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Structures and stabilities of UPbn (n ≤ 18) clusters: A first-principles global optimization calculation

MATERIALS TODAY COMMUNICATIONS 2023年 36卷

作者： Gao, Chun Guo, Guina Hu, Shuxian Liu, Haitao Zou, Wenli Zhang, Ping Yan, Jun Inst Appl Phys & Computat Math Lab Computat Phys Beijing 100088 Peoples R China Northwest Univ Inst Modern Phys Xian 710127 Shaanxi Peoples R China Univ Sci & Technol Beijing Sch Math & Phys Beijing 100083 Peoples R China Shaanxi Key Lab Theoret Phys Frontiers Xian 710127 Shaanxi Peoples R China Peking Univ Ctr Appl Phys & Technol Beijing 100871 Peoples R China

Endohedrally doped clusters have received much attention because they can act as superatoms and have great potential as the building blocks for cluster-assembled materials. We have carried out a comprehensive study based on the combination of the particle swarming optimization algorithm and the relativistic density functional theory, to obtain geometric structures and stabilities of lead (Pb) clusters doped with one uranium (U) atom. A gradual evolution pattern was observed with the increasing number of Pb atoms, from exohedrally doped structures to quasi-endohedral structures, and finally to endohedrally doped structures. At least 12 and 11 Pb atoms were necessary to encapsulate the U atom completely in the calculation without and with the spin-orbit coupling (SOC) effect respectively. The UPb16 cluster was represented as a highly stable endohedral cage structure with the U atom around its center. In addition, the crucial role of the SOC effect was emphasized. We hope that our findings will provide a fundamental understanding of actinide-doped lead clusters, which may be quite different from their light element analogues.

关键词： Intermetalloid cluster Endohedrally doped cluster particle swarm optimization algorithm Density functional theory Relativistic effect

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Intelligent optimization algorithm-based electricity pricing strategy for smart building clusters

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Wang, Hui Liao, Xu Ji, Xiu Gong, Shanggao Meng, Xiangping Wang, Jiarui Changchun Inst Technol Sch Elect Engn & Informat Technol Changchun Peoples R China Natl & Local Joint Engn Res Ctr Smart Distribut Ne Changchun Peoples R China State Grid JILIN Elect Power Res Inst Changchun Peoples R China

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strate

关键词： smart buildings peer-to-peer power sharing retail electricity provider market bargaining particle swarm optimization algorithm alternating direction multiplier method

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algorithms to Model and Optimize a Stand-Alone Photovoltaic-Diesel-Battery System: An Application in Rural Libya

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 523-529页

作者： Elbaz, Abdurazaq Tahir Guneser, Muhammet Karabuk Univ Engn Fac Elect & Elect Dept TR-78100 Karabuk Turkey

This paper introduces a new optimum calculation technique for a stand-alone hybrid photovoltaic-diesel-battery system (PDBS), which meets the energy requirements of a small village in southern Libya. The bat algorithm design strategy is applied to reduce the annual cost of the system, taking into consideration the controlled electricity restriction and the optimal numbers of PV panels, diesel generators, and batteries. Comparative tests are performed using MATLAB for the bat algorithm with the grey wolf search algorithm and particle swarm optimization, demonstrating that the bat algorithm determines the optimum size of the PDBS effectively at a lower expense. Results then indicate that, taking into account the reliability characteristics, this has a significant effect on optimum capacity, load supply, and cost.

关键词： annual system cost bat algorithm grey wolf optimization algorithm particle swarm optimization algorithm photovoltaic-diesel-battery system

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Research on error analysis and calibration method of 3-PUU parallel robot

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2024年

作者： Chen, Mingfang Liang, Hongjian He, Chaoyin Zhang, Yongxia Huang, Liang'en Kunming Univ Sci & Technol Fac Mech & Elect Engn 727 Jingming South RdWujiaying St Kunming 650500 Yunnan Peoples R China

Kinematic calibration is necessary for the pose accuracy improvement of the parallel robots. However, difficulties occur in traditional calibration methods, such as exceeding a large number of error parameters and error accumulation. In this paper, a 3-PUU parallel robot is taken as the research object, and the closed-loop vector method and differential theory are employed to establish an error model. Through quantitative analysis of geometric parametric errors, redundant parameters within the calibration algorithm are meticulously eliminated. By treating the dominant error terms as optimization variables for the mechanism's parameters, the calibration problem is transformed into a nonlinear system optimization challenge, which effectively avoids the problem of multiple error parameters and accumulation of errors in the traditional method. Further, using the particle swarm algorithm to compute the minimum value of the objective function, one can obtain the actual structural parametric errors of the robot. These errors are then utilized to correct the kinematic model, enabling the calibration of the mechanism's parameters and ultimately enhancing operation accuracy. Lastly, simulation and experimental validation of the algorithm are carried out. The simulation results indicate that the end-effector position error converges to zero infinitely;and the experimental results indicate that the maximum error of the end-effector in the x, y, z directions and the maximum position error are reduced from 8.53, 11.67, 3.29, and 12.56 mm to 1.09, 1.32, 0.98, and 1.75 mm, respectively. The standard deviation of the position error is reduced from 2.43 to 0.32 mm. The mean error is reduced from 7.76 to 1.02 mm. To sum up, the operation accuracy and stability of the robot are greatly improved.

关键词： Parallel robot error model kinematic calibration particle swarm optimization algorithm pose accuracy

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Testing study of creep model for tailing cemented backfill and its secondary development based on FLAC^3D

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ROCK AND SOIL MECHANICS 2012年第SUPPL. 1期33卷 112-116页

作者： Zhao Kui He Wen Xiong Liang-xiao Yang Xin Wang Xiao-jun Jiangxi Univ Sci & Technol Engn Res Inst Ganzhou 341000 Jiangxi Peoples R China Jiangxi Univ Sci & Technol Minist Educ Engn Res Ctr High Efficiency Dev & Applicat Tech Ganzhou 341000 Jiangxi Peoples R China Ningbo Univ Fac Architectural Civil Engn & Enviroment Ningbo 315211 Zhejiang Peoples R China Chongqing Univ Coll Civil Engn Chongqing 400030 Peoples R China Jiangxi Univ Sci & Technol Sch Resources & Environm Engn Ganzhou 341000 Jiangxi Peoples R China

Great many results about the creep properties of rock have been achieved, while few reports on the cemented backfill's creep characters were obtained. In this paper, creep property of cemented backfill is characterized by Hoek-Kelvin model. Cemented backfill creep experiments are firstly conducted in the laboratory, then the parameters of Hoek-Kelvin creep model are identified using particle swarm optimization algorithm. Meanwhile, the sensitivity of creep parameters to the stress level is studied. Results show that the numerical value of EH and. vary little at different loading stress levels, and EK is very sensitive to the change of loading stress level. The proposed creep model for backfill is developed with the software FLAC(3D);and it has been used to numerically simulate multi-stage loading on cemented backfill. The numerical results agree well with the experimental ones.

关键词： cemented backfill creep model particle swarm optimization algorithm FLAC(3D)

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Collaborative Planning of Distribution Network, Data Centres and Renewable Energy in the Power Distribution IoT via Interval optimization

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ENERGIES 2024年第15期17卷 3623页

作者： Su, Lei Wu, Wenxiang Feng, Wanli Qin, Junda Ao, Yuqi State Grid Hubei Elect Power Res Inst Wuhan 430074 Peoples R China State Grid Smart Grid Res Inst Co Ltd State Key Lab Adv Power Transmiss Technol Beijing 102209 Peoples R China

With the development of the power distribution Internet of Things (IoT), the escalating power demand of data centers (DCs) poses a formidable challenge to the operation of distribution networks (DNs). To address this, the present study considers the operational flexibility of DCs and its impact on DNs and constructs a collaborative planning framework of DCs, renewable energy sources (RESs), and DNs. This framework employs the interval optimization method to mitigate uncertainties associated with RES output, wholesale market prices, carbon emission factors, power demand, and workloads, and the collaborative planning model is transformed into an interval optimization problem (IOP). On this basis, a novel hybrid solution method is developed to solve the IOP, where an interval order relation and interval possibility method are employed to transform the IOP into a deterministic optimization problem, and an improved integrated particle swarm optimization algorithm and gravitational search algorithm (IIPSOA-GSA) is presented to solve it. Finally, the proposed planning framework and solution algorithm are directly integrated into an actual integrated system with a distribution network and DC to verify the effectiveness of the proposed method.

关键词： data center uncertainty interval optimization collaborative planning particle swarm optimization algorithm gravitational search algorithm

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Design and key technology of the energy consumption management system for the liquid cooling data center

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ENERGY SCIENCE & ENGINEERING 2023年第3期11卷 1284-1293页

作者： Lai, Yibo Fang, Xiang Han, Rongjie Xuan, Yi Huang, Jiabin Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou Peoples R China Hangzhou Power Supply Co State Grid Zhejiang Elect Power Co Ltd Hangzhou 310009 Peoples R China

In view of the serious problem of energy consumption waste in the application process of liquid cooling data center, a new energy consumption management system of liquid cooling data center is constructed in this research. Energy consumption predictor, resource controller and resource configurator are used to monitor and manage energy consumption and optimize resource allocation of liquid cooling data center. The S3C2440A microprocessor with the internal core of ARM920T is adopted as the core controller of the energy consumption data collection system, and the energy consumption sampling circuit is designed with the voltage transformer and the current transformer, and attenuation network is adopted to prevent frequency aliasing in data sampling. particle swarm optimization algorithm is used to identify the parameters in the model estimator, and the resource coordinator is used to solve the power consumption and the performance models. When using multiple physical servers to simulate the data center environment, the experimental structure shows that the system in the research can control the overall energy consumption of the server within 260 W, and the prediction error of the model estimator is kept lower than 2.4%.

关键词： energy consumption management system energy consumption sampling circuit liquid cooling data center particle swarm optimization algorithm resource configurator

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A fuzzy logic-based method for solving the scheduling problem in the cloud environments using a non-dominated sorted algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第17期31卷 e5185-e5185页

作者： Wakil, Karzan Badfar, Arshad Dehghani, Pooyan Sadati, Seyed Mojtaba Shoja Navimipour, Nima Jafari Sulaimani Polytech Univ Res Ctr Sulaimani Iraq Islamic Azad Univ Dept Comp Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Management South Tehran Branch Tehran Iran Shahid Beheshti Univ Elect Engn Dept Tehran Iran Islamic Azad Univ Islamshahr Branch Young Researchers & Elite Club Islamshahr *** Iran

Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution. Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms.

关键词： cloud computing multi-objective optimization particle swarm optimization algorithm task scheduling

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Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

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MECHANICS & INDUSTRY 2021年第1期22卷

作者： Kang, Jianxiong Lu, Yanjun Luo, Hongbo Li, Jie Hou, Yutao Zhang, Yongfang Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Univ Technol Sch Printing Packaging Engn & Digital Media Techn Xian 710048 Peoples R China

The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

关键词： Wear assessment cylinder liner support vector regression fuzzy uncertainty particle swarm optimization algorithm

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Optimized Control Strategy for Photovoltaic Hydrogen Generation System with particle swarm algorithm

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ENERGIES 2022年第4期15卷 1472-1472页

作者： He, Hongyang Lu, Zhigang Guo, Xiaoqiang Shi, Changli Jia, Dongqiang Chen, Chao Guerrero, Josep M. Yanshan Univ Elect Engn Dept Qinhuangdao 066000 Hebei Peoples R China Chinese Acad Sci Inst Elect Engn Beijing 100190 Peoples R China State Grid Beijing Elect Power Co Beijing 100051 Peoples R China State Grid Jiaxing Elect Power Supply Co Jiaxing 314033 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

关键词： particle swarm optimization algorithm hybrid energy storage hydrogen generation system optimized energy storage capacity configuration

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