Improvement of interior ballistic performance utilizing particle swarm optimization
✍ Scribed by Hazem El Sadek, Xiaobing Zhang, Mahmoud Rashad, Cheng Cheng.
- Tongue
- English
- Leaves
- 11
- Category
- Library
No coin nor oath required. For personal study only.
✦ Synopsis
Research Article. Mathematical Problems in Engineering. Hindawi Publishing Corporation. Volume 2014, Article ID 156103, 10 pages. http://dx.doi.org/10.1155/2014/156103.
This paper investigates the interior ballistic propelling charge design using the optimization methods to select the optimum charge design and to improve the interior ballistic performance. The propelling charge consists of a mixture propellant of seven-perforated granular propellant and one-hole tubular propellant. The genetic algorithms and some other evolutionary algorithms have complex evolution operators such as crossover, mutation, encoding, and decoding. These evolution operators have a bad performance represented in convergence speed and accuracy of the solution. Hence, the particle swarm optimization technique is developed. It is carried out in conjunction with interior ballistic lumped-parameter model with the mixture propellant. This technique is applied to both single-objective and multiobjective problems. In the single-objective problem, the optimization results are compared with genetic algorithm and the experimental results. The particle swarm optimization introduces a better performance of solution quality and convergence speed. In the multiobjective problem, the feasible region provides a set of available choices to the charge’s designer. Hence, a linear analysis method is adopted to give an appropriate set of the weight coefficients for the objective functions. The results of particle swarm optimization improved the interior ballistic performance and provided a modern direction for interior ballistic propelling charge design of guided projectile.Content:Introduction.
Problem formulation.
Formulation of particle swarm optimization algorithm.
Application of PSO algorithm to interior ballistic model.
Conclusion.
Acknowledgments.
References.
✦ Subjects
Военные дисциплины;Баллистика и динамика выстрела;Внутренняя баллистика
📜 SIMILAR VOLUMES
Particles, information link, memory, and cooperation are discussed in this introduction to particle swarm optimization. Starting with a simple but efficient parametric version, this manual shows how to adapt the basic principles for an enhanced, fully adaptive version. All source programs are either
Издательство InTech, 2007, -548 pp.<div class="bb-sep"></div>In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has
Technical report ARL-TR-6709, Weapons and Materials Research Directorate, ARL, November 2013. — 46 p.<div class="bb-sep"></div>The equilibrium thermodynamic properties of propellants are routinely calculated by standard computer codes such as NASA-Lewis, BAGHEERA, or BLAKE. This thermodynamic inform