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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Zhang, Yi He, Wei Hu, Ling Lou, Zicheng Zhang, Daqin Wang, Zheng Southwest Petr Univ Sch Mechatron Engn Xindu Ave 8 Chengdu 610500 Sichuan Peoples R China

Rotary combination seals of composite impactors are susceptible to failure and relaxation in high-pressure, high-temperature deep-well environments. The aim of this study is to analyze the effect of stress relaxation on the performance of the seals and to assess their performance under different operating conditions. The study adopts GA-PSO-BP algorithm to predict the leakage, simulates the contact pressure change of the sealing surface before and after stress relaxation by Abqus software, and calculates the leakage rate by using MATLAB software in combination with the thermoelastic flow lubrication model and the finite element analysis data, to establish the prediction model dataset. The study also optimised the neural network prediction model and verified the accuracy of the model experimentally. The results show that increasing pressure leads to an increase in contact pressure, but relaxation increases contact length and decreases maximum pressure, and increasing compression increases and then decreases contact pressure and increases the peak value, but relaxation decreases the peak value and increases the risk of leakage. The increase in speed and temperature both lead to an increase in leakage rate, and the GA-PSO-BP model predicts more accurately the rotary combination seals with a higher leakage rate after considering the stress relaxation.

关键词： rotary combination seal stress relaxation BP neural network genetic algorithm particle swarm optimization algorithm leakage

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A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

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ENERGIES 2025年第5期18卷 1136-1136页

作者： Du, Jie Chen, Shuaizhi Pan, Linlin Liu, Yubao Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Precis Reg Earth Modeling & Informat Ctr Nanjing 210044 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing 210044 Peoples R China

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

关键词： wind speed predicting signal decomposition hybrid deep learning model particle swarm optimization algorithm

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optimization algorithm-Based Fault-Tolerant Resilient Control for Helicopter System: The Finite-Time Case

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2025年第2期20卷 021007页

作者： Satheesh, T. Anisha, T. M. Sakthivel, R. Elayabharath, V. T. Ren, Y. Karpagam Acad Higher Educ Dept Math Coimbatore 641021 India Bharathiar Univ Dept Appl Math Coimbatore 641046 India Sungkyunkwan Univ Dept Math Suwon 440746 South Korea Anhui Normal Univ Sch Math & Stat Wuhu 241000 Anhui Peoples R China

This paper centers on the finite-time control problem of the helicopter system in the midst of external disturbances, gain fluctuations, and actuator faults. Precisely, the finite-time boundedness and input-output finite-time stability are obtained concurrently to preclude huge undesirable values for both state and output, respectively, during specific transients. In particular, a particle swarm optimization setup is put forward for lowering the cost function and enhancing the overall reliability of the system. Furthermore, the fault-tolerant resilient control scheme is tailored to exhibit strong resistance to failures in the actuator and variations in the gain matrix. By blending the Lyapunov stability theory with the concept of finite time, we attain the essential requirements to ensure the stability of the closed-loop system over a finite span of time. Subsequently, the explicit methodology for obtaining the gain matrix is laid out pursuant to the established requirements. In particular, simulation results are presented to analyze the potential and significance of the devised control scheme.

关键词： helicopter system fault-tolerant resilient control finite-time theory particle swarm optimization algorithm

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An effective optimization algorithm for hydrogen fuel cell-based hybrid energy system: A sustainable microgrid approach

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1341-1355页

作者： Abu, Sayem M. Hannan, M. A. Rahman, S. A. Long, Chua Yaw Ker, Pin Jern Wong, Richard T. K. Jang, Gilsoo Univ Tenaga Nas Dept Mech Engn COE Kajang 43000 Malaysia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea Univ Tenaga Nas Dept Elect & Elect Engn COE Kajang 43000 Malaysia

--This paper presents the optimization of the performance of hybrid energy system (HES) integrating PV systems, hydrogen fuel cells, batteries, and supercapacitors for improved energy management and reduced hydrogen consumption in microgrid applications. The optimization is achieved using a particle swarm optimization (PSO) algorithm to adjust the proportional-integral controller. Key performance metrics, such as fuel flow rate, hydrogen consumption, and system stability, are targeted. Comparative analysis shows hydrogen consumption reduced by 18.35%, fuel flow rate by 19.87%, and oxygen utilization improved by 1.04%, resulting in better fuel cell efficiency. PV power consumption decreased by 17.53%, and battery and supercapacitor performance improved by 9.71% and 32%, respectively. The PSO-optimized system also reduces grid dependence by 2.21% and enhances stability by 7.58%. These results demonstrate the critical role of the fuel cell in the HES and highlight how PSO can advance sustainable energy systems by improving microgrid efficiency and reducing grid reliance.

关键词： Hybrid energy system Hydrogen fuel cell battery Supercapacitor particle swarm optimization algorithm

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Seismic analysis of coastal slope stability considering strength nonlinearity and tension cut-off

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MARINE GEORESOURCES & GEOTECHNOLOGY 2025年第4期43卷 586-606页

作者： Hu, Shihong Zhao, Lianheng Li, Liang Zuo, Shi Huang, Dongliang Ni, Xueqian Cent South Univ Sch Civil Engn Changsha Hunan Peoples R China Cent South Univ Key Lab Heavy haul Railway Engn Struct Minist Educ Changsha 410075 Hunan Peoples R China Hunan Prov Key Lab Disaster Prevent Mitigat Rail Transit Engn Struct Changsha 410075 Hunan Peoples R China Changsha Univ Coll Civil Engn Changsha 410022 Hunan Peoples R China

Tensile stresses may lead to cracks forming at the crest of coastal slopes and subsequent slope failure, particularly under seismic load conditions. Traditional shear strength failure criteria often overestimate the tensile strength of geomaterials, and in turn the factor of safety against sliding. This paper presents an improved nonlinear analysis method for assessing the stability of slopes, while considering tension cut-off in the failure criterion as well as the detrimental effects of inertial seismic loading and pore water pressure. The method employs the principle of energy consumption to obtain solutions for the geometry of the potential sliding surface and stress distribution, while the critical slope height and the stability factor are determined by means of a particle swarm optimization algorithm. Comparison with existing methods is conducted to validate the effectiveness and accuracy of this method. Results showed that the tensile strength of geomaterials had a significant impact on the stability of the slope and its associated failure mechanism, particularly for the vertical slope. This study could avoid the assumptions of the dilatancy angle on the velocity discontinuity and critical slope sliding surface, and reflect the tensile and nonlinear characteristics of the geomaterials well.

关键词： Coastal slope stability nonlinear properties tensile strength seismic load pore water pressure energy consumption analysis particle swarm optimization algorithm

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Virtually constrained generalized relative motion modeling and a control parameter optimizer for automatic carrier landing

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2024年第3期96卷 448-457页

作者： Zhang, Yiwei Li, Daochun Kan, Zi Yao, Zhuoer Xiang, Jinwu Beihang Univ Sch Aeronaut Sci & Engn Beijing Peoples R China Tianmushan Lab Hangzhou Zhejiang Peoples R China

PurposeThis paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is ***/methodology/approachA novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control *** control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea ***/valueThe proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

关键词： Automatic carrier landing system Virtually constrained generalized relative motion particle swarm optimization algorithm Control parameter optimization

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Collaborative planning of electric vehicle integrated charging and swapping stations and distribution network for carbon emission reduction

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ENERGY REPORTS 2024年 12卷 5846-5862页

作者： Liu, Wenjin Xin, Shijin Zhang, Zhaoxu Fan, Changhui Hao, Guojie Xu, Qing State Grid Gansu Elect Power Co Baiyin Power Supply Co Baiyin 730900 Peoples R China

Electric vehicle (EV) integration into the distribution network (DN) is a significant measure for reducing carbon dioxide emissions, yet existing studies have not fully explored the carbon reduction potential of the EV. This article proposes an innovative collaborative planning framework for electric vehicle integrated charging and swapping stations (EVICSS) and DN for carbon emission reduction. Initially, the division of responsibilities and mechanism analysis for emission reduction in DN are presented. Subsequently, the ladder carbon price model is presented to incentivize carbon mitigation efforts. And, considering the architecture and functioning mode of the EVICSS, the operational model for EVICSS equipment is established to deliver high-quality services to EV users, taking into account fast charging stations (FCS), fast battery swapping stations (FBSS), and cascade energy storage system (CESS) comprehensively. Taking into account the economic implications of carbon trading, a collaborative planning strategy for EVICSS and the DN is formulated to minimize overall costs while maximizing carbon emission reductions. Then an modified integrated gravitational search algorithm and particle swarm optimization algorithm (MIGSA-PSOA) is customizedly developed to solve this complicated optimization problem. Finally, case studies based a real DN are conducted to illustrate the effectiveness of the collaborative planning model. The results demonstrated it can effectively respond to DN power regulation and realize carbon emission reduction. Compared with independent planning, collaborative planning can increase the planning cost and total cost to 15.6% and 33.1% respectively.

关键词： Electric vehicle Collaborative planning Electric vehicle integrated charging and swapping stations Low carbon Gravitational search algorithm particle swarm optimization algorithm

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Short-term wind speed forecasting based on a hybrid model that integrates PSO-LSSVM and XGBoost

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1138-1143页

作者： Shi, Yanhua Zhengzhou Univ Technol Sch Informat Engn 18 Yingcai St Zhengzhou 450053 Henan Peoples R China

A groundbreaking method is proposed to mitigate the impact of unpredictable fluctuations in wind velocity on wind power generation. This innovative approach integrates the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) and XGBoost models in a harmonious manner. Initially, the raw wind speed data is subjected to wavelet threshold denoising to reduce noise and volatility. For short-term wind speed prediction, a PSO-LSSVM-XGBoost model is introduced. After the initial wind speed sequence undergoes wavelet threshold denoising, the enhanced sequence is forecasted using the LSSVM model, with its hyperparameters optimized through the PSO algorithm. The errors, obtained by subtracting the predicted values from the original data, are compensated using XGBoost. The final forecast results combine the rectified error data with the initial projected results. Experimental findings demonstrate the model's remarkable capability to enhance prediction performance and accuracy.

关键词： wind speed prediction least squares support vector machine particle swarm optimization algorithm deep learning combination model

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A novel design model of flow channel paths for additive manufacturing

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RAPID PROTOTYPING JOURNAL 2024年第6期30卷 1230-1248页

作者： Li, Dongfei Wang, Hongtao Dai, Ning Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing Peoples R China

PurposeThis paper aims to propose a method for automatic design of additive manufacturing (AM) flow channel paths driven by path length and pressure loss. The research focuses on the automatic design of channel paths, intending to achieve the shortest flow channel length or minimum pressure loss and improve the design efficiency of AM ***/methodology/approachThe initial layout of the flow channels is redesigned to consider the channels print supports. Boundary conditions and constraints are defined according to the redesigned channels layout, and the equation consisting of channel length and pressure loss is used as the objective function. Then the path planning simulation is performed based on particle swarm algorithm. The proposed method describes the path of flow channels using spline cures. The spline curve is controlled by particle (one particle represents a path), and the particle is randomly generated within the design space. After the path planning simulation is completed, the generated paths are used to create 3D *** study 1 demonstrates the automatic design of hydraulic spool valve. Compared to conventional spool valve, the pressure loss was reduced by 86% and the mass was reduced by 83%. The design results of case study 2 indicate that this approach is able to find the shortest channel path with lower computational ***/valueThe automatic design method of flow channel paths driven by path length and pressure loss presented in this paper provides a novel solution for the creation of AM flow components.

关键词： Additive manufacturing Flow channel path Automatic design Pressure loss particle swarm optimization algorithm Hydraulic valve

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Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第8期75卷 1493-1509页

作者： Saemi, Saeed Komijan, Alireza Rashidi Tavakkoli-Moghaddam, Reza Islamic Azad Univ Dept Ind Engn Cent Tehran Branch Tehran Iran Islamic Azad Univ Dept Ind Engn Firoozkooh Branch Firoozkooh Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

This study examines the problem of crew pairing and rostering under COVID-19 conditions. To keep the cockpit crew members safe against COVID-19, the problem is investigated in such a way that each cockpit crew member spends less daily sit time period in the airports and returns to his/her home base at the end of the day to take a rest. This study aims to introduce a Mixed-Integer Linear Programming (MILP) formulation for the problem. In order to solve the problem, three meta-heuristic algorithms including Genetic algorithm (GA), Firefly algorithm (FA), and particle swarm optimization (PSO) are applied based on a new chromosome representation. The proposed algorithms could obtain solutions with the least possible number of cockpit crew members to cover the existing flights by considering some rules and regulation related to employing cockpits. Moreover, the findings indicate that the algorithms can provide solutions near the optimal solutions (1.94%, 2.49%, and 2.43% gaps for the GA, FA, and PSO on average, respectively) for the small-scale instances extracted from the data sets. Additionally, the proposed GA can find lower-cost solutions in comparison to the FA and PSO in approximately similar CPU time for problem instances with different sizes.

关键词： COVID-19 conditions scheduling integrated crew pairing and rostering problem meta-heuristics genetic algorithm particle swarm optimization algorithm

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