Multi-objective optimization of laser perforated fuel filter parameters based on artificial neural network and genetic algorithm--颗粒学报PARTICUOLOGY

Wang, Y., Zhang, T., Chen, L., & Tao, W. (2025). Multi-objective optimization of laser perforated fuel filter parameters based on artificial neural network and genetic algorithm. Particuology, 96, 57-70. https://doi.org/10.1016/j.partic.2024.10.016

Multi-objective optimization of laser perforated fuel filter parameters based on artificial neural network and genetic algorithm

Yifan Wang, Tianyi Zhang, Lei Chen^*, Wenquan Tao

Key Laboratory of Thermo-Fluid Science and Engineering, MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

10.1016/j.partic.2024.10.016

Volume 96, January 2025, Pages 57-70

Received 6 September 2024, Revised 20 October 2024, Accepted 22 October 2024, Available online 2 November 2024, Version of Record 19 November 2024.

E-mail: chenlei@mail.xjtu.edu.cn

Highlights

• Multi-objective optimization method is used for the perforated filter.

• Neural network is used to predict the performance of fuel filters.

• Pareto optimal frontier of fuel filter optimization is obtained.

• Optimal value is obtained by order preference of a similarity ideal solution.

Abstract

In precise fuel circuit systems, the filtration of particulate impurities seriously affects the efficiency and service life of various components. For filtration process intensification of high-pressure fuel laser perforated filters, the two-phase flow characteristics in filters is studied. The size, position and number of filtration holes are taken as optimization variables, and take the filtration efficiency and flow pressure drop as optimization objectives. Computational fluid dynamics (CFD) is used to simulate the two-phase motion of continuous phase and discrete particles in a periodic unit. Artificial neural networks (ANN) are utilized for objectives prediction, and the NSGA-II genetic algorithm is employed for multi-objective optimization, resulting in the Pareto front solution set. Furtherly, the reasonable solution is selected by introducing TOPSIS to ensure that two optimization indexes are relatively smaller and balanced. The optimized filter element scheme allows the filter to have a pressure drop of less than 3.2 MPa under high pressure and a filtration efficiency of over 80% for spherical particle impurities with a diameter of 5 μm or more.

Graphical abstract

Keywords

Fuel filter; Multiphase flow; Neural network; Genetic algorithm; Multi-objective optimization; Particle

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