نوع مقاله : مقاله پژوهشی
عنوان مقاله English
نویسندگان English
This paper presents an uncertain optimal design methodology for minimizing the total mass of a re-entry space capsule, taking into account geometric manufacturing and assembly tolerance uncertainties. Uncertain optimal design refers to the process of optimizing a design while considering the effects of uncertainties, ensuring that the final solution is both robust and stable. The proposed approach integrates multi-objective optimization with variance-based robustness analysis. Initially, the optimal design point is determined under deterministic conditions using a genetic algorithm. Subsequently, the design's robustness against potential parameter variations is assessed through an iterative process. If the initial design does not satisfy the robustness criteria, the algorithm iteratively updates the design constraints to identify a new optimal point until a robust solution is achieved. The multi-objective optimization framework is implemented using the All-At-Once (AAO) approach, and Latin Hypercube Sampling (LHS) is employed to explore the geometric uncertainty space and construct response surfaces for objective functions and constraints. Variance analysis quantifies the influence of geometric uncertainties on the optimal responses. Results show that the robust optimized capsule is 10.7% lighter than the baseline design, while exhibiting improved static stability margins. This mass reduction is accomplished through intelligent aerodynamic reshaping and the elimination of the need for balancing mass. Robustness evaluation confirms that all critical design functions maintain a minimum 2σ safety margin against geometric uncertainties, validating the reliability of the proposed design. The methodology effectively balances mass minimization with stability requirements, demonstrating particular relevance for aerospace applications where both performance and safety are paramount.
کلیدواژهها English
