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Development and simulation of a gridded thermionic cathode electron gun for a high-energy photon source

Nuclear Science and Techniques 2023年第3期34卷 73-81页

作者： Sheng-Chang Wang Da-Yong He Cai Meng Jing-Yi Li Zu-Sheng Zhou Jin-Dong Liu Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China

A gridded thermionic cathode electron gun was developed for the linear accelerator of the High Energy Photon Source(HEPS).An electron gun should provide a large maximum bunch charge with a wide adjustable *** satisfy these requirements,the shape of the electrode was optimized using a multi-objective genetic algorithm.A large bunch charge with an adjustable range was achieved using the grid-limited gun,the flow of which was analyzed using 3-D *** electron gun has been manufactured and tested,and the measured data of the grid-limited current and simulation results are compared and discussed in this study.

关键词： Electron gun multi-objective genetic algorithm Grid CST

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Optimization of the Structural Parameters of a Cyclone by Using a genetic algorithm

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CHEMICAL ENGINEERING & TECHNOLOGY 2022年第9期45卷 1691-1700页

作者： Song, Chen Wang, Jiarui Wang, Simin Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol 28 Xianning VVest Rd Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

A mathematical model is proposed to optimize a two-stage separation cyclone. The model combines the discrete phase model, multi-objective genetic algorithm, and response surface model. The model was validated by experimental data with an average deviation of 9.87 %. The effects of three structural parameters on the comprehensive performance of this cyclone are discussed. By applying a central composite design method, the cyclone was optimized and three optimal designs were obtained, with an average pressure drop reduction of 6.05 %, and 2.34-fold improvement in separation efficiency over the original design. The proposed model and results are of great significance for the further design of this type of cyclone.

关键词： multi-objective genetic algorithm Optimal design Structural parameters Two-stage separation Ultrafine particles

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Enhancing Steel Wheel Ventilation Efficiency Through multi-objective Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第8期49卷 11661-11682页

作者： Bogrekci, I. Demircioglu, P. Sasmaz, M. E. Unal, C. Aydin Adnan Menderes Univ Fac Engn Dept Mech Engn Aydin Turkiye Tech Univ Munich Inst Mat Sci TUM Sch Engn & Design Garching Germany Jantsa Wheel Ind Design Ctr Aydin Turkiye

This study focuses on the optimization of ventilation hole design in steel wheels used for heavy commercial vehicles. The primary objective is to reduce the weight of the wheel while ensuring compliance with radial fatigue and cornering fatigue test requirements. Four distinct ventilation types were parametrized using ANSYS Mechanical, with the von Mises stress on the disk, number of ventilations, and wheel weight serving as design parameters. Stress analysis and weight comparisons were performed between wheels featuring different ventilation types and an ellipse ventilation wheel. Incorporating the design of experiment (DoE) and response surface optimization (RSO) module in ANSYS Workbench 2022 R1 was employed to compare and evaluate the obtained values. Subsequently, the multi-objective genetic algorithm (MOGA-II) method was employed for optimization, aiming to identify the optimal design. The optimization process, utilizing a maximum of 20 iterations, a convergence stability percentage of 2%, and a maximum allowable Pareto percentage of 70%, yielded 1, 3, 3, and 3 candidate design points for round, slot, trapezoid, and halfmoon-type ventilation holes, respectively. Among the various ventilation types considered, the halfmoon-type ventilation hole exhibited the most promising results. Compared to the current design, the optimized wheel achieved a weight reduction of 0.9 kg (2.05%). This outcome demonstrates the effectiveness of the proposed methodology. Although lighter designs were not attainable while maintaining the same stress values for the other three ventilation types, the halfmoon-type ventilation hole was ultimately selected as the preferred design.

关键词： Design of experiment multi-objective genetic algorithm Finite element method Radial fatigue test Response surface optimization

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A data-driven multi-objective optimization approach for enhanced methanol yield and exergy loss minimization in direct hydrogenation of CO2

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APPLIED THERMAL ENGINEERING 2024年 251卷

作者： Samad, Abdul Saghir, Husnain Musawwir, Abdul Ahmad, Iftikhar Caliskan, Hakan Natl Univ Sci & Technol Sch Chem & Mat Engn Islamabad 44000 Pakistan Friedrich Alexander Univ Erlangen Nurnberg Dept Chem & Biol Engn Erlangen Germany Usak Univ Fac Engn & Nat Sci Dept Mech Engn TR-64200 Usak Turkiye

The stability and control of process industries have historically faced obstacles due to inherent uncertainties in their operations. This work focuses on leveraging machine learning models as a surrogate to facilitate real-time optimization of operating conditions of the direct hydrogenation of CO 2 to enhance methanol production while minimizing exergy losses. Numerous studies have explored steady-state exergy analysis and the impact of various process conditions on methanol production. However, there is a notable absence of research investigating the influence of exergy destruction on methanol production under dynamic conditions. Initially, a commercial software Aspen HYSYS was utilized to design, model, and simulate the methanol synthesis plant. Exergy analysis was conducted to quantify the process exergy losses, exergy efficiency, and potential areas for improvement. The process model transitioned to a dynamic mode by incorporating +/- 5% uncertainty into critical operating conditions, i.e. , temperature, pressure, and molar flow rate, simulating realworld variability and resulting in a dataset of 370 samples. Two machine learning models;the Gaussian process regression (GPR) and artificial neural network (ANN) model were developed using the data samples to predict the process exergy losses and molar flow rate of methanol produced. To enhance the predictive capabilities of these deployed models, Bayesian optimization was employed for hyperparameter tuning. The developed models were employed as surrogates in multi -objective genetic algorithm (MOGA) environments with the aim of maximizing the production of methanol with minimum exergy losses under uncertainty. The optimized process conditions, derived from MOGA-based methods, underwent cross -validation using the Aspen model. This analysis revealed that the methanol synthesis plant exhibited an exergy loss of 2425 kW, an exergy efficiency of 97.78%, and an improvement potential of 53.74 kW. The GPR and ANN models

关键词： multi-objective genetic algorithm Exergy losses Carbon capture and conversion Energy recovery Machine learning Renewable methanol

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Modelling and application of joint maintenance grouping and workload smoothing for an automotive plant

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第8期62卷 2832-2852页

作者： Truong, Minh-Tuan Vu, Hai-Canh Do, Phuc Iung, Benoit Voisin, Alexandre Univ Lorraine CRAN UMR 7039 CNRS Vandoeuvre Les Nancy France Roberval Univ Technol Compiegne Compiegne France

In the maintenance optimisation framework, grouping maintenance is a promising solution for maintenance planning of multi-component systems, in which maintenance activities are performed together to reduce maintenance costs. One of the most widely identified challenges in real applications of grouping maintenance is that it may disturb the maintenance workload balance (smoothness), causing many difficulties in production and/or labour scheduling and inventory management. In this study, we propose a joint optimisation approach for maintenance grouping and workload balancing to address the above challenge. First, a mathematical model of the joint optimisation problem was derived. A multi-objective grouping optimisation approach based on the Weighted Sum model and genetic algorithm was implemented to determine the Pareto-optimal grouping solution. The proposed approach was applied to a real case study of an automotive plant comprising 40 production lines with 1090 components. The results highlighted the advantages, effectiveness, and flexibility of the proposed maintenance approach in real-world applications.

关键词： Maintenance grouping workload smoothing automotive maintenance multi-objective genetic algorithm multi-component system

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A multi-objective optimization approach for green supply chain network design for the sea cucumber ( Apostichopus japonicus ) industry

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 927卷 172050页

作者： Ren, Anqi Yu, Lixingbo Zhao, Xintao Jia, Fei Han, Fengfan Hou, Haochen Liu, Ying Dalian Ocean Univ Minist Educ Key Lab Environm Controlled Aquaculture 52 Heishijiao St Dalian 116023 Peoples R China Dalian Ocean Univ Coll Marine Technol & Environm 52 Heishijiao St Dalian 116023 Peoples R China Dalian Bangchui Isl Sea Cucumber Dev Co Ltd 987 Wuyi Rd Dalian 116100 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Peoples R China

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber ( Apostichopus japonicus ) is an aquatic product of high economic value;however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016 -world method were used to assess each aquaculture model ' s global warming potential (GWP), as the environmental performance indicator. In addition, multi -objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating highenergy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in C

关键词： Life cycle assessment Green supply chain Network design multi-objective genetic algorithm M-TOPSIS

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Optimization design of trapezoidal flow field proton exchange membrane fuel cell combined with computational fluid dynamics, surrogate model, and multi-objective optimization algorithm

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IONICS 2024年第6期30卷 3375-3389页

作者： Wang, Changjiang Yu, Zeting Wu, Haonan Wang, Daohan Shandong Univ Sch Energy & Power Engn Jinan 250061 Peoples R China Shandong Univ Sch Elect Engn Jinan 250061 Peoples R China

The flow field structure plays the key roles in the operating reliability and power output of proton exchange membrane fuel cell (PEMFC). This study investigates and compares two typical structural parameters of PEMFC with the trapezoidal flow channel (TFC) and the trapezoidal flow channel with block (TFCB). A three-dimensional (3-D) multiphase TFC model is first developed, and then the multi-objective optimization is performed by using the trained artificial neural network (ANN) surrogate model and non-dominated sorting genetic algorithm (NSGA-II). Finally, the technique for order preference by similarity to an ideal solution (TOPSIS) is used to investigate the optimized structural parameters of TFC. The results show the net power output and the oxygen uniformity index of the optimized TFC are increased by 19.77% and 21.92% compared with the straight flow channel (SFC). Furthermore, it is also found using block in the trapezoidal flow channel (TFCB) can increase the performance of PEMFC, and it exhibits a 25.10% improvement for the net power output and 27.88% for oxygen uniformity index, respectively.

关键词： Proton exchange membrane fuel cell Trapezoidal flow field optimization Block Computational fluid dynamics Artificial neural network multi-objective genetic algorithm

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Simulation study of high-quality electron beam injector for external injection of laser plasma wakefield acceleration

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第7期57卷

作者： Liu, Lanxin Zhong, Jianhua Guan, Jiabao Dai, Zeyi Xia, Guoxing Wang, Jike Nie, Yuancun Wuhan Univ Inst Adv Studies Wuhan 430072 Peoples R China Univ Manchester Dept Phys & Astron Manchester England

The laser wakefield acceleration (LWFA) with external injection requires high-quality, ultra-short electron bunches, which can be produced by using a photocathode injector based on the room-temperature RF electron gun. In this paper, the physics design of such a photocathode injector system is discussed for two operating modes: low charge (10 pC) and high charge (50 pC). The front end of the injector was optimized by combining the beam dynamics simulation software ASTRA with the multi-objective genetic algorithm NSGA-II. The downstream section was simulated using CSRtrack and ASTRA to optimize the magnetic chicane and match the Twiss parameters. A slit collimator was inserted in the middle of the chicane to filter part of the electrons, and hence shorten the bunch length and reduce the relative energy spread. The obtained beam parameters meet the requirements for an external injection of the LWFA, where the electrons can be further accelerated from 100 MeV to 1.5 GeV. Such a photocathode injector combined with the LWFA has the potential to be applied in the accumulation injection of a 1.5 GeV storage ring at Wuhan Advanced Light Source.

关键词： Laser wakefield acceleration External injection Photocathode injector Beam dynamics multi-objective genetic algorithm

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Simulation Optimization-Based model for Decision-Making in the stroke clinical pathway

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Boareto, Pedro A. Moretti, Leonardo N. Safanelli, Juliana Liberato, Rafaela B. Moro, Carla H. C. Pecora Junior, Jose E. Moro, Claudia M. C. B. Coelho, Leandro dos S. Loures, Eduardo F. R. Deschamps, Fernando Santos, Eduardo A. Portela Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program PPGEPS 1155 Imaculada Conceicao St BR-80215901 Curitiba PR Brazil Univ Fed Parana Elect Engn Grad Program Curitiba PR Brazil Joinville Stroke Registry 238 Getulio Vargas Ave BR-89202000 Joinville SC Brazil Municipal Hosp Sao Jose 238 Getulio Vargas Ave BR-89202000 Joinville SC Brazil Univ Fed Parana 632 Prefeito Lothario Meissner Ave BR-80210170 Curitiba PR Brazil Pontificia Univ Catolica Parana Hlth Technol Grad Program Grad Program Hlth Technol 1155 Imaculada Conceicao St BR-80215901 Curitiba PR Brazil Univ Fed Parana Polytech Ctr Dept Mech Engn 230 Cel Francisco H dos Santos AveBloc 4 BR-81530000 Curitiba PR Brazil

Healthcare is highly complex, sensitive and needs constant improvements. Several works have already been developed to support those processes. However, finding the optimum solution takes much work and time. multi-objective genetic algorithms (MOGA) improve the results by finding the optimal trade-off between multiple conflicting objectives and exploring the problem space more thoroughly. This study presents an enhanced framework that integrates Process Mining (PM), Discrete Event Simulation (DES), and multi-objective genetic algorithms (MOGAs) into an optimized, end-to-end pipeline. This framework builds upon an existing non-optimized approach to enable decision-makers to explore and implement Key Performance Indicator (KPI)-oriented solutions directly from raw log data. The framework navigates vast and complex solution spaces by embedding MOGAs into the KPI-oriented simulation process, delivering optimized scenarios with improved performance, boosting decision-making efficiency. The clinical stroke pathway, covering symptoms' onset to hospital discharge, was utilized as a case. This research demonstrates how optimization techniques with classical techniques into one unified framework can accelerate healthcare improvements, offering scalable applications to other domains beyond stroke care. The results demonstrate that using MOGA leads to improved solutions compared to the non-optimized framework, and this approach can be evaluated in short periods due to its performance. The findings underscore the solutions' sensitivity to changes in simulation parameters, emphasizing the importance of considering multiple objectives when dealing with complex decision-making problems in the healthcare industry. Future studies are suggested to extend the model, compare the effectiveness of different optimization methods within the framework, and test the framework's applicability to other domains.

关键词： multi-objective genetic algorithm Stroke Decision making support Optimization Healthcare Discrete Event Simulation

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An Artificial Neural Network Model for Infill Well Placement and Control Optimization During Foam Injection in Heterogeneous Oil Reservoirs

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年第7期50卷 4575-4597页

作者： Nwanwe, Onyebuchi Ivan Izuwa, Nkemakolam Chinedu Ohia, Nnaemeka Princewill Kerunwa, Anthony Fed Univ Technol Owerri Dept Petr Engn Owerri Nigeria

Infill well placement and well control optimization can be conducted by trial and error and direct optimization methods using reservoir simulators which are computationally expensive and time-consuming. The challenges faced by these methods can be solved by coupling reservoir simulation proxy models with optimization algorithms to quickly obtain an optimal solution with minimal computational effort. In this study, artificial neural network (ANN) models were coupled with genetic algorithms for infill well placement and/or well control optimization at the onset of foam injection since they are computationally inexpensive. Evaluation of whether or not to drill infill wells at the onset of foam injection is important to confirm a more profitable option in a given reservoir, and this was conducted using the coupled ANN-GA approach. For the infill well case, coupled well placement and well control optimization were considered while for the no infill well case, only well control optimization was considered. For the two scenarios, an optimal solution that maximized net present value and return on investment and minimized payout time was determined using ANN and a multi-objective genetic algorithm (MOGA). Results of the current study showed that drilling infill wells was more profitable than not drilling infill wells since oil produced and the NPV obtained for the infill well case was greater than that obtained for the no infill well case. Hence, coupling ANN-MOGA reduced the computational cost and time for determining the optimal solution, and this approach can serve as an alternative to making field development planning decisions based on engineering judgment, which can sometimes be erroneous.

关键词： Foam flooding Infill well placement optimization Well control optimization Net present value Return on investment Payout time multi-objective genetic algorithm

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