工业工程最新文献_第3页

Effects of antibiotics and heavy metals on ARGs in Danjiangkou Reservoir

IF 5.3 2区环境科学与生态学

Emerging Contaminants Pub Date : 2025-03-20 DOI: 10.1016/j.emcon.2024.100453

Jing Li , Xuanzi Guo , Xingxing Long , Jiangyan Wu , Weijia Zhang , Yanrong Zhu , Chunhui Xi , Yao Zhang

{"title":"Effects of antibiotics and heavy metals on ARGs in Danjiangkou Reservoir","authors":"Jing Li , Xuanzi Guo , Xingxing Long , Jiangyan Wu , Weijia Zhang , Yanrong Zhu , Chunhui Xi , Yao Zhang","doi":"10.1016/j.emcon.2024.100453","DOIUrl":"10.1016/j.emcon.2024.100453","url":null,"abstract":"<div><div>Antibiotic resistance genes (ARGs) have attracted more and more attention due to their potential exposure hazards. The Danjiangkou Reservoir (DJKR) is the source of water for the Middle Route Project under the South-to-North Water Transfer Scheme in China. To clarify the distribution of ARGs and their influencing factors in DJKR (including Danjiang Reservoir (DR) and Hanjiang River Reservoir (HR)), we used metagenomic analysis to investigate the ARGs. The results showed that the most abundant bacteria of both parts were Proteobacteteria. Antibiotic efflux (58.2 %) and alteration of antibiotic targets (69.4 %) were the main mechanisms in DR and HR. The composition of ARG species was similar in the two parts, but the number of ARG isoforms in HR was significantly higher than that in DR. ARG Intl1 was detected in both DR and HR. Network analysis showed a significant correlation between mobile genetic elements (MGEs) and ARGs. Heavy metals also showed a significant correlation with ARGs. Interestingly, the relationship between heavy metals and ARGs were more significant than that between antibiotics and ARGs.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100453"},"PeriodicalIF":5.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusion-Based Constitutive Model (FuCe): Toward Model-Data Augmentation in Constitutive Modeling

IF 3.4

国际机械系统动力学学报(英文) Pub Date : 2025-03-20 DOI: 10.1002/msd2.70005

Tushar, Sawan Kumar, Souvik Chakraborty

{"title":"Fusion-Based Constitutive Model (FuCe): Toward Model-Data Augmentation in Constitutive Modeling","authors":"Tushar, Sawan Kumar, Souvik Chakraborty","doi":"10.1002/msd2.70005","DOIUrl":"https://doi.org/10.1002/msd2.70005","url":null,"abstract":"<p>Constitutive modeling is crucial for engineering design and simulations to accurately describe material behavior. However, traditional phenomenological models often struggle to capture the complexities of real materials under varying stress conditions due to their fixed forms and limited parameters. While recent advances in deep learning have addressed some limitations of classical models, purely data-driven methods tend to require large data sets, lack interpretability, and struggle to generalize beyond their training data. To tackle these issues, we introduce “Fusion-based Constitutive model (FuCe): Toward model-data augmentation in constitutive modeling.” This approach combines established phenomenological models with an Input Convex Neural Network architecture, designed to train on the limited and noisy force-displacement data typically available in practical applications. The hybrid model inherently adheres to necessary constitutive conditions. During inference, Monte Carlo dropout is employed to generate Bayesian predictions, providing mean values and confidence intervals that quantify uncertainty. We demonstrate the model's effectiveness by learning two isotropic constitutive models and one anisotropic model with a single fiber direction, across six different stress states. The framework's applicability is also showcased in finite element simulations across three geometries of varying complexities. Our results highlight the framework's superior extrapolation capabilities, even when trained on limited and noisy data, delivering accurate and physically meaningful predictions across all numerical examples.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 1","pages":"86-100"},"PeriodicalIF":3.4,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Application of the Novel Kolmogorov–Arnold Networks for Predicting the Fundamental Period of RC Infilled Frame Structures

IF 3.4

国际机械系统动力学学报(英文) Pub Date : 2025-03-18 DOI: 10.1002/msd2.70004

Shan Lin, Kaiyang Zhao, Hongwei Guo, Quanke Hu, Xitailang Cao, Hong Zheng

{"title":"The Application of the Novel Kolmogorov–Arnold Networks for Predicting the Fundamental Period of RC Infilled Frame Structures","authors":"Shan Lin, Kaiyang Zhao, Hongwei Guo, Quanke Hu, Xitailang Cao, Hong Zheng","doi":"10.1002/msd2.70004","DOIUrl":"https://doi.org/10.1002/msd2.70004","url":null,"abstract":"<p>The fundamental period is a crucial parameter in structural dynamics that informs the design, assessment, and monitoring of structures to ensure the safety and stability of buildings during earthquakes. Numerous machine-learning and deep-learning approaches have been proposed to predict the fundamental period of infill-reinforced concrete frame structures. However, challenges remain, including insufficient prediction accuracy and excessive computational resource demands. This study aims to provide a new paradigm for accurately and efficiently predicting fundamental periods, namely, Kolmogorov–Arnold networks (KANs) and their variants, especially radial basis function KANs (RBF-KANs). KANs are formulated based on the Kolmogorov–Arnold representation theorem, positioning them as a promising alternative to multilayer perceptron. In this research, we compare the performance of KANs against fully connected neural networks (FCNNs) in the context of fundamental period prediction. The mutual information method was employed for the analysis of dependencies between features in the FP4026 data set. Nine predictive models, including KANs, F-KANs, FCNN-2, FCNN-11, CatBoost, Support Vector Machine, and others, were constructed and compared, with hyperparameters determined by Optuna, which will highlight the optimal model amongst the F-KANs models. Numerical results manifest that the highest performance is yielded by the KANs with <i>R</i><sup>2</sup> = 0.9948, which offers an explicit form of the formula. Lastly, we further dive into the explainability and interpretability of the KANs, revealing that the number of stories and the opening percentage features have a significant effect on the fundamental period prediction results.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 1","pages":"67-85"},"PeriodicalIF":3.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digitalizing greenhouse trials: An automated approach for efficient and objective assessment of plant damage using deep learning

IF 8.2

Artificial Intelligence in Agriculture Pub Date : 2025-03-17 DOI: 10.1016/j.aiia.2025.03.001

Laura Gómez-Zamanillo , Arantza Bereciartúa-Pérez , Artzai Picón , Liliana Parra , Marian Oldenbuerger , Ramón Navarra-Mestre , Christian Klukas , Till Eggers , Jone Echazarra

{"title":"Digitalizing greenhouse trials: An automated approach for efficient and objective assessment of plant damage using deep learning","authors":"Laura Gómez-Zamanillo , Arantza Bereciartúa-Pérez , Artzai Picón , Liliana Parra , Marian Oldenbuerger , Ramón Navarra-Mestre , Christian Klukas , Till Eggers , Jone Echazarra","doi":"10.1016/j.aiia.2025.03.001","DOIUrl":"10.1016/j.aiia.2025.03.001","url":null,"abstract":"<div><div>The use of image based and, recently, deep learning-based systems have provided good results in several applications. Greenhouse trials are key part in the process of developing and testing new herbicides and analyze the response of the species to different products and doses in a controlled way. The assessment of the damage in the plant is daily done in all trials by visual evaluation by experts. This entails time consuming process and lack of repeatability. Greenhouse trials require new digital tools to reduce time consuming process and to endow the experts with more objective and repetitive methods for establishing the damage in the plants.</div><div>To this end, a novel method is proposed composed by an initial segmentation of the plant species followed by a multibranch convolutional neural network to estimate the damage level. In this way, we overcome the need for costly and unaffordable pixelwise manual segmentation for damage symptoms and we make use of global damage estimation values provided by the experts.</div><div>The algorithm has been deployed under real greenhouse trials conditions in a pilot study located in BASF in Germany and tested over four species (GLXMA, TRZAW, ECHCG, AMARE). The results show mean average error (MAE) values ranging from 5.20 for AMARE and 8.07 for ECHCG for the estimation of PDCU value, with correlation values (R<sup>2</sup>) higher than 0.85 in all situations, and up to 0.92 in AMARE. These results surpass the inter-rater variability of human experts demonstrating that the proposed automated method is appropriate for automatically assessing greenhouse damage trials.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 280-295"},"PeriodicalIF":8.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Super-Element Differential-Quadrature Discrete-Time Transfer Matrix Method for Efficient Transient Analysis of Rotor Systems

IF 3.4

国际机械系统动力学学报(英文) Pub Date : 2025-03-16 DOI: 10.1002/msd2.70002

Kai Xie, Xiaoting Rui, Bin He, Jinghong Wang

{"title":"Super-Element Differential-Quadrature Discrete-Time Transfer Matrix Method for Efficient Transient Analysis of Rotor Systems","authors":"Kai Xie, Xiaoting Rui, Bin He, Jinghong Wang","doi":"10.1002/msd2.70002","DOIUrl":"https://doi.org/10.1002/msd2.70002","url":null,"abstract":"<p>Efficient transient analysis is critical in rotor dynamics. This study proposes the super-element (SE) differential-quadrature discrete-time transfer matrix method (DQ-DT-TMM), a novel approach that eliminates the requirement for initial component accelerations and effectively handles beam and solid finite element (FE) models with high-dimensional degrees of freedom (DOFs) in rotor systems. The primary methodologies of this approach include: (1) For the beam substructure FE dynamic equation, the Craig–Bampton method is employed for the order reduction of internal coordinates, followed by the differential-quadrature method for temporal discretization. Using SE technology, the internal accelerations are condensed into the boundary accelerations, and the transfer equation and matrix for beam SEs are derived. (2) For the solid substructure FE dynamic equation formulated in the rotating reference frame, in addition to applying the procedures used for beam substructures, rigid multipoint constraints are introduced to condense the boundary coordinates for hybrid modeling with lumped parameter components. The transfer equation is subsequently formulated in the inertial reference frame, enabling the derivation of the transfer matrix for solid SEs. Comparative analysis with full-order FE models in commercial software demonstrates the advantages of the SE DQ-DT-TMM for linear rotor systems: (i) Accurately captures system dynamics using only a few primary modes. (ii) Achieves a 99.68% reduction in computational time for a beam model with 1120 elements and a 99.98% reduction for a solid model with 75 361 elements. (iii) Effectively recovers dynamic responses at any system node using recovery techniques. This research develops a computationally efficient framework for the transient analysis of large-scale rotor systems, effectively addressing the challenges associated with high-dimensional DOF models in conventional DT-TMMs.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 1","pages":"141-159"},"PeriodicalIF":3.4,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatiotemporal distribution and environmental risk assessment of 6PPDQ in the Schuylkill River

IF 5.3 2区环境科学与生态学

Emerging Contaminants Pub Date : 2025-03-15 DOI: 10.1016/j.emcon.2025.100501

Kavya Somepalli, Gangadhar Andaluri

{"title":"Spatiotemporal distribution and environmental risk assessment of 6PPDQ in the Schuylkill River","authors":"Kavya Somepalli, Gangadhar Andaluri","doi":"10.1016/j.emcon.2025.100501","DOIUrl":"10.1016/j.emcon.2025.100501","url":null,"abstract":"<div><div>Tire wear particles (TWPs) and associated contaminants, including microplastics, benzothiazoles, polycyclic aromatic hydrocarbons (PAHs), N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD), its byproduct 6PPD-Quinone (6PPDQ), and heavy metals, are emerging pollutants in aquatic ecosystems. 6PPD, a commonly used tire antioxidant, reacts with ozone to form 6PPDQ, a toxic compound linked to acute mortality in aquatic species, such as Coho salmon. Despite its known impact, data on 6PPDQ in northeastern U.S. freshwater systems, including the Schuylkill River, remain limited. This study examined the spatiotemporal distribution of 6PPDQ in the Schuylkill River and assessed its environmental risks. It also identified key contamination sources and seasonal trends. We analyzed 6PPDQ concentrations at 16 locations across different seasons using the EPA 1634 Draft Method. Their relationship with traffic volume, population density, and tire-related industrial proximity was evaluated. Concentrations ranged from non-detectable to 17.95ng/L, with urban regions exhibiting higher levels. A moderate positive correlation (r=0.416) between 6PPDQ concentrations and Average Annual Daily Traffic (AADT) suggests traffic as a significant source. Population density and industrial proximity also contributed to contamination. Based on the EPA freshwater screening value (11ng/L), two sites posed high risks, while 88% were at medium risk. Risk levels peaked in October, when increased precipitation and reduced flow exacerbated contamination. These findings highlight the seasonal intensification of 6PPDQ pollution, emphasizing the need for stormwater management and long-term monitoring to mitigate risks and assess seasonal dynamics in freshwater systems.</div></div>","PeriodicalId":11539,"journal":{"name":"Emerging Contaminants","volume":"11 2","pages":"Article 100501"},"PeriodicalIF":5.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TGFN-SD: A text-guided multimodal fusion network for swine disease diagnosis

IF 8.2

Artificial Intelligence in Agriculture Pub Date : 2025-03-14 DOI: 10.1016/j.aiia.2025.03.002

Gan Yang , Qifeng Li , Chunjiang Zhao , Chaoyuan Wang , Hua Yan , Rui Meng , Yu Liu , Ligen Yu

{"title":"TGFN-SD: A text-guided multimodal fusion network for swine disease diagnosis","authors":"Gan Yang , Qifeng Li , Chunjiang Zhao , Chaoyuan Wang , Hua Yan , Rui Meng , Yu Liu , Ligen Yu","doi":"10.1016/j.aiia.2025.03.002","DOIUrl":"10.1016/j.aiia.2025.03.002","url":null,"abstract":"<div><div>China is the world's largest producer of pigs, but traditional manual prevention, treatment, and diagnosis methods cannot satisfy the demands of the current intensive production environment. Existing computer-aided diagnosis (CAD) systems for pigs are dominated by expert systems, which cannot be widely applied because the collection and maintenance of knowledge is difficult, and most of them ignore the effect of multimodal information. A swine disease diagnosis model was proposed in this study, the Text-Guided Fusion Network-Swine Diagnosis (TGFN-SD) model, which integrated text case reports and disease images. The model integrated the differences and complementary information in the multimodal representation of diseases through the text-guided transformer module such that text case reports could carry the semantic information of disease images for disease identification. Moreover, it alleviated the phenotypic overlap problem caused by similar diseases in combination with supervised learning and self-supervised learning. Experimental results revealed that TGFN-SD achieved satisfactory performance on a constructed swine disease image and text dataset (SDT6K) that covered six disease classification datasets with accuracy and F1-score of 94.48 % and 94.4 % respectively. The accuracies and F1-scores increased by 8.35 % and 7.24 % compared with those under the unimodal situation and by 2.02 % and 1.63 % compared with those of the optimal baseline model under the multimodal fusion. Additionally, interpretability analysis revealed that the model focus area was consistent with the habits and rules of the veterinary clinical diagnosis of pigs, indicating the effectiveness of the proposed model and providing new ideas and perspectives for the study of swine disease CAD.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 2","pages":"Pages 266-279"},"PeriodicalIF":8.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Health Monitoring of Thin Shell Structures

IF 3.4

国际机械系统动力学学报(英文) Pub Date : 2025-03-13 DOI: 10.1002/msd2.12141

Ihtisham Khalid, Zahid Ahmed Qureshi, Faisal Siddiqui, Selda Oterkus, Erkan Oterkus

{"title":"Structural Health Monitoring of Thin Shell Structures","authors":"Ihtisham Khalid, Zahid Ahmed Qureshi, Faisal Siddiqui, Selda Oterkus, Erkan Oterkus","doi":"10.1002/msd2.12141","DOIUrl":"https://doi.org/10.1002/msd2.12141","url":null,"abstract":"<p>Thin plate and shell structures are extensively used in aerospace, naval, and energy sectors due to their lightweight and efficient load-bearing properties. Structural Health Monitoring (SHM) implementations are becoming increasingly important in these industries to reduce maintenance costs, improve reliability, and ensure safe operations. This study presents an efficient triangular inverse shell element for thin shell structures, developed using discrete Kirchhoff assumptions within the inverse finite element method (iFEM) framework. The proposed inverse formulation is efficient and requires fewer strain sensors to achieve accurate and reliable displacement field reconstruction than existing inverse elements based on the First Order Shear Deformation Theory (FSDT). These features are critical to iFEM-based SHM strategies for improving real-time efficiency while reducing project costs. The inverse element is rigorously validated using benchmark problems under in-plane, out-of-plane, and general loading conditions. Also, its performance is compared to an existing competitive inverse shell element based on FSDT. The inverse formulation is further evaluated for robust shape-sensing capability, considering a real-world structural configuration under a practicable sparse sensor arrangement. Additional investigation includes defect characterization and structural health assessment using damage index criteria. This research contributes toward developing more reliable and cost-effective monitoring solutions by highlighting the potential application of the proposed inverse element for SHM frameworks designed for thin shell structures.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 1","pages":"20-39"},"PeriodicalIF":3.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.12141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safeguarding Pipeline Integrity Through Stacked Ensemble Learning and Data Fusion

IF 3.4

国际机械系统动力学学报(英文) Pub Date : 2025-03-13 DOI: 10.1002/msd2.12142

Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan

{"title":"Safeguarding Pipeline Integrity Through Stacked Ensemble Learning and Data Fusion","authors":"Hussein A. M. Hussein, Sharafiz B. Abdul Rahim, Faizal B. Mustapha, Prajindra S. Krishnan","doi":"10.1002/msd2.12142","DOIUrl":"https://doi.org/10.1002/msd2.12142","url":null,"abstract":"<p>This research presents a novel approach to pipeline Structure Health Monitoring (SHM) by utilizing frequency response function signals and integrating advanced data-driven techniques to detect and evaluate vibration responses regarding loose bolts, scale deposits within pipelines, and cracks at pipeline supports, aiming to determine the effectiveness of utilizing artificial neural networks (ANN) and an ensemble learning approach in detecting the aforementioned damages through a data-driven approach. The research starts by recording 6500 samples captured by two accelerometers, related to 11 replicated pipeline structural scenarios. The research demonstrated the potential of principal component analysis (PCA) in dimensionality reduction, achieving approximately 81% reduction in data set 1 acquired by accelerometer 1 and around 79.5% in data set 2 acquired by accelerometer 2, without significant loss of information. Additionally, two ANN base models were employed for fault recognition and classification, achieving over 99.88% accuracy and mean squared error values ranging from 0.00006 to 0.00019. A significant innovation of this work lies in the implementation of an ensemble learning approach, which integrates the strengths of the base models, showcasing outstanding performance that was proved consistent across multiple iterations, effectively mitigating the weaknesses of the base models and providing a reliable fault classification and prediction system. This research underscores the effectiveness of combining PCA, ANN, k-fold cross-validation, and ensemble learning techniques in pipeline SHM for improved reliability and safety. The findings highlight the potential for broader applications of this methodology in real-world scenarios, addressing urgent challenges faced by infrastructure owners and operators.</p>","PeriodicalId":60486,"journal":{"name":"国际机械系统动力学学报(英文)","volume":"5 1","pages":"129-140"},"PeriodicalIF":3.4,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/msd2.12142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143707591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Wireless Power Transfer System with High Misalignment Tolerance Using Diagonal Crossed Solenoid Magnetic Coupler*

Chinese Journal of Electrical Engineering Pub Date : 2025-03-11 DOI: 10.23919/CJEE.2025.000099

Zhizhong Li;Yuandong Zhang;Samson S. Yu;Guidong Zhang

{"title":"A Hybrid Wireless Power Transfer System with High Misalignment Tolerance Using Diagonal Crossed Solenoid Magnetic Coupler*","authors":"Zhizhong Li;Yuandong Zhang;Samson S. Yu;Guidong Zhang","doi":"10.23919/CJEE.2025.000099","DOIUrl":"https://doi.org/10.23919/CJEE.2025.000099","url":null,"abstract":"During wireless charging, misalignments commonly occur in the transmission between the transmitting and receiving pads, including misalignments in the forward, backward, lateral and vertical directions. Unavoidable misalignments can result in changes in system parameters, thus affecting charging performance. A novel diagonally crossed solenoid magnetic coupler (DCSMC) is developed as a solution. The DCSMC integrated into a wireless power transfer (WPT) system with a hybrid topology enables superior misalignment tolerance in the X, Y, Z and XY diagonal directions while maintaining load-independent voltage output characteristics. A simplified parameter design method is developed to optimize the misalignment tolerance performance of a hybrid WPT system in multiple directions. Finally, a hardware prototype of a WPT system is constructed with an operating frequency of 200 kHz and a power of 200 W. The experimental results show that the hybrid WPT system, operating under loads from 40 Ω to 80 Ω, can tolerate misalignments of ±90 mm (40.9%) in both the <tex>$X$</tex> and Y axes, maintaining as small as a 5% fluctuation in output voltage. In addition, the WPT system can handle a maximum vertical displacement of +40 mm along the Z-axis and XY-diagonal misalignments of ±40 mm (12.8%).","PeriodicalId":36428,"journal":{"name":"Chinese Journal of Electrical Engineering","volume":"11 1","pages":"138-150"},"PeriodicalIF":0.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0