A new approach for the solution of transient eddy-current problems involving pieces with one direction of symmetry is presented. The approach is applicable to pieces with translational or rotational symmetry. A Fourier series decomposition of the solution is introduced in the direction of symmetry, which converts the original 3D numerical problem into a series of independent 2D problems. The decomposition has significant benefits in terms of computational time and numerical noise reduction, and is inherently parallelisable.
{"title":"A hybrid spectral-spatial formulation for the calculation of the transient eddy-current response","authors":"A. Skarlatos, Chritophe Reboud","doi":"10.3233/jae-230131","DOIUrl":"https://doi.org/10.3233/jae-230131","url":null,"abstract":"A new approach for the solution of transient eddy-current problems involving pieces with one direction of symmetry is presented. The approach is applicable to pieces with translational or rotational symmetry. A Fourier series decomposition of the solution is introduced in the direction of symmetry, which converts the original 3D numerical problem into a series of independent 2D problems. The decomposition has significant benefits in terms of computational time and numerical noise reduction, and is inherently parallelisable.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139170956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades.
{"title":"The application of artificial intelligence and machine learning in the design process for electromagnetic devices","authors":"David A. Lowther","doi":"10.3233/jae-230104","DOIUrl":"https://doi.org/10.3233/jae-230104","url":null,"abstract":"Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jinpeng Shi, Donglai Wang, Yan Zhao, Chengze Li, Aijun Zhang
The radiation of adjacent field sources has a specific spatial correlation. In order to suppress electromagnetic disturbance and improve the electromagnetic compatibility of secondary equipment, the electric field’s spatial coupling characteristics and distribution law should be mastered. Therefore, a method for predicting the spatial electric field generated by substation switching operation based on the Atomic Orbital Search-Graph Convolution Network- Long and Short-Term Memory (AOS-GCN-LSTM) model is presented to deal with this problem. First, the GCN is used to construct graph data according to node characteristics and topology information. The feature selection uses the Maximum Information Coefficient (MIC) to extract the spatial correlation of the adjacent field source radiation. At the same time, the LSTM is used to capture the temporal correlation characteristics of different position field strengths in space. Then, the AOS is used to optimize the model with a hyperparameter. In addition, the simulation data of the full-wave simulation model of the spatial electric field generated by switch operation in a 220 kV GIS substation is an example of verification. The results show that the prediction error of the proposed method is below 3%, and it has strong adaptability to the application environment and good prediction performance.
{"title":"Predicting of spatial electric field generated by substation switch operation based on AOS-GCN-LSTM Model","authors":"Jinpeng Shi, Donglai Wang, Yan Zhao, Chengze Li, Aijun Zhang","doi":"10.3233/jae-230089","DOIUrl":"https://doi.org/10.3233/jae-230089","url":null,"abstract":"The radiation of adjacent field sources has a specific spatial correlation. In order to suppress electromagnetic disturbance and improve the electromagnetic compatibility of secondary equipment, the electric field’s spatial coupling characteristics and distribution law should be mastered. Therefore, a method for predicting the spatial electric field generated by substation switching operation based on the Atomic Orbital Search-Graph Convolution Network- Long and Short-Term Memory (AOS-GCN-LSTM) model is presented to deal with this problem. First, the GCN is used to construct graph data according to node characteristics and topology information. The feature selection uses the Maximum Information Coefficient (MIC) to extract the spatial correlation of the adjacent field source radiation. At the same time, the LSTM is used to capture the temporal correlation characteristics of different position field strengths in space. Then, the AOS is used to optimize the model with a hyperparameter. In addition, the simulation data of the full-wave simulation model of the spatial electric field generated by switch operation in a 220 kV GIS substation is an example of verification. The results show that the prediction error of the proposed method is below 3%, and it has strong adaptability to the application environment and good prediction performance.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138684003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eniz Museljic, K. Roppert, L. Domenig, Alice Reinbacher Köstinger, M. Kaltenbacher
This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified on synthetic as well as measurement data.
{"title":"Employing automatic differentiation and neural networks for parameter identification of an energy based hysteresis model","authors":"Eniz Museljic, K. Roppert, L. Domenig, Alice Reinbacher Köstinger, M. Kaltenbacher","doi":"10.3233/jae-230107","DOIUrl":"https://doi.org/10.3233/jae-230107","url":null,"abstract":"This paper is about the parameter identification of an energy based hysteresis model from measurements by employing automatic differentiation and neural networks. We first introduce the energy based hysteresis model and the parameters which are to be identified. Then we show how the model can benefit from automatic differentiation. After that we incorporate a parametrization of the energy based hysteresis model via distribution functions and identify the parameters of the distribution function. Then, the hysteresis model is sampled and the generated datasets are used to train neural networks to predict the hysteresis parameters. The described methods are tested and verified on synthetic as well as measurement data.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an analytical method to calculate the on-load magnetic field, Back electromotive force (BEMF) and torque of the surface-mounted permanent-magnet vernier machine (SMPMVM) accounting for slots, tooth-tips, flux modulation pole slots (FMPS) and the shape of magnet. The on-load magnetic field is predicted according to the surface-current method of permanent magnet, subdomain model and the superposition principle. BEMF and torque are calculated based on the magnetic field. The results show that THD of open-circuit and on-load radial flux density in the SMPMVM with concentric magnet poles is smaller than that in the conventional SMPMVM with eccentric magnet poles. 30th and 50th harmonic orders are obvious between two motors. Moreover, the peak cogging torque of the motor with concentric magnet poles is almost six times of the peak cogging torque of motor with eccentric magnet poles. The finite-element and experimental results confirm that the developed analytical method has high accuracy for predicting the magnetic field, BEMF, cogging torque and on-load electro-magnetic torque of SMPMVM with different shape of magnet.
{"title":"Analytical calculation of magnetic field in surface-mounted permanent-magnet vernier motors","authors":"Ningning Ren, Le Fan, Keke Yang","doi":"10.3233/jae-230047","DOIUrl":"https://doi.org/10.3233/jae-230047","url":null,"abstract":"This paper proposes an analytical method to calculate the on-load magnetic field, Back electromotive force (BEMF) and torque of the surface-mounted permanent-magnet vernier machine (SMPMVM) accounting for slots, tooth-tips, flux modulation pole slots (FMPS) and the shape of magnet. The on-load magnetic field is predicted according to the surface-current method of permanent magnet, subdomain model and the superposition principle. BEMF and torque are calculated based on the magnetic field. The results show that THD of open-circuit and on-load radial flux density in the SMPMVM with concentric magnet poles is smaller than that in the conventional SMPMVM with eccentric magnet poles. 30th and 50th harmonic orders are obvious between two motors. Moreover, the peak cogging torque of the motor with concentric magnet poles is almost six times of the peak cogging torque of motor with eccentric magnet poles. The finite-element and experimental results confirm that the developed analytical method has high accuracy for predicting the magnetic field, BEMF, cogging torque and on-load electro-magnetic torque of SMPMVM with different shape of magnet.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138493458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to the special issue Deep learning for analysis and synthesis in electromagnetics","authors":"M. E. Mognaschi","doi":"10.3233/jae-239003","DOIUrl":"https://doi.org/10.3233/jae-239003","url":null,"abstract":"","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Kyrgiazoglou, Stefanos Gerardis, Dionisis Loizos, Panagiotis-Avgerinos Politis, T. Theodoulidis
Machining technology plays a major role in modern manufacturing industries due to the growing demand for equipment and products for aerospace, automotive, precision machinery sectors etc. A key issue associated with any type of machining operation is the accelerated tool wear which leads to shorter tool life, thus to low machining precision and decreased productivity. In this paper an electromagnetic Non-Destructive Testing method (NDT), and in particular Eddy Current Testing (ECT), is used to detect the wear of used cutting tools vs a new one.
{"title":"Eddy current testing in drill-bit tools","authors":"A. Kyrgiazoglou, Stefanos Gerardis, Dionisis Loizos, Panagiotis-Avgerinos Politis, T. Theodoulidis","doi":"10.3233/jae-230130","DOIUrl":"https://doi.org/10.3233/jae-230130","url":null,"abstract":"Machining technology plays a major role in modern manufacturing industries due to the growing demand for equipment and products for aerospace, automotive, precision machinery sectors etc. A key issue associated with any type of machining operation is the accelerated tool wear which leads to shorter tool life, thus to low machining precision and decreased productivity. In this paper an electromagnetic Non-Destructive Testing method (NDT), and in particular Eddy Current Testing (ECT), is used to detect the wear of used cutting tools vs a new one.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Infrared thermography is an imaging technique that can be used to inspect materials for flaws and various degradations in a non destructive way. In this work, we focused on the use of fast models to recover information about the material properties from experimental measurements recorded over time.Two different modelling approaches are compared to each other and to experimental data acquired on a composite plate. Then, the model based inverse problem consisting in estimating the plate properties is discussed.
{"title":"Transient thermographic signal analysis for thinning detection in composite plate","authors":"Christophe Reboud, Audrey Vigneron, Anastassios Skarlatos","doi":"10.3233/jae-230170","DOIUrl":"https://doi.org/10.3233/jae-230170","url":null,"abstract":"Infrared thermography is an imaging technique that can be used to inspect materials for flaws and various degradations in a non destructive way. In this work, we focused on the use of fast models to recover information about the material properties from experimental measurements recorded over time.Two different modelling approaches are compared to each other and to experimental data acquired on a composite plate. Then, the model based inverse problem consisting in estimating the plate properties is discussed.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tong Wu, Yuanyuan Wang, Xiaoguang Li, Yu Tao, Chaofeng Ye
The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%.
顶针管的可靠性对维护核电站的安全起着至关重要的作用。需要对缺陷深度进行量化和预测，以支持运行决策。本文介绍了一种基于涡流测试（ECT）数据分析的顶针管壁缺陷量化方法。然后，研究了使用人工神经网络（ANN）预测检测深度的方法。根据管子的位置将其分为两种形状和四个区域，并通过平均插值法对每个区域和每种形状的数据进行扩展。为每个区域的每个形状构建了基于 ANN 的预测模型。实验结果表明，该模型可以根据前三年的信号预测下一年的信号，平均绝对百分比误差小于 16%。
{"title":"Detection and prediction of thimble tube defects using artificial neural networks","authors":"Tong Wu, Yuanyuan Wang, Xiaoguang Li, Yu Tao, Chaofeng Ye","doi":"10.3233/jae-230132","DOIUrl":"https://doi.org/10.3233/jae-230132","url":null,"abstract":"The reliability of thimble tubes plays a critical role for maintaining the safety of a nuclear power plant. The defect depth needs to be quantified and predicted to support the operational decision-making. This paper presents a method to quantify the defects on thimble tube wall based on the analyzation of eddy current testing (ECT) data. Then, a method using artificial neural network (ANN) to predict the detect depth is studied. The tubes are divided into 2 shapes and four regions according to their positions and the data of each region and each shape is expanded by mean interpolation. A prediction model based on ANN is constructed for each shape in each region. The experimental results show that the model can predict the signal of the next year according to the signal of the previous three years with mean absolute percentage error less than 16%.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139083944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonello Tamburrino, Alessandro Sardellitti, Filippo Milano, Vincenzo Mottola, Marco Laracca, Luigi Ferrigno
This paper introduces Buckingham’s 𝜋 theorem in the context of Non-Destructive Testing & Evaluation (NDT&E). Its application leads to easier problems to handle by reducing the number of variables involved. In this sense, dimensional analysis can provide the foundation for in-line, real-time and low-cost inspection methods that are fully compatible with the requirements of the Industry 4.0 and NDE 4.0 paradigms. In order to show the impact of the Buckingham’s 𝜋 theorem in NDT&E, we consider a practical case of interest, i.e. the simultaneous estimation of thickness and electrical conductivity of metallic plates via Eddy Current Testing. An initial numerical analysis is carried out with the aim to show the metrological performance of the method. The results obtained show that the method combines good accuracy with low computational costs.
{"title":"Application of dimensional analysis to ECT in the era of NDE 4.0","authors":"Antonello Tamburrino, Alessandro Sardellitti, Filippo Milano, Vincenzo Mottola, Marco Laracca, Luigi Ferrigno","doi":"10.3233/jae-230133","DOIUrl":"https://doi.org/10.3233/jae-230133","url":null,"abstract":"This paper introduces Buckingham’s 𝜋 theorem in the context of Non-Destructive Testing & Evaluation (NDT&E). Its application leads to easier problems to handle by reducing the number of variables involved. In this sense, dimensional analysis can provide the foundation for in-line, real-time and low-cost inspection methods that are fully compatible with the requirements of the Industry 4.0 and NDE 4.0 paradigms. In order to show the impact of the Buckingham’s 𝜋 theorem in NDT&E, we consider a practical case of interest, i.e. the simultaneous estimation of thickness and electrical conductivity of metallic plates via Eddy Current Testing. An initial numerical analysis is carried out with the aim to show the metrological performance of the method. The results obtained show that the method combines good accuracy with low computational costs.","PeriodicalId":50340,"journal":{"name":"International Journal of Applied Electromagnetics and Mechanics","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138683999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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