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GEOENERGY SCIENCE AND ENGINEERING 2024年 243卷

作者： Yin, Hu Zhong, Zhiyong Duan, Mubai Wu, Xianzhu Li, Qian Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China Southwest Petr Univ Oil & Gas Well Construction Safety Inst Chengdu 610500 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Chengdu 610051 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Drilling & Prod Technol Res Inst Guanghan 618300 Peoples R China

In the drilling process of exploiting natural gas, improper wellbore pressure control will lead to gas kicks, which will cause formation gas to enter the wellbore. If the gas reaches the ground, toxic gases such as H2S will cause environmental pollution and even large amount of drilling fluid overflow will further cause major safety accidents such as blowout. Bullheading killing is to inject killing fluid by applying pressure at the wellhead, and push the gas in the wellbore back to the formation. However, it is difficult to obtain formation parameters in high temperature and high pressure environment, and the distribution in wellbore gas is unclear, which makes it difficult to accurately simulate the well killing process and provide reference for actual construction. Therefore, through the characteristics of wellhead overflow in the process of gas kicks, combined with particle swarm optimization (pso) algorithm, the formation parameters are solved, and then a new leakage model is derived to describe the resistance of gas being pushed back to the formation. Well W2 and Well X3 are taken as examples to verify the model. The results show that the error between wellhead pressure and actual wellhead pressure data is 6.3% and 6.9% respectively, which shows satisfactory calculation accuracy. The specific process of obtaining construction parameters for this engineering technology is put forward. The research in this paper is of great significance for modeling and guiding well killing operation by bullheading killing.

关键词： Bullheading Gas kicks Leakage model pso algorithm Well control

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Comprehensive energy, exergy, and environmental evaluation of integrated heat pipe photovoltaic/thermal systems using multi-objective optimization

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APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Ghasempour, Roghayeh Salehi, Mohammad Fakouriyan, Samaneh Univ Tehran Coll Interdisciplinary Sci & Technol Sch Energy Engn Renewable Energy Dept Tehran Iran Univ Tehran Coll Interdisciplinary Sci & Technol Sch Energy Engn Nanofluid & Graphene Based Nanostruct Res Lab NGNR Tehran Iran Tyndall Natl Inst Int Energy Res Ctr IERC Cork Ireland

The depletion of fossil fuels and their environmental emissions have led to a tendency toward the implementation of sustainable energy sources. This study aims to evaluate the energy, exergy, and environmental performance of photovoltaic/thermal systems-based heat pipes. Furthermore, the electrical exergy and thermal efficiency of the photovoltaic/thermal system are analyzed. In this regard, two methods of multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOpso) are utilized. The simulated heat pipes are regarded as thermosyphon and pulsating type with water and acetone as the working fluid. The novelty of this study lies in the comparative analysis of thermosyphon and pulsating heat pipes with water and acetone as working fluids in PV/T systems, combined with the application of advanced multi-objective optimization algorithms, offering a comprehensive. The result demonstrates that acetone, as a working fluid, can help achieve better performance compared to water, and also pulsating heat pipe illustrates greater performance rather than the thermosyphon heat pipe. Moreover, the result reveals that the electrical exergy efficiency of the photovoltaic panel equipped with thermosyphon heat pipe with acetone has increased by 14.2 % compared to the photovoltaic panels without the heat pipe. In addition, utilizing thermosyphon base photovoltaic/thermal system with acetone reduces the production of greenhouse gases by 894.5 CO2 eq kg/m(2).year, which is a further reduction of 156.8 CO2 eq kg/m(2).year compared to the photovoltaic panel without a heat pipe.

关键词： Photovoltaic/thermal system Thermosyphon heat pipe Pulsating heat pipe Exergy analysis Environmental analysis pso algorithm

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An energy-aware clustering approach based on Gini coefficient and Ipso applied for energy-constrained applications

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TELECOMMUNICATION SYSTEMS 2025年第2期88卷 1-16页

作者： Wan, Hang Gao, Jiaqi David, Michael Derigent, William Zhao, Haiyan Chang, Yufang Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China Univ Lorraine CRAN CNRS UMR 7039 F-54516 Vandoeuvre Les Nancy France

In recent years, wireless sensor networks are playing an increasingly pivotal role in the monitoring of energy-constrained environments. However, the limited power of nodes restricts the network's life cycle and service quality. In this paper, we propose an energy-aware dynamic reorganization clustering protocol based on the Gini coefficient and an improved particle swarm optimization algorithm (DGIpso). The concept of Gini coefficient is applied to address uneven clustering in this hierarchical method. An improved pso method is proposed for optimal cluster head selection, which considers residual energy, distance, and individual Gini coefficient factors. Additionally, a dynamic reorganization mechanism based on the Gini coefficient is introduced to enhance transmission performance over the network's lifespan. Experimental results demonstrate that the proposed DGIpso protocol significantly outperforms LEACH, R-LEACH, pso-C, pso-WZ, and C3HA in network lifetime, energy efficiency, data throughput, and communication stability across various network scales. By incorporating Gini coefficients and heuristic algorithms into dynamic reorganization, this approach effectively addresses uneven energy distribution in clustering protocols, providing innovative strategies for monitoring in resource-constrained applications.

关键词： WSN Energy efficiency Hierarchical clustering protocol Dynamic network reorganization pso algorithm Gini coefficient

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Chiller System Modeling using pso Optimization based NARX approach 12

Chiller System Modeling using PSO Optimization based NARX ap...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Song, Zilong Li, Xiaoli Wang, Kang Li, Yang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Beijing Univ Technol Engn Res Ctr Digital Community Beijing 100124 Peoples R China Commun Univ China CUC Sch Int Studies Beijing 100024 Peoples R China

ISBN: (纸本)9798350321050

The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (pso) algorithm are used to establish a nonlinear autoregressive neural network model (pso-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the pso-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.

关键词： NARX Modeling chiller system Data-driven pso algorithm

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Modeling and Optimization of Energy Conversion Performance of a Free-Piston Linear Engine

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INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY 2024年 1-18页

作者： Hung, Nguyen Ba Lim, Ocktaeck Hanoi Univ Sci & Technol Sch Mech Engn Hanoi 100000 Vietnam Univ Ulsan Sch Mech Engn Ulsan 44610 South Korea

A model-based study is carried out to optimize the energy conversion performance of a free-piston linear engine (FPLE) based on its design parameters. The FPLE is modeled by dynamic, linear alternator, and thermodynamic models. Besides, a mathematical model using particle swarm optimization (pso) algorithm is established to optimize the FPLE energy conversion performance. To validate the FPLE models, the simulation results of in-cylinder pressure and electric power output of the engine are compared with the corresponding experimental results. Influences of design parameters, including air gap (gap) between translator and stator, number of coil turns in stator (Ncoil), and thickness of permanent magnet in translator (hm), on the FPLE dynamic, thermodynamic, power, and energy conversion characteristics are studied. The simulation results indicate that the in-cylinder pressure is increased when increasing gap and reducing Ncoil and hm;however, it also causes the electric power output and conversion efficiency of energy to reduce. By using pso algorithm, the optimum conversion efficiency of energy is quickly found at gap = 1.9 mm, Ncoil = 252, and hm = 16 mm. This study shows a successful combination of simulation models and pso algorithm to simulate and optimize the energy conversion performance of the FPLE.

关键词： Free-piston Linear engine pso algorithm

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Adaptive Particle Swarm Optimization algorithm and Application Model Based on Diversity-Driven Optimization

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IEEE ACCESS 2024年 12卷 170707-170720页

作者： Ming, Jingwei Xie, Zhiqiang Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin 150000 Peoples R China

The rapid development of Internet of Things technologies has led to the continuous increase and complexity of data feature dimensions. Therefore, a diversity-driven optimization-based adaptive particle swarm optimization feature selection algorithm was proposed to improve the accuracy of feature selection for high-dimensional data. The diversity drive of particles was constructed through population diversity. Secondly, the feature space segmentation method of the algorithm was improved. An adaptive population size adjustment mechanism was proposed. In 12 data sets with different dimensions, the proposed method had an advantage over other methods in terms of average accuracy. The average accuracy of these two classifiers increased by an average of 12.71% and 9.89%, respectively. The average time cost of the proposed method running 30 times on 12 data sets was 343.83ms, which was an average reduction of 44.02% compared to the other three algorithms. Therefore, diversity-driven optimization methods can enhance the algorithmic particle optimization speed. The proposed algorithm requires lower computational costs and superior feature selection accuracy for high-dimensional data feature selection. This algorithm has positive application value in high-dimensional data feature selection problems.

关键词： Diversity-driven optimization pso algorithm adaptive adjustment adaptive adjustment feature importance feature importance feature selection feature selection KNN classifier KNN classifier KNN classifier

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Particle Swarm Optimization and Long Short Term Memory algorithms for Financial Brand Data Prediction under Internet of Things

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2024年第4期40卷 827-847页

作者： Shen, Zheng-shun Li, Huai-bin Dongbei Univ Finance & Econ Sch Business Adm Dalian 116025 Peoples R China Huaiyin Normal Univ Sch Econ & Management Huaian 223001 Peoples R China

This study aims to improve the predictive ability of financial brand data, help investors and decision-makers better identify different financial brands' performance, and reduce unnecessary financial risks. The stock prices of various financial brands are undertaken as the experimental objects. The characteristics of financial data and the original model network structure are analyzed based on the Internet of Things (IoT). The long short-term memory (LSTM) neural and the particle swarm optimization algorithm (pso algorithm) are adopted to optimize the model's parameters. The autoregressive integrated moving average model (ARIMA) algorithm is used to analyze the spatio-temporal data, and a data prediction model for the financial brand based on the ARIMA-pso-LSTM algorithm is proposed. In addition, the validity and effectiveness of the proposed model are proved through the analysis and testing of specific example data. It is found that the model based on the IoT shows higher processing speed in contrast to the conventional method;the model proposed in this study shows smaller errors and improved accuracy in data prediction than the pso-LSTM model and the LSTM model;the model presents better fitting effects and better performance compared with the latest research model. The experimental results reveal that combining the three algorithms can integrate the advantages of various algorithms, and the three algorithms can complement each other, thereby improving the ability of financial data analysis. The model established in this study shows high accuracy and good adaptability in identifying financial brands and predicting financial data.

关键词： pso algorithm LSTM financial brand data prediction model optimization

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Overvoltage risk regulation strategy with distributed energy application in a distribution network based on the Stackelberg game

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Qiu, Zekai Wang, Jianbo Zhang, Xiaoqing Lei, Yuhang Tong, Chenjie Lu, Yufan Chang, Xiaoqiang State Grid Shaanxi Elect Power Co Ltd Power Res Inst Xian Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

Along with the increasing low-carbon demand of the power system, the access of a high percentage of renewable energy resources to the distribution network has a large impact on the voltage fluctuation of the system and reduces the operational reliability. In this paper, we consider utilizing the reactive capacity of distributed resources to participate in system voltage regulation to reduce node loss of load probability (LOLP) caused by node overvoltage faults and propose an overvoltage risk regulation strategy for the interaction between distribution network operators (DSOs) and distributed users in the framework of the Stackelberg game. First, the nodes are clustered and analyzed based on the two-dimensional indexes of node voltage regulation ability, and different voltage regulation compensation tariffs are assigned. Second, the cost-benefit model of voltage regulation for the leader and follower sides and the node LOLP model are constructed to measure the reliability of the system. The Stackelberg game is used to co-optimize the two parties' compensation tariffs and voltage regulation strategies. The optimal solution of voltage regulation under the equilibrium of the game is obtained by solving using the particle swarm optimization (pso) algorithm. Based on the IEEE-33 node system, a case study is carried out to verify that the proposed overvoltage risk regulation strategy can maximize the benefits of the regulator participants while enhancing the operational reliability of the system.

关键词： distributed system operator distributed resource reactive power voltage regulation K-means++ clustering algorithm Stackelberg game pso algorithm

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2D scale-3 fractional Euler wavelets optimization algorithm for fractional-order differential equations

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JOURNAL OF COMPUTATIONAL SCIENCE 2024年 83卷

作者： Zhou, Fengying Zhang, Jiakun Jiangxi Sci & Technol Normal Univ Sch Math Sci Nanchang 330036 Jiangxi Peoples R China

A numerical scheme combining particle swarm optimization(pso) optimization algorithm for solving fractional- order differential equations is developed by using 2D scale-3 fractional Euler wavelets, which are constructed via orthogonal Euler polynomials. The fractional integration formulas for scale-3 fractional Euler wavelets are derived under Riemann-Liouville fractional integral. The error estimation is given through the generalized fractional-order Taylor expansion and the convergence analysis of 2D scale-3 fractional Euler wavelets expansion is studied. According to the fractional integration formulas, together with the collocation method, a numerical technique is created by discretizing the fractional-order differential equation into a system of equations. Some linear and nonlinear problems are provided and pso algorithm is applied to the numerical scheme. The numerical results are analyzed and compared with existing findings. This not only confirms the feasibility and effectuality of the proposed method but also demonstrates that pso algorithm can enhance the numerical scheme's performance.

关键词： Scale-3 fractional Euler wavelets Fractional-order differential equation pso algorithm Collocation method

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A ground motion construction method considering spectrum characteristics as supplement for conditional mean spectrum

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BULLETIN OF EARTHQUAKE ENGINEERING 2025年第2期23卷 579-603页

作者： Zhang, Hui Yu, Dinghao Li, Gang Dong, Zhiqian Dalian Univ Technol Fac Infrastruct Engn Dalian 116024 Liaoning Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China

In the performance-based earthquake engineering, the ground motion (GM) records are generally selected for performing nonlinear time history analysis to evaluate the performance of the structure. However, due to the limited GM database, it is difficult to select GM sequences with high intensity by amplitude-scaled method for condition mean spectrum (CMS) to reasonably evaluate the performance of the structure. To solve the problem, an efficient method for constructing GM sequences is proposed as a supplement to traditional CMS. To construct GM sequences that meets the specific response spectrum sequence, a target response spectrum (conditional spectrum) sequence with physical meaning is simulated firstly based on the CMS and Latin hypercube sampling. Then, the time domain spectral matching (TDSM) method is introduced to generate GM sequences matching the target response spectrum sequences constructed above by adding adjustment wavelet functions so that available ground acceleration time history with high intensity and long period can be obtained from the limited GM database. However, in the previous studies, the coefficient used to constrain the adjustment wavelet functions usually is taken as an empirical constant value, which makes the efficiency of the iterative calculation unable to be guaranteed by the TDSM method. The particle swarm optimization method is introduced to optimize the TDSM method to find the best coefficient used to constrain the adjustment wavelet functions to improve the calculation efficiency and accuracy. To verify the effectiveness of the proposed method, the GM sequences selected by amplitude-scaled and proposed method are input into single-degree-of-freedom and multi-degree-of-freedom systems. The result shows that the proposed method for constructing GM sequences can be used as a supplement for the traditional CMS.

关键词： Ground motion construction method Spectrum characteristics Conditional mean spectrum pso algorithm The time domain spectral matching method

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