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2025 Vol.30, Issue 3 Preview Page

Original Article

MULTI-OBJECTIVE DESIGN OPTIMIZATION OF REENTRY CAPSULE USING A MACHINE LEARNING-BASED AERO-THERMODYNAMIC SURROGATE MODEL

기계 학습 기반 열공력 대리모델을 이용한 재진입 캡슐의 다목적 형상 최적 설계

S. Kang1
,
J. Gwon2
,
H. Yeo2
,
H. Nam2
,
K.H. Kim23†
강 성원1
,
권 준하2
,
여 훈정2
,
남 한솔2
,
김 규홍23†
1Department of Mechanical and Aerospace Engineering, Seoul National University
2Department of Aerospace Engineering, Seoul National University
3Institute of Advanced Aerospace Technology, Seoul National University
1서울대학교 기계항공공학부
2서울대학교 항공우주공학과
3서울대학교 항공우주신기술연구소
*Corresponding Author
30 September 2025. pp. 20-40
PDF XML Citation
Abstract

This study presents a rapid and efficient analysis framework for the design of reentry vehicles operating in hypersonic flow environments, by proposing a multi-objective optimization method based on machine learning surrogate models. While computational fluid dynamics (CFD) provides highly accurate results, its high computational cost makes it impractical for early design phases where a wide range of flow conditions and geometric variations must be explored iteratively. To address this limitation, surrogate models based on Kriging and deep learning were developed using sample data generated from empirical aero-thermodynamic models that represent the aerodynamic and thermal characteristics of reentry vehicles. These surrogate models were then integrated with a genetic algorithm to enable rapid optimization. The results demonstrate that the surrogate models can reliably replace the empirical models and offer significant advantages in terms of computational efficiency, confirming their potential as practical design tools at the engineering level.

Keywords
Hypersonic flow
Multi-objective optimization
Reentry capsule
Deep-learning based model
Machine learning
Hypersonic aerothermodynamic
키워드
극초음속 유동
다목적 최적화
재진입 캡슐
딥러닝 기반 모델
기계학습
극초음속 열공력
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Information
  • Publisher :Korean Society for Computational Fluids Engineering
  • Publisher(Ko) :한국전산유체공학회
  • Journal Title :Journal of Computational Fluids Engineering
  • Journal Title(Ko) :한국전산유체공학회지
  • Volume : 30
  • No :3
  • Pages :20-40
  • Received Date : 2025-05-16
  • Revised Date : 2025-07-31
  • Accepted Date : 2025-09-02
  • DOI :https://doi.org/10.6112/kscfe.2025.30.3.020
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