On the application of surrogate regression models for aerodynamic coefficient prediction
Computational fluid dynamics (CFD) simulations are nowadays been intensively used in aeronautical industries to analyse the aerodynamic performance of different aircraft configurations within a design process. These simulations allow to reduce time and cost compared to wind tunnel experiments or fli...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Instituto Nacional de Técnica Aeroespacial (INTA) |
| Repositorio: | DIGITAL.INTA Repositorio Digital del Instituto Nacional de Técnica Aeroespacial |
| OAI Identifier: | oai:digital.inta.es:20.500.12666/613 |
| Acceso en línea: | https://link.springer.com/article/10.1007/s40747-021-00307-y http://hdl.handle.net/20.500.12666/613 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Aerodynamic analysis Computational fluid dynamics Surrogate modeling Regression Support vector machines for regression |
| Sumario: | Computational fluid dynamics (CFD) simulations are nowadays been intensively used in aeronautical industries to analyse the aerodynamic performance of different aircraft configurations within a design process. These simulations allow to reduce time and cost compared to wind tunnel experiments or flight tests. However, for complex configurations, CFD simulations may still take several hours using high-performance computers to deliver results. For this reason, surrogate models are currently starting to be considered as a substitute of the CFD tool with a reasonable prediction. This paper presents a review on surrogate regression models for aerodynamic coefficient prediction, in particular for the prediction of lift and drag coefficients. To compare the behaviour of the regression models, three different aeronautical configurations have been used, a NACA0012 airfoil, a RAE2822 airfoil and 3D DPW wing. These databases are also freely provided to the scientific community to allow other researchers to make further comparison with other methods. |
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