Layout design optimization of a space propulsion system using hybrid optimization algorithm

Abstract

The layout design problem of a propulsion system is complex and time-consuming process. This is mainly due to geometrical and performance constraints and system requirements. In addition, layout design optimization of a space propulsion system is non-linear, non-convex, and multimodal, which makes it difficult to implement conventional optimization methods to this class of design problems. This paper presents a hybrid optimization algorithm using genetic algorithm and sequential quadratic programming for optimal layout design of a space propulsion system. Previous research works mainly focused on the layout design components with constant parts. However, the approach adopted in this paper involves both variable mass component and hybrid optimization algorithm (GA-SQP) of a space propulsion system. The proposed hybrid optimization algorithm explores globally the design search space to locate the most promising region using genetic algorithm, whereas gradient-based sequential quadratic programming algorithm is used to reduce the computational time with a high degree of accuracy. The results obtained show that the proposed method provides an effective way of solving layout design optimization problem using both variable mass components method and a hybrid optimization for optimal layout design of a space propulsion system.

Design optimization of a thrust chamber using a mass-based model to improve the geometrical and performance parameters of low-thrust space propulsion systems

“…25 Optimization-based techniques are used widely in all aspects of engineering, especially aerospace design. 4,5,9,26–28…”

“…25 Optimization-based techniques are used widely in all aspects of engineering, especially aerospace design. 4,5,9,[26][27][28] Population based, nongradient, stochastic direct search optimization methods are attractive choices for such problems because they are easy to implement and effective for highly nonlinear problems. GAs are effective tools for exploration of large-scale, nonlinear design spaces that are used for the design optimization of combustors and, when combined with gradientbased search techniques, can provide more computationally efficient means of identifying near optimal designs.…”

A dual-system cooperative co-evolutionary algorithm for satellite equipment layout optimization

“…Liu et al 21 adopted an improved Monte Carlo method, called Wang-Landau sample method, and local search based on gradient to solve the SELOP. Shafaee et al 22 proposed a hybrid search strategy, which mixed GA and sequential quadratic programming for the spacecraft propellant system layout optimization problem. Fakoor and Taghinez 23 had proposed a hybrid optimization method, which mixed simulated annealing optimization and quasi-Newton methods for the satellite layout optimization problem.…”

“…The evaluation index of computational performance (computational accuracy or computational accuracy and robustness) is usually employed to evaluate an algorithm, taking no account of computational efficiency. [4][5][6][14][15][16][20][21][22] It's worth noting that the verification of the algorithms for SELOP developed for engineering application is not perfect. Besides the physical experiment, how to develop and improve other ways of the verification is worthy of further discussion.…”

“…As stated above evaluation criteria, taking no account of computational efficiency. In traditional literatures about SELOP, [4][5][6][14][15][16][20][21][22] computational accuracy, or assisted by standard deviation was usually used as evaluation indices of algorithms. The experimental results of case 1 show that the proposed NDCCDE/ DPSO obtains the best computational accuracy of I and robustness (computational success ratio and standard deviation), compared with other six algorithms (including four dual-system algorithms and two single-system algorithms based upon Potter's CC).…”