Research on Flexible Multi-body Dynamics Structure Optimization of Artilleries

doi:10.3969/j.issn.1000-1093.2017.01.004

Abstract

Abstract: In allusion to the problem of that the existing structural dynamics optimization methods cannot optimize the flexible parts in flexible multi-body dynamic systems, a method combining multidisciplinary agent models and the improved nondominated sorting genetic algorithm (NSGA-II) is proposed. Based on an experimentally authenticated rigid-flexible coupled multibody model, a surrogate model with good generalization ability and forecasting accuracy is established with RBF-BP neural network. In the proposed model, the muzzle vibration parameters are used as outputs, and the modal parameters of flexible part and some general structural parameters are taken as inputs. Nondominated sorting genetic algorithm is used to improve the muzzle vibration characteristics, and the max-min criterion is adopted to select a solution from the Pareto front. The optimized effect is compared with the optimized result of the original model. The result shows that the proposed method can be used for optimization of artillery flexible multi-body dynamics structure. Key

Key words: ordnancescienceandtechnology, flexiblemulti-bodyoptimization, muzzlevibration, RBF-BPneuralnetwork, surrogatemodel, geneticalgorithm

CLC Number:

TJ302

XIAO Hui, YANG Guo-lai, SUN Quan-zhao. Research on Flexible Multi-body Dynamics Structure Optimization of Artilleries[J]. Acta Armamentarii, 2017, 38(1): 27-34.

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第38卷
第1期2017 年1月兵工学报ACTA
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