Integral Elastic Centralizer based on Gray Correlation and Multi-island Genetic Algorithm Optimization of Structural Parameters Integral

Mingjun Yang; Yuansong Wei

doi:10.54691/ecxydf35

Authors

Mingjun Yang
Yuansong Wei

DOI:

https://doi.org/10.54691/ecxydf35

Keywords:

Integral Elastic Centralizer, Reset Force, Grey Correlation, Neural Network, Multi-Island Genetic Algorithm, Structure Optimization.

Abstract

Based on grey correlation and RBF neural network-multi-island genetic algorithm, combined with finite element simulation to optimize the structural parameters of the monolithic elastic righting device to improve its reset force performance. The righting device is a circumferentially symmetric structure, and the model is simplified to a single bow piece model of 1/6. The bow piece chord length, width, and thickness are selected as the key parameters, and a three-factor, four-level orthogonal experiment is conducted to analyze the influence of each parameter with the help of the gray correlation degree method. The Latin supergroup square sampling method was used to randomly sample the influence factors to obtain reasonable training samples and validation data. The chord length, width, and thickness are taken as input parameters, the reset force value is taken as output, the RBF neural network-multi-island genetic algorithm optimization model is constructed, and the grey correlation degree is used as the weight to guide the optimization direction. After optimization, the optimal parameter combinations of chord length 231 mm, width 44 mm, and thickness 4.6 mm were obtained, at which time the predicted value of reset force reached 19760.39 N. Simulation and validation results show that the relative error between simulated and predicted values is only 1.1%, and the reset force is improved by 50.76% compared with that before optimization, which fully verifies the feasibility of the optimization model.

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