بهینه‌سازی طراحی چندموضوعی یک ماهواره‌بر بازگشت‌پذیر انعطاف‌پذیر تحت عدم‌ قطعیت مبتنی بر قابلیت اطمینان

Abstract

Complex systems design problems entail a suitable structure in which all disciplines including their coupled relationships have been considered and modeled at the same time. These types of design problems involve time and computational cost challenges. Multi-Disciplinary Design Optimization (MDO) methods have been developed to address these issues simultaneously. The aim is to provide a proper design structure for an uncertainty-based reentry trajectory design optimization problem under the control restrictions and structural constraints of a Reusable Flexible Launch Vehicle (RFLV) alongside the determination of optimal skin thickness and thermal protection system thickness concerning the design criteria of the flexible structure in such a way that the final design would meet the desired reliability. Trajectory, structure, aerodynamics, aeroelasticity, and thermal protection system are considered as involved disciplines in the design problem. The study's purpose will be to obtain an optimal trajectory to meet all the control and structure restrictions while estimating optimal body skin and thermal protection thicknesses based on structural design criteria evaluating in re-entry trajectory are in process. The flexible launch vehicle body has been considered as a free-free Bernoulli-Euler beam for bending variation and D'alembert's principle for inertia force in static model with the aim of assessing structural design standards. The 3DOF longitudinal dynamic equations plus the first bending mode have been considered. By Chebyshev polynomial interpolation, the angle of attack scope has been achievable and then the trajectory optimization problem has been transformed to a discrete nonlinear programming problem (NLP), which leads to numerical integration of state equation and satisfying all path constraints in Bolza optimal control problem. All highly nonlinear uncertainty-based constraints in the model have led to taking advantage of the evolutionary optimization algorithm that here it has been implemented by Non-Dominated Sorting Genetic Algorithm (NSGA-II). Finally, epistemic and aleatory uncertainties have been applied through Probability Theory to estimate the reliability of constraints that had been affected by uncertainties. The result shows a 75 percent decrease through utilizing the evolutionary multi-objective technique against the gradient-based algorithm in design space optimization regarding computational cost in recalling objective functions. The other conclusion is that the sequential reliability analysis structure modeling efficiency is much better compared to the parallel one.