A New Method for Optimizing the Jet-Cleaning Performance of Self-Cleaning Screen Filters: The 3D CFD-ANN-GA Framework DOI Open Access
Zheng Qin, Zhen Chen, Rui Chen

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1194 - 1194

Published: April 15, 2025

The jet-type self-cleaning screen filter integrates industrial jet-cleaning technology into the process of filters in drip irrigation system, which has advantages low water consumption, high cleaning capacity, and wide applicability compared to traditional filters. However, its commercialization faces challenges as optimal jet mode optimization method have not been determined. This study proposes a framework that combines computational fluid dynamics (CFD), artificial neural networks (ANN), genetic algorithms (GA) for optimizing parameters improve performance. results show that, among main influencing nozzle, incident section diameter d V-groove half angle β most significant effects on peak wall shear stress, action area, consumption cleaning. ANN higher accuracy predicting performance (R2 = 0.9991, MAE 9.477), it can effectively replace CFD model parameters. resulted 1.34% reduction 16.82% 7.6% increase area base model. combining CFD, ANN, GA provide an parameter scheme

Language: Английский

A New Method for Optimizing the Jet-Cleaning Performance of Self-Cleaning Screen Filters: The 3D CFD-ANN-GA Framework DOI Open Access
Zheng Qin, Zhen Chen, Rui Chen

et al.

Processes, Journal Year: 2025, Volume and Issue: 13(4), P. 1194 - 1194

Published: April 15, 2025

The jet-type self-cleaning screen filter integrates industrial jet-cleaning technology into the process of filters in drip irrigation system, which has advantages low water consumption, high cleaning capacity, and wide applicability compared to traditional filters. However, its commercialization faces challenges as optimal jet mode optimization method have not been determined. This study proposes a framework that combines computational fluid dynamics (CFD), artificial neural networks (ANN), genetic algorithms (GA) for optimizing parameters improve performance. results show that, among main influencing nozzle, incident section diameter d V-groove half angle β most significant effects on peak wall shear stress, action area, consumption cleaning. ANN higher accuracy predicting performance (R2 = 0.9991, MAE 9.477), it can effectively replace CFD model parameters. resulted 1.34% reduction 16.82% 7.6% increase area base model. combining CFD, ANN, GA provide an parameter scheme

Language: Английский

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