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21 pages, 2204 KB  
Article
Adhesion Control of High-Speed Train Based on Improved Nonlinear Kalman Filter
by Haotian Gan, Song Wang, Junqi Lu and Haoran Ou
Appl. Sci. 2025, 15(19), 10524; https://doi.org/10.3390/app151910524 - 29 Sep 2025
Abstract
In the operation of high-speed trains, the effective transmission of traction force heavily relies on the adhesion between the wheel and the rail. Excessive traction or braking force may exceed the adhesion limit, causing wheel creep or slide, which threatens both equipment and [...] Read more.
In the operation of high-speed trains, the effective transmission of traction force heavily relies on the adhesion between the wheel and the rail. Excessive traction or braking force may exceed the adhesion limit, causing wheel creep or slide, which threatens both equipment and safety. To address this, a state estimation method based on the SVD-ACKF (singular value decomposition adaptive cubature Kalman filter) is proposed for high-precision estimation of train speed. Combined with an extremum-seeking algorithm, a closed-loop adhesion control strategy is developed to maintain train operations near the maximum adhesion point. Simulation results show that the method ensures accurate tracking under varying rail conditions and noise, while the control algorithm maintains adhesion utilization above 90%, thereby meeting operational demands and enhancing railway safety. Full article
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21 pages, 3434 KB  
Article
Deep Learning-Based Compliance Assessment for Chinese Rail Transit Dispatch Speech
by Qiuzhan Zhao, Jinbai Zou and Lingxiao Chen
Appl. Sci. 2025, 15(19), 10498; https://doi.org/10.3390/app151910498 - 28 Sep 2025
Abstract
Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms [...] Read more.
Rail transit dispatch speech plays a critical role in ensuring the safety of urban rail operations. To enable automated and accurate compliance assessment of dispatch speech, this study proposes an improved deep learning model to address the limitations of conventional approaches in terms of accuracy and robustness. Building upon the baseline Whisper model, two key enhancements are introduced: (1) low-rank adaptation (LoRA) fine-tuning to better adapt the model to the specific acoustic and linguistic characteristics of rail transit dispatch speech, and (2) a novel entity-aware attention mechanism that incorporates named entity recognition (NER) embeddings into the decoder. This mechanism enables attention computation between words belonging to the same entity category across different commands and recitations, which helps highlight keywords critical for compliance assessment and achieve precise inter-sentence element alignment. Experimental results on real-world test sets demonstrate that the proposed model improves recognition accuracy by 30.5% compared to the baseline model. In terms of robustness, we evaluate the relative performance retention under severe noise conditions. While Zero-shot, Full Fine-tuning, and LoRA-only models achieve robustness scores of 72.2%, 72.4%, and 72.1%, respectively, and the NER-only variant reaches 88.1%, our proposed approach further improves to 89.6%. These results validate the model’s significant robustness and its potential to provide efficient and reliable technical support for ensuring the normative use of dispatch speech in urban rail transit operations. Full article
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23 pages, 7556 KB  
Article
On-Site Monitoring and a Hybrid Prediction Method for Noise Impact on Sensitive Buildings near Urban Rail Transit
by Yanmei Cao, Yefan Geng, Jianguo Chen and Jiangchuan Ni
Buildings 2025, 15(17), 3227; https://doi.org/10.3390/buildings15173227 - 7 Sep 2025
Viewed by 748
Abstract
The environmental noise impact on sensitive buildings and residents, generated by urban rail transit systems, has attracted increasing attention from the public and various levels of management. Owing to the diversity of building types and the complexity of noise propagation paths, the accurate [...] Read more.
The environmental noise impact on sensitive buildings and residents, generated by urban rail transit systems, has attracted increasing attention from the public and various levels of management. Owing to the diversity of building types and the complexity of noise propagation paths, the accurate prediction of noise levels adjacent to structures through traditional experimental or empirical formula-based methods is challenging. In this paper, on-site multi-dimensional noise monitoring of the noise source affecting the sensitive buildings was first carried out, and a hybrid prediction method combining normative formulas, numerical simulations, and experimental research is proposed and validated. This approach effectively addresses the shortcomings of traditional prediction methods in terms of source strength determination, propagation path distribution, and accuracy of results. The results show that, while predicting or assessing the noise impact on sensitive buildings and interior residents, it is important to properly consider the impact of background noise (such as road traffic) as well as vibration radiation noise of bridge structures. The predicted results obtained by using this method closely match the measured results, with errors controlled within 3 dB(A). The noise prediction error in front of buildings is controlled within 2 dB(A), fully meeting the requirements for environmental noise assessment. Full article
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24 pages, 7537 KB  
Article
A Mathematical Methodology for the Detection of Rail Corrugation Based on Acoustic Analysis: Toward Autonomous Operation
by César Ricardo Soto-Ocampo, Juan David Cano-Moreno, Joaquín Maroto and José Manuel Mera
Mathematics 2025, 13(17), 2815; https://doi.org/10.3390/math13172815 - 1 Sep 2025
Viewed by 457
Abstract
In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that [...] Read more.
In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article
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21 pages, 2893 KB  
Article
Intelligent Fault Diagnosis System for Running Gear of High-Speed Trains
by Shuai Yang, Guoliang Gao, Ziyang Wang, Shengfeng Zeng, Yikai Ouyang and Guanglei Zhang
Sensors 2025, 25(17), 5269; https://doi.org/10.3390/s25175269 - 24 Aug 2025
Viewed by 997
Abstract
Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by [...] Read more.
Conventional rail transit train running gear fault diagnosis mainly depends on routine maintenance inspections and manual judgment. However, these approaches lack robustness under complex operational environments and elevated noise levels, rendering them inadequate for real-time performance and the rigorous accuracy standards demanded by modern rail transit systems. Furthermore, many existing deep learning–based methods suffer from inherent limitations in feature extraction or incur prohibitive computational costs when processing multivariate time series data. This study represents one of the early efforts to introduce the TimesNet time series modeling framework into the domain of fault diagnosis for rail transit train running gear. By utilizing an innovative multi-period decomposition strategy and a mechanism for reshaping one-dimensional data into two-dimensional tensors, the framework enables advanced temporal-spatial representation of time series data. Algorithm validation is performed on both the high-speed train running gear bearing fault dataset and the multi-mode fault diagnosis datasets of gearbox under variable working conditions. The TimesNet model exhibits outstanding diagnostic performance on both datasets, achieving a diagnostic accuracy of 91.7% on the high-speed train bearing fault dataset. Embedded deployment experiments demonstrate that single-sample inference is completed within 70.3 ± 5.8 ms, thereby satisfying the real-time monitoring requirement (<100 ms) with a 100% success rate over 50 consecutive tests. The two-dimensional reshaping approach inherent to TimesNet markedly enhances the capacity of the model to capture intrinsic periodic structures within multivariate time series data, presenting a novel paradigm for the intelligent fault diagnosis of complex mechanical systems in train running gears. The integrated human–machine interaction system includes a comprehensive closed-loop process encompassing detection, diagnosis, and decision-making, thereby laying a robust foundation for the continued development of train running gear predictive maintenance technologies. Full article
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33 pages, 7645 KB  
Article
Evaluation of Rail Corrugation and Roughness Using In-Service Tramway Bogie Frame Vibrations: Addressing Challenges and Perspectives
by Krešimir Burnać, Ivo Haladin and Katarina Vranešić
Infrastructures 2025, 10(8), 209; https://doi.org/10.3390/infrastructures10080209 - 12 Aug 2025
Viewed by 453
Abstract
Rail corrugation and roughness represent typical irregularities on railway and tramway tracks, which cause increased dynamic forces, high-frequency vibrations, reduced riding comfort, shorter track lifespan, higher maintenance costs, and increased noise levels. Roughness and corrugation can be measured by evaluating the unevenness of [...] Read more.
Rail corrugation and roughness represent typical irregularities on railway and tramway tracks, which cause increased dynamic forces, high-frequency vibrations, reduced riding comfort, shorter track lifespan, higher maintenance costs, and increased noise levels. Roughness and corrugation can be measured by evaluating the unevenness of the rail longitudinal running surface, which can be conducted using handheld devices or trolleys (directly on the track). Alternatively, vehicle or track-based indirect methods offer practical solutions for determining the condition of the rail running surface. This paper presents a methodology for rail corrugation and roughness evaluation, using bogie frame vibration data from an instrumented in-service tramway vehicle operating on Zagreb’s tramway network. Furthermore, it investigates the effects of various factors on the evaluation method, including wheel roughness, lateral positioning, signal processing methods, horizontal geometry, wheel–rail contact force, and tramway vehicle vibroacoustic characteristics. It was concluded that a simplified methodology that did not include transfer functions or wheel roughness measurements yielded relatively good results for evaluating rail corrugation and roughness across several wavelength bands. To improve the presented methodology, future research should assess the vehicle’s vibroacoustic characteristics with experimental hammer impact tests, measure the influence of wheel roughness on wheel–rail contact and bogie vibrations, and refine the measurement campaign by increasing test runs, limiting speed variation, and conducting controlled tests. Full article
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18 pages, 9390 KB  
Article
An Integrated SEA–Deep Learning Approach for the Optimal Geometry Performance of Noise Barrier
by Hao Wu, Lingshan He, Ziyu Tao, Duo Zhang and Yunke Luo
Machines 2025, 13(8), 670; https://doi.org/10.3390/machines13080670 - 31 Jul 2025
Cited by 1 | Viewed by 431
Abstract
The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating [...] Read more.
The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R2 = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article
(This article belongs to the Special Issue Advances in Noise and Vibrations for Machines)
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23 pages, 1794 KB  
Review
Noise Annoyance in Physical Sciences: Perspective 2015–2024
by Jacek Lukasz Wilk-Jakubowski, Radoslaw Harabin, Lukasz Pawlik and Grzegorz Wilk-Jakubowski
Appl. Sci. 2025, 15(12), 6559; https://doi.org/10.3390/app15126559 - 11 Jun 2025
Viewed by 933
Abstract
The current level of civilization development results in the widespread presence of devices that generate sound waves. Even in the so-called quiet zones, infrasound can be recorded, which, despite the lack of audibility, causes undesirable physiological reactions or affects the environment. Research on [...] Read more.
The current level of civilization development results in the widespread presence of devices that generate sound waves. Even in the so-called quiet zones, infrasound can be recorded, which, despite the lack of audibility, causes undesirable physiological reactions or affects the environment. Research on noise pollution and its effects on human health and the environment is increasingly prevalent. Thus, the problem of noise should be considered an important and increasingly real problem. In the presented article, an advanced review of the literature on the noise was carried out in order to systematize the issues, diagnose trends, and identify research gaps. The literature review included 1952 articles present in the Scopus database. After selecting the material, 112 documents were qualified for full analysis. Publications were grouped based on selected categories, cross-analyzed for statistical correlations, and described on the basis of content. The findings indicate the dominant areas of research interest in noise and its sources and reveal the most widespread methodological trends such as increased interest in the area of engineering (66.67% increase) and air transport (19.04% increase); an increased frequency of use of the experimental method (28.12% increase); and the rising interest of noise annoyance in China (150% increase). On the other hand, the largest drops of interest occur in Earth and planetary sciences (decrease of 50%), in road transportation (21.87%), in conceptual papers (decrease of 16.21%), and a reduced number of affiliations of authors from Germany (decrease of 45.45%). Outcomes indicate a proposal for future research to fill the identified gaps in the literature. Full article
(This article belongs to the Special Issue Recent Advances in Soundscape and Environmental Noise)
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18 pages, 9989 KB  
Article
Study on Vibration Characteristics and Transmission Path of Mountain Rack Trains Based on the OPTA Method
by Liangzhao Qi, Xingqiao Deng, Liyuan Zeng, Chenglong Dong, Yixin Xu, Shisong Wang and Yucheng Liu
Machines 2025, 13(6), 482; https://doi.org/10.3390/machines13060482 - 3 Jun 2025
Viewed by 471
Abstract
The Dujiangyan–Siguniangshan mountain rack railway project is China’s first mountain rail transit. Most of its lines are located in mountainous areas and close to natural ecological protection areas, which have strict restrictions on the vibration and noise of train operation. At the same [...] Read more.
The Dujiangyan–Siguniangshan mountain rack railway project is China’s first mountain rail transit. Most of its lines are located in mountainous areas and close to natural ecological protection areas, which have strict restrictions on the vibration and noise of train operation. At the same time, the vibration of mountain rack railway trains is also an important factor affecting the safety and riding comfort of trains. However, due to the multi-source vibration of gear teeth, wheels, rails, and suspensions, it is difficult to clearly define the vibration characteristics and vibration transmission path of the train, which has a serious impact on its vibration noise suppression and optimization. To this end, this study proposed a set of evaluation methods for the vibration characteristics and transfer paths of mountain rack trains based on a combination of dynamics and operational transfer path analysis (OTPA). Considering the interaction between the dynamic behaviors of the primary and secondary suspensions, the gear tooth contact behavior, the wheel–rail contact behavior and the dynamic behaviors of the track system, a dynamic model of a mountain rack train based on the finite element method was established, and the effectiveness of the model was verified through field experiments. On this basis, the OTPA method was used to establish a vibration transfer path model between the secondary suspension and the center of mass of the car body, and it was used to analyze the vibration mechanism and transfer path of the train body at the rated speed (20 km/h) and the limited speed (30 km/h). This study is of great significance for suppressing the vibration noise of mountain rack trains, reducing the impact on the ecological environment and improving ride comfort. Full article
(This article belongs to the Section Vehicle Engineering)
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36 pages, 4752 KB  
Article
A New Concept of Hybrid Maglev-Derived Systems for Faster and More Efficient Rail Services Compatible with Existing Infrastructure
by Jesus Felez, Miguel A. Vaquero-Serrano, David Portillo, Santiago Antunez, Giuseppe Carcasi, Angela Nocita, Michael Schultz-Wildelau, Lorenzo A. Parrotta, Gerardo Fasano and Pietro Proietti
Sustainability 2025, 17(11), 5056; https://doi.org/10.3390/su17115056 - 30 May 2025
Viewed by 1958
Abstract
Magnetic levitation (maglev) technology offers significant advantages for rail transport, including frictionless propulsion, reduced noise, and lower maintenance costs. However, its widespread adoption has been limited due to the need for a dedicated infrastructure incompatible with conventional rail networks. The MaDe4Rail project, funded [...] Read more.
Magnetic levitation (maglev) technology offers significant advantages for rail transport, including frictionless propulsion, reduced noise, and lower maintenance costs. However, its widespread adoption has been limited due to the need for a dedicated infrastructure incompatible with conventional rail networks. The MaDe4Rail project, funded by Europe’s Rail Joint Undertaking (ERJU), explores Maglev-Derived Systems (MDSs) as means to integrate maglev-inspired solutions into existing railway corridors with minimal modifications. This paper focuses on the so-called “hybrid MDS” configuration, which refers to levitating systems that can operate on existing rail infrastructure. Unlike current maglev systems, which require dedicated tracks, the proposed MDS system is designed to operate on conventional rail tracks, allowing for its compatibility with traditional trains and ensuring the interoperability of lines. In order to identify the most viable solution, two different configurations have been analysed. The evaluated scenario could benefit from the introduction of hybrid MDSs based on magnetic levitation, where a group of single vehicles, also called pods, is used in a virtual coupling configuration. The objective of this case study is to increase the capacity of traffic on the existing railway line by significantly reducing travel time, while maintaining a similar energy consumption to that of the current conventional trains operating on this line. Simulation results indicate that the hybrid MDS can optimise railway operations by taking advantage of virtual coupling to improve traffic flow, reducing travel times and energy consumption with the optimisation of the aerodynamic drag. The system achieves a balance between increased speed and energy efficiency, making it a viable alternative for future rail transport. An initial cost–benefit analysis suggests that the hybrid MDS could deliver substantial economic advantages, positioning it as a promising solution for enhancing European railway networks with minimal infrastructure investment. Full article
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13 pages, 3176 KB  
Communication
The Design of a Closed-Loop Piezoelectric Friction–Inertia XY Positioning Platform with a Centimeter Travel Range
by Zheng-Rong Guo, Hong-Sheng Tan, Chin-Shun Chang, Ing-Shouh Hwang, En-Te Hwu and Hsien-Shun Liao
Actuators 2025, 14(6), 265; https://doi.org/10.3390/act14060265 - 28 May 2025
Viewed by 546
Abstract
Friction–inertia piezoelectric actuators can perform long-range positioning with nanometer resolution. However, friction and inertia are not easy to control and can influence the actuator’s performance. The present study proposes a friction–inertia-type piezoelectric XY positioning platform with a simple structure, which uses magnets to [...] Read more.
Friction–inertia piezoelectric actuators can perform long-range positioning with nanometer resolution. However, friction and inertia are not easy to control and can influence the actuator’s performance. The present study proposes a friction–inertia-type piezoelectric XY positioning platform with a simple structure, which uses magnets to provide stable normal force and friction. Sliders and rails were used to provide long travel ranges of 80 mm and 70 mm in the X and Y directions, respectively. Compact optical encoders were installed on the platform to enhance the positioning accuracy. With a three-phase positioning strategy involving both stepping and closed-loop methods, the system achieved a positioning accuracy of 3 µm (0.03%) and a repeatability of 325 nm (0.0033%) over a 10 mm long travel range. The positioning resolution was 4.7 nm, which was primarily limited by optical encoder noise under the closed-loop control mode. An astigmatic optical profilometer was used for the wide-range and high-resolution surface imaging of the XY positioning platform. Full article
(This article belongs to the Section Precision Actuators)
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28 pages, 3106 KB  
Article
Integrated Control Strategies of EGR System and Fuel Injection Pressure to Reduce Emissions and Fuel Consumption in a DI Engine Fueled with Diesel-WCOME Blends and Neat Biodiesel
by Giorgio Zamboni and Massimo Capobianco
Energies 2025, 18(11), 2791; https://doi.org/10.3390/en18112791 - 27 May 2025
Viewed by 556
Abstract
A wide experimental campaign was developed on an automotive turbocharged diesel engine, using two blends between diesel oil and waste cooking oil methyl esters (WCOME) and neat biodiesel. A conventional B7 diesel oil was considered as a reference fuel. The two blends, respectively, [...] Read more.
A wide experimental campaign was developed on an automotive turbocharged diesel engine, using two blends between diesel oil and waste cooking oil methyl esters (WCOME) and neat biodiesel. A conventional B7 diesel oil was considered as a reference fuel. The two blends, respectively, included 40 and 70% of WCOME, on a volumetric basis. The influence of biodiesel was analyzed by testing the engine in two part-load operating conditions, applying proper control strategies to the exhaust gas recirculation (EGR) circuit and rail pressure, to assess the interactions between the engine management and the tested fuels. The variable nozzle turbine (VNT) was controlled to obtain a constant level of intake pressure in the two experimental points. Referring to biodiesel effects at constant operating mode, higher WCOME content generally resulted in better efficiency and soot emission, while NOX emission was negatively affected. EGR activation allowed for limited NO formation but with penalties in soot emission. Furthermore, interactions between the EGR circuit and turbocharger operations and control led to higher fuel consumption and lower efficiency. Finally, the increase in rail pressure corresponded to better soot emission and penalties in NOX emission. Combining all these effects, the selection of EGR rate and rail pressure values higher than the standard levels resulted in better efficiency, NOX, and soot emissions when comparing blends and neat biodiesel to conventional B7, granting advantages not only with regard to greenhouse gas emissions. Combustion parameters were also assessed, showing that combustion stability and combustion noise were not negatively affected by biodiesel use. Combustion duration was reduced when using WCOME and its blend, even if the center of combustion was slightly shifted along the expansion stroke. The main contribution of this investigation to the scientific and technical knowledge on biodiesel application to internal combustion engines is related to the development of tests on diesel–biodiesel blends with high WCOME content or neat WCOME, identifying their effects on NOX emissions, the definition of integrated strategies of HP EGR system, fuel rail pressure, and VNT for the simultaneous reduction in NOX and soot emissions, and the detailed assessment of the influence of biodiesel on a wide range of combustion parameters. Full article
(This article belongs to the Special Issue Performance and Emissions of Advanced Fuels in Combustion Engines)
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28 pages, 9195 KB  
Article
Enhancing Sealing Performance Predictions: A Comprehensive Study of XGBoost and Polynomial Regression Models with Advanced Optimization Techniques
by Weiru Zhou and Zonghong Xie
Materials 2025, 18(10), 2392; https://doi.org/10.3390/ma18102392 - 20 May 2025
Cited by 1 | Viewed by 933
Abstract
Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and [...] Read more.
Motors, as the core carriers of pollution-free power, realize efficient electric energy conversion in clean energy systems such as electric vehicles and wind power generation, and are widely used in industrial automation, smart home appliances, and rail transit fields with their low-noise and zero-emission operating characteristics, significantly reducing the dependence on fossil energy. As the requirements of various application scenarios become increasingly complex, it becomes particularly important to accurately and quickly design the sealing structure of motors. However, traditional design methods show many limitations when facing such challenges. To solve this problem, this paper proposes hybrid models of machine learning that contain polynomial regression and optimization XGBOOST models to rapidly and accurately predict the sealing performance of motors. Then, the hybrid model is combined with the simulated annealing algorithm and multi-objective particle swarm optimization algorithm for optimization. The reliability of the results is verified by the mutual verification of the results of the simulated annealing algorithm and the particle swarm optimization algorithm. The prediction accuracy of the hybrid model for data outside the training set is within 2.881%. Regarding the prediction speed of this model, the computing time of ML is less than 1 s, while the computing time of FEA is approximately 9 h, with an efficiency improvement of 32,400 times. Through the cross-validation of single-objective optimization and multi-objective optimization algorithms, the optimal design scheme is a groove depth of 0.8–0.85 mm and a pre-tightening force of 80 N. The new method proposed in this paper solves the limitations in the design of motor sealing structures, and this method can be extended to other fields for application. Full article
(This article belongs to the Section Materials Simulation and Design)
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13 pages, 4280 KB  
Article
Performance Characteristics of the Battery-Operated Silicon PIN Diode Detector with an Integrated Preamplifier and Data Acquisition Module for Fusion Particle Detection
by Allan Xi Chen, Benjamin F. Sigal, John Martinis, Alfred YiuFai Wong, Alexander Gunn, Matthew Salazar, Nawar Abdalla and Kai-Jian Xiao
J. Nucl. Eng. 2025, 6(2), 15; https://doi.org/10.3390/jne6020015 - 15 May 2025
Viewed by 956
Abstract
We present the performance and application of a commercial off-the-shelf Si PIN diode (Hamamatsu S14605) as a charged particle detector in a compact ion beam system (IBS) capable of generating D–D and p–B fusion charged particles. This detector is inexpensive, widely available, and [...] Read more.
We present the performance and application of a commercial off-the-shelf Si PIN diode (Hamamatsu S14605) as a charged particle detector in a compact ion beam system (IBS) capable of generating D–D and p–B fusion charged particles. This detector is inexpensive, widely available, and operates in photoconductive mode under a reverse bias voltage of 12 V, supplied by an A23 battery. A charge-sensitive preamplifier (CSP) is mounted on the backside of the detector’s four-layer PCB and powered by two ±3 V lithium batteries (A123). Both the detector and CSP are housed together on the vacuum side of the IBS, facing the fusion target. The system employs a CF-2.75-flanged DB-9 connector feedthrough to supply the signal, bias voltage, and rail voltages. To mitigate the high sensitivity of the detector to optical light, a thin aluminum foil assembly is used to block optical emissions from the ion beam and target. Charged particles generate step responses at the preamplifier output, with pulse rise times in the order of 0.2 to 0.3 µs. These signals are recorded using a custom-built data acquisition unit, which features an optical fiber data link to ensure the electrical isolation of the detector electronics. Subsequent digital signal processing is employed to optimally shape the pulses using a CR-RCn filter to produce Gaussian-shaped signals, enabling the accurate extraction of energy information. Performance results indicate that the detector’s baseline RMS ripple noise can be as low as 0.24 mV. Under actual laboratory conditions, the estimated signal-to-noise ratios (S/N) for charged particles from D–D fusion—protons, tritons, and helions—are approximately 225, 75, and 41, respectively. Full article
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17 pages, 2943 KB  
Article
Experimental Study on Noise Reduction Performance of Vertical Sound Barrier in Elevated Rail Transit
by Lizhong Song, Yisheng Zhang, Quanmin Liu, Yunke Luo and Ran Bi
Buildings 2025, 15(10), 1621; https://doi.org/10.3390/buildings15101621 - 11 May 2025
Viewed by 793
Abstract
With the large-scale construction of rail transit in mainland China, the noise problem caused by passing trains has become increasingly prominent. The vertical sound barrier is currently the most effective noise control measure for rail transit. However, the noise reduction performance of the [...] Read more.
With the large-scale construction of rail transit in mainland China, the noise problem caused by passing trains has become increasingly prominent. The vertical sound barrier is currently the most effective noise control measure for rail transit. However, the noise reduction performance of the vertical sound barrier at different train speeds remains unclear. This study focuses on the box-girder cross-sections of an elevated urban rail transit line with and without vertical sound barriers, conducting field tests during train passages. Based on the test results, the influence of train speed on noise levels at both cross-sections was investigated, the sound source characteristics were analyzed, and the noise reduction performance of the vertical sound barriers at different speeds was explored. The findings indicate the following: Regardless of the presence of sound barriers, within the speed range of 20 to 80 km/h, the linear sound pressure levels at the track-side and beam-side measurement points exhibit a strong linear correlation with speed, while the correlation is weaker at the beam-bottom measurement points. As speed increases, the wheel–rail noise increases by approximately 1.5 dB compared to the structural noise at the same speed. Vertical sound barriers significantly reduce mid-to-high-frequency noise, but in the low frequency band between 20 and 63 Hz, the noise increases, likely due to secondary structural noise radiated by the self-vibration of the barriers when trains pass. At speeds of 20, 40, 60, and 80 km/h, the insertion loss at measurement points located 7.5 m from the track centerline ranges from 6.5 to 9.0, 8.5 to 10.5, 7.5 to 9.5, and 7.5 to 10.2 dB, respectively. At 25 m from the track centerline, the insertion loss ranges from 1.5 to 2.5, 6.0 to 6.5, 5.5 to 6.0, and 5.0 to 6.0 dB, respectively. The noise reduction capability of the vertical sound barrier initially increases and then decreases with higher speeds, and the rate of reduction slows as speed increases. This research will provide a reference and basis for determining speed limits in the rail transit sections equipped with sound barriers. Full article
(This article belongs to the Special Issue Vibration Prediction and Noise Assessment of Building Structures)
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