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Multiscale Modeling and Reconstruction of Joint Motion: Finite Element Optimization Based on particle swarm algorithm

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IEEE ACCESS 2025年 13卷 57639-57649页

作者： Zhu, Jiaju Zhang, Zhong Liu, Runnan Ren, Meixue Ma, Guodong Northeast Normal Univ Sch Phys Educ Changchun 130024 Jilin Peoples R China Jilin Univ Sch Mech & Aerosp Engn Changchun 130015 Jilin Peoples R China Harbin Engn Univ Sch Ship Engn Harbin 150006 Heilongjiang Peoples R China Jilin Inst Phys Educ Changchun 130022 Jilin Peoples R China Jilin Sport Univ Human Movement Sci Coll Changchun 130022 Jilin Peoples R China Dongshin Univ Sports Prescript Dept Naju 58245 Jeollanam Do South Korea

This study employs finite element analysis (FEA) to address the challenge of motion reconstruction in three-dimensional (3D) human body modeling, specifically focusing on joint movement. High-resolution CT and MRI scans of the knee joint were utilized to construct an accurate 3D reconstruction model, while the particle swarm algorithm was implemented to optimize joint positioning. Experimental evaluations were conducted to assess the influence of varying unit numbers and joint rotation angles on reconstruction accuracy. The results demonstrate that increasing the number of units significantly improves the model's continuity and precision. Notably, with 64 units, the discontinuities in the knee region were nearly eliminated, and the entire reconstruction process was completed in just 6.69 milliseconds. Moreover, adjustments in joint rotation angles effectively reduced reconstruction errors, bringing them down to 1.317 cm at a sampling frequency of 1/60. Additionally, iterative reconstruction decreased the error in the knee region by approximately 30% compared to non-iterative methods. The optimization process facilitated by the particle swarm algorithm revealed that most particles achieved high fitness levels after the initial iteration, and a considerable proportion shifted to the foreground region during the second iteration once fitness values dropped below 0.2. Ultimately, the study substantiates the accuracy and reliability of the proposed method for joint area reconstruction. These findings indicate that the FEA-based 3D reconstruction framework is highly effective in capturing complex joint movements and deformations, providing valuable insights for both biomechanical research and clinical applications.

关键词： Three-dimensional displays Image reconstruction Finite element analysis Optimization Solid modeling Point cloud compression Accuracy Analytical models Mathematical models particle swarm optimization Three-dimensional reconstruction joint motion finite element analysis particle swarm algorithm

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A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven

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APPLIED ENERGY 2025年 379卷

作者： Lan, Tian Huang, Lianzhong Ma, Ranqi Wang, Kai Ruan, Zhang Wu, Jianyi Li, Xiaowu Chen, Li Dalian Maritime Univ Marine Engn Coll Dalian 116026 Peoples R China China Merchants Energy Shipping Co Ltd Zhongshan 518067 Guangdong Peoples R China Dalian Shipbldg Ind Co Ltd Dalian 116011 Peoples R China

Fuel consumption prediction plays an irreplaceable role in the assessment of ship emissions and energy efficiency optimization. However, in practical and diverse situations with different ship types, routes, and operating conditions, single-algorithm-based fuel consumption prediction models fail to operate effectively. Additionally, conventional fusion models that lack construction and optimization mechanisms also lack adaptability. To tackle this issue, this paper proposes a dual-adaptive prediction method. Initially, the particle swarm Optimization (PSO) algorithm is utilized to initialize each base-model. Next, the Binary particle swarm Optimization (BPSO) algorithm is employed to construct an adaptive Blending architecture. Finally, the Phasor particle swarm Optimization (PPSO) algorithm is used for adaptive cascaded optimization of the fusion model. Based on the aforementioned approach, the BPSO-Blending-PPSO model, which consistently maintains optimal performance, is constructed and validated using operational data from two ships. The results demonstrate that the BPSOBlending-PPSO model reduces the RMSE value by 5.2886-46.7492 % compared to single-algorithm models, effectively adapting to various sailing conditions of different ships. This method not only provides a new perspective to improve the predictive accuracy and robustness of ship fuel consumption models but also exhibits good scalability by incorporating various novel prediction algorithms into the base-model pool.

关键词： Ship fuel consumption prediction particle swarm algorithm Blending fusion strategy Adaptive prediction Cascade optimization

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Research on energy group structure optimization method for pressure water reactor plate-type fuel based on particle swarm algorithm

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ANNALS OF NUCLEAR ENERGY 2025年 218卷

作者： Li, Jiannan Chen, Ling Li, Song Zhang, Yongfa Hao, Jianli Naval Univ Engn Coll Nucl Sci & Technol Wuhan 430033 Peoples R China

The energy group structure significantly affects the accuracy and efficiency of cross-section and transport calculations. Plate-type reactors have significant differences in neutron dynamics compared to traditional rod reactors, which means that traditional energy group structures are not suitable for plate-type reactors. Therefore, it is necessary to explore new energy group structures to adapt to the complex structure of plate-type reactor cores. The paper proposes an optimization method suitable for the energy group structure of plate-type fuel based on the particle swarm algorithm. To ensure the universality of the obtained multi group structure in the plate-type fuel structure, the sensitivity of the plate elements parameters has been analyzed. Multiple examples are set up for optimization, with the average difference in k infinity between multi-group and coarse-group transport calculations as the objective function, and the optimal energy group boundary position is sought by updating generation by generation. Based on the above method, the 15-group, 29-group, 37-group, and 45-group structures suitable for multi-group transport calculations of pressurized water reactor plate-type cores are optimized. Numerical verification shows that the group structure proposed in this paper has high computational accuracy for various operating conditions of pressurized water reactor plate-type cores, and significantly improves computational efficiency compared to the traditional 361-group structure.

关键词： Energy structure particle swarm algorithm Plate-type core Cross section calculation Energy group condensation

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Multi-Objective Optimization for Rectangular Microchannel Using Response Surface Model Coupled With particle swarm algorithm

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Journal of Thermal Science and Engineering Applications 2025年第6期17卷 061008页

作者： Wei, Hongmei Yu, Ruien Lu, Huishan Shanxi Provincial Key Laboratory for Advanced Manufacturing Technology North University of China Taiyuan Shanxi 030051 China Department of Mechanical Engineering North University of China No. 3 Xueyuan Road Taiyuan Shanxi 030051 China

With the development of microelectronics and micro-electromechanical systems, the performance requirements for microchannels are becoming increasingly higher and more complex. This study aims to improve the overall performance of rectangular microchannels using the multi-objective particle swarm optimization algorithm (MOPSOA). First, the response surface methodology (RSM) was adopted to fit the thermal resistance function. Three-dimensional contour plots and response surface plots were created to analyze the interaction between fin thickness, channel width, and channel depth, aiming to understand their impact on thermal resistance values. Second, a mathematical model for the MOPSOA was developed with the objective functions being thermal resistance and pressure drop. Next, the Pareto optimal solution set for thermal resistance and pressure drop was determined by conducting simulations, and the K-mean clustering method was employed to identifying the four representative solutions. The results indicate a high level of accuracy in the thermal resistance function fitted by the RSM, with correlation coefficients R2= 0.9981 and adjusted correlation coefficient adj R2= 0.9961 respectively. Finally, the performance of a microchannel heat sink was assessed using the computational fluid dynamics method, and the optimized heating surface has a maximum temperature of 11 °C and a maximum pressure drop of 5.292 kPa lower than the non-optimized one. Additionally, the temperature distribution on the substrate is more uniform. This revealed a superior heat transfer capability and lower pressure drop, resulting in a more comprehensive and efficient performance.

关键词： Pressure drop heat and mass transfer response surface methodology thermal resistance function heat transfer enhancement micro/nanoscale heat transfer particle swarm algorithm Thermal resistance algorithms heat transfer Response surface methodology heat transfer in manufacturing thermal systems Microchannels Heat sinks rectangular microchannels

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Maximum power point tracking control of photovoltaic systems using a hybrid improved whale particle swarm optimization algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 1789-1803页

作者： Zhang, Lianghao Wang, Shuang Ni, Zihao Li, Fashe Kunming Univ Sci & Technol Yunnan Key Lab Clean Energy & Energy Storage Techn Kunming Peoples R China Kunming Univ Sci & Technol State Key Lab Complex Nonferrous Met Resources Cle Kunming Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn 68 Wenchang Rd121 St Kunming Yunnan 650093 Peoples R China

Maximum power point tracking (MPPT) control techniques are commonly employed to maximize the benefits and enhance the operational efficiency of photovoltaic systems in challenging outdoor environments. However, when the photovoltaic array is subjected to partial shading conditions, the conventional MPPT methods exhibit inadequate global maximum power point (GMPP) tracking performance. To this end, in this study, an effective MPPT control method for photovoltaic systems is proposed based on a hybrid improved whale particle swarm optimization algorithm (IWPOA). The control method integrates the advantages of the Improved Whale Optimization algorithm (IWOA) and the particle swarm algorithm (PSO) with additional control and stochastic elements. Validation analysis under a variety of conditions reveals that IWPOA significantly improves MPPT, achieving a tracking speed of approximately 0.13 s, which represents a 18.75-53.57% enhancement over other comparative algorithms. The mean tracking accuracy of IWPOA is 99.21%, and it offers superior tracking stability, making it suitable for diverse applications in MPPT.

关键词： Improved whale optimization algorithm maximum power point tracking partial shading conditions particle swarm algorithm photovoltaic array

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particle swarm algorithm-based damage-mitigating control law analysis and synthesis for liquid-propellant rocket engine

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第10期233卷 3810-3818页

作者： Cheng, Yuqiang Wu, Jianjun Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Hunan Peoples R China

The damage-mitigating control is a novel technique to ameliorate the reliability and safety of liquid-propellant rocket engines by achieving an optimized trade-off level between overall dynamic performance of the liquid-propellant rocket engine and structural durability of some selected critical damageable components under the condition of no impact on the achievement of the launch and flight mission. Thus, it is needed to be solved for the damage-mitigating control that the global optimization of the best trade-off between the damage of the critical damageable components and the performance of rocket engine. The major challenge should focus on: (i) to construct model of a certain rocket engine system dynamics, critical components structural dynamics, and damage dynamics;(ii) to optimize open loop feed-forward control law based on liquid-propellant rocket engine system dynamic model, structural and damage dynamics model, by using particle swarm optimization algorithm;(iii) to synthesize an intelligent damage-mitigating control system using the optimized open loop control law. In this paper, synthesis procedure of damage mitigation is introduced;structure and damage dynamic model of damageable components are formulated. The results of the simulation computation show that the synthesized control laws are implemented and achieve the effect of damage mitigating for the liquid-propellant rocket engine. It can provide important theoretical and practical value not only for improving the safety and reliability of the liquid-propellant rocket engine, but also for the complex thermo-flow-mechanical systems such as airplane engines, automobile engines, and fossil-fueled power plant because their service life is very critical too.

关键词： Damage-mitigating control reusable liquid-propellant rocket engine particle swarm algorithm system dynamics damage accumulation damage-mitigating control law

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particle swarm algorithm for discretization of decision tables

Particle swarm algorithm for discretization of decision tabl...

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International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Zheng, Xinwei Dai, Jianhua Key Lab E Business Market Applicat Technol Guangd Guangzhou 510320 Guangdong Peoples R China Guangdong Univ Business Studies Sch Informat Sci Guangzhou 510320 Guangdong Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9783037855744

Discretization of attributes with real values is an important problem in data mining. From the viewpoint of discernibility, consistency needs to be satisfied and cuts set for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. A particle swarm algorithm for discretization of decision tables is proposed.

关键词： particle swarm algorithm Discretization Decision table

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particle swarm algorithm applied in the layout optimization for console

Particle swarm algorithm applied in the layout optimization ...

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2nd IEEE International Conference on Advanced Computer Control

作者： Xu, Yuqing Meng, Qingxin Yang, Zhi Harbin Engn Univ Coll Mech & Elect Engn Harbin Peoples R China

ISBN: (纸本)9781424458479

Components on console are usually arranged by manual layout according to ergonomics standards and experiences in nuclear power plant. It is hard to balance many criterions, for it depends much on designer's judgments and preferences. This method leads to a random layout results. particle swarm optimization is a solution to complex optimization problems with constraints, and has been applied successfully in layout and optimization fields. Fitness function was structured according to the evaluating indicators which include operation frequency and order and relationships between individuals. The optimization objective was evaluated by the hand moving distance. This method could be adopted to optimize the layout of components on human-machine interface in order to ensure safety and efficiency of the system. The troubles of subjective feelings and randomness are avoided. An example of 13 components is implemented to show the layout procedure.

关键词： layout optimization particle swarm algorithm console

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particle swarm algorithm-Based Integrated Tax and Finance Control Platform Construction 2

Particle Swarm Algorithm-Based Integrated Tax and Finance Co...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sangeetha, M. Kaliappan, S. Raja, S. Ravichandran, P. Diana, I. Elsie Sagar, B.S. Department of Computer Science and Engineering VSB Engineering College Karur India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Saravanampatti Coimbatore India Department of Mechanical Engineering Sri krishna College of Engineering and Technology Tamilnadu Coimbatore India Department of Mechatronics Engineering Kongu Engineering College Tamil Nadu Erode India Tamilnadu Dindigul624622 India School of Electrical and Electronics Engineering REVA University India

ISBN: (纸本)9798350305418

With the rapid development of modern society, enterprises have also stepped into an era in which the modernization process is accelerating. There are more and more enterprise groups participating in market competition, and the degree of conglomeration is getting deeper and deeper. This trend has led to the scale of the group company. With the continuous expansion, the disconnection between the group's financial work and the group's business and even other links in the group's development process has become a common problem that plagues most enterprise groups. Enterprise groups must face the embarrassing situation of the failure of financial control work. Governance and disclosure of financial status and other aspects are still far from perfect. Some companies have suffered losses, abnormal financial conditions, and even bankruptcy for various reasons, especially because of the outbreak and spread of the financial crisis. The business is in serious financial trouble. On the basis of the particle swarm algorithm, crossover and mutation operations of the genetic algorithm are added to construct the particle swarm genetic hybrid algorithm. Through the example simulation, it can be seen that this method is used to complete the frequency domain design of the control system, which is simpler and more effective than the traditional correction method. Therefore, in this context, how to build a relatively complete financial management and control integration system to ensure the efficient operation of the group's business and the smooth achievement of the overall strategic goal has important research value. By reviewing the research on the integration of financial management and control in academia, it is found that the theoretical community has not given a detailed financial management and control integration system framework. © 2023 IEEE.

关键词： control integration Governance particle swarm algorithm Traditional Genetic algorithm

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Design optimization of wind turbine tower with lattice-tubular hybrid structure using particle swarm algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2016年第15期25卷 743-758页

作者： Chen, Junling Yang, Rongchang Ma, Renle Li, Jinwei Tongji Univ Dept Struct Engn Shanghai 200092 Peoples R China Tongji Architectural Design Grp Co Ltd Shanghai Peoples R China

As the size of wind turbine grows, the cost of the commonest tubular structural system will also increase because of the increasing cost of transportation, assembly, erection and servicing. A lattice-tubular hybrid structural system, which is composed of a four-angle cross-shaped lattice structure at the bottom and a tubular structure at the top, is proposed for large wind turbine systems. The welding processes can be totally avoided and the fatigue strengths effectively improved because all members can be assembled by bolts in site after cutting and drilling in the factory. The ultimate bearing capacities of combined cross-shaped members subjected to axial compressions are obtained by a series of numerical analyses. The column curve of four-angle-combined cross-shaped members is obtained by fitting numerical results with a piecewise function. The particle swarm optimization algorithm is adopted to optimize the shape and size of the lattice partition in this study. The constraints including stress, slenderness ratio and frequency are applied to find the minimum weight of the lattice partition in the hybrid tower. The optimal results show that the proposed structural system is feasible and can resolve the disadvantages of the traditional tubular system in the fabricating, mounting and transporting. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： wind turbines lattice tower hybrid structure particle swarm algorithm cross-shaped section structural optimization

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