Data-Driven Nonintrusive Model-Order Reduction for Aerodynamic Design Optimization
| dc.contributor.author | Moni, Abhijith | |
| dc.contributor.author | Yao, Weigang | |
| dc.contributor.author | Malekmohamadi, Hossein | |
| dc.date.acceptance | 2024-03-21 | |
| dc.date.accessioned | 2024-05-20T13:35:46Z | |
| dc.date.available | 2024-05-20T13:35:46Z | |
| dc.date.issued | 2024-05-13 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. | |
| dc.description.abstract | Fast and accurate evaluation of aerodynamic characteristics is essential for aerodynamic design optimization because aircraft programs require many years of design and optimization. Therefore, it is imperative to develop sufficiently fast, robust, and accurate computational tools for industry routine analysis. This paper presents a nonintrusive machine-learning method for building reduced-order models (ROMs) using an autoencoder neural network architecture. An optimization framework was developed to identify the optimal solution by exploring the low-dimensional subspace generated by the trained autoencoder. To demonstrate the convergence, stability, and reliability of the ROM, a subsonic inverse design problem and a transonic drag minimization problem of the airfoil were studied and validated using two different parameterization strategies. The robustness and accuracy demonstrated by the method suggest that it is valuable in parametric studies, such as aerodynamic design and optimization, and requires only a small fraction of the cost of full-order modeling. | |
| dc.funder | No external funder | |
| dc.funder.other | De Montfort University Doctoral College Scholarship fund | |
| dc.identifier.citation | Moni, A., Yao, W. and Malekmohamadi, H. (2024) Data-Driven Nonintrusive Model-Order Reduction for Aerodynamic Design Optimization. AIAA Journal, | |
| dc.identifier.doi | https://doi.org/10.2514/1.J063080 | |
| dc.identifier.uri | https://hdl.handle.net/2086/23800 | |
| dc.language.iso | en | |
| dc.peerreviewed | Yes | |
| dc.publisher | American Institute of Aeronautics and Astronautics | |
| dc.subject | CFD | |
| dc.subject | Aerodynamics | |
| dc.subject | Machine Learning (ML) | |
| dc.subject | Reduced Order Modelling | |
| dc.title | Data-Driven Nonintrusive Model-Order Reduction for Aerodynamic Design Optimization | |
| dc.type | Article |