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

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Particuology 2025年第1期96卷 57-70页

作者： Yifan Wang Tianyi Zhang Lei Chen Wenquan Tao Key Laboratory of Thermo-Fluid Science and Engineering MOESchool of Energy and Power EngineeringXi'an Jiaotong UniversityXi'an710049China

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.

关键词： Fuel filter multiphase flow Neural network Genetic algorithm multi-objective optimization Particle

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multi-objective optimization of ultra-high performance concrete based on life-cycle assessment and machine learning methods

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Frontiers of Structural and Civil Engineering 2025年第1期19卷 143-161页

作者： Min WANG Mingfeng DU Xiaoying ZHUANG Hui LV Chong WANG Shuai ZHOU China Merchants Chongqing Communications Technology Research and Design Institute Co. Ltd.Chongqing 400067China College of Materials Science and Engineering Chongqing UniversityChongqing 400045China Department of Mathematics and Physics Leibniz Universität HannoverHannover 30167Germany Department of Geotechnical Engineering College of Civil EngineeringTongji UniversityShanghai 200092China

Ultra-high performance concrete(UHPC)has gained a lot of attention lately because of its remarkable properties,even if its high cost and high carbon emissions run counter to the current development *** lower the cost and carbon emissions of UHPC,this study develops a multi-objective optimization framework that combines the non-dominated sorting genetic algorithm and 6 different machine learning methods to handle this *** key features of UHPC are filtered using the recursive feature elimination approach,and Bayesian optimization and random grid search are employed to optimize the hyperparameters of the machine learning prediction *** optimal mix ratios of UHPC are found by applying the multi-objective algorithm non-dominated sorting genetic algorithm-Ⅲ and multiobjective evolutionary algorithm based on adaptive geometric *** results are evaluated by technique for order preference by similarity to ideal solution and validated by *** outcomes demonstrate that the compressive strength and slump flow of UHPC are correctly predicted by the machine learning *** multiobjective optimization produces Pareto fronts,which illustrate the trade-off between the mix’s compressive strength,slump flow,cost,and environmental sustainability as well as the wide variety of possible *** research contributes to the development of cost-effective and environmentally sustainable UHPC,and aids in robust,intelligent,and sustainable building practices.

关键词： ultra-high performance concrete machine learning multi-objective optimization life-cycle assessment

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multi-objective optimization of cascaded packed bed thermal energy storage unit based on response surface and factor analysis methods

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APPLIED ENERGY 2025年 386卷

作者： Chen, Chengxu Du, Xiaoze Yang, Lizhong Romagnoli, Alessandro North China Elect Power Univ Key Lab Power Stn Energy Transfer Convers & Syst Minist Educ Beijing 102206 Peoples R China Lanzhou Univ Technol Sch Energy & Power Engn Lanzhou 730050 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore Nanyang Technol Univ Energy Res Inst NTU 1 CleanTech Loop Singapore 637141 Singapore

The cascaded multi-layer packed bed thermal energy storage (TES) unit with varying fill ratios is proposed to enhance its thermal performance. A concentric dispersion model for simulating thermal fluid heat transfer is developed and experimentally validated. Based on this, four designs are explored to examine the effect of the filling ratio of phase change materials with different melting points on the thermal performance of the packed bed TES system, including that of balanced-layer, top-heavy-layer, middle-heavy-layer and bottom-heavy-layer. The multi-factor and multi-objective optimization is conducted by response surface and factor analysis methods. Differs from the previous studies that only designed several configurations with different phase change material filling ratios, the present sudy focuses on the interaction between the filling ratio and the thermal performances, as well as the optimal filling ratio of each layer to achieve the best thermal performance. The results show that the bottom-heavy-layer has the shortest charging time of 950 min and the highest energy utilization of 61.72 %, while the top-heavy-layer has the highest charging exergy efficiency of 84.7 % and the largest TES capacity of 96.88 MWh. As for the multi-objective optimization, the optimized value of comprehensive evaluation indicator F is 1.7112, and the corresponding charging time, energy utilization, TES capacity, and charging exergy efficiency is 778 min, 0.62, 99.76 MWh, and 0.83, respectively. This research establishes a foundation for the advanced optimization of phase change material filling ratios and comprehensive system-level evaluation.

关键词： Thermal energy storage Cascaded packed bed Response surface method Phase change material multi-objective optimization

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multi-objective optimization and analysis of divided-layer varying-network magnetic circuit based axial field flux-switching magnetic gear composite motors

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JOURNAL OF POWER ELECTRONICS 2025年第3期25卷 458-468页

作者： Cheng, Jinru Zhang, Wei Yin, Xin Ding, Feiyang Nantong Univ Sch Elect Engn & Automat Nantong Peoples R China

The axial field flux-switching magnetic gear composite machine (AFFSMGCM) is a new type of magnetic field modulation machine with a dual-rotor. Due to the complicated structure of the AFFSMGCM and the nonlinear characteristic of dual magnetic fields coupling, a divided-layer varying-network magnetic circuit (VNMC) method is developed to optimize the machine to obtain a high calculation accuracy and reduce operation time. First, an accurate VNMC model is established according to the magnetic field distribution of an AFFSMGCM. The magnetic field coupling of the rotor and stator permanent magnets (PMs) is performed by the rotary magnetic modulation ring (RMMR). Thus, the magnetic circuit of the RMMR is divided into two layers to reduce the influence of magnetic saturation and leakage flux on the calculation accuracy of the permeances. Then the particle swarm optimization (PSO) method is used to achieve multi-objective optimization of the AFFSMGCM based on the divided-layer VNMC for achieving a large torque, low torque ripple and high efficiency. Next, a multi-physics field coupling analysis is carried out to verify the optimized AFFSMGCM. Finally, a prototype is built and experiments are carried out to validate the AFFSMGCM.

关键词： Varying-network magnetic circuit multi-objective optimization Composite machine multi-physics field coupling analysis

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multi-objective optimization of remote sensing satellite constellation in terms of maximum revisit time and system total cost in the geography of Türkiye

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2025年第2期40卷 1087-1098页

作者： Tola, Ceyhun Mutlu, Utku Aselsan Inc Technol & Strategy Management Div TR-06200 Ankara Turkiye Istanbul Tech Univ Grad Sch Aeronaut & Astronaut Engn TR-34469 Istanbul Turkiye Istanbul Tech Univ Philosophy Program TR-34469 Istanbul Turkiye

Minimizing the maximum revisit time of satellite constellations is crucial for the rapid detection of changes, especially in disaster, defense and intelligence contexts. This study aims to design a Walker Delta satellite constellation to cover T & uuml;rkiye's geography, collecting data with short revisit times and low costs. A Matlab script was created to calculate the maximum revisit time parametrically in the Systems Tool Kit software by configuring satellite constellation configurations, and this script was integrated into the ModeFrontier software to create an optimization loop using the multi-objective Genetic Algorithm method. The optimization has four objectives: minimizing the number of satellites, the number of orbital planes, the vertical half-angle of the sensor, and the maximum revisit time. As a result of the study, it was concluded that to achieve a maximum revisit time of fewer than 120 minutes in the region where data is to be collected, there should be a minimum of 33 satellites, at least 11 orbital planes, and a minimum vertical half-angle of 15 degrees for the sensor. A satellite constellation configuration was chosen from the optimized Paretooptimal solution set, meeting cost and revisit time criteria.

关键词： Satellite constellation revisit time remote sensing orbital mechanics multi-objective optimization

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multi-objective optimization of PC-SAFT parameters for ionic liquids from density and viscosity data using entropy scaling

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FLUID PHASE EQUILIBRIA 2025年 595卷

作者： Melfi, Diego T. Scurto, Aaron M. Univ Kansas Dept Chem & Petrol Engn Lawrence KS 66045 USA Univ Kansas Ctr Environm Beneficial Catalysis Lawrence KS 66046 USA

Equations of state using the Statistical Associating Fluid Theory (SAFT EoS) have found tremendous success in the thermodynamic modeling of ionic liquids (ILs) and mixtures. Traditionally, SAFT EoS parameters are fit to pure component pressure-volume-temperature (PVT) (density) data and vapor pressure data. We have recently combined the PC-SAFT EoS with entropy scaling theory to correlate and predict the viscosity of ILs and IL mixtures. We found that the PC-SAFT EoS parameters for ionic liquids regressed to PVT data can sometimes lead to relatively large deviations in the viscosity correlations, especially at high pressure. Here, we investigate the effect of including viscosity data along with PVT data for the PC-SAFT parameter regression of two series of 1-nalkyl-3-methyl imidazolium ionic liquids ([CnMIm][Tf2N] and [CnMIm][BF4]). From analyzing the Pareto fronts, the inclusion of viscosity data to PVT data for PC-SAFT parameters resulted in only a small loss in accuracy for the density, but with much improved viscosity correlations through entropy scaling. We found that the parameter sets obtained from density and viscosity data regression are less prone to numerical pitfalls, i.e. fictitious SAFT critical points, than the parameter sets obtained from PVT data alone. In addition, the predicted (kij=0) phase equilibrium (VLE) of ionic liquids and mixtures with CO2, CH4, and water were equal to, if not better than the predictions using PVT data alone. Overall, the use of pure PVT and viscosity data in the parameterization of PCSAFT yields a more widely applicable prediction method for both thermodynamic and transport properties.

关键词： PC-SAFT Viscosity Entropy scaling Equation of state multi-objective optimization

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multi-objective optimization of cryogenic propellant zero boil-off storage: Modeling, optimization method and performance enhancement

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ENERGY 2025年 320卷

作者： Zhang, Wujie Xu, Liangze Zhang, Jiaxu Zheng, Zhaoqi Miao, Ruijiao Huang, Yonghua Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China

Cryocooler-based zero boil-off schemes are promising for long-term storage of cryogenic propellants. To date, no studies have addressed the fluid-thermal coupling between key components of the zero boil-off system, as well as the trade-offs among the cooling power, insulation performance and energy utilization efficiency. To predict and optimize the performance of a cryocooler-based zero-boil-off system, a systemic model integrating theoretical calculations and computational fluid dynamics was developed. Additionally, a multi-objective optimization method was designed based on a modified hyperplane generation approach and a self-adaptive crossover operator. The accuracy of the model was validated using the results of liquid nitrogen experiments from the literature. On the same computation platform, the proposed optimization method demonstrated superior capabilities in avoiding local optima and accelerating convergence compared with the original Non-dominated Sorting Genetic Algorithm-III: the temperature uniformity of the cold shield improved by at least 10.44 %, and the time cost was reduced by 32.25 %. In addition, conflicts were identified among the cooling power, temperature uniformity of the cold shield, and parasitic heat leakage, whereas the temperature uniformity showed a positive correlation with heat leakage through multilayer insulation. These findings provide guidelines for the multi-objective design of cryogenic propellant storage systems for future space missions.

关键词： Cryogenic propellants Variable density multilayer insulation Cold shield Zero boil-off storage technology multi-objective optimization

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multi-objective optimization control for shield cutter wear and cutting performance using LightGBM and enhanced NSGA-II

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AUTOMATION IN CONSTRUCTION 2025年 171卷

作者： Yin, Ziwei Jiao, Jianwei Xie, Ping Luo, Hanbin Wei, Linchun Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Wuhan Peoples R China Shanghai Tunnel Engn Co Ltd Shanghai Peoples R China

Varying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance cutter wear and cutting performance. To address these issues, a multi-objective optimization (MOO) framework based on the Light Gradient Boosting Machine (LightGBM) algorithm and the enhanced non-dominated sorting genetic-II (NSGA-II) algorithm is proposed to predict and optimize the cutter wear and cutting performance. To validate this framework, a shield tunneling project in China is presented. The results show that the efficiency and accuracy of predicting and optimizing the two objectives have been improved compared with other common methods. This MOO framework is valuable for operators to formulate rational operational control strategies.

关键词： Cutter wear Cutting performance multi-objective optimization The light gradient boosting machine (LightGBM) Enhanced non-dominated sorting genetic-II (NSGA-II) algorithm

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multi-objective optimization analysis of on-demand design in multi-source heating system for low carbon development

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ENERGY 2025年 324卷

作者： Wang, Weichen Sun, Jingchao Yan, Su Yuan, Yuxing Xiao, Tianshun Chen, Baoqi Du, Tao Na, Hongming Northeastern Univ SEP Key Lab Ecoind Shenyang 110819 Liaoning Peoples R China Minist Educ Engn Res Ctr Frontier Technol Low Carbon Steelmaki Shenyang 110819 Liaoning Peoples R China Inst Frontier Technol Low Carbon Steelmaking Shenyang 110819 Liaoning Peoples R China

A multi-energy complementary heating system (MEHS) is essential for the development of low-carbon building heating. However, limited research has been conducted on the optimal heat load allocation. In light of heat balancing, heat source operation, and energy resource limits, this study proposes a MEHS model based on life cycle assessment, with costs, CO2 emissions, and energy efficiency serving as the objective functions. By optimizing, the Pareto frontier solution is obtained, and the relationships between the objective and the heat load allocation ratios of heating subsystems are analyzed. Finally, the impact of the power industry's low-carbon transition and increasing carbon trading prices on the MEHS is investigated. The results show that the optimal heat load allocation ratios for the air source heat pump, natural gas-fired, biomass-fired, coal-fired, thermal storage electric boiler, and ground source heat pump heating subsystems are 44.42 %, 13.41 %, 0.71 %, 32.18 %, 0.56 %, and 8.72 %, respectively. Costs and CO2 emissions during operation account for more than 80 % of the total life cycle impact. CO2 emissions can be reduced by 16 % when the power grid CO2 emission factor is reduced to 0.2530 kg CO2/kWh, and by 4.5 % when the carbon price increases to 200 CNY/t.

关键词： multi-source heating system CO2 emission reduction Design of heat load system multi-objective optimization

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multi-objective optimization of a combined cooling, heating and power system integrated with reformed methanol high-temperature proton exchange membrane fuel cell

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 3674-3691页

作者： Zhong, Zhaoda Gao, Xin Zhu, Jimin Zhao, Wenyu Li, Na Araya, Samuel Simon Liso, Vincenzo Aalborg Univ DOE Pontoppidanstraede 111 DK-9220 Aalborg Denmark Tech Univ Carolo Wilhelmina Braunschweig Inst Internal Combust Engines & Fuel Cells IVB Braunschweig Germany Tech Univ Denmark DTU Construct Kongens Lyngby Denmark Luxembourg Inst Sci & Technol LIST Mat Res & Technol Dev Esch Belval Belvaux Sanem Luxembourg

Reformed methanol high-temperature proton exchange membrane fuel cell (RM HT-PEMFC) systems demonstrate potential for both mobile and stationary applications. However, optimizing key variables is challenging due to the complex coupling of heat flows across various temperature levels. This study develops a combined cooling, heating and power system by integrating the RM HT-PEMFC with a double-effect LiBr-H2O absorption refrigeration cycle. The proposed system is optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), targeting system exergy efficiency, specific CO2 emissions, and exergy cost per unit product. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to determine the optimal values of objectives: an exergy efficiency of 43.12%, specific CO2 emissions of 0.510 kg/kWh, and exergy cost per unit product of 167.59 USD/GJ, representing improvements of 20.73%, reduction of 17.10%, and 1.07% compared to baseline. The optimized ranges for key parameters are identified as follows: stack temperature (173.94-179.91 degrees C), steam to carbon ratio (1.78-1.80), current density (0.20-0.40 A/cm2), and cathode stoichiometry (2.29-2.52).

关键词： Absorption refrigeration cycle environmental analysis HT-PEMFC multi-objective optimization NSGA-II

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