Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity...Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach（HHA） based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle（ASLV）.A non-learning random function（NLRF） is proposed to con-trol low-level meta-heuristics（LLMHs） that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.展开更多
文摘Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization（MDO） process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach（HHA） based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle（ASLV）.A non-learning random function（NLRF） is proposed to con-trol low-level meta-heuristics（LLMHs） that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.
