Finocyl Grain Design Using the Genetic Algorithm in Combination with Adaptive Basis Function Construction

Abstract

This study deals with the application of optimization in Finocyl grain design with ballistic objective functions using a genetic algorithm. The classical sampling method is used for space filling; a level-set method is used for simulating the evaluation of a burning surface of the propellant grain. An algorithm is developed beside the level-set code that prepares the initial grain configuration using a computer-aided design (CAD) to export generated models to the level-set code. The lumped method is used to perform internal ballistic analysis. A meta-model is used to surrogate the level-set method in an optimization design loop. Finally, a case study is done to verify the proposed algorithm. Observed results show that the grain design method reduced the design time significantly, and this algorithm can be used in designing any grain type.

A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning

“…The first reason is the inadequate performance of the optimization technique, owing to which it cannot identify the optimal solution. Nevertheless, the genetic algorithm has successfully optimized the grain configuration design [1,7]. Even in this study, the searchability of the algorithm is sufficient because the optimal design using the settings listed in Table 3 is successful, as described in Section 4.…”

“…Depending on the mission, various area profiles, such as progressive, neutral, and regressive, can be applied. The grain design focuses on modifying the set of configuration variables to generate the desired area profile [1][2][3]. In addition to the area profile, several factors, such as manufacturability, must be considered in the design of the grain configuration.…”

“…Finocyl has a complex configuration combining various cross-sectional shapes. This configuration can generate most area profiles and is used in high-performance propulsion systems because of its high loading density [1][2][3][4][5]. However, in the design of this configuration, computer-aided design (CAD) programs or high-accuracy numerical analysis code must be performed to obtain the burning surface area.…”

Integrated Optimization of First-Stage SRM and Ascent Trajectory of Multistage Launch Vehicles

“…Once the optimal pressure or thrust laws are known, a parametric optimization of the variables describing the finocyl grain configuration can be carried out to minimize the least square error between the desired and the estimated thrust profile [26,27]. This postprocess procedure leads to slight modifications of the thrust profile, whose impact on the launcher trajectory can be assessed by re-optimizing the trajectory with an imposed thrust; however, in the authors' experience, the difference between the two trajectories is minimal.…”

A Special Mutation Operator in the Genetic Algorithm for Fixed Point Problems

“…The relationship between the genetic algorithm and the fixed points is a two-way relationship. In this sense, in some studies, fixed point properties have been used to improve the performance of genetic algorithms [13][14][15][16][17][18], and in some studies, updated models of genetic algorithms have been used to solve fixed point problems [19][20][21][22].…”