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<p>This paper presents a conceptual design for a reusable unmanned space vehicle based on multidisciplinary optimization (MDO). The MDO integrates geometry definition, weight analysis, propulsion analysis, aerothermal analysis, and trajectory analysis. To enhance modeling fidelity during conceptual design, heat flux estimation considers wall temperature, and fuel weight is estimated based on a given mission profile. Aerodynamic properties are evaluated using modified Newtonian theory and digital DATCOM, and convective heating is predicted through approximate convective-heating equations coupled with radiative equilibrium (radiative cooling). The optimization process employs a real-coded multiobjective genetic algorithm (MOGA) combined with a design space adaptation method to address the complexity and interdependence of the disciplines involved and explore high-performance solutions with the proper design space. The optimization simultaneously minimizes vehicle weight and total surface heat flux at critical regions such as the stagnation point and leading edges. Results indicate that the adaptive design space method improves solution performances. Furthermore, analysis of variance (ANOVA) identifies key geometric parameters influencing weight and heat flux.</p>

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Mission-Based Multidisciplinary Optimization of a Reusable Unmanned Space Vehicle

  • 期刊论文

  • 发表日期：

    2025年8月23日

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  • Jongho Jung,
  • Jaehyoung Park,
  • Dabeen Jeong,
  • Shinkyu Jeong

摘要

This paper presents a conceptual design for a reusable unmanned space vehicle based on multidisciplinary optimization (MDO). The MDO integrates geometry definition, weight analysis, propulsion analysis, aerothermal analysis, and trajectory analysis. To enhance modeling fidelity during conceptual design, heat flux estimation considers wall temperature, and fuel weight is estimated based on a given mission profile. Aerodynamic properties are evaluated using modified Newtonian theory and digital DATCOM, and convective heating is predicted through approximate convective-heating equations coupled with radiative equilibrium (radiative cooling). The optimization process employs a real-coded multiobjective genetic algorithm (MOGA) combined with a design space adaptation method to address the complexity and interdependence of the disciplines involved and explore high-performance solutions with the proper design space. The optimization simultaneously minimizes vehicle weight and total surface heat flux at critical regions such as the stagnation point and leading edges. Results indicate that the adaptive design space method improves solution performances. Furthermore, analysis of variance (ANOVA) identifies key geometric parameters influencing weight and heat flux.