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Artillery structural dynamic responses uncertain optimization based on robust Nash game method
Fengjie Xu,Guolai Yang,Liqun Wang 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.9
To coordinate the contradiction between artillery launching performance indexes under parameter uncertainty, this paper proposes an artillery structural dynamic responses optimization method based on robust Nash game theory. First, a multi-flexible body dynamic model for a 155 mm caliber artillery is established, which coupling the interior ballistic model, recoil force model, and balance mechanism model. Secondly, the live firing experiment is carried out to verify the accuracy of the established multi-flexible model. Then the muzzle vibration and maximum chamber pressure are selected as the players in the game. Because these two indexes can represent the most critical contradictory indexes of artillery, namely the firing accuracy and power. Afterward, to reduce the computational time, the BP neural network surrogate model is constructed to replace the original multi-flexible body dynamic model. Finally, the double-loop approach is adopted to search for the robust Nash equilibrium. The inner loop optimization is used to determine the worst-case scenario caused by the parameter uncertainty. The outer loop optimization is referred to as the robust Nash equilibrium solution process. The results show that the artillery structural dynamics responses have been significantly improved.
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Yuwei Yao,Liqun Wang,Guolai Yang,Fengjie Xu,Lei Li 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.3
The interval perturbation method is an effective and successful tool in the uncertainty analysis; however, it suffers from the deficiency in the required differential information, which limits its application in complex engineering problems. To end this, this paper uses the radial basis neural network to formulate the derivative information, and its fine accuracy is demonstrated by a mathematical example. Moreover, a new interval analysis method combining interval perturbation and radial basis neural network differentiation, abbreviated as RBNNIPM is proposed. Furthermore, RBNNIPM is applied to calculate the boundaries of yield stress in a three-bar truss, and the detailed assessment proves that RBNNIPM has both high efficiency and high precision. Finally, an electromagnetic buffer model is established to certificate the practicability of RBNNIPM in practical engineering.
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