Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.
{"title":"Quantum Optimization of Reconfigurable Intelligent Surfaces for Mitigating Multipath Fading in Wireless Networks","authors":"Emanuel Colella;Luca Bastianelli;Valter Mariani Primiani;Zhen Peng;Franco Moglie;Gabriele Gradoni","doi":"10.1109/JMMCT.2024.3494037","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3494037","url":null,"abstract":"Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"403-414"},"PeriodicalIF":1.8,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142736643","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}
In addressing the challenges posed by low-frequency airborne transient electromagnetics (ATEM), it is necessary to take into account the considerations of accuracy, computational efficiency, and the scale and intricacy of the physical domain. This becomes particularly crucial when dealing with large-scale, complex issues, with the aim of mitigating the computational resource burden associated with managing such complexities. In order to further meet the aforementioned criteria, a Perfectly Matched Monolayer (PMM) model has been introduced into the Random Forest Regression (RFR) framework. The RFR-based PMM model has demonstrated exceptional accuracy through the utilization of Bagging's integrated learning methodology, while also reducing the computational resource requirements for processing time. In comparison to traditional machine learning models, our model has exhibited significant advantages in terms of training stability, model efficiency, and parallelization capabilities. To verify and establish the reliability of this approach, three-dimensional numerical simulations of the ATEM problem were conducted. The proposed model in this study has exhibited superior accuracy, efficiency, and versatility in addressing the low-frequency ATEM problem, integrating with the FDTD method.
{"title":"Efficient Physical Truncation of Low-Frequency ATEM Problems in Specific Geometries by Using Random Forest Regression Based PMM Model","authors":"Naixing Feng;Shuiqing Zeng;Huan Wang;Yuxian Zhang;Zhixiang Huang","doi":"10.1109/JMMCT.2024.3491835","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3491835","url":null,"abstract":"In addressing the challenges posed by low-frequency airborne transient electromagnetics (ATEM), it is necessary to take into account the considerations of accuracy, computational efficiency, and the scale and intricacy of the physical domain. This becomes particularly crucial when dealing with large-scale, complex issues, with the aim of mitigating the computational resource burden associated with managing such complexities. In order to further meet the aforementioned criteria, a Perfectly Matched Monolayer (PMM) model has been introduced into the Random Forest Regression (RFR) framework. The RFR-based PMM model has demonstrated exceptional accuracy through the utilization of Bagging's integrated learning methodology, while also reducing the computational resource requirements for processing time. In comparison to traditional machine learning models, our model has exhibited significant advantages in terms of training stability, model efficiency, and parallelization capabilities. To verify and establish the reliability of this approach, three-dimensional numerical simulations of the ATEM problem were conducted. The proposed model in this study has exhibited superior accuracy, efficiency, and versatility in addressing the low-frequency ATEM problem, integrating with the FDTD method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"10 ","pages":"1-7"},"PeriodicalIF":1.8,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753858","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 : 2024-10-29DOI: 10.1109/JMMCT.2024.3487779
Chang Yang;Dan Jiao
In this paper, we develop a kernel-independent and purely algebraic method, Nested Pseudo-Skeleton Approximation (NPSA) algorithm, to generate a low-rank ${mathcal H}^{2}$ -matrix representation of electrically large surface integral equations (SIEs). The algorithm only uses $O(NlogN)$ entries of the original dense SIE matrix of size $N$ to generate the ${mathcal H}^{2}$ -representation. It also provides a closed-form expression of the cluster bases and coupling matrices with respect to original matrix entries. The resultant ${mathcal H}^{2}$ -matrix is then directly solved for electrically large scattering analysis. Numerical experiments have demonstrated the accuracy and efficiency of the proposed algorithm. In addition to surface integral equations, the proposed algorithms can also be applied to solving other electrically large integral equations.
{"title":"Nested Pseudo Skeleton Approximation Algorithm for Generating ${mathcal H}^{2}$-Matrix Representations of Electrically Large Surface Integral Equations","authors":"Chang Yang;Dan Jiao","doi":"10.1109/JMMCT.2024.3487779","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3487779","url":null,"abstract":"In this paper, we develop a kernel-independent and purely algebraic method, Nested Pseudo-Skeleton Approximation (NPSA) algorithm, to generate a low-rank \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-matrix representation of electrically large surface integral equations (SIEs). The algorithm only uses \u0000<inline-formula><tex-math>$O(NlogN)$</tex-math></inline-formula>\u0000 entries of the original dense SIE matrix of size \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 to generate the \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-representation. It also provides a closed-form expression of the cluster bases and coupling matrices with respect to original matrix entries. The resultant \u0000<inline-formula><tex-math>${mathcal H}^{2}$</tex-math></inline-formula>\u0000-matrix is then directly solved for electrically large scattering analysis. Numerical experiments have demonstrated the accuracy and efficiency of the proposed algorithm. In addition to surface integral equations, the proposed algorithms can also be applied to solving other electrically large integral equations.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"393-402"},"PeriodicalIF":1.8,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636427","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 : 2024-10-08DOI: 10.1109/JMMCT.2024.3475988
Mai Le;Alan Yao;Amie Zhang;Hieu Le;Zhaoyang Chen;Xuqing Wu;Lihong Zhao;Jiefu Chen
Solid-state batteries can offer enhanced safety and potentially higher energy density compared to traditional lithium-ion batteries. However, their power density remains a challenge due to limited ionic conductivity in composite electrodes caused by non-ideal microstructures. Laborious experimental processes and time-consuming data analysis algorithms are obstacles to establishing structure–performance correlations and optimizing electrode microstructure. In this paper, we present a machine learning approach to predict the effective conductivity of a composite electrode based on scanning electron microscopy images, using binary images composed of conductive and non-conductive regions and an ionic conductivity value of the conductive region. We show that our proposed method is two orders of magnitude more efficient than conventional numerical schemes such as the finite difference method.
{"title":"Expediting Ionic Conductivity Prediction of Solid-State Battery Electrodes Using Machine Learning","authors":"Mai Le;Alan Yao;Amie Zhang;Hieu Le;Zhaoyang Chen;Xuqing Wu;Lihong Zhao;Jiefu Chen","doi":"10.1109/JMMCT.2024.3475988","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3475988","url":null,"abstract":"Solid-state batteries can offer enhanced safety and potentially higher energy density compared to traditional lithium-ion batteries. However, their power density remains a challenge due to limited ionic conductivity in composite electrodes caused by non-ideal microstructures. Laborious experimental processes and time-consuming data analysis algorithms are obstacles to establishing structure–performance correlations and optimizing electrode microstructure. In this paper, we present a machine learning approach to predict the effective conductivity of a composite electrode based on scanning electron microscopy images, using binary images composed of conductive and non-conductive regions and an ionic conductivity value of the conductive region. We show that our proposed method is two orders of magnitude more efficient than conventional numerical schemes such as the finite difference method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"375-382"},"PeriodicalIF":1.8,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517850","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 : 2024-09-30DOI: 10.1109/JMMCT.2024.3470557
Dhirodaatto Sarkar;Jue Wang;Alex Chortos
Finding applications in fields such as manipulation platforms and gas sensors, various strategies have been developed to enhance scale and resolution of resistive heating arrays, including integration of diodes/transistors. However, emerging applications in soft robotics and wearable devices prioritize systems that can be fabricated over large areas using low-cost materials, and benefit from simplified control. Utilizing common row/column electrodes to address heating elements, matrix addressing reduces the complexity of control inputs. Passive matrices require no semiconductor components, further minimizing device complexity. Despite these advantages, thermal and electrical crosstalk hinder passive matrix addressing. In this study, we present a novel systematic analysis of the crosstalk in passive matrix resistive heating arrays, addressing both electrical and thermal couplings. We employ theoretical and computational approaches to investigate the effects of materials and array geometry on crosstalk. Through COMSOL multiphysics simulations, we quantify crosstalk as a function of the conductivity of the constituent materials and array geometry. The computational approach allows us to decouple the effects of electrical and thermal crosstalk. Additionally, Pattern Search is used to optimize array designs, minimizing crosstalk and voltage input and revealing trade-offs at various array scales (illustrated in a 16 × 16 array). Furthermore, we study the significant impact of thermal patterns and control methods on crosstalk by implementing progressive scan. This work provides insights and optimization strategies for the design of resistive heating arrays used as actuators or sensors in soft robotics and wearable devices, highlighting its practical significance in the advancement of these emerging applications.
{"title":"Crosstalk Analysis in Passively Addressed Soft Resistive Heating Arrays","authors":"Dhirodaatto Sarkar;Jue Wang;Alex Chortos","doi":"10.1109/JMMCT.2024.3470557","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3470557","url":null,"abstract":"Finding applications in fields such as manipulation platforms and gas sensors, various strategies have been developed to enhance scale and resolution of resistive heating arrays, including integration of diodes/transistors. However, emerging applications in soft robotics and wearable devices prioritize systems that can be fabricated over large areas using low-cost materials, and benefit from simplified control. Utilizing common row/column electrodes to address heating elements, matrix addressing reduces the complexity of control inputs. Passive matrices require no semiconductor components, further minimizing device complexity. Despite these advantages, thermal and electrical crosstalk hinder passive matrix addressing. In this study, we present a novel systematic analysis of the crosstalk in passive matrix resistive heating arrays, addressing both electrical and thermal couplings. We employ theoretical and computational approaches to investigate the effects of materials and array geometry on crosstalk. Through COMSOL multiphysics simulations, we quantify crosstalk as a function of the conductivity of the constituent materials and array geometry. The computational approach allows us to decouple the effects of electrical and thermal crosstalk. Additionally, Pattern Search is used to optimize array designs, minimizing crosstalk and voltage input and revealing trade-offs at various array scales (illustrated in a 16 × 16 array). Furthermore, we study the significant impact of thermal patterns and control methods on crosstalk by implementing progressive scan. This work provides insights and optimization strategies for the design of resistive heating arrays used as actuators or sensors in soft robotics and wearable devices, highlighting its practical significance in the advancement of these emerging applications.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"353-365"},"PeriodicalIF":1.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408685","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}
The synergistic effects between total ionizing dose (TID) and transient dose rate (TDR) effects are explored. To implement the analysis, a stable 3D parallel numerical scheme is specially developed. Using the control volume finite element tearing and interconnect (CV-FETI) method, the discontinuous boundary conditions can be included with the usual numerical properties. Newton's method is employed to overcome the nonconvergence brought by the nonlinear property of the drift- diffusion model. Compared to with the commercial COMSOL Multiphysics software, our CV-FETIM shows strong numerical stability on unstructured meshes. The proposed method is validated by comparing the numerical results with those calculated using commercial software. Then, this new solver is applied to simulate MOSFET, STI-based LDMOSFET, and FinFET. By adjusting the dose rate, oxide traps, and interface traps, the independent TID, independent TDR, and TID-TDR synergistic effects are investigated. On picosecond or nanosecond timescales, the duration, amplitude, and decline rate of the radiation-induced photocurrent are studied. Moreover, the influence of interface traps on different surfaces is compared. The numerical results indicated that the developed numerical scheme possesses good stability, accuracy, and applicability.
{"title":"A Stabilized Numerical Scheme to Simulate Synergistic Effect of TID and TDR in Semiconductor Devices","authors":"Tan-Yi Li;Yanning Chen;Nian-En Zhang;Da-Wei Wang;Qi-Wei Zhan;Qi-Chao Wang;Guang-Rong Li;Dongyan Zhao;Wen-Yan Yin","doi":"10.1109/JMMCT.2024.3469280","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3469280","url":null,"abstract":"The synergistic effects between total ionizing dose (TID) and transient dose rate (TDR) effects are explored. To implement the analysis, a stable 3D parallel numerical scheme is specially developed. Using the control volume finite element tearing and interconnect (CV-FETI) method, the discontinuous boundary conditions can be included with the usual numerical properties. Newton's method is employed to overcome the nonconvergence brought by the nonlinear property of the drift- diffusion model. Compared to with the commercial COMSOL Multiphysics software, our CV-FETIM shows strong numerical stability on unstructured meshes. The proposed method is validated by comparing the numerical results with those calculated using commercial software. Then, this new solver is applied to simulate MOSFET, STI-based LDMOSFET, and FinFET. By adjusting the dose rate, oxide traps, and interface traps, the independent TID, independent TDR, and TID-TDR synergistic effects are investigated. On picosecond or nanosecond timescales, the duration, amplitude, and decline rate of the radiation-induced photocurrent are studied. Moreover, the influence of interface traps on different surfaces is compared. The numerical results indicated that the developed numerical scheme possesses good stability, accuracy, and applicability.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"366-374"},"PeriodicalIF":1.8,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142438600","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 : 2024-09-19DOI: 10.1109/JMMCT.2024.3463748
Yinpeng Wang
The accurate acquisition of unknown multiphysics fields in specified regions is vital for industrial production. Traditional computational approaches often require dense mesh generation to achieve precise numerical results, leading to substantial computational resource consumption and extended processing times. However, recent advancements in deep learning (DL) have introduced alternative solutions to computational physics problems. This paper presents a novel multiphysics field solver that integrates operator learning with classical finite element methods (FEM). The overall structure of the framework is a Transformer based on the attention mechanism, with a loss function incorporating physical constraints. The network takes the result of a coarse grid finite element calculation as input, while the output target is the value of a dense grid computation. Compared to traditional DL frameworks, the proposed architecture consistently maintains low error rates across a range of input resolutions. Additionally, the high efficiency of graphics processing units (GPUs) enables fully trained networks to generate solutions in quasi-real time, demonstrating significant potential for practical applications.
{"title":"Deep Multiphysics Fields Solver Established on Operator Learning Transformer and Finite Element Method","authors":"Yinpeng Wang","doi":"10.1109/JMMCT.2024.3463748","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3463748","url":null,"abstract":"The accurate acquisition of unknown multiphysics fields in specified regions is vital for industrial production. Traditional computational approaches often require dense mesh generation to achieve precise numerical results, leading to substantial computational resource consumption and extended processing times. However, recent advancements in deep learning (DL) have introduced alternative solutions to computational physics problems. This paper presents a novel multiphysics field solver that integrates operator learning with classical finite element methods (FEM). The overall structure of the framework is a Transformer based on the attention mechanism, with a loss function incorporating physical constraints. The network takes the result of a coarse grid finite element calculation as input, while the output target is the value of a dense grid computation. Compared to traditional DL frameworks, the proposed architecture consistently maintains low error rates across a range of input resolutions. Additionally, the high efficiency of graphics processing units (GPUs) enables fully trained networks to generate solutions in quasi-real time, demonstrating significant potential for practical applications.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"341-352"},"PeriodicalIF":1.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408684","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 : 2024-09-18DOI: 10.1109/JMMCT.2024.3464373
Ge Cao;Zhen Peng
The radio wave propagation channel is central to the performance of wireless communication systems. In this paper, we introduce a novel machine learning-empowered methodology for wireless channel modeling. The key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes. Our approach offers several significant advantages, including the flexibility to adjust antenna radiation patterns and transmitter/receiver locations, the capability to predict radio path loss maps, and the scalability of large-scale wireless scenes. As a result, it lays the groundwork for an end-to-end pipeline for network planning and deployment optimization. The proposed work is validated in various outdoor and indoor radio environments.
{"title":"RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments","authors":"Ge Cao;Zhen Peng","doi":"10.1109/JMMCT.2024.3464373","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3464373","url":null,"abstract":"The radio wave propagation channel is central to the performance of wireless communication systems. In this paper, we introduce a novel machine learning-empowered methodology for wireless channel modeling. The key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes. Our approach offers several significant advantages, including the flexibility to adjust antenna radiation patterns and transmitter/receiver locations, the capability to predict radio path loss maps, and the scalability of large-scale wireless scenes. As a result, it lays the groundwork for an end-to-end pipeline for network planning and deployment optimization. The proposed work is validated in various outdoor and indoor radio environments.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"330-340"},"PeriodicalIF":1.8,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368554","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}
Simulating electromagnetic waves within biological tissues is critical for assessing electromagnetic effects in biological environment. Precise modeling of biological tissues in computational electromagnetics is therefore necessary. The Cole-Cole dispersive model based on the fractional power functions can more accurately describe the electrical characteristics of biological tissues in a wide frequency range than the typical dispersive model based on the integer power functions. Previous research on the time-domain simulation of the Cole-Cole medium is mainly based on the finite difference time domain (FDTD) method. Recently, researchers proposed a DEH scheme (Maxwell's equations with field variables D, E and H) discontinuous Galerkin time domain (DGTD) method to simulate wave propagation in the Cole-Cole dispersive media. However, it lacks the perfectly matched layer (PML) to truncate the Cole-Cole background media. This paper fills this gap by developing a PML for Cole-Cole background media in the DGTD method.
模拟生物组织内的电磁波对于评估生物环境中的电磁效应至关重要。因此，有必要在计算电磁学中对生物组织进行精确建模。与基于整数幂函数的典型色散模型相比，基于分数幂函数的 Cole-Cole 色散模型能更准确地描述生物组织在宽频率范围内的电特性。以往对 Cole-Cole 介质的时域模拟研究主要基于有限差分时域（FDTD）方法。最近，研究人员提出了一种 DEH 方案（带有场变量 D、E 和 H 的麦克斯韦方程）非连续伽勒金时域（DGTD）方法来模拟 Cole-Cole 色散介质中的波传播。然而，该方法缺乏完美匹配层（PML）来截断科尔-科尔背景介质。本文通过在 DGTD 方法中开发科尔-科尔背景介质的 PML，填补了这一空白。
{"title":"Perfectly Matched Layer for Cole–Cole Dispersive Media in DGTD Method","authors":"Xuebin Qin;Xuan Wu;Shuo Wang;Xiaoying Zhao;Yuanguo Zhou;Qiang Ren","doi":"10.1109/JMMCT.2024.3462529","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3462529","url":null,"abstract":"Simulating electromagnetic waves within biological tissues is critical for assessing electromagnetic effects in biological environment. Precise modeling of biological tissues in computational electromagnetics is therefore necessary. The Cole-Cole dispersive model based on the fractional power functions can more accurately describe the electrical characteristics of biological tissues in a wide frequency range than the typical dispersive model based on the integer power functions. Previous research on the time-domain simulation of the Cole-Cole medium is mainly based on the finite difference time domain (FDTD) method. Recently, researchers proposed a DEH scheme (Maxwell's equations with field variables D, E and H) discontinuous Galerkin time domain (DGTD) method to simulate wave propagation in the Cole-Cole dispersive media. However, it lacks the perfectly matched layer (PML) to truncate the Cole-Cole background media. This paper fills this gap by developing a PML for Cole-Cole background media in the DGTD method.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"320-329"},"PeriodicalIF":1.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368555","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 : 2024-09-04DOI: 10.1109/JMMCT.2024.3454451
Venkat Prasad Padhy;Dipanjan Gope;Sadasiva M. Rao;N. Balakrishnan
It is well-known that one of the parameters useful to detect low-observable stealth targets, specifically aircraft, is to monitor the Radar Cross Section (RCS) enhancement in the medium surrounding the aircraft due to the wake vortex generated during the flight. The acoustic wave induced by the wake vortex creates dielectric constant fluctuations because of compressions and rarefactions in the propagating medium. The subject matter of this work is to develop a method to rigorously predict RCS in the presence of wake vortex. First, the solution of flow over an aircraft is obtained, then permittivity of the medium is computed using flow parameters, providing the coupling term between the electromagnetic and acoustic phenomenon. Then Electromagnetic (EM) scattering from the inhomogeneity due to the flow in the vicinity of the aircraft and in its wake region is computed for an incident plane wave in lateral and longitudinal directions. The scattering problem is solved using Integral Equation (IE). The scattered far-field is interpreted using the conventional radar equation and the enhancement in RCS and detectability are presented. It is believed that this is the first time that an IE method is used to study the EM scattering problem from an aerodynamic flow and its detectability. It is shown in this paper that the presence of wake vortex can enhance the detectable range by around 2 km at 250 MHz giving an early warning advantage of around 15 seconds.
{"title":"Towards the Detection of Low-Observable Flying Object in the Presence of Wake Vortex Flow","authors":"Venkat Prasad Padhy;Dipanjan Gope;Sadasiva M. Rao;N. Balakrishnan","doi":"10.1109/JMMCT.2024.3454451","DOIUrl":"https://doi.org/10.1109/JMMCT.2024.3454451","url":null,"abstract":"It is well-known that one of the parameters useful to detect low-observable stealth targets, specifically aircraft, is to monitor the Radar Cross Section (RCS) enhancement in the medium surrounding the aircraft due to the wake vortex generated during the flight. The acoustic wave induced by the wake vortex creates dielectric constant fluctuations because of compressions and rarefactions in the propagating medium. The subject matter of this work is to develop a method to rigorously predict RCS in the presence of wake vortex. First, the solution of flow over an aircraft is obtained, then permittivity of the medium is computed using flow parameters, providing the coupling term between the electromagnetic and acoustic phenomenon. Then Electromagnetic (EM) scattering from the inhomogeneity due to the flow in the vicinity of the aircraft and in its wake region is computed for an incident plane wave in lateral and longitudinal directions. The scattering problem is solved using Integral Equation (IE). The scattered far-field is interpreted using the conventional radar equation and the enhancement in RCS and detectability are presented. It is believed that this is the first time that an IE method is used to study the EM scattering problem from an aerodynamic flow and its detectability. It is shown in this paper that the presence of wake vortex can enhance the detectable range by around 2 km at 250 MHz giving an early warning advantage of around 15 seconds.","PeriodicalId":52176,"journal":{"name":"IEEE Journal on Multiscale and Multiphysics Computational Techniques","volume":"9 ","pages":"383-392"},"PeriodicalIF":1.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579242","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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