Probabilistic Analysis of Gun Barrel Ablation Life Based on the Modified Response Surface Model

2014 ◽  
Vol 1004-1005 ◽  
pp. 1076-1083 ◽  
Author(s):  
Wei Zhou ◽  
Jun Fang
Keyword(s):  
Response Surface ◽  
Estimation Error ◽  
Optimal Solution ◽  
Solution Space ◽  
Response Surface Model ◽  
Parameters Estimation ◽  
Surface Model ◽  
Gun Barrel ◽  
Stochastic Fem ◽  
Low Efficiency

The randomness of structural and material parameters needs to be considered in the reliability analysis of gun barrel ablation life. However, the traditional method, like Stochastic FEM sampling, results in huge computing workload and low efficiency. This paper proposed a modified response surface model for estimating the gun barrel ablation life. In which, the estimation error of the response surface model is the optimization goal. Gauss-Newton method (GNM) is used to get the optimal solution whose initial value is solved by Genetic-algorithm (GA). After that, ablation life can be calculated by the optimized response surface model. GA is effective in global solution space searching, while GNM is effective in local searching. The new method takes full advantages of both GA and GNM in parameters estimation. The simulation result shows that the combination of GA and GNM obtains a higher precision of ablation estimation and greatly improves the computational efficiency.

Response surface model and genetic algorithm-based multi-objective optimization of stator structures of hollow-type traveling wave ultrasonic motors

2016 ◽  
Vol 231 (12) ◽  
pp. 2187-2199 ◽  
Author(s):  
Niu Zijie ◽  
Sun Zhijun ◽  
Zhu Hua ◽  
Zhang Jun
Keyword(s):  
Genetic Algorithm ◽  
Response Surface ◽  
Traveling Wave ◽  
Optimal Solution ◽  
Response Surface Model ◽  
Adaptive Genetic Algorithm ◽  
Surface Model ◽  
Hollow Structures ◽  
Ultrasonic Motors ◽  
Before And After

The stators of hollow-type traveling wave ultrasonic motors have certain problems stemming from their complex and hollow structures, significant differences between the two orthogonal modal frequencies, incomplete separation of the design model and interferential model, low-vibration amplitude, and significant localized inner stress during vibration, etc. In this paper, a dimensional parameterized finite elemental model for the motor was established by utilizing the finite elemental method. Afterwards, modal assurance criteria were used to identify the vibration models with various objectives for optimization established from this and integrating multiple objectives for optimization into a single optimization objective. Then a response surface model was established in the design space the Latin-hypercube random sampling method. Finally, a globally optimal solution was obtained according to the self-adaptive genetic algorithm and the response surface model. In order to prove the reasonableness of the optimized result, the stators are processed according to the sizes determined before and after the optimization. This paper describes the vibration of stators tested by a Doppler vibration tester. The Z-direction amplitude of the optimized stator changed from 1.0 µm to 2.5 µm. According to the testing results, the structural optimization plan used in this paper is reasonable and obviously helpful for vibration optimization of the stator.

Optimization Design of Pressure Shell in Underwater Vehicle Based on Response Surface Model

2009 ◽  
Vol 419-420 ◽  
pp. 89-92
Author(s):  
Zhuo Yi Yang ◽  
Yong Jie Pang ◽  
Zai Bai Qin
Keyword(s):  
Finite Element ◽  
Response Surface ◽  
Optimization Design ◽  
Engineering Application ◽  
Element Model ◽  
Response Surface Model ◽  
Design Stage ◽  
Surface Model ◽  
Design Variables ◽  
Structure Strength

Cylinder shell stiffened by rings is used commonly in submersibles, and structure strength should be verified in the initial design stage considering the thickness of the shell, the number of rings, the shape of ring section and so on. Based on the statistical techniques, a strategy for optimization design of pressure hull is proposed in this paper. Its central idea is that: firstly the design variables are chosen by referring criterion for structure strength, then the samples for analysis are created in the design space; secondly finite element models corresponding to the samples are built and analyzed; thirdly the approximations of these analysis are constructed using these samples and responses obtained by finite element model; finally optimization design result is obtained using response surface model. The result shows that this method that can improve the efficiency and achieve optimal intention has valuable reference information for engineering application.

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