## 4 Conclusions  
This study presents an integrated computational framework combining 3D computational fluid dynamics (CFD), artificial neural networks (ANNs), and genetic algorithms (GAs) to optimize jet-cleaning performance. The main conclusions are as follows:  
1. Among the main influencing parameters of the nozzle, the incident cross-section diameter d and V-groove half-angle \(\beta\) have the greatest influence on the peak wall shear stress, action area, and cleaning water consumption, with a total contribution rate of about 97.00%, 98.43%, and 98.01, which should be treated as key influencing parameters in the optimization.
2. The CFD numerical simulation model is verified to have an error of 5% or less, which meets the accuracy requirements and can be used to obtain the basic data for prediction and optimization instead of physical tests.
3. Among the nine commonly used AI algorithms tested, the artificial neural network ANN has the best prediction performance (\(R^{2}=0.9991\), \(MAE=9.477\)). The prediction model results were within 3% error from the CFD simulation results, which has  
Figure 15: Performance comparison of the optimization and the initial model.  
high prediction accuracy and can replace the CFD numerical simulation model for predicting the jet-cleaning performance over the full parameter range.
* The optimization resulted in a 1.34% reduction in the peak wall shear stress, a 16.82% reduction in cleaning water consumption, and a 7.6% increase in the action area for the optimal model compared to the base model. It is investigated that the genetic algorithm GA optimization framework based on the artificial neural network ANN prediction model can achieve the prediction of jet cleaning performance.  
The CFD-ANN-GA optimization framework has high accuracy and generalization capability and saves time in performance optimization. In the process of the subsequent improvement and upgrading of jet self-cleaning screen filters, the optimization framework proposed in this paper can be applied to consider the structural optimization of multiple nozzles in order to broaden the application scope of this optimization framework.  
Z.Q.: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing--original draft. Z.C.: Conceptualization, Investigation, Methodology, Writing--original draft. R.C.: Conceptualization, Project administration, Resources, Supervision, Validation, Writing--review and editing. J.Z.: Conceptualization, Funding acquisition, Investigation, Methodology, Writing--review and editing. N.L.: Investigation, Writing--review and editing. M.L.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Xinjiang Province Science and Technology Development Plan Projects, grant number 2024 LQ04003; the Agricultural GG Project of the Xinjiang Production and Construction Corps (NYHXGG), grant number 2023 AA30502; the Corps Technology Achievement Transformation Project, grant number 2023 BA003; and the National Key Research and Development Program, grant number 2022 YFD1900405.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.