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Dynamic modeling method and parameter calibration of subarea thin-layer elements in bolted connection area of diaphragm coupling

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PHYSICA SCRIPTA 2025年第3期100卷 036008-036008页

作者： Wen, Huabing Gao, Shuaishuai Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212100 Peoples R China

Aiming at the finite element modeling problem on the dynamic characteristics of multi-layer elastic diaphragm bolted joints, and adopting the equivalent modeling method of subarea thin-layer elements, a parameter identification method that can accurately and efficiently identify the material parameters of each area of subarea thin-layer elements is proposed. Taking the diaphragm coupling in the marine propulsion shaft system as an example, the accuracy of the simplified model is verified by comparing the dynamics characteristics of the vibrational mode, driving point mechanical impedance, and vibration level drop between test and simulation. The variation of material parameters in each region under different bolt preloads was studied, and the effects of different material parameters on the dynamic properties were analyzed, in which the elastic modulus had a greater effect on the model dynamic characteristics, while Poisson's ratio and density had a smaller effect. The research results show that the subarea thin-layer element modeling method can effectively simplify the dynamic modeling method of the diaphragm coupling under the consideration of bolt preload, and the parameter identification based on the multi-objective genetic algorithm can accurately identify the material parameters of each area of the subarea thin-layer element, which can be used to analyze the dynamic characteristics of the multilayered elastic diaphragm bolted couplings.

关键词： subarea thin-layer element diaphragm coupling dynamic characteristics parameter identification multi-objective genetic algorithm

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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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A Two-Stage multi-objective genetic-Fuzzy Mining algorithm

A Two-Stage Multi-Objective Genetic-Fuzzy Mining Algorithm

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6th IEEE International Workshop on genetic and Evolutionary Fuzzy Systems (GEFS)

作者： Chen, Chun-Hao He, Ji-Syuan Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781467358996

In this paper, we propose a two-stage multi-objective fuzzy mining algorithm for dealing with linguistic knowledge discovery. In the first stage, the multi-objective genetic algorithm is used to derive a set of non-dominated membership functions (Pareto solutions) with two objective functions. In the second stage, the clustering technique is utilized to find representative solutions from the Pareto solutions. The representative solutions could be employed to mine fuzzy association rules according to the favorites of decision makers. Experiments on a simulation dataset are made and the results show the effectiveness of the proposed algorithm.

关键词： multi-objective genetic algorithm clustering technique membership function taxonomy fuzzy association rule

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Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage System

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Energy Engineering 2025年第1期122卷 285-306页

作者： Hongliang Hao Caifeng Wen Feifei Xue Hao Qiu Ning Yang Yuwen Zhang Chaoyu Wang Edwin E.Nyakilla Department of New Energy Storage Peking University Ordos Research Institute of EnergyOrdos017010China School of Energy and Power Engineering Inner Mongolia University of TechnologyHohhot010080China Key Laboratory of Wind Energy and Solar Energy Utilization Technology Inner Mongolia University of TechnologyHohhot010051China

Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy *** paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,***/Simulink was used for simulation *** optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 ***,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations *** proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.

关键词： Electric-thermal hybrid storage modal decomposition multi-objective genetic algorithm capacity optimization allocation operation strategy

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Thermal-hydraulic characteristics and optimization of coiled-tube resistance furnace based on response surface method and multi-objective genetic algorithms

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Case Studies in Thermal Engineering 2025年 72卷

作者： Ruochen Zhang Yinglong Wu Juan Xiao Simin Wang School of Chemical Engineering and Technology Xi'an Jiaotong University Xi'an 710049 PR China

The electrification transformation of the petrochemical industry is imminent, driven by the imperative to reduce carbon emissions. This paper presents a coiled-tube resistance furnace and investigates the flow and heat transfer performance of heavy residual oil within the tube through numerical simulation methods. The furnace's coiled-tube structure was optimized using a multi-objective genetic algorithm (MOGA) combined with a genetic aggregation response surface model, with inlet velocity, coil diameter, pitch, and tube diameter as design variables. The numerical model was experimentally validated and demonstrated the reliability of the simulation approach. Sensitivity analysis identified inlet velocity and coil diameter as the most influential parameters. Consequently, the optimal structural parameters were determined as a coil diameter of 200 mm, a pitch of 60 mm, and a tube diameter of 30 mm. Compared to the original structure, the optimized design led to a 63.7 % increase in the average heat transfer coefficient and a 44.6 % reduction in the average pressure drop. Additionally, a new correlation formula was developed by incorporating the Dean number, coil curvature, and dimensionless pitch as correction factors. The formula shows excellent fitting accuracy and robustness, providing a theoretical basis for the design of coiled tube resistance furnaces.

关键词： Coiled-tube Resistance furnace Heat transfer enhancement multi-objective genetic algorithm Fitting correlations

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Sequencing Mixed-model Production Systems by Modified multi-objective genetic algorithms

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Chinese Journal of Mechanical Engineering 2010年第5期23卷 537-546页

作者： WANG Binggang Department of Industry and Business Administration Henan University of Urban Construction Pingdingshan 467044 China

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many ***,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly *** far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly *** paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate *** objectives are considered simultaneously：minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly *** integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are *** the above problems are non-deterministic polynomial-hard（NP-hard）,a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals＇ *** feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing *** sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.

关键词： mixed-model production system sequencing parallel machine buffers multi-objective genetic algorithm multi-objective simulated annealing algorithm

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Effects of diversity control in single-objective and multi-objective genetic algorithms

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JOURNAL OF HEURISTICS 2007年第1期13卷 1-34页

作者： Chaiyaratana, Nachol Piroonratana, Theera Sangkawelert, Nuntapon King Mongkuts Inst Technol N Bangkok Res & Dev Ctr Intelligent Syst Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Prod Engn Bangkok 10800 Thailand King Mongkuts Inst Technol N Bangkok Dept Elect Engn Bangkok 10800 Thailand

This paper covers an investigation on the effects of diversity control in the search performances of single-objective and multi-objective genetic algorithms. The diversity control is achieved by means of eliminating duplicated individuals in the population and dictating the survival of non-elite individuals via either a deterministic or a stochastic selection scheme. In the case of single-objective genetic algorithm, onemax and royal road R-1 functions are used during benchmarking. In contrast, various multi-objective benchmark problems with specific characteristics are utilised in the case of multi-objective genetic algorithm. The results indicate that the use of diversity control with a correct parameter setting helps to prevent premature convergence in single-objective optimisation. Furthermore, the use of diversity control also promotes the emergence of multi-objective solutions that are close to the true Pareto optimal solutions while maintaining a uniform solution distribution along the Pareto front.

关键词： benchmarking diversity control multi-objective genetic algorithm single-objective genetic algorithm

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Enhancing agricultural sustainability: Optimizing crop planting structures and spatial layouts within the water-land-energy-economy-environment-food nexus

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GEOGRAPHY AND SUSTAINABILITY 2025年第3期6卷

作者： Wu, Haowei Li, Zhihui Deng, Xiangzheng Zhao, Zhe Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Chinese Acad Sci Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Liaoning Univ Fac Econ Sch Econ Shenyang 110136 Peoples R China

In the new phase of sustainable development, agriculture is seeking sustainable management of the water-land-energy-economy-environment-food nexus. At present, there are few studies on optimizing crop planting structure and analyzing its spatial layout with consideration of natural and socio-economic factors. Herein, we proposed a framework for addressing this issue. In this framework, the NSGA-II algorithm was used to construct the multi-objective optimization model of crop planting structures with consideration of water and energy consumption, greenhouse gas (GHG) emissions, economic benefits, as well as food, land, and water security constraints, while the model for planting spatial layout optimization was established with consideration of crop suitability using the MaxEnt model and the improved Hungarian algorithm. This framework was further applied in the Black Soil Region of Northeast China (BSRNC) for analyzing optimized crop planting structures and spatial layouts of three main crops (rice, maize, and soybean) under various scenarios. This study showed that the sown area of rice in the BSRNC decreased by up to 40.73 % and 35.30 % in the environmental priority scenario and economic-environmental balance scenario, respectively, whereas that of soybean increased by up to 112.44 % and 63.31 %, respectively. In the economic priority scenario, the sown area of rice increased by up to 93.98 %. Expanding the sown area of soybean was effective in reducing GHG emissions. On the contrary, rice production led to greater environmental costs though it provided higher economic returns. Among the three crops, maize exhibited an advantage in balancing environmental and economic benefits. Hegang-Jixi area in the northeast of the BSRNC was identified as the key area with the most intense crop planting transfer among different scenarios. Overall, this framework provides a new methodology for optimizing crop planting structures and spatial layouts with consideration of the nexus

关键词： Planting structure optimization Crop spatial layout multi-objective genetic algorithm Food security Agricultural sustainability Water-land-energy-economy-environment-food nexus

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multi-objective Optimization of Activation Time and Discharge Time of Thermal Battery Using a genetic algorithm Approach

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ENERGIES 2020年第24期13卷 6477页

作者： Li, Qing Shao, Yu-Qiang Liu, Huan-Ling Shao, Xiao-Dong Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China China Elect Technol Grp Corp 18th Res Inst Tianjin 300384 Peoples R China

Activation time and discharge time are important criteria for the performance of thermal batteries. In this work a heat transfer analysis is carried out on the working process of thermal batteries. The effects of the thicknesses of heat pellets which are divided into three groups and that of the thickness of insulation layers on activation time and discharge time of thermal batteries are numerically studied using Fluent 15.0 when the sum of the thickness of heating plates and insulation layers remain unchanged. According to the numerical results, the optimal geometric parameters are obtained by using multi-objective genetic algorithm. The results show that the activation time is mainly determined by the thickness of the bottom heat pellet, while the discharge time is determined by the thickness of the heat pellets and that of the insulation layers. The discharge time of the optimized thermal battery is increased by 4.08%, and the activation time is increased by 1.23%.

关键词： thermal batteries activation time discharge time multi-objective genetic algorithm

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Applying multi-objective genetic algorithms in green building design optimization

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BUILDING AND ENVIRONMENT 2005年第11期40卷 1512-1525页

作者： Wang, WM Zmeureanu, R Rivard, H Concordia Univ Ctr Bldg Studies Dept Bldg & Environm Engn Montreal PQ H3G 1M8 Canada Ecole Technol Super Dept Construct Engn Montreal PQ Canada

Since buildings have considerable impacts on the environment, it has become necessary to pay more attention to environmental performance in building design. However, it is a difficult task to find better design alternatives satisfying several conflicting criteria, especially, economical and environmental performance. This paper presents a multi-objective optimization model that could assist designers in green building design. Variables in the model include those parameters that are usually determined at the conceptual design stage and that have critical influence on building performance. Life cycle analysis methodology is employed to evaluate design alternatives for both economical and environmental criteria. Life cycle environmental impacts are evaluated in terms of expanded cumulative exergy consumption, which is the sum of exergy consumption due to resource inputs and abatement exergy required to recover the negative impacts due to waste emissions. A multi-objective genetic algorithm is employed to find optimal solutions. A case study is presented and the effectiveness of the approach is demonstrated for identifying a number of Pareto optimal solutions for green building design. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： building design green building life cycle assessment life cycle cost multi-objective genetic algorithm

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