Optimization Design And Numerical Simulation On Cased Telescoped Ammunition Gun

The CTA is a new kind of ammunition with special charge structure. As the projectile is embedded in the cartridge case, the body is of cylinder or other regular shapes. This special charge structure enables the gun system of CTA to reload bigger caliber weapons without changing original turret size, thus improving the power and survivability. Thanks to the advantages of improving cannon power and compacting cannon structure, the CTA weapon system has been developed rapidly.In this paper, the launching process of35mm CTA weapon system was used as the engineering background. Both a zero-dimensional interior ballistic model and a one-dimensional two-phase flow model of CTA were established according to the classical and modern interior ballistic theories, combined with the special structure of ammunition and the concept of secondary ignition-serial combustion. The variation of mean values of combustion gas with time and the distribution characteristics of some physical parameters in the chamber were simulated numerically. Moreover, the effect of multi-parameter on the interior ballistic performance was analyzed. And the numerical stimulation results agree well with the experiment results.On this basis, an optimization design model of interior ballistics for CTA gun was established according to the requirements of interior ballistic performances and the design objectives of gun system. The pattern search method, genetic algorithm and stimulated annealing algorithm were used to optimize the interior performance, respectively. Results show that these three methods have a good consistency and indicate that they are all effective for the optimal design of CTA gun interior ballistic in theory. All the three optimal results and the capabilities of the three methods were compared. It is show that the pattern search method has the highest efficiency in optimizing, the genetic algorithm for the second, the stimulated annealing algorithm in the last place. However, the genetic algorithm and the stimulated annealing algorithm can converge to the globally optimal solution more effectively. Especially when dealing with some optimal problems with constraints, the stimulated annealing algorithm has better convergence. Therefore, the conclusions above would have certain guiding significance for the selection of optimal method.