An integrated framework for solid rocket motor grain design optimization. (June 2014)

• Record Type: Journal Article
• Authors: Dong-Hui, Wang; Yang, Fei; Fan, Hu; Wei-Hua, Zhang
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 228:Number 7(2014:Jul.)
• Page Start: 1156
• Page End: 1170
• Publication Date: 2014-06
• Subjects: Grain design -- solid rocket motor -- hybrid optimization -- level set; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410013486589
• Languages: English
• ISSNs: 0954-4100

Abstract

Traditional grain designs, which identify the best combination of geometrical parameters to improve the grain performance and meet the flight-mission requirements, are often performed manually. In this article, an integrated framework is presented to perform the design optimization of solid rocket motor propellant grains. In the proposed framework, the level set method is adapted to solid propellant burnback analysis and this technique does not have any restriction on the grain configuration and is capable of handling grains of multi-stage and various burning forms. Along with the level set method, a dedicated algorithm is developed using application programming interfaces of commercial computer-aided design software to transform the initial grain shape into a special data file that can be fed to the level set codes to activate the burnback analysis. Moreover, a hybrid optimization method incorporating genetic algorithm and sequential quadratic programming is exploited to improve the grain design efficiency. Finally, two case studies have been performed to verify the feasibility and general-purpose characteristics of the proposed grain design optimization environment. The results obtained show that the proposed design framework facilitates the grain optimization process and various grain design requirements can be met. The design cycle has been remarkably reduced because of the introduction of hybrid optimization method.