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Aerothermal Design Based on Machine Learning and Multi-source Data Fusion
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Guang YANG, Meijing TAN, Chunsheng NIE, Linsen ZHANG, Lun ZHANG
Missiles and Space Vehicles | 2025, 48(6) : 10 - 18
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Missiles and Space Vehicles | 2025, 48(6): 10-18
• Launch Vehicle and Missile •
Aerothermal Design Based on Machine Learning and Multi-source Data Fusion
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Guang YANG, Meijing TAN, Chunsheng NIE, Linsen ZHANG, Lun ZHANG
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  • Science and Technology on Space Physics Laboratory, China Academy of Launch Vehicle Technology, Beijing, 100076
Published: 2025-12-25 doi: 10.7654/j.issn.2097-1974.20250602
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This research addresses the challenge of high computational resource demands and extended simulation cycles associated with three-dimensional aerothermal numerical simulations for complex-shaped hypersonic vehicles. To overcome this limitation, the application of machine learning-based multi-source data fusion methods in aerodynamic thermal design is investigated, utilizing substantial datasets accumulated during past development projects. The characteristics of various data types, including aerodynamic thermal engineering/numerical simulation and ground/flight test data, are analyzed. Employing Latin hypercube sampling and batch submission techniques, a numerical simulation dataset is constructed, and a multi-source heterogeneous aerodynamic thermal database is established. Grid normalization algorithms for configurations involving rudder rotation and localized deformation are developed. Based on clustering and region matching algorithms, simulation data are partitioned, extracted, and statistically analyzed. Deep learning-based approaches for aerodynamic thermal data fusion and intelligent agent modeling are researched, with predictive accuracy validated using a specific lifting body aerodynamic configuration.

machine learning  /  neural network  /  multi-source  /  data fusion  /  aeroheating
Guang YANG, Meijing TAN, Chunsheng NIE, Linsen ZHANG, Lun ZHANG. Aerothermal Design Based on Machine Learning and Multi-source Data Fusion[J]. Missiles and Space Vehicles, 2025 , 48 (6) : 10 -18 . DOI: 10.7654/j.issn.2097-1974.20250602
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Year 2025 volume 48 Issue 6
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Article Info
doi: 10.7654/j.issn.2097-1974.20250602
  • Receive Date：2025-07-06
  • Online Date：2026-01-20
  • Published：2025-12-25
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  • Received：2025-07-06
  • Revised：2025-12-01
Affiliations
    Science and Technology on Space Physics Laboratory, China Academy of Launch Vehicle Technology, Beijing, 100076
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