Aerodynamic optimization of a coaxial rotor system using a deep learning-based multi-fidelity surrogate model
This paper introduces a multi-fidelity surrogate-based optimization framework that employs deep learning techniques for aerodynamic optimization of the X2TD coaxial rotor system in hover. To integrate data across three fidelity levels, a Deep Learning-Based Multilevel Hierarchical Kriging (DLMHK) mo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2528120 |
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