoil experimental data has been developed. ASPIRE integrates century-old historical data with modern reports, forming an unparalleled resource of real-world pressure measurements. This addresses a critical gap left by prior repositories, which relied primarily on numerical simulations. Demonstrative results for three airfoils show that ADAPT accurately predicts $C_p$ distributions and aerodynamic coefficients across varied flow conditions, achieving a mean absolute error in enclosed area ($\text{MAE}_\text{enclosed}$) of 0.029. ASPIRE and ADAPT lay the foundation for an interactive airfoil analysis tool driven by a large language model, enabling users to perform design tasks based on natural language questions rather than explicit technical input. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Airfoil Models Lee, Howon Save, Aanchal Seshadri, Pranay Rauleder, Juergen Fluid Dynamics The development of a Large Airfoil Model (LAM), a transformative approach for answering technical questions on airfoil aerodynamics, requires a vast dataset and a model to leverage it. To build this foundation, a novel probabilistic machine learning approach, A Deep Airfoil Prediction Tool (ADAPT), has been developed. ADAPT makes uncertainty-aware predictions of airfoil pressure coefficient ($C_p$) distributions by harnessing experimental data and incorporating measurement uncertainties. By employing deep kernel learning, performing Gaussian Process Regression in a ten-dimensional latent space learned by a neural network, ADAPT effectively handles unstructured experimental datasets. In tandem, Airfoil Surface Pressure Information Repository of Experiments (ASPIRE), the first large-scale, open-source repository of airfoil experimental data has been developed. ASPIRE integrates century-old historical data with modern reports, forming an unparalleled resource of real-world pressure measurements. This addresses a critical gap left by prior repositories, which relied primarily on numerical simulations. Demonstrative results for three airfoils show that ADAPT accurately predicts $C_p$ distributions and aerodynamic coefficients across varied flow conditions, achieving a mean absolute error in enclosed area ($\text{MAE}_\text{enclosed}$) of 0.029. ASPIRE and ADAPT lay the foundation for an interactive airfoil analysis tool driven by a large language model, enabling users to perform design tasks based on natural language questions rather than explicit technical input. |
| title | Large Airfoil Models |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2410.08392 |