PreprintArticleVersion 1This version is not peer-reviewed
Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis
Version 1
: Received: 3 September 2024 / Approved: 4 September 2024 / Online: 4 September 2024 (08:35:58 CEST)
How to cite:
He, X.-G.; Huang, B.; Peng, L.-K.; Chen, J. Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis. Preprints2024, 2024090345. https://doi.org/10.20944/preprints202409.0345.v1
He, X.-G.; Huang, B.; Peng, L.-K.; Chen, J. Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis. Preprints 2024, 2024090345. https://doi.org/10.20944/preprints202409.0345.v1
He, X.-G.; Huang, B.; Peng, L.-K.; Chen, J. Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis. Preprints2024, 2024090345. https://doi.org/10.20944/preprints202409.0345.v1
APA Style
He, X. G., Huang, B., Peng, L. K., & Chen, J. (2024). Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis. Preprints. https://doi.org/10.20944/preprints202409.0345.v1
Chicago/Turabian Style
He, X., Li-kun Peng and Jia Chen. 2024 "Researches on Orthogonal Experiments of Submersible Proportional Short-Circuit Blowing Model Test Bench and BP Neural Network Prediction&Pearson Correlation Analysis" Preprints. https://doi.org/10.20944/preprints202409.0345.v1
Abstract
Short-circuit blowing is an important technical means to ensure the rapid surfacing of submersible. In order to study the impact of seven multiple manipulation factors of three levels on blowing, a proportional short-circuit blowing model test bench had been built and L18(37) orthogonal experiments were carried out; then, using Back propagation neural network and Pearson correlation analysis, the experimental data were trained and predicted, correlation between individual factor and blowing was further studied as supplement of orthogonal experiments. It had been proved that both multi-factor combination and individual including blowing duration, sea tank back pressure, the gas blowing pressure of the cylinder group, and sea valve flowing area had larger influence, whose correlation coefficients were 0.6535, 0.8105, 0.5569, 0.5373, of which the F-ratio of blowing duration is over critical value 3.24. And statistical evaluation indicators of Back propagation neural network were between 10e-2 and 10e-13, relative error was less than 3%, and prediction error accuracy was 100%. Based on the results above, a reasonable prediction method for submersible short-circuit blowing had been formed and suggestions on engineering design and operations were proposed, including advantage of short-circuit blowing method, initial condition settings and manipulation strategy.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
