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Online tunable sound absorbing acoustic metamaterial with optional aperture and adjustable cavity

APPLIED ACOUSTICS 2025年 231卷

作者： Yang, Xiaocui Shen, Xinmin Peng, Wenqiang Zhu, Dawei Song, Haichao Hu, Daochun Shen, Cheng Xu, Ming Zhu, Nannan Shi, Qi Nanjing Vocat Univ Ind Technol Engn Training Ctr 1 North Yangshan Rd Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Multifunct Lightweight Mat & Struct M 29 Yudao St Nanjing 210016 Jiangsu Peoples R China Army Engn Univ PLA Field Engn Coll Dept Mech Engn 88 Houbiaoying St Nanjing 210014 Jiangsu Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn 108 Deya St Changsha 410073 Hunan Peoples R China Jiangsu Shenglifang Environm Protect Technol Co Lt 1 Huayangang Nanjing 210003 Jiangsu Peoples R China

An online tunable acoustic metamaterial with the optional aperture and adjustable cavity is proposed to satisfy noise reduction requirements for various frequency ranges. Its sound absorption properties can be preliminarily optimized by the joint of three-dimensional finite element simulation model and particle swarm optimization algorithm. For the various frequency ranges of [250 Hz, 2000 Hz], [400 Hz, 800 Hz], [400 Hz, 1200 Hz], [600 Hz, 1400 Hz], and [1000 Hz, 1400 Hz], the theoretical average sound absorption coefficients increase from 0.3583 to 0.4604, from 0.6378 to 0.7591, from 0.4689 to 0.5799, from 0.5017 to 0.6305, from 0.7243 to 0.8754 respectively, which demonstrate the necessity of the parameter optimization. Meanwhile, the actual average sound absorption coefficients are further improved by direct optimization with tunable sample, which raise from 0.4236 to 0.4475, from 0.7232 to 0.7429, from 0.5421 to 0.5628, from 0.5906 to 0.6135, and from 0.8441 to 0.8630 respectively. This online tunable acoustic metamaterial can be treated as a universal sample to gain maximum sound absorption performance in a certain frequency range and a teaching appliance in the physics experiments on acoustic metamaterials, which show the excellent application prospects.

关键词： Online tunable acoustic metamaterial Sound absorption performance Optional aperture Adjustable cavity Finite element simulation particle swarm algorithm

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Prediction of lipid oxidation and polycyclic aromatic hydrocarbons in grilled sausages based on optimized back propagation neural network and computer vision

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2025年第5期19卷 3384-3402页

作者： Mei, Hebao Xing, Wei Hu, Manzi Zhou, Hui Chen, Gongwei Cai, Kezhou Xu, Baocai Hefei Univ Technol Key Lab Anim Food Green Mfg & Resource Min Anhui P Hefei 230009 Peoples R China Anhui Agr Univ Sch Anim Sci & Technol Hefei 230036 Peoples R China Hefei Univ Technol Engn Res Ctr Bioproc 420 Feicui Rd Hefei 230601 Anhui Peoples R China

This study developed a model to predict multiple quality parameters of grilled sausages using a computer vision system (CVS) integrated with a back propagation neural network (BP-NN) optimized by particle swarm optimization (PSO). The non-contact characteristic of CVS allowed for the quick and non-destructive acquisition of color information, which was used as input variables. Peroxide value (POV), 2-thiobarbituric acid reactive substances (TBARS), and polycyclic aromatic hydrocarbons (PAHs) were used as output layer parameters. The TBARS, POV, and PAH4 models outperformed other prediction models in terms of accuracy, with prediction error under 10%, a maximum mean squared error (MSE) of 0.037, and correlation coefficients (R) above 0.9. In contrast, the PAH15 prediction model had a prediction error under 16%, an MSE of 0.083, and correlation coefficients ranging from 0.76 to 0.83. Furthermore, sensitivity analysis revealed the a-value as the most influential parameter for predicting lipid oxidation and PAH4 levels in grilled sausages. Overall, these results suggest that the color parameters extracted by the CVS combined with the PSO-BP-NN model have great potential for predicting lipid oxidation and PAHs formation during grilled sausage processing in a rapid and non-destructive manner.

关键词： Computer vision system Artificial neural network particle swarm algorithm Lipid oxidation Polycyclic aromatic hydrocarbons

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Optimal design and numerical studies of negative stiffness device-TMD controlled systems using PSO algorithm

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2025年 189卷

作者： Chen, Peng Wang, Bin Ma, Kaiqiang Sichuan Univ Dept Civil Engn Chengdu Sichuan Peoples R China Sichuan Inst Bldg Res Chengdu Peoples R China

In recent years, research on negative stiffness devices (NSDs) for vibration control has increased. This study examines the combination of NSDs with tuned mass dampers (TMDs) and explores the NSD-TMD as a nonlinear energy sink (NES). The particle swarm optimization (PSO) algorithm was employed to determine the optimal parameters for the TMD, NSD-TMD, and NES systems. The optimization procedure proves that the PSO algorithm is suitable for solving the optimization problem of the complex systems with strong nonlinearity. The overall procedure is effective and demonstrates strong convergence. The force transmissibility curves of the optimized systems were compared, revealing that the NSD-TMD achieved the best vibration performance, while the NES system demonstrated the target energy transfer (TET) property. Another interesting characteristic of the NSDTMD system is that it does not necessarily require a large additional mass or damping factor to achieve optimal performance. An actual vertical vibration control scenario for a pedestrian bridge was analyzed using various control strategies. Human-induced force excitations were simulated using a code-defined method. The acceleration responses of an uncontrolled pedestrian bridge and the three optimized control systems were compared in both time and frequency domains. Results further verified the force transmissibility curves. The NSD-TMD system outperformed the other two systems, particularly in reducing the first-order response. Conversely, the NES system proved less effective for controlling human-induced vibration with harmonic force excitations.

关键词： Negative stiffness device Tuned mass damper particle swarm algorithm Optimal design

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Congestion management and energy optimization in multi-market electricity systems

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JOURNAL OF ENERGY STORAGE 2025年 112卷

作者： Xie, Xiaomin Liu, Hui Du, Xuanfu Anhui Vocat & Tech Coll Intelligent Mfg Coll Hefei 230011 Peoples R China

The increasing importance of energy exchange between electricity markets across regions, commonly referred to as "market integration", has emerged as a critical challenge in modern power networks. This study introduces a novel framework for managing congestion in transmission networks and optimizing energy and reserve settlement in interconnected electricity markets. The framework allows market participants to simultaneously bid in multiple markets and exchange energy through interconnecting transmission lines. Each market independently manages energy generation and storage, while a coordinating entity ensures optimal allocation of transmission network capacity to maintain system security. The study examines three methods for simultaneous energy and reserve settlement, each designed to enhance efficiency and minimize energy waste in multi-market systems. The framework is validated through simulations on a 3-region, 15-bus system, and the IEEE RTS-96 system, demonstrating its applicability and performance across diverse market configurations. The particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) metaheuristic optimization algorithm is employed to solve the problem, effectively managing congestion and optimizing market operations. Simulation results using the IEEE RTS-96 system showed that integrating market interactions reduced congestion by 100 % across critical transmission lines within five iterations. The third reserve settlement method reduced overall market costs by 8.3 % compared to the second method, with a 6.2 % cost reduction in Market C. Furthermore, storage price reductions of up to 53.3 % were achieved compared to conventional methods, showcasing the economic and operational benefits of the proposed approach.

关键词： Multiple electricity markets Energy market Storage market Market coupling Congestion management particle swarm algorithm

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Design and Analysis of the High Torque Density Circumferentially Excited Permanent Magnet Motor Based on PSO

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第1期39卷 772-781页

作者： Han, Jichao Bian, Xizhao Qiu, Jiayu Ge, Baojun Sun, Yutian Harbin Univ Sci & Technol Harbin 150080 Peoples R China Harbin Inst Large Elect Machinery Harbin 150040 Peoples R China

In order to further improve the torque density of the permanent magnet motor, a novel 650 kW circumferentially excited permanent magnet (CEPM) motor is proposed and designed. The response surface method and particle swarm optimization (PSO) algorithm are used to obtain the optimal design of the circumferentially excited permanent magnet motor. The electromagnetic performance is analyzed under the different operating conditions. The mathematical model of three-dimensional end transient electromagnetic filed is calculated to obtain the end leakage magnetic flux and end component losses, which provides the heat source for thermal analysis. Novel air-water cooling dual self-circulation ventilation system with the axial-flow fan is proposed. The complex fluid flow and the temperature distribution of each component are determined in the circumferentially excited permanent magnet motor based on three-dimensional fluid-solid thermal coupled model. The highest temperature of the components is determined. This novel ventilation cooling system can effectively take away the heat in the circumferentially excited permanent magnet motor. A prototype of circumferentially excited permanent magnet motor is manufactured. The designs and analyses are validated.

关键词： Circumferentially excited permanent magnet motor particle swarm algorithm electromagnetic performance end component losses thermal analysis

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Surface Settlement Prediction of Rectangular Pipe-Jacking Tunnel Based on the Machine-Learning algorithm

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2024年第1期15卷 04023061-04023061页

作者： Hu, Da Hu, Yongjia Yi, Shun Liang, Xiaoqiang Li, Yongsuo Yang, Xian Hunan City Univ Hunan Engn Res Ctr Struct Safety & Disaster Preve 518 Yingbin East Rd Yiyang 413000 Peoples R China Hunan Prov Key Lab Key Technol Hydropower Dev 16 Xiangzhang East Rd Changsha 410014 Peoples R China

The construction disturbance mechanism of rectangular pipe-jacking tunnels is more intricate than that of circular tunnels, leading to potential issues such as excessive accumulation and deformation of the surrounding formation, which can result in engineering disasters. However, there is currently a lack of reliable methods for predicting these disturbances. Machine-learning techniques have the capability to analyze the influence of multiple independent variables on a dependent variable, offering a new approach for predicting surface settlement in the construction of rectangular pipe-jacking tunnels. To address the sensitivity of existing machine-learning models to initial parameters, an improved particle swarm optimization (IPSO) method is employed. This method incorporates an adaptive mutation technique, adaptive inertia weight, and postoptimization method for mutant particles to enhance the particle size and determine the probability of obtaining the optimal value. By leveraging the strong mapping and nonlinear fitting abilities of the backpropagation (BP) algorithm, the IPSO-BP algorithm model is developed and compared with the BP, support vector machine, and random forest (RF) models using actual monitoring data. The findings indicate that in the presence of specific noise in the surface settlement data, the IPSO-BP prediction model demonstrates an enhanced accuracy of 26%, 25%, and 10% for the left amplitude. This approach can serve as a valuable reference for settlement prediction in similar projects. (c) 2023 American Society of Civil Engineers.

关键词： Machine learning Surface settlement Tunnel construction Rectangular pipe jacking particle swarm algorithm

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Investigation into the evolution of airport cement pavement distresses based on association rule mining

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CONSTRUCTION AND BUILDING MATERIALS 2025年 462卷

作者： He, Yinzhang Xiong, Kun Yang, Zeyu Zhang, Jiupeng Li, Yan Hu, Qinshi Li, Yuanfa Huang, Guojing Zhang, Mingliang Tan, Xiaoyong Changan Univ Sch Highway Xian 710000 Peoples R China CAAC Key Lab lntelligent Construct & Maintenance Xian 710064 Peoples R China Northwestern Polytech Univ Sch Civil Aviat Xian 710072 Peoples R China East China Jiaotong Univ Sch Civil Engn & Architecture Nanchang 330013 Peoples R China

This study endeavors to enhance the comprehension of damage mechanism in airport cement pavements, analyzing patterns to pinpoint strategies that extend the service life of these pavements. First, a distress dataset was established through surveys conducted at five airports in northern China to explore the intrinsic patterns between these distresses. Then, the development and evolution of these distresses were meticulously investigated using an improved Binary particle swarm Optimization-Ant Colony algorithm (BPSO-ACA). The results demonstrated that the improved algorithm outperforms traditional methods, reducing computation time by 38.97 % and 9.85 % compared to the two Apriori algorithms, respectively. The analysis revealed that the evolution of airport cement pavement distresses followed a consistent, interconnected three-stage progression. To prolong the lifespan of airport cement pavements, it is crucial to implement robust lifecycle management, which involves strategic maintenance measures at critical phases of distress evolution to either mitigate or arrest their advancement.

关键词： Airport pavement Distress evolution Association rule mining particle swarm algorithm Ant colony algorithm

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The design and operation optimization for conductive rubber heated snow melting bridge deck pavement

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NUMERICAL HEAT TRANSFER PART A-APPLICATIONS 2025年第11期86卷 3453-3471页

作者： Ma, Zipeng Wei, Haibin Jiang, Boyu Wei, Dongsheng Chen, Jinhao Jilin Univ Sch Transportat Changchun 130022 Peoples R China

In order to solve the problem of ice and snow disasters on roads, this study proposed a new technology for active deicing of bridge decks using conductive rubber heating system (CRHS) as the heat source. The existing measures to improve the operation efficiency of snowmelt pavement often only optimize 1-2 influencing factors, and lack of optimization of multiple factors such as paving design, operation and environment. Therefore, in order to improve the operation efficiency, this study carried out multi-objective optimization on the design and operation of conductive rubber heated snow melting bridge deck pavement. The bridge deck pavement scheme of conductive rubber heating system was proposed, and its two-dimensional numerical model of heat transfer was established. The laying spacing of conductive rubber (s), heating power (p), ambient temperature (t) and wind speed (v) were used as input parameters for system operation optimization. Predictive models for total heating time (THT) and the loss of energy rate (LER) were developed using response surface methodology (RSM). The specific effects of input parameters on THT and LER and their interactions were analyzed by thermal simulation. Aiming at less THT and lost energy (LE), the optimal input parameters of conductive rubber heating system were obtained by the multi-objective particle swarm algorithm. The results indicated that shorter THT and lower LE could be achieved when the laying spacing (s) was 0.25 m, the ambient temperature (t) was -3 degrees C, the wind speed (v) was 2 m/s, and the input power was 550 W/m2. And adjusting s was more effective than adjusting p to obtain low THT and LER. The results and recommendations can effectively guide the operation of conductive rubber heating system. The multi-objective optimization research idea of improving the operation efficiency of snowmelt pavement in this paper can provide reference for similar deicing pavement applications.

关键词： Active deicing conductive rubber heating system particle swarm algorithm response surface methodology thermal simulation

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Optimal design of district metered areas based on improved particle swarm optimization method for water distribution systems

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WATER SUPPLY 2022年第11期22卷 7930-7944页

作者： Yu, Tingchao Zhang, Xiangqiu Long, Zhihong Zhou, Hua Liu, Xiaowei Zhejiang Univ Zhejiang Key Lab Drinking Water Safety & Distribut Hangzhou 310058 Peoples R China Zhejiang Univ Inst Municipal Engn Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China Guangzhou Water Supply Co Ltd Guangzhou 510600 Peoples R China Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Zhejiang Univ Inst Marine technol & Engn Ocean Coll Hangzhou 310058 Peoples R China

Although partitioning of water distribution systems (WDSs) into district metered areas (DMAs) is challenging, it can be effectively used for refined management and leakage control. A two-step novel process for DMA partitioning is proposed in this study, i.e. clustering and dividing. The first step is to cluster nodes through improved METIS graph partitioning method. The second step is to optimize the location of flow meters and gate valves on boundary pipes by obtaining the feasible solutions. The good solutions that constitute the Pareto front was produced,which could be tough and time-consuming task. The paper proposed the innovative and efficient dividing phase: (a) selecting the important boundary pipes by hydraulic analysis;(b) using the improved particle swarm optimization algorithm;(c) proposing three-objective functions. The proposed method is applied to Modena and EXNET networks to demonstrate its feasibility.

关键词： district metered areas flowmeter gate valves particle swarm algorithm water distribution system

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Multiobjective Optimization of Roadheader Shovel-Plate Parameters Using Gray Weight and particle swarm Optimization

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IEEE ACCESS 2022年 10卷 104555-104566页

作者： Li, Qiang Liu, Songyong Gao, Mengdi China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Jiangsu Peoples R China Suzhou Univ Sch Mech & Elect Engn Suzhou 234000 Peoples R China

As the working efficiency and life span of the shovel plate of a roadheader directly influence its performance, optimization of the shovel-plate parameters is crucial. For optimizing the shovel-plate parameters, the variations in the loading capacity and shovel grubbing force with respect to the shovel-plate parameters are determined in this study. Moreover, the ideal point method and gray weight method are proposed for multiobjective optimization. The gray weights of the loading capacity and shovel resistance are determined by investigating existing molded products and through the gray decision method. Thereafter, the particle swarm optimization (PSO) algorithm is applied for multiobjective optimization of the shovel-plate parameters. Considering the EBZ230-type roadheader shovel plate as an example, parameter optimization through multiobjective optimization decreases the mass of the shovel plate by 14.3% and the shovel resistance by 7.3%, while increasing the loading capacity by 1.4%. To demonstrate the influence of optimization, coal and rock excavation with a shovel is simulated using the optimized parameters in an ANSYS-Workbench environment. The results indicate that the maximum stress at the front of the shovel plate decreases by 22.1%, minimum fatigue life increases by 139.6%, and minimum safety factor increases by 30.3%. The obtained results establish that, in multiobjective optimization based on PSO, the ideal point method and gray weight method optimize the shovel-plate parameters. This optimization can provide a theoretical basis and reference values for the design of roadheader shovel plates and can be applied for multiobjective optimization in engineering as well.

关键词： Optimization Linear programming particle swarm optimization Vibrations Heuristic algorithms Coal mining Excavation Multiobjective optimization particle swarm algorithm roadheader shovel plate

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