Surge over-voltages may induce magnetic saturation, flux instability in power components and undermining reliability. To address trade-off between computational efficiency and accuracy of the fixed-step finite element method (FEM) under transients, this paper presents an adaptive time-stepping FEM (ATS-FEM) driven by higher-order truncation-error estimation, with Schur complement preconditioning integrated to optimize memory usage for accelerating parallel matrix solution. Three typical magnetic components often used in strong magnetic launch and propulsion systems are simulated and validated in comparison with that of commercial software. It is shown that our developed ATS-FEM can dynamically adjust the time steps but with high numerical accuracy maintained, and it also has the capability for capturing localized saturation, radial gradients, and permeability drops in high-current regions of the magnetic components.
{"title":"An Adaptive Time-Stepping Finite Element Method With Schur-Complement Preconditioning for Surge Simulation of Magnetic Components","authors":"Zhe Chen;Yanning Chen;Yi-Yao Wang;Hao-Xuan Zhang;Yin-Da Wang;Rongchuan Bai;Zhengwei Du;Yingzong Liang;Fang Liu;Hao Xie;Wen-Yan Yin","doi":"10.1109/JMMCT.2025.3606993","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3606993","url":null,"abstract":"Surge over-voltages may induce magnetic saturation, flux instability in power components and undermining reliability. To address trade-off between computational efficiency and accuracy of the fixed-step finite element method (FEM) under transients, this paper presents an adaptive time-stepping FEM (ATS-FEM) driven by higher-order truncation-error estimation, with Schur complement preconditioning integrated to optimize memory usage for accelerating parallel matrix solution. Three typical magnetic components often used in strong magnetic launch and propulsion systems are simulated and validated in comparison with that of commercial software. It is shown that our developed ATS-FEM can dynamically adjust the time steps but with high numerical accuracy maintained, and it also has the capability for capturing localized saturation, radial gradients, and permeability drops in high-current regions of the magnetic components.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"421-432"},"PeriodicalIF":1.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-28DOI: 10.1109/JMMCT.2025.3603902
Kiran Ravindran;Abhijith B. Narendranath;Kalarickaparambil Joseph Vinoy
Numerical electromagnetic computations must often accommodate random geometric representations while handling biological tissues, and engineered components with manufacturing tolerances. Meshless time-domain radial point interpolation method (RPIM) offers advantages to quantitatively analyze such geometric uncertainties using polynomial chaos expansion (PCE). Formulations for geometric uncertainties may require variations in mesh or node distribution for each analyzed sample, leading to high computational requirement for re-meshing. The proposed geometric stochastic RPIM (G-SRPIM) overcomes this with a single domain model by expressing the shape function matrix of RPIM in a stochastic framework. The uncertainty is quantified in G-SRPIM through a novel way by which its random support domain moment matrices are organized in a block structure, and inverted using Schur's complement and Neumann approximation, exploiting the underlying symmetry. The proposed method is validated by analyzing a parallel plate waveguide with a slit exhibiting random variations, a realistic 3D bio-electromagnetic problem involving a section of human head, and an iris filter with random variations in its iris dimensions. Standard deviation upto $45 %$ of the average inter-node distance is tested without jeopardizing the stability. The accuracy of our approach is compared with Monte-Carlo (MC) simulations on a deterministic RPIM using the Kolmogorov-Smirnov (KS) test. Additionally, results are compared with MC simulation on CST Studio Suite 2018 and stochastic collocation (SC). The proposed method exhibits superior execution time compared to SC and MC-based non-intrusive implementations, underscoring its efficiency and reliability in handling geometric uncertainties in microwave components.
在处理生物组织和具有制造公差的工程部件时，数值电磁计算必须经常适应随机几何表示。无网格时域径向点插值法（RPIM）具有利用多项式混沌展开（PCE）定量分析几何不确定性的优势。几何不确定性的公式可能需要每个分析样本的网格或节点分布的变化，导致重新网格划分的高计算需求。提出的几何随机RPIM （G-SRPIM）通过在随机框架中表示RPIM的形状函数矩阵，克服了这一问题。在G-SRPIM中，不确定性是通过一种新颖的方法来量化的，通过这种方法，它的随机支持域矩矩阵被组织成一个块结构，并使用舒尔补和诺依曼近似来反演，利用潜在的对称性。通过分析具有随机变化的狭缝平行板波导、涉及人体头部部分的现实三维生物电磁问题以及虹膜尺寸随机变化的虹膜滤波器，验证了所提方法的有效性。在不影响稳定性的情况下，测试平均节点间距离的标准偏差可达45%。我们的方法的准确性与蒙特卡罗（MC）模拟的确定性RPIM使用Kolmogorov-Smirnov （KS）测试进行了比较。此外，还将结果与CST Studio Suite 2018上的MC模拟和随机配置（SC）进行了比较。与基于SC和mc的非侵入式实现相比，该方法具有更好的执行时间，强调了其在处理微波元件几何不确定性方面的效率和可靠性。
{"title":"A Meshless Time-Domain Method for Geometric Uncertainty Quantification","authors":"Kiran Ravindran;Abhijith B. Narendranath;Kalarickaparambil Joseph Vinoy","doi":"10.1109/JMMCT.2025.3603902","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3603902","url":null,"abstract":"Numerical electromagnetic computations must often accommodate random geometric representations while handling biological tissues, and engineered components with manufacturing tolerances. Meshless time-domain radial point interpolation method (RPIM) offers advantages to quantitatively analyze such geometric uncertainties using polynomial chaos expansion (PCE). Formulations for geometric uncertainties may require variations in mesh or node distribution for each analyzed sample, leading to high computational requirement for re-meshing. The proposed geometric stochastic RPIM (G-SRPIM) overcomes this with a single domain model by expressing the shape function matrix of RPIM in a stochastic framework. The uncertainty is quantified in G-SRPIM through a novel way by which its random support domain moment matrices are organized in a block structure, and inverted using Schur's complement and Neumann approximation, exploiting the underlying symmetry. The proposed method is validated by analyzing a parallel plate waveguide with a slit exhibiting random variations, a realistic 3D bio-electromagnetic problem involving a section of human head, and an iris filter with random variations in its iris dimensions. Standard deviation upto <inline-formula><tex-math>$45 %$</tex-math></inline-formula> of the average inter-node distance is tested without jeopardizing the stability. The accuracy of our approach is compared with Monte-Carlo (MC) simulations on a deterministic RPIM using the Kolmogorov-Smirnov (KS) test. Additionally, results are compared with MC simulation on CST Studio Suite 2018 and stochastic collocation (SC). The proposed method exhibits superior execution time compared to SC and MC-based non-intrusive implementations, underscoring its efficiency and reliability in handling geometric uncertainties in microwave components.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"396-406"},"PeriodicalIF":1.5,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.1109/JMMCT.2025.3602986
Ting Zang;Gaobiao Xiao
This paper presents an efficient optimization algorithm for synthesizing the discrete array factor, which extends the optimization domain to the invisible region to mitigate aliasing effect, thereby achieving well-controlled radiation patterns. By further lowering the level of the sidelobes in part of the visible region, the algorithm allows to shape the radiation patterns of sparse arrays with desired characteristics, such as uniform main lobe ripples and low sidelobe levels. Some evanescent modes have been added to compensate for the additional degrees of freedom caused by the increased optimization range, so that the number of the extreme points to be controlled is still approximately equal to the number of degrees of freedom (NDF), maintaining the monotonic convergence property of the algorithm. Numerical examples and FEKO simulation results validate the effectiveness and the accuracy of the proposed method.
{"title":"An Efficient Method for Synthesizing Sparse Arrays With Well-Controlled Discrete Array Factors","authors":"Ting Zang;Gaobiao Xiao","doi":"10.1109/JMMCT.2025.3602986","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3602986","url":null,"abstract":"This paper presents an efficient optimization algorithm for synthesizing the discrete array factor, which extends the optimization domain to the invisible region to mitigate aliasing effect, thereby achieving well-controlled radiation patterns. By further lowering the level of the sidelobes in part of the visible region, the algorithm allows to shape the radiation patterns of sparse arrays with desired characteristics, such as uniform main lobe ripples and low sidelobe levels. Some evanescent modes have been added to compensate for the additional degrees of freedom caused by the increased optimization range, so that the number of the extreme points to be controlled is still approximately equal to the number of degrees of freedom (NDF), maintaining the monotonic convergence property of the algorithm. Numerical examples and FEKO simulation results validate the effectiveness and the accuracy of the proposed method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"388-395"},"PeriodicalIF":1.5,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An efficient hybrid approach based on combining the bidirectional recurrent neural network with knowledge-based neural network is presented to predict jitter in a chain of CMOS inverters in the presence of multiple noise sources. The new method achieves a reasonable accuracy and provides for efficient training using input data obtained from both a circuit simulator as well as analytical relations. The proposed approach can also estimate jitter for each inverter in the chain by only employing the accurate training data associated with the first inverter, resulting in a significant increase in speed compared to conventional approaches.
{"title":"Knowledge-Based Bidirectional Recurrent Neural Network Approach for Efficient Prediction of Jitter in a Chain of CMOS Inverters","authors":"Ahsan Javaid;Ramachandra Achar;Jai Narayan Tripathi","doi":"10.1109/JMMCT.2025.3602632","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3602632","url":null,"abstract":"An efficient hybrid approach based on combining the bidirectional recurrent neural network with knowledge-based neural network is presented to predict jitter in a chain of CMOS inverters in the presence of multiple noise sources. The new method achieves a reasonable accuracy and provides for efficient training using input data obtained from both a circuit simulator as well as analytical relations. The proposed approach can also estimate jitter for each inverter in the chain by only employing the accurate training data associated with the first inverter, resulting in a significant increase in speed compared to conventional approaches.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"407-420"},"PeriodicalIF":1.5,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01DOI: 10.1109/JMMCT.2025.3593872
Suyash Kushwaha;Chintu Bhaskara Rao;Shamini P R;Sourajeet Roy;Rohit Sharma
In this paper, novel copper graphene heterogeneous interconnect structures are proposed which retain the ease of fabrication while having far better electrical performance when compared to the conventional copper interconnects. In the nanoscale regime, signal integrity (SI) of the copper interconnects degrades significantly. To address the signal integrity issues, these heterogeneous interconnects are developed at 7 nm technology nodes which are further used to make the crossbar arrays for neuromorphic computing. The proposed copper graphene heterogeneous interconnects were designed by stacking the layers of copper and multilayer graphene nanoribbons (MLGNRs) one over the other and a detailed signal integrity analysis is done based on the quantities like the per unit length Resistance, Insertion Loss (IL), Return Loss (RL), eye diagrams, surface charge density and volume current density. The results shows that the proposed interconnects outperformed the copper interconnects based on each and every SI quantity. Finally, in the application example, the best performing heterogeneous interconnects are used to create larger crossbar arrays with sizes 64 × 64, 128 × 128. Further, the key performance matrices such as the delay time, the rise time and the fall time are analyzed and compared with the conventional crossbars made from the copper interconnects. The results in application example proved that the heterogeneous interconnects performs better than the copper interconnects for neuromorphic computing.
{"title":"Performance Enhanced Copper-Graphene Hetero Interconnect Structures in Crossbar Arrays for Neuromorphic Computing","authors":"Suyash Kushwaha;Chintu Bhaskara Rao;Shamini P R;Sourajeet Roy;Rohit Sharma","doi":"10.1109/JMMCT.2025.3593872","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3593872","url":null,"abstract":"In this paper, novel copper graphene heterogeneous interconnect structures are proposed which retain the ease of fabrication while having far better electrical performance when compared to the conventional copper interconnects. In the nanoscale regime, signal integrity (SI) of the copper interconnects degrades significantly. To address the signal integrity issues, these heterogeneous interconnects are developed at 7 nm technology nodes which are further used to make the crossbar arrays for neuromorphic computing. The proposed copper graphene heterogeneous interconnects were designed by stacking the layers of copper and multilayer graphene nanoribbons (MLGNRs) one over the other and a detailed signal integrity analysis is done based on the quantities like the per unit length Resistance, Insertion Loss (IL), Return Loss (RL), eye diagrams, surface charge density and volume current density. The results shows that the proposed interconnects outperformed the copper interconnects based on each and every SI quantity. Finally, in the application example, the best performing heterogeneous interconnects are used to create larger crossbar arrays with sizes 64 × 64, 128 × 128. Further, the key performance matrices such as the delay time, the rise time and the fall time are analyzed and compared with the conventional crossbars made from the copper interconnects. The results in application example proved that the heterogeneous interconnects performs better than the copper interconnects for neuromorphic computing.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"379-387"},"PeriodicalIF":1.5,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the impact of noise on time-reversal imaging and propose an approach that significantly enhances the detection of objects in noisy environments. Our method involves the decomposition of the time-reversal operator at a single frequency, known for its sensitivity to noise. We utilize a specific autoencoder architecture to denoise the generated dataset from a multi-static data matrix (MDM), effectively separating the signal sub-space from the noise sub-space, even at low signal-to-noise ratios (SNRs) ranging from −5 dB to high levels of SNR. This dataset is generated by simulating scatterers mounted at various locations within a two-dimensional (2D) grid, each with different SNRs.
{"title":"Enhancing DORT Method Performance in Time-Reversal Microwave Imaging Through Denoising Autoencoder","authors":"Hamed Rezaei;Amir Nader Askarpour;Abdolali Abdipour","doi":"10.1109/JMMCT.2025.3589191","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3589191","url":null,"abstract":"We investigate the impact of noise on time-reversal imaging and propose an approach that significantly enhances the detection of objects in noisy environments. Our method involves the decomposition of the time-reversal operator at a single frequency, known for its sensitivity to noise. We utilize a specific autoencoder architecture to denoise the generated dataset from a multi-static data matrix (MDM), effectively separating the signal sub-space from the noise sub-space, even at low signal-to-noise ratios (SNRs) ranging from −5 dB to high levels of SNR. This dataset is generated by simulating scatterers mounted at various locations within a two-dimensional (2D) grid, each with different SNRs.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"360-369"},"PeriodicalIF":1.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examines the computational challenges associated with modeling liver tumors using microwave ablation (MWA), while highlighting the limitations of conventional methods and advocating for the use of MWA in conjunction with artificial intelligence as a more promising approach. The proposed innovative antenna design, which comprises a coaxial line featuring a tapered outer conductor and a dipole antenna, aims to produce a nearly spherical ablation zone without the need for any additional matching network. Capable of operating at both 2.45 GHz and 5.8 GHz with minor structural modifications, it offers flexibility in tumor ablation systems. The research further incorporates and compares the sigmoidal model, a well-established computational method, and a recently developed parametric model for evaluating temperature-dependent properties in modeling the 3-D liver tissue, identifying differences in the ablation zone during MWA. Additionally, since both under and over ablation are major concerns during the MWA procedure, resulting in damage to healthy tissue and tumor recurrence, respectively, this study introduces a Taguchi Artificial Neural Networks (TNN) framework for the prediction of ablation zone in advance, thereby, significantly reducing the number of required training datasets without compromising performance metrics.
{"title":"Optimized Microwave Ablation With a Novel Applicator: Integration of Taguchi Neural Networks for Enhanced Predictive Accuracy of Ablation Zone","authors":"Suyash Kumar Singh;Brij Kumar Bharti;Amar Nath Yadav;Ajay Kumar Dwivedi","doi":"10.1109/JMMCT.2025.3589163","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3589163","url":null,"abstract":"This study examines the computational challenges associated with modeling liver tumors using microwave ablation (MWA), while highlighting the limitations of conventional methods and advocating for the use of MWA in conjunction with artificial intelligence as a more promising approach. The proposed innovative antenna design, which comprises a coaxial line featuring a tapered outer conductor and a dipole antenna, aims to produce a nearly spherical ablation zone without the need for any additional matching network. Capable of operating at both 2.45 GHz and 5.8 GHz with minor structural modifications, it offers flexibility in tumor ablation systems. The research further incorporates and compares the sigmoidal model, a well-established computational method, and a recently developed parametric model for evaluating temperature-dependent properties in modeling the 3-D liver tissue, identifying differences in the ablation zone during MWA. Additionally, since both under and over ablation are major concerns during the MWA procedure, resulting in damage to healthy tissue and tumor recurrence, respectively, this study introduces a Taguchi Artificial Neural Networks (TNN) framework for the prediction of ablation zone in advance, thereby, significantly reducing the number of required training datasets without compromising performance metrics.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"348-359"},"PeriodicalIF":1.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-10DOI: 10.1109/JMMCT.2025.3587386
Satish Kumar;Gopi Ram;Durbadal Mandal;Rajib Kar
In order to optimize the synthesis of Asymmetric Time-Modulated Circular Antenna Array (ATMCAA) and Symmetric Time-Modulated Circular Antenna Array (STMCAA), this work presents the Novel Particle Swarm Optimization Algorithm (NPSO). Inter-element spacing and uniform current excitation are maintained by regulating the switching time sequence and progressive phase delay of each element. A distinct cost function is developed for each of the two case studies. Using 20- and 36-element examples, several low side-lobe designs synthesized from ATMCAA and STMCAA are compared with traditional circular arrays. Through the manipulation of switching time sequence and progressive phase delay, the cost function is optimized to simultaneously reduce the side-lobe level (SLL) and directivity in ATMCAA and STMCAA. When it comes to antenna array synthesis, NPSO performs better than other algorithms, such as cat swarm optimization and invasive weed optimization. This study demonstrates how effective NPSO is at optimizing antenna arrays in order to improve higher communication reliability and signal quality.
{"title":"Optimal Configuration and Performance Enhancement of Time-Modulated Circular Antenna Arrays","authors":"Satish Kumar;Gopi Ram;Durbadal Mandal;Rajib Kar","doi":"10.1109/JMMCT.2025.3587386","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3587386","url":null,"abstract":"In order to optimize the synthesis of Asymmetric Time-Modulated Circular Antenna Array (ATMCAA) and Symmetric Time-Modulated Circular Antenna Array (STMCAA), this work presents the Novel Particle Swarm Optimization Algorithm (NPSO). Inter-element spacing and uniform current excitation are maintained by regulating the switching time sequence and progressive phase delay of each element. A distinct cost function is developed for each of the two case studies. Using 20- and 36-element examples, several low side-lobe designs synthesized from ATMCAA and STMCAA are compared with traditional circular arrays. Through the manipulation of switching time sequence and progressive phase delay, the cost function is optimized to simultaneously reduce the side-lobe level (SLL) and directivity in ATMCAA and STMCAA. When it comes to antenna array synthesis, NPSO performs better than other algorithms, such as cat swarm optimization and invasive weed optimization. This study demonstrates how effective NPSO is at optimizing antenna arrays in order to improve higher communication reliability and signal quality.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"334-347"},"PeriodicalIF":1.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-02DOI: 10.1109/JMMCT.2025.3585550
Wen-Tao Bao;Joseph D. Kotulski;Jin-Fa Lee
This paper presents an automatic mesh refinement method designed to accurately capture resonant responses in high-quality factor devices using surface integral equations. To validate the method, a solution-based error estimator is proposed to evaluate solution quality and identify elements requiring local mesh refinement. The sensitivity of the local error distribution to frequencies near numerical resonance is examined. To effectively capture the resonant behavior, an automatic h–refinement strategy, combined with frequency sweeping, is introduced. Numerical experiments on slotted cavities with high-quality factor are provided. In addition, the advantages of the proposed error estimator over the widely used residual error estimator are discussed.
{"title":"Automatic Mesh Refinement Process for High-Quality Factor Resonant Cavities Using the Method of Moments","authors":"Wen-Tao Bao;Joseph D. Kotulski;Jin-Fa Lee","doi":"10.1109/JMMCT.2025.3585550","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3585550","url":null,"abstract":"This paper presents an automatic mesh refinement method designed to accurately capture resonant responses in high-quality factor devices using surface integral equations. To validate the method, a solution-based error estimator is proposed to evaluate solution quality and identify elements requiring local mesh refinement. The sensitivity of the local error distribution to frequencies near numerical resonance is examined. To effectively capture the resonant behavior, an automatic <italic>h</i>–refinement strategy, combined with frequency sweeping, is introduced. Numerical experiments on slotted cavities with high-quality factor are provided. In addition, the advantages of the proposed error estimator over the widely used residual error estimator are discussed.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"324-333"},"PeriodicalIF":1.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-02DOI: 10.1109/JMMCT.2025.3584998
Reza Ilka;Jiangbiao He;Jingjing Yang;Jose E. Contreras;Carlos G. Cavazos;Weijun Yin
Power transformers serve as indispensable elements in nearly every electrical power system. Ensuring the continuous operation of power transformers is pivotal in maintaining the reliability and safety of the power network. Hotspot temperature (HST) in windings is a key factor that indicates the health condition of a power transformer. To determine the temperature of the transformer windings, it is essential to obtain the temperature distribution inside the transformer. This paper introduces a high-fidelity multi-physics modeling and simulation framework focused on predicting the reliability of large power transformers. The methodology relies on the application of three-dimensional (3D) finite element analysis (FEA) and computational fluid dynamics (CFD). In particular, electromagnetic modeling and simulation using FEA are conducted to calculate transformer losses. Subsequently, a thermal-hydraulic model is established to determine the temperature distribution. More importantly, this is to identify the HST in the transformer windings, which is further utilized to determine the transformer lifetime. Additionally, a sensitivity analysis is carried out to evaluate how the properties of the cooling oil affect both temperature distribution and HST. Finally, experimental results are provided to confirm the multi-physics modeling and simulation results.
{"title":"FEA and CFD Based Multi-Physics Modeling, Simulation, and Validation of Oil-Immersed Power Transformers","authors":"Reza Ilka;Jiangbiao He;Jingjing Yang;Jose E. Contreras;Carlos G. Cavazos;Weijun Yin","doi":"10.1109/JMMCT.2025.3584998","DOIUrl":"https://doi.org/10.1109/JMMCT.2025.3584998","url":null,"abstract":"Power transformers serve as indispensable elements in nearly every electrical power system. Ensuring the continuous operation of power transformers is pivotal in maintaining the reliability and safety of the power network. Hotspot temperature (HST) in windings is a key factor that indicates the health condition of a power transformer. To determine the temperature of the transformer windings, it is essential to obtain the temperature distribution inside the transformer. This paper introduces a high-fidelity multi-physics modeling and simulation framework focused on predicting the reliability of large power transformers. The methodology relies on the application of three-dimensional (3D) finite element analysis (FEA) and computational fluid dynamics (CFD). In particular, electromagnetic modeling and simulation using FEA are conducted to calculate transformer losses. Subsequently, a thermal-hydraulic model is established to determine the temperature distribution. More importantly, this is to identify the HST in the transformer windings, which is further utilized to determine the transformer lifetime. Additionally, a sensitivity analysis is carried out to evaluate how the properties of the cooling oil affect both temperature distribution and HST. Finally, experimental results are provided to confirm the multi-physics modeling and simulation results.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"304-314"},"PeriodicalIF":1.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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