Purpose -The purpose of this paper is the optimal design of a reentry vehicle configuration to minimize the mission cost which is equal to minimize the heat absorbed (thermal protection system mass) and structural mass and to maximize the drag coefficient (trajectory errors and minimum final velocity). Design/methodology/approach -There are two optimization approaches for solving this problem: multiobjective optimization (lead to Pareto optimal solutions); and single-objective optimization (lead to one optimal solution). Single-objective genetic algorithms (GA) and multiobjective Genetic Algorithms (MOGA) are employed for optimization. In second approach, if there are n objectives (n þ 1) GA run is needed to find nearest point (optimum point), which leads to increase the time processing. Thus, a modified GA called single run GA (SRGA) is presented as third approach to avoid increasing design time. It means if there are n objectives, just one GA run is enough. Findings -Two multi module function -Ackley and bump function -are selected for examination the third approach. Results of MOGA, GA and SRGA are presented which show SRGA approach can find the nearest point in much shorter time with acceptable accuracy. Originality/value -GA, MOGA and SRGA approaches are applied to multidisciplinary design optimization of a reentry vehicle configuration and results show the efficiency of SRGA in complex design optimization problem.

No abstract

An intense simplification in production, storage and handling of hydrogen peroxide develop a renewed interest in hydrogen peroxide thrusters especially for low cost attitude control or orbit correction (orbit maintenance). Chemical decomposition, aerothermodynamics flow and structure demand different optimum conditions such as tank pressure, catalyst bed pressure, concentration of H 2 O 2 and geometry. These parameters play important role in propulsion system's mass and performance. Discipline conflicts are solved by Multidisciplinary Design Optimization (MDO) techniques with synchronized optimization for all subsystems respect to any criteria and limitations. In this paper, monopropellant propulsion system design optimization algorithm is presented and result of the design algorithm is validated. Results of the design algorithm have been compared with data of two different operational thrusters. According to the results, the proposed model can suitably predict total mass and performance with errors below than 10%. Then, MDO framework is proposed for the monopropellant propulsion system. Optimum propellant mass, thrust level, mass flow rate, nozzle geometry, catalyst bed length and diameter, propellant tank mass, feeding subsystem mass and total mass are derived using hybrid optimization (GA+SQP) for two space missions.

No abstract

Monopropellant propulsion systems are widely used especially for low cost attitude control or orbit correction (orbit maintenance). To optimize the total propulsion system, subsystems should be optimized. Chemical decomposition, aerothermodynamics, and structure disciplines demand different optimum condition such as tank pressure, catalyst bed length and diameter, catalyst bed pressure, and nozzle geometry. Subsystem conflicts can be solved by multidisciplinary design optimization (MDO) technique with simultaneous optimization of all subsystems with respect to any criteria and limitations. In this paper, monopropellant propulsion system design algorithm is presented and the results of the proposed algorithm are validated. Then, multidisciplinary design optimization of hydrazine propulsion system is proposed. The goal of optimization can be selected as minimizing the total mass (including propellant), minimizing the propellant mass (maximizing the Isp), or minimizing the dry mass. Minimum total mass, minimum propellant mass, and minimum dry mass are derived using MDO technique. It is shown that minimum total mass, minimum dry mass, and minimum propellant mass take place in different conditions. The optimum parameters include bed-loading, inlet pressure, mass flow, nozzle geometry, catalyst bed length and diameter, propellant tank mass, specific impulse (Isp), and feeding mass which are derived using genetic algorithm (GA).