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A physically based constitutive model for 41CrS4 steel based on particle swarm optimization algorithm

MATERIALS RESEARCH EXPRESS 2024年第10期11卷 106514页

作者： Wei, Xiaoxiao Tan, Fan Yang, Peipei Pan, Hongchen State Grid Gansu Elect Power Res Inst Lanzhou 730070 Peoples R China Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China Lanzhou Univ Technol Sch Mat Sci & Engn Lanzhou 730050 Peoples R China State Grid Lanzhou Elect Power Supply Co Lanzhou 730050 Peoples R China Evergrande New Energy Automot R&D Inst Global Head Mat Engn Dept Shanghai 201600 Peoples R China

Utilizing the Gleeble-3500 thermal simulation apparatus, a thermal compression assay was performed on 41CrS4 steel within the temperature range of 900 degrees C to 1200 degrees C, featuring a strain rate of 0.01 to 5 s-1, to derive its flow stress curve. The evaluation of the Arrhenius equation parameters was adeptly carried out by deploying a sophisticated particle swarm optimization algorithm. Through rigorous analysis, the correlation coefficient and the mean absolute deviation were calculated to quantify the alignment between the predictive accuracy of the developed model and the empirical data. The findings demonstrate the ability of the particle swarm optimization algorithm to significantly enhance the precision of the constitutive model. This augmented level of accuracy substantively increases the model's utility and reliability for simulations of high-temperature material forming processes.

关键词： 41CrS4 steel high-temperature constitutive model particle swarm optimization algorithm

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A particle swarm optimization algorithm based on an improved deb criterion for constrained optimization problems

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PEERJ COMPUTER SCIENCE 2022年 8卷 e1178页

作者： Sun, Ying Shi, Wanyuan Gao, Yuelin North Minzu Univ Collaborat Innovat Ctr Sci Comp & Intelligent Pro Yinchuan Ningxia Peoples R China North Minzu Univ Sch Math & Informat Sci Yinchuan Ningxia Peoples R China

To solve the nonlinear constrained optimization problem, a particle swarm optimization algorithm based on the improved Deb criterion (CPSO) is proposed. Based on the Deb criterion, the algorithm retains the information of 'excellent' infeasible solutions. The algorithm uses this information to escape from the local best solution and quickly converge to the global best solution. Additionally, to further improve the global search ability of the algorithm, the DE strategy is used to optimize the personal best position of the particle, which speeds up the convergence speed of the algorithm. The performance of our method was tested on 24 benchmark problems from IEEE CEC2006 and three real-world constraint optimization problems from CEC2020. The simulation results show that the CPSO algorithm is effective.

关键词： particle swarm optimization algorithm Constrained optimization problems Deb criterion

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Research on particle swarm optimization algorithm of electromechanical hybrid braking control strategy based on road surface recognition

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年

作者： Zheng, Zhu-An Liang, Cang Yancheng Inst Technol Sch Automot Engn Yancheng Peoples R China

Concerning the issue of low energy recovery efficiency of compound braking in Battery electric vehicles, an electromechanical composite braking control strategy based on road recognition and particle swarm optimization algorithm is proposed, which is different from the braking force control strategy of front and rear wheels with fixed ratio distribution before optimization. By analyzing the structure and working principle of the compound braking system of electric vehicle, a road surface identifier based on fuzzy algorithm is designed to track the peak adhesion coefficient of the road surface to obtain the maximum braking force of the brake. In addition, particle swarm optimization (PSO) is used to modify the motor braking force, so as to maximize the efficiency of energy recovery, and CRUISE and MATLAB are used in simulation environment to carry out joint simulation analysis. The results show that compared with the control strategy before optimization, the proposed control strategy not only ensures the vehicle braking stability but also has shorter braking distance, shorter braking time, larger motor braking torque, and slower decrease of battery State of Charge (SOC) value. Under the cycle condition of New European Driving Cycle (NEDC), Federal Test Procedure (FTP) and Extra Urban Driving Cycle (EUDC), the State of Charge of the battery increased by 1.98%, 2.1%, and 0.5%, respectively.

关键词： Compound braking Energy recovery fuzzy algorithm particle swarm optimization algorithm road recognition

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Location of natural gas leakage sources on offshore platform by a multi-robot system using particle swarm optimization algorithm

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2020年 84卷 103636-103636页

作者： Ma, Tao Liu, Shuhai Xiao, Huaping China Univ Petr Coll Mech & Transportat Engn Beijing 102249 Peoples R China China Univ Petr Lab Petr & Petrochem Robot Beijing 102249 Peoples R China

The offshore platform serves as the infrastructure for offshore mining. In order to guarantee safe operation of the platform and the development of the platform in the direction of intelligence, a natural gas leakage intelligent detection system with fast response is necessary to prevent potential accident when leakage occurs. In this work, a method to locate leaked natural gas is proposed based on the particle swarm optimization algorithm for the multi-robots to achieve. First, the advection diffusion equation is calculated to simulate the leakage gas transmission using finite difference method in different scene layouts. At the same time,a multi-robot collaborative detection strategy for gas detection, gas tracking, and gas source localization is proposed using an improved particle swarm optimization algorithm. After that, the process of multi robots search for leak sources was simulated. In addition, the control algorithm is analyzed, and the results show that the strategy has a high detection rate. Further analysis shows that the number of robots has increased from 2 to 7, the time it takes for the multi-robot system to successfully locate the leak source decreased;when the number of robots is constant, the time it takes for the multi-robot to successfully locate two leak sources is longer than the time to locate a single leak. Finally, search strategies based on particle swarm optimization, ant colony algorithm and cuckoo search algorithm are used to simulate in a single leak source environment. When the number of robots is changed from 3 to 7, compared with the other two algorithms, the accuracy of the search strategy search based on particle swarm is increased by 2%-28%, and the search time is accelerated by *** particle swarm optimization is superior than two others for control of multi-robot system in leakage source location.

关键词： Offshore platform Natural gas leak particle swarm optimization algorithm Gas source search

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Application and Improvement of the particle swarm optimization algorithm in Source-Term Estimations for Hazardous Release

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ATMOSPHERE 2023年第7期14卷 1168-1168页

作者： Lu, Jinshu Huang, Mengqing Wu, Wenfeng Wei, Yonghui Liu, Chong Zhejiang Ocean Univ Dept Naval Architecture & Maritime Zhoushan 316022 Peoples R China

Hazardous gas release can pose severe hazards to the ecological environment and public safety. The source-term estimation of hazardous gas leakage serves a crucial role in emergency response and safety management practices. Nevertheless, the precision of a forward diffusion model and atmospheric diffusion conditions have a significant impact on the performance of the method for estimating source terms. This work proposes the particle swarm optimization (PSO) algorithm coupled with the Gaussian dispersion model for estimating leakage source parameters. The method is validated using experimental cases of the prairie grass field dispersion experiment with various atmospheric stability classes. The results prove the effectiveness of this method. The effects of atmospheric diffusion conditions on estimation outcomes are also investigated. The estimated effect in extreme atmospheric diffusion conditions is not as good as in other diffusion conditions. Accordingly, the Gaussian dispersion model is improved by adding linear and polynomial correction coefficients to it for its inapplicability under extreme diffusion conditions. Finally, the PSO method coupled with improved models is adapted for the source-term parameter estimation. The findings demonstrate that the estimation performance of the PSO method coupled with improved models is significantly improved. It was also found that estimated performances of source parameters of two correction models were significantly distinct under various atmospheric stability classes. There is no single optimal model;however, the model can be selected according to practical diffusion conditions to enhance the estimated precision of source-term parameters.

关键词： source-term parameter estimation forward dispersion model particle swarm optimization algorithm gas release atmospheric diffusion conditions

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Research and Application of the Transient Electromagnetic Method Inversion Technique Based on particle swarm optimization algorithm

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IEEE ACCESS 2020年 8卷 198307-198316页

作者： Xu, Zhengyu Fu, Zhihong Zhang, Jing Chongqing Univ Sch Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Chongqing Triloop Prospecting Technol Co Ltd Chongqing 402660 Peoples R China

The transient electromagnetic (TEM) method is widely used in shallow surface engineering geological surveys due to its advantages such as light weight, high efficiency, and strong resolution. However, interpretation and inversion of TEM data is a complicated process. The traditional algorithm of TEM inversion employs the "smoke ring" fast imaging method, which can only reflect the approximate morphology of the stratigraphic model, and the inversion accuracy is low. Therefore, this method cannot meet the requirements of high-precision inversion. In this article, we present the particle swarm optimization (PSO) algorithm for TEM inversion. First of all, the response of the rectangular loop source TEM based on electric dipole integration was calculated and compared with the analytical solution results of the rectangular loop source and the accuracy of the algorithm was verified. Then, we introduced the solution process of the particle swarm optimization algorithm and analyzed the influence of particle swarm optimization algorithm parameter selection on the accuracy of the inversion result and the convergence speed. Next, a layered medium model was established. The particle swarm optimization algorithm and "smoke ring" fast imaging method were used to perform inversion calculation. The results show that the PSO algorithm has the advantages of high efficiency and accuracy. Finally, we examined the effectiveness of the particle swarm optimization algorithm for TEM data processing by inverting survey data from an Air-raid shelter on the campus of Chongqing University in China and comparing the results with those from the "smoke ring" fast imaging. The research works in this article provide new methods and techniques for TEM data processing.

关键词： particle swarm optimization Transient analysis Imaging Conductivity Convergence Inverse problems Transient electromagnetic method particle swarm optimization algorithm fast imaging inversion

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optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on particle swarm optimization algorithm

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第6期28卷 1885-1893页

作者： Liu, Li Chen, Tong Gao, Shijie Liu, Ye Yang, Shuguo Wang, Xinli Heilongjiang Bayi Agr Univ Coll Engn Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Bayi Agr Univ Coll Econ & Management Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Jianshan Farm Co Ltd Beidahuang Grp Nenjiang 161499 Heilongjiang Peoples R China

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 -1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis.

关键词： agricultural machinery allocation particle swarm optimization algorithm path planning

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Accelerometer calibration based on improved particle swarm optimization algorithm of support vector machine

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SENSORS AND ACTUATORS A-PHYSICAL 2024年 369卷

作者： Zhao, Xin Ji, Yong-xiang Ning, Xiao-lei China HuaYin Ordnance Test Ctr Huayin 714200 Peoples R China

Aiming at the problem that the deterministic errors caused by non-orthogonal installation, calibration factor, zero bias and other factors in production and in the use of accelerometers need to be calibrated by high-precision instruments, support vector machine regression is used to process the original data output by the accelerometer, and the processed data of each axis are used to establish a parameter calibration model without reference datum through the relationship between the output value of each axis of accelerometer, gravity acceleration and coaxial reversal in the paper. Then, the adaptive mutation rate is used to dynamically adjust the number of reverse learning particles, and the particles of particle swarm optimization algorithm are selected and adjusted according to the reverse learning, which solves the problems that particle swarm optimization algorithm tends to fall into localoptimum and the convergence speed is slow, through which a fast, accurate and simple calibration can be realized, and the performance of particle swarm optimization algorithm is improved. The calibration experiment shows that the improved particle swarm optimization algorithm has higher accuracy and faster convergence speed than the particle swarm optimization algorithm, and the calibration parameter accuracy is higher than that of the least square method, which does not need the datum of each axis. The calibration model proposed in this paper can realize a benchmark-free calibration outside the laboratory. At the same time, the improved particle swarm optimization algorithm can obtain calibration parameters with higher accuracy and faster speed in the rapid calibration, which provides the idea of a new model for accelerometer calibration and expands the application environment of accelerometer.

关键词： Calibration Accelerometer Reverse learning particle swarm optimization algorithm

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Research on cutting stock optimization of rebar engineering based on building information modeling and an improved particle swarm optimization algorithm

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DEVELOPMENTS IN THE BUILT ENVIRONMENT 2023年 13卷

作者： Ren, Ke Jia, Lu Huang, Jiantao Wu, Min Nanchang Univ Sch Infrastruct Engn Nanchang 330031 Peoples R China Nanchang Univ Res Inst Urban Rural Construct & Dev Nanchang 330031 Peoples R China

Rebar is one of the most important construction materials in engineering projects. For reinforced concrete structures, rebar consumption accounts for about 16% of the total project cost. Total project cost could be significantly reduced by precise computation on the cutting length of rebars. In this paper, a novel two-stage framework of rebar cutting stock is proposed to produce minimum residual rate of rebar and the minimum number of cutting patterns. In the first stage, a fast and accurate rebar lofting technique based on building in-formation modeling (BIM) is designed to automatically generate rebar-cutting data. In the second stage, a greedy strategy-based adaptive particle swarm optimization algorithm (GAPSO) is developed to optimize the rebar-cutting scheme. Finally, the proposed framework is used in the reinforcement laying-off of a comprehensive indemnificatory housing project, and the result verified the practicability and efficiency of the framework.

关键词： Rebar cutting stock Building information modeling (BIM) optimization Parametric automatic lofting particle swarm optimization algorithm

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Research on an optimization Method for Injection-Production Parameters Based on an Improved particle swarm optimization algorithm

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ENERGIES 2022年第8期15卷 2889页

作者： Dong, Yukun Zhang, Yu Liu, Fubin Zhu, Zhengjun China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China PetroChina Tarim Oilfield Co Res Inst Explorat & Dev Korla 841000 Peoples R China

The optimization of injection-production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes injection-production parameters by combining an improved particle swarm optimization algorithm to study the relationship between injection-production parameters and the net present value. In the process of injection-production parameter optimization, the particle swarm optimization algorithm has shortcomings, such as being prone to fall into local extreme points and slow in convergence speed. Curve adaptive and simulated annealing particle swarm optimization algorithms are proposed to further improve the optimization ability of the particle swarm optimization algorithm. Taking the Tarim oil field as an example, in different stages, the production time, injection volume and flowing bottom hole pressure were used as input variables, and the optimal net present value was taken as the goal. The injection-production parameters were optimized by improving the particle swarm optimization algorithm. Compared with the particle swarm algorithm, the net present value of the improved scheme was increased by about 3.3%.

关键词： particle swarm optimization algorithm injection-production optimization curve adaptation

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