Performance Prediction and Structural Parameter Optimization of Control Rod Hydraulic Buffer Based on GA-BP Neural Network

Zhang Xiangwen; Fan Chenguang; He An; Wu Chuang; Yang Yujing

doi:10.13832/j.jnpe.2023.06.0162

Volume 44 Issue 6

Dec. 2023

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Zhang Xiangwen, Fan Chenguang, He An, Wu Chuang, Yang Yujing. Performance Prediction and Structural Parameter Optimization of Control Rod Hydraulic Buffer Based on GA-BP Neural Network[J]. Nuclear Power Engineering, 2023, 44(6): 162-169. doi: 10.13832/j.jnpe.2023.06.0162

Citation:

Zhang Xiangwen, Fan Chenguang, He An, Wu Chuang, Yang Yujing. Performance Prediction and Structural Parameter Optimization of Control Rod Hydraulic Buffer Based on GA-BP Neural Network[J]. Nuclear Power Engineering, 2023, 44(6): 162-169. doi: 10.13832/j.jnpe.2023.06.0162

Citation:

Zhang Xiangwen, Fan Chenguang, He An, Wu Chuang, Yang Yujing. Performance Prediction and Structural Parameter Optimization of Control Rod Hydraulic Buffer Based on GA-BP Neural Network[J]. Nuclear Power Engineering, 2023, 44(6): 162-169. doi: 10.13832/j.jnpe.2023.06.0162

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Performance Prediction and Structural Parameter Optimization of Control Rod Hydraulic Buffer Based on GA-BP Neural Network

doi: 10.13832/j.jnpe.2023.06.0162

1.
School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu, 610031, China
2.
Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province, Chengdu, 610031, China

Received Date: 2023-03-05
Rev Recd Date: 2023-08-12
Publish Date: 2023-12-15

Abstract

Abstract

In order to predict the buffer performance of hydraulic buffers of control rod assemblies by back-propagation (BP) neural network model improved by genetic algorithm (GA) and to realize the optimization of structural parameters. In this study, we simulated the falling rod in hydrostatic water for a specific control rod assembly hydrodynamic buffer. By changing the adjustable parameters of the test and setting up different test conditions, a large number of test data were obtained. The maximum impact force of control rod assembly in the process of rod falling was predicted by GA-BP neural network, and an optimized mathematical model was constructed. The nonlinear programming function (fmincon) is used to solve the problem, and a more optimal combination of structural parameters is obtained. The results show that the GA-BP neural network model has higher prediction accuracy compared with the BP neural network mdoel, and the fmincon function can realize fast solution of the optimal mathematical model of the maximum impact force of the control rod assembly. Therefore, the optimization method in this paper can provide some reference for the structural optimization design of hydraulic buffers.
- Control rod hydraulic buffer,
- Buffer performance test,
- Maximum impact force,
- GA-BP neural network,
- Parameter optimization

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References(16)

References

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