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

IF 4.1 2区 材料科学 Q2 ENGINEERING, CHEMICAL Particuology Pub Date : 2024-11-02 DOI:10.1016/j.partic.2024.10.016
Yifan Wang, Tianyi Zhang, Lei Chen, Wenquan Tao
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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.

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基于人工神经网络和遗传算法的激光穿孔燃油过滤器参数的多目标优化
在精密燃油回路系统中,颗粒杂质的过滤严重影响着各种部件的效率和使用寿命。为了强化高压燃料激光穿孔过滤器的过滤过程,研究了过滤器中的两相流动特性。以过滤孔的大小、位置和数量为优化变量,以过滤效率和流动压降为优化目标。计算流体动力学(CFD)用于模拟连续相和离散颗粒在周期性单元中的两相运动。利用人工神经网络(ANN)进行目标预测,并采用 NSGA-II 遗传算法进行多目标优化,最终得到帕累托前沿解集。此外,还通过引入 TOPSIS 来选择合理的解决方案,以确保两个优化指标相对较小且平衡。优化后的滤芯方案使过滤器在高压下的压降小于 3.2 MPa,对直径在 5 μm 以上的球形颗粒杂质的过滤效率超过 80%。
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来源期刊
Particuology
Particuology 工程技术-材料科学:综合
CiteScore
6.70
自引率
2.90%
发文量
1730
审稿时长
32 days
期刊介绍: The word ‘particuology’ was coined to parallel the discipline for the science and technology of particles. Particuology is an interdisciplinary journal that publishes frontier research articles and critical reviews on the discovery, formulation and engineering of particulate materials, processes and systems. It especially welcomes contributions utilising advanced theoretical, modelling and measurement methods to enable the discovery and creation of new particulate materials, and the manufacturing of functional particulate-based products, such as sensors. Papers are handled by Thematic Editors who oversee contributions from specific subject fields. These fields are classified into: Particle Synthesis and Modification; Particle Characterization and Measurement; Granular Systems and Bulk Solids Technology; Fluidization and Particle-Fluid Systems; Aerosols; and Applications of Particle Technology. Key topics concerning the creation and processing of particulates include: -Modelling and simulation of particle formation, collective behaviour of particles and systems for particle production over a broad spectrum of length scales -Mining of experimental data for particle synthesis and surface properties to facilitate the creation of new materials and processes -Particle design and preparation including controlled response and sensing functionalities in formation, delivery systems and biological systems, etc. -Experimental and computational methods for visualization and analysis of particulate system. These topics are broadly relevant to the production of materials, pharmaceuticals and food, and to the conversion of energy resources to fuels and protection of the environment.
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