Optimization of the reefed parachute using genetic algorithm
Abstract
Purpose
The purpose of this paper is to find optimal reef parameters to minimize the maximum instantaneous opening load for a reefed parachute with geometry and environmental parameters given in the model.
Design/methodology/approach
The dynamic model Drop Test Vehicle Simulation (DTVSim) is used to model the inflation and descent of the reefed parachute system. It is solved by the fourth-order Runge–Kutta method, and the opening load values are thereby obtained. A parallel genetic algorithm (GA) code is developed to optimize the reefed parachute. A penalty scheme is used to have the maximum dynamic pressure restricted within a certain range.
Findings
The simulation results from DTVSim fit well with experimental data from drop tests, showing that the simulator has high accuracy. The one-stage and two-stage reefed parachute systems are optimized by GA and their maximum opening loads are decreased by 43 and 25 per cent, respectively. With the optimal reef parameters, two of the peaks in the opening load curve are almost equal. The velocity, loiter time and flight path angle of the parachute system all change, but these changes have no negative effect on the parachute’s operational performance.
Originality/value
An optimization method for reefed parachute design is proposed for the first time. This methodology can be used in the preliminary design phase for a reefed parachute system and significantly improve design efficiency.
Keywords
Acknowledgements
This work was co-supported by the National Natural Science Foundation of China (No. 11272345), Funding of Jiangsu Innovation Program for Graduate Education (KYLX16_0402) and the Fundamental Research Funds for the Central Universities. They are gratefully acknowledged by the author.
Citation
Yang, X., Yu, L. and Zhao, X.-S. (2017), "Optimization of the reefed parachute using genetic algorithm", Engineering Computations, Vol. 34 No. 6, pp. 1923-1938. https://doi.org/10.1108/EC-05-2016-0163
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited