Research on a prediction model for gate water seal performance based on neural networks
To address the issues of large computational workload and low efficiency in the study of gate water seal performance,a prediction model for gate water seal performance based on a neural network algorithm was established. First,a finite element simulation of the P-type water seal device was conducted,resulting in the contact width,contact stress,and seal head displacement under 106 working conditions. Next,a prediction model was built based on the BP neural network algorithm,with input features being preload and reservoir water pressure,and output features being contact stress,contact width,and seal head displacement. Finally,the prediction model was trained using water seal simulation examples from 20 working conditions,and the trained model was then used to predict the simulation results for the remaining working conditions. The model's prediction results were satisfactory,with average prediction errors of 0.48％,0.63％ and 0.71％ for contact width,contact stress,and seal head displacement,respectively. The results indicate that the gate water seal performance prediction model based on neural networks can achieve good prediction results with only a small number of training samples,significantly reducing the workload in the study of gate water seal performance.
water sealBP neural networkprediction modelfinite element analysis
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