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Isogeometric shape optimization for widening band gaps of periodic composite plates

EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2024年 103卷

作者： Yin, Shuohui Huang, Jiahui Zou, Zhihui Bui, Tinh Quoc Cong, Yu Yu, Tiantang Zhang, Gongye Xiangtan Univ Sch Mech Engn & Mech Xiangtan 411105 Hunan Peoples R China Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX USA Duy Tan Univ Duy Tan Res Inst Computat Engn DTRICE 6 Tran Nhat Duat1 Dist Ho Chi Minh City Vietnam Duy Tan Univ Fac Civil Engn Da Nang City 550000 Vietnam Univ Evry Univ Paris Saclay LMEE F-91020 Evry France Hohai Univ Dept Engn Mech Nanjing 210098 Peoples R China Southeast Univ Sch Civil Engn Jiangsu Key Lab Engn Mech Nanjing 210096 Jiangsu Peoples R China

This paper presents an isogeometric shape optimization methodology to widen the band gap of periodic com-posite plates by smoothly optimizing its thickness profile. To save the computational cost, two NURBS surfaces are used in the isogeometric optimization method. While a fine NURBS surface (analysis mesh) is used for modeling the band gap of periodic composite plates within the isogeometric analysis (IGA) framework, a coarser NURBS design surface, i.e., the design mesh, is employed to describe the periodic plate thickness profile exactly and the position of control points are taken as optimal thickness variables. The particle swarm optimization (PSO) algorithm is adopted to solve the constrained dynamic maximization problems. On the other hand, the level set method and triangular domain integration technique are employed to describe the two-phase material interface of periodic composite plates. Numerical examples are provided to validate the accuracy and reliability of the present method. The numerical results showed that the present method can provide an accurate analysis and smooth plate thickness profile in the optimization process. The band gap of periodic composite plates is maximized effectively by the design of the plate thickness profile using the present optimization approach.

关键词： Shape optimization Isogeometric analysis Band gap particle swarm algorithm Mindlin plate

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Multifactor Recommendation-based Video Caching Strategy in Mobile Edge Computing 21

Multifactor Recommendation-based Video Caching Strategy in M...

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21st IEEE International Conference on Ubiquitous Computing and Communications / 5th IEEE International Conference on Data Science and Computational Intelligence / 21st IEEE International Conference on Computer and Information Technology

作者： Liu, Qian Wu, Ying Liu, Qilie Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Minist Educ Mobile Commun Technol Key Lab Chongqing 400065 Peoples R China

ISBN: (纸本)9781665477260

Mobile edge computing (MEC) can provide users with high-quality video services by placing computing and storage capacity close to the user. Transferring large size video files usually consumes more time and energy, and the emergence of edge caching can effectively solve this problem. Current caching schemes generally consider the impact of only one or two factors among attributes such as ratings and reviews, without considering the impact of multiple factors together on the recommender system. In this paper, we propose a video caching strategy based on multi-factor recommendation (VCSMFR) to solve the above problem. First, the video file ratings and corresponding rankings are obtained by a recommendation algorithm that fuses multi-factor data (e.g., reviews, directors, and actors). Then, an optimized particle swarm algorithm is used to make caching decisions for files stored on the edge MEC server to solve the problem that traditional particle swarm algorithms are prone to local convergence. Simulation results show that the recommendation algorithm proposed in this paper can analyze user and video information more carefully by fusing multiple factors, and improve the cache hit rate by 11% over the traditional caching scheme. In addition, the greedy algorithm is introduced into the optimized particle swarm algorithm, which improves the local search ability as well as the convergence of the algorithm and achieves faster caching decisions.

关键词： mobile edge computing recommender systems video caching particle swarm algorithm multi-factor recommendations

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Review of Neuro-Space Mapping Modeling for Heteromorphic RFID Antennas 10

Review of Neuro-Space Mapping Modeling for Heteromorphic RFI...

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IEEE 10th Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yan, Shuxia Chen, Zhifeng Shi, Weiguang Feng, Feng Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Tianjin Univ Sch Elect Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781665489546

With the development of technology, the radio frequence (RF) antenna gain estimation methods need to overcome some problems, such as complex modeling processes, long modeling cycles, and low model accuracy. The traditional methods are unable to meet modern device modeling requirements. This paper introduces an estimation framework, which combine neuro-space mapping (Neuro-SM) model with the particle swarm optimiser algorithm. Simulation results show that this method can improve the accuracy of existing models.

关键词： radio frequence (RF) antenna neuro-space mapping particle swarm algorithm

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Application of Metaheuristic algorithms for Optimizing Longitudinal Square Porous Fins

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Computers, Materials & Continua 2021年第4期67卷 73-87页

作者： Samer H.Atawneh Waqar A.Khan Nawaf N.Hamadneh Adeeb M.Alhomoud College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Department of Mechanical Engineering College of EngineeringPrince Mohammad bin Fahd UniversityAl Khobar31952Saudi Arabia Department of Basic Sciences College of Science and Theoretical StudiesSaudi Electronic UniversityRiyadh11673Saudi Arabia

The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms.A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 *** temperature of the base surface is higher than the fin surface,and the fin tip is kept adiabatic or cooled by convection heat *** other pertinent parameters include Rayleigh number(100≤Ra≤10^(4)),Darcy number,(10^(−4)≤Da≤10^(−2)),relative thermal conductivity ratio of solid phase to fluid(1000≤kr≤8000),Nusselt number(10≤Nu≤100),porosity(0.1≤φ≤0.9).The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic *** computer science,metaheuristic algorithms are one of the most widely used techniques for optimization *** this research,three metaheuristic algorithms,including the firefly algorithm(FFA),particle swarm algorithm(PSO),and hybrid algorithm(FFAPSO)are employed to examine the performance of square *** is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms.

关键词： Optimization firefly algorithm particle swarm algorithm hybrid algorithms porous media entropy generation rate

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Speed Tracking Control of Unmanned Patrol Vehicle based on particle swarm Optimization 33

Speed Tracking Control of Unmanned Patrol Vehicle based on P...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Zheng, Jinlong Li, Meng Yang, Zeyun Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China Shandong Longyi Aviat Technol Co Ltd Jining 272000 Peoples R China

ISBN: (纸本)9781665440899

Unmanned patrol vehicle is a typical mobile robot, which is an important equipment to realize intelligent patrol in outdoor environment such as industrial and mining enterprises, judicial prisons, docks and warehouses. In order to solve the problem of long speed regulation time and poor stability of inspection unmanned vehicle in complex environment, this paper designs a fuzzy PID speed control method based on particle swarm optimization algorithm. On the basis of conventional fuzzy PID, combined with particle swarm optimization algorithm, the membership function of fuzzy controller is optimized to reduce the dependence of fuzzy control on expert experience. Simulation results show that this method can reduce the speed regulation time and improve the stability of speed control.

关键词： particle swarm algorithm Fuzzy PID control Unmanned patrol vehicle Parameter setting

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A fuzzy PI controller for piezoelectric fast mirror based on particle swarm optimization 16

A fuzzy PI controller for piezoelectric fast mirror based on...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Dong, Yutong Li, Cong Liu, Lei Yang, Hongjie Chen, Te Cao, Guixing Northwestern Polytech Univ Shaanxi Aerosp Flight Vehicle Design Key Lab Xian Peoples R China China Acad Space Technol Inst Telecommun & Nav Satellites Beijing Peoples R China

ISBN: (纸本)9781665422482

In this paper, the hysteresis model and electric-mechanical dynamic model in series are presented to model the complex dynamic behavior of piezoelectric fast steering mirror. To realize its precise control, a compound controller is designed by combining the feedforward control based on Bouc-Wen inverse model and fuzzy PI control. Then, the parameters of fuzzy PI are adjusted by particle swarm optimization algorithm. Finally, the simulation and experimental results verify the effectiveness of the proposed control method compared with the conventional PI control.

关键词： piezoelectric fast steering mirror hysteresis fuzzy PI particle swarm algorithm

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A mathematical model for balancing safety and economy of UAVs in forest firefighting 2

A mathematical model for balancing safety and economy of UAV...

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2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Peng, Zixin Zhu, Genglin Li, Mu Zeng, Ruifeng Cheng, Sibo Wang, Kaisheng Guangdong Univ Technol Sch Electromech Engn Guangzhou Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Forest fires have a tremendous negative impact on the global ecology and the national economy. Advances in information machine technology have paved the way for the use of Unmanned Aerial Vehicles (UAVs) to fight forest fires, including monitoring fires and expanding signal propagation. However, in practical applications, we must consider the characteristics of UAVs such as monitoring range and the difficulties in balancing safety and economics. We take the 2019-2020 bushfires in Victoria and New South Wales as a case study and explore how best to combine two types of UAVs, SSA drones and radio repeater drones, to balance safety and cost-effectiveness. In this paper, we first employ the particle swarm algorithm to reasonably schedule the minimum number of SSA drones patrolling the sky while maximizing their surveillance area for the fire area. Then, we propose a Topology Spanning Tree (TST) algorithm to determine the locations of Emergency Operations Centers (EOCs) and Radio Repeater drones and the minimum number required. Then, we use the Ant Colony algorithm to find the shortest path of the drones from the EOCs to the designated patrol location to reduce the loss of their flight power. Finally, we set up a multiple regression equation to find the minimum cost using the size and frequency of fire events as parameters.

关键词： Ant Colony algorithm Forest Fire particle swarm algorithm Topological Spanning Tree algorithm

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Optimal charging of lithium-ion batteries based on lithium precipitation suppression

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JOURNAL OF ENERGY STORAGE 2024年 82卷

作者： Yu, Changzhou Huang, Siqi Xu, Haizhen Yan, Jiale Rong, Kang Sun, Meimei Hefei Univ Sch Adv Mfg Engn Hefei Anhui Peoples R China Naval Aviat Univ Zhifu Rd Yantai Peoples R China

With the development of the times, lithium-ion batteries have gradually become the main power source for new energy electric vehicles. The development of new energy electric vehicle charging technology cannot be separated from the progress of lithium-ion battery charging technology. Due to the secondary reaction of lithium precipitation during the lithium-ion battery charging process, there is a higher demand for lithium-ion battery charging strategies. This paper introduces an optimized charging method for suppressing lithium precipitation based on the second-order RC equivalent circuit model of lithium-ion batteries. An adaptability function is established to determine the lithium precipitation suppression value and temperature increase value, and the particle swarm algorithm is used to optimize the current value for each charging stage. The effectiveness of this charging method based on the particle swarm algorithm is verified through contrasted simulations with the method of suppressing lithium precipitation at 20 C in MATLAB/Simulink, which proves its efficiency and superiority.

关键词： Lithium -ion battery particle swarm algorithm Suppress lithium precipitation Optimized charging

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Real-world data-driven charging strategies for incorporating health awareness in electric buses

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JOURNAL OF ENERGY STORAGE 2024年 92卷

作者： Li, Taotao Zhang, Wencan Huang, Guangshan He, Hancheng Xie, Yi Zhu, Taoxi Liu, Guote Foshan Univ Sch Mechatron Engn Foshan 528200 Peoples R China Guangdong Tianshu New Energy Technol Co Ltd Foshan 528300 Peoples R China

Electric buses are progressively supplanting conventional fuel-powered vehicles in urban public transportation. However, owing to the extensive daily mileage and frequent charging-discharging cycles, power batteries accelerate aging, augmenting operation and maintenance costs for electric buses. To tackle this issue, the present study initially develops a thermo-electric-health coupling model using actual charge-discharge data obtained from power batteries to cover its aging loss, state of charge, and temperature effects. Then, by employing the particle swarm Optimization and Model Predictive Control methods, the fast charging strategy, maximum health charging strategy, and balanced charging strategy are formulated to manage lithium-ion battery aging effectively, enhance charging efficiency, and ensure thermal safety. The practical application and simulation results demonstrate that, under the proposed fast charging strategy, the maximum health charging strategy, and the balanced charging strategy, it takes 1809 s, 4282 s, and 2523 s for the power battery State of Charge to increase from 50 % to 95 %. Moreover, after undergoing 60 charge cycles, the power battery experiences a health degradation of only 0.47 %, 0.21 %, and 0.3 %, respectively. Compared to the conventional charging mode, the proposed charging strategy effectively mitigates power battery aging across various charging scenarios, ensuring safety and high charging efficiency.

关键词： Real -world data Battery health Charging strategy Model predictive control particle swarm algorithm

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Two-Stage, Three-Layer Stochastic Robust Model and Solution for Multi-Energy Access System Based on Hybrid Game Theory

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PROCESSES 2024年第12期12卷 2656页

作者： Wu, Guodong Li, Xiaohu Wang, Jianhui Zhang, Ruixiao Bao, Guangqing Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Grid Gansu Elect Power Co Lanzhou 730030 Peoples R China State Grid Gansu Elect Power Co Elect Power Sci Res Inst Lanzhou 730030 Peoples R China SouthWest Petr Univ Sch Elect & Informat Engn Chengdu 610500 Peoples R China

This paper proposes a two-stage, three-layer stochastic robust model and its solution method for a multi-energy access system (MEAS) considering different weather scenarios which are described through scenario probabilities and output uncertainties. In the first stage, based on the principle of the master-slave game, the master-slave relationship between the grid dispatch department (GDD) and the MEAS is constructed and the master-slave game transaction mechanism is analyzed. The GDD establishes a stochastic pricing model that takes into account the uncertainty of wind power scenario probabilities. In the second stage, considering the impacts of wind power and photovoltaic scenario probability uncertainties and output uncertainties, a max-max-min three-layer structured stochastic robust model for the MEAS is established and its cooperation model is constructed based on the Nash bargaining principle. A variable alternating iteration algorithm combining Karush-Kuhn-Tucker conditions (KKT) is proposed to solve the stochastic robust model of the MEAS. The alternating direction method of multipliers (ADMM) is used to solve the cooperation model of the MEAS and a particle swarm algorithm (PSO) is employed to solve the non-convex two-stage model. Finally, the effectiveness of the proposed model and method is verified through case studies.

关键词： weather scenarios hybrid game stochastic robust variable alternating iteration particle swarm algorithm

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