Pub Date : 2025-12-12DOI: 10.1109/JSEN.2025.3640716
Xiaoman Guo;Zishi Shen;Xianmin Chen;Xinkun Zhou;Gang Chen;Hu Sun
To address the challenge of cross-region feature distribution shifts in corrosion damage monitoring using ultrasonic-guided wave, this study proposes a transfer learning method based on convolutional neural networks and representation subspace distance (CNNs-RSD). This method aims to improve damage localization accuracy and generalization capability of guided wave signals across diverse structures. This approach extracts damage-sensitive features from Lamb wave in various structures, providing a foundation for subsequent domain adaptation regression (DAR). Simultaneously, representation subspace distance (RSD) is introduced as the domain-adaptive regression module to minimize the geometric distance between source and target feature subspaces from a subspace alignment perspective, which effectively mitigating the regression performance degradation caused by scale perturbation in traditional feature alignment method. To validate the effectiveness of the proposed method, a corrosion damage dataset based on aluminum plate was constructed, and multiple transfer experiments were designed. Damage data corresponding to a 20 mm defect from aluminum plate sample 1 were used as the source domain, and cross-domain recognition tests were subsequently conducted on aluminum plate sample 2 with four different damage sizes (15, 20, 25, and 30 mm). Furthermore, additional validation was performed on two new aluminum plates containing real corrosion defects. The results demonstrate that the CNN-RSD method outperforms comparative models, including 1-D CNN (1D-CNN), CNN-KGW, and gMLP, in terms of mean absolute error (MAE) and localization relative error (LRE), exhibiting superior positioning accuracy and robustness. It also maintains robust positioning performance in real-damage verification, thereby highlighting its cross-domain transferability and potential for engineering applications.
{"title":"A Cross-Domain Corrosion Identification Algorithm of Aircraft Structures Based on Domain Adaptation Regression","authors":"Xiaoman Guo;Zishi Shen;Xianmin Chen;Xinkun Zhou;Gang Chen;Hu Sun","doi":"10.1109/JSEN.2025.3640716","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3640716","url":null,"abstract":"To address the challenge of cross-region feature distribution shifts in corrosion damage monitoring using ultrasonic-guided wave, this study proposes a transfer learning method based on convolutional neural networks and representation subspace distance (CNNs-RSD). This method aims to improve damage localization accuracy and generalization capability of guided wave signals across diverse structures. This approach extracts damage-sensitive features from Lamb wave in various structures, providing a foundation for subsequent domain adaptation regression (DAR). Simultaneously, representation subspace distance (RSD) is introduced as the domain-adaptive regression module to minimize the geometric distance between source and target feature subspaces from a subspace alignment perspective, which effectively mitigating the regression performance degradation caused by scale perturbation in traditional feature alignment method. To validate the effectiveness of the proposed method, a corrosion damage dataset based on aluminum plate was constructed, and multiple transfer experiments were designed. Damage data corresponding to a 20 mm defect from aluminum plate sample 1 were used as the source domain, and cross-domain recognition tests were subsequently conducted on aluminum plate sample 2 with four different damage sizes (15, 20, 25, and 30 mm). Furthermore, additional validation was performed on two new aluminum plates containing real corrosion defects. The results demonstrate that the CNN-RSD method outperforms comparative models, including 1-D CNN (1D-CNN), CNN-KGW, and gMLP, in terms of mean absolute error (MAE) and localization relative error (LRE), exhibiting superior positioning accuracy and robustness. It also maintains robust positioning performance in real-damage verification, thereby highlighting its cross-domain transferability and potential for engineering applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 2","pages":"3389-3399"},"PeriodicalIF":4.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1109/JSEN.2025.3640827
Kenesbaeva Periyzat Ismaylovna;Azimbek Khudoyberdiev;Hee-Cheol Kim
Traditional mental workload (MW) classification methods often rely on handcrafted features and achieve modest accuracy (70%–85%) while focusing on single modalities or static fusion, thus missing complementary information across sensors. Recent multimodal fusion approaches, such as attention-based weighting, averaging, or majority voting, often fail to accurately assess the relative informativeness of each modality, especially when one sensor becomes unreliable. We introduce CogniMoE, an end-to-end multimodal framework that learns from raw physiological signals with three innovations: 1) a high-efficiency on-the-fly scalogram generation pipeline using FP16 arithmetic that overcomes traditional storage bottlenecks reducing disk space usage by 98% while enabling seamless GPU processing; 2) parallel per-modality CNN–LSTM branches with attention and dynamic dropout that robustly extract modality-specific spatial–temporal features, outperforming single-stream encoders; and 3) an interpretable mixture of experts (MoE) gating mechanism that replaces static fusion with instance-level adaptive weighting, ensuring robustness by dynamically suppressing unreliable modalities in real time. Evaluations on the MAUS, CLAS, and WESAD datasets demonstrate that CogniMoE consistently outperforms both traditional methods (with average accuracies of 70%–85%) and recent state-ofthe- art (SOTA) approaches (up to 92% accuracy), achieving accuracies of 94%, 92%, and 98%, respectively. In addition, the MoE gating mechanism improves classification accuracy by approximately 5% on average over nonadaptive fusion strategies while dynamically adjusting modality importance based on individual participant characteristics.
{"title":"CogniMoE: End-to-End Multimodal Mental Workload Classification via On-the-Fly Scalogram Generation and MoE Gating","authors":"Kenesbaeva Periyzat Ismaylovna;Azimbek Khudoyberdiev;Hee-Cheol Kim","doi":"10.1109/JSEN.2025.3640827","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3640827","url":null,"abstract":"Traditional mental workload (MW) classification methods often rely on handcrafted features and achieve modest accuracy (70%–85%) while focusing on single modalities or static fusion, thus missing complementary information across sensors. Recent multimodal fusion approaches, such as attention-based weighting, averaging, or majority voting, often fail to accurately assess the relative informativeness of each modality, especially when one sensor becomes unreliable. We introduce CogniMoE, an end-to-end multimodal framework that learns from raw physiological signals with three innovations: 1) a high-efficiency on-the-fly scalogram generation pipeline using FP16 arithmetic that overcomes traditional storage bottlenecks reducing disk space usage by 98% while enabling seamless GPU processing; 2) parallel per-modality CNN–LSTM branches with attention and dynamic dropout that robustly extract modality-specific spatial–temporal features, outperforming single-stream encoders; and 3) an interpretable mixture of experts (MoE) gating mechanism that replaces static fusion with instance-level adaptive weighting, ensuring robustness by dynamically suppressing unreliable modalities in real time. Evaluations on the MAUS, CLAS, and WESAD datasets demonstrate that CogniMoE consistently outperforms both traditional methods (with average accuracies of 70%–85%) and recent state-ofthe- art (SOTA) approaches (up to 92% accuracy), achieving accuracies of 94%, 92%, and 98%, respectively. In addition, the MoE gating mechanism improves classification accuracy by approximately 5% on average over nonadaptive fusion strategies while dynamically adjusting modality importance based on individual participant characteristics.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"26 3","pages":"5213-5228"},"PeriodicalIF":4.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11298422","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This work reports the successful integration and processing of hundreds of GaN on Silicon wafer lots devoted to 100V Monolithic GaN power device within a standard 8-inch silicon fab primarily dedicated to BCD/CMOS technology production. By addressing key challenges related to gallium cross-contamination and equipment compatibility with thicker GaN on Si substrates, a comprehensive contamination management strategy was developed and implemented. This strategy includes dedicated equipment classification, backside wafer protection, optimized cleaning procedures for GaN etching tools, and rigorous monitoring using TXRF measurements. The approach enabled reliable, high-mechanical yield of GaN wafer device fabrication without impacting existing BCD/CMOS production lines, demonstrating the feasibility of coexisting GaN and silicon technologies in a shared manufacturing environment. This achievement paves the way for cost-effective scaling of GaN power device production within mainstream semiconductor fabs.
{"title":"Feasibility Demonstration of GaN on Si Process for R&D and Manufacturing on Existing 200mm Si-Fab","authors":"Luisito Livellara;Michele Molgg;Guido Pietrogrande;Selene Colombo;Daria Doria;Ivana Patoprsta;Costanza Adamo;Alessia Azzopardo;Paolo Colpani","doi":"10.1109/TSM.2025.3642930","DOIUrl":"https://doi.org/10.1109/TSM.2025.3642930","url":null,"abstract":"This work reports the successful integration and processing of hundreds of GaN on Silicon wafer lots devoted to 100V Monolithic GaN power device within a standard 8-inch silicon fab primarily dedicated to BCD/CMOS technology production. By addressing key challenges related to gallium cross-contamination and equipment compatibility with thicker GaN on Si substrates, a comprehensive contamination management strategy was developed and implemented. This strategy includes dedicated equipment classification, backside wafer protection, optimized cleaning procedures for GaN etching tools, and rigorous monitoring using TXRF measurements. The approach enabled reliable, high-mechanical yield of GaN wafer device fabrication without impacting existing BCD/CMOS production lines, demonstrating the feasibility of coexisting GaN and silicon technologies in a shared manufacturing environment. This achievement paves the way for cost-effective scaling of GaN power device production within mainstream semiconductor fabs.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 1","pages":"148-155"},"PeriodicalIF":2.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1109/TPS.2025.3602512
Hui Chen;Xian Cheng;Guowei Ge;Shuai Du;Qinwei Zhang;Wanlong Zhang;Shuo Chen;Chenxi Wang
Self-voltage sharing capacitor pattern (SSCP) could meet the compact uniform voltage distribution demand of tank multibreak vacuum circuit breakers (VCBs). However, the consistency analysis of postarc sheath evolution in series-connected breaks with grading capacitors has drawn little attention in previous studies. This article focused on the dynamic development of postarc currents in series-connected vacuum interrupters (VIs) with various grading capacitor patterns. The concept of series-connected sheath consistency was introduced to quantitatively characterize the postarc sheath enhancement effect in series-connected VIs with SSCP, and the particle-in-cell (PIC) computational model was established. Furthermore, the influence of series-connected breaks, shield potential, and contact distance on postarc sheath evolution was investigated, which indicated that SSCP could affect the evolution process of postarc currents, sheath potential, and sheath thickness. Compared to conventional grading capacitor patterns (CGCPs), the postarc current peak was reduced by 20%. The maximum value of the consistency coefficient of SSCP is 0.20 (the ideal value is 0) in series-connected breaks. This article could be used for the evaluation of postarc sheath evolution consistency of series-connected SSCP, which promotes the advancement of ultrahigh-voltage multibreak tank VCBs.
{"title":"Consistency Analysis of Postarc Sheath Evolution in Self-Voltage Sharing Interrupters for Multibreak Vacuum Circuit Breakers","authors":"Hui Chen;Xian Cheng;Guowei Ge;Shuai Du;Qinwei Zhang;Wanlong Zhang;Shuo Chen;Chenxi Wang","doi":"10.1109/TPS.2025.3602512","DOIUrl":"https://doi.org/10.1109/TPS.2025.3602512","url":null,"abstract":"Self-voltage sharing capacitor pattern (SSCP) could meet the compact uniform voltage distribution demand of tank multibreak vacuum circuit breakers (VCBs). However, the consistency analysis of postarc sheath evolution in series-connected breaks with grading capacitors has drawn little attention in previous studies. This article focused on the dynamic development of postarc currents in series-connected vacuum interrupters (VIs) with various grading capacitor patterns. The concept of series-connected sheath consistency was introduced to quantitatively characterize the postarc sheath enhancement effect in series-connected VIs with SSCP, and the particle-in-cell (PIC) computational model was established. Furthermore, the influence of series-connected breaks, shield potential, and contact distance on postarc sheath evolution was investigated, which indicated that SSCP could affect the evolution process of postarc currents, sheath potential, and sheath thickness. Compared to conventional grading capacitor patterns (CGCPs), the postarc current peak was reduced by 20%. The maximum value of the consistency coefficient of SSCP is 0.20 (the ideal value is 0) in series-connected breaks. This article could be used for the evaluation of postarc sheath evolution consistency of series-connected SSCP, which promotes the advancement of ultrahigh-voltage multibreak tank VCBs.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"54 1","pages":"203-218"},"PeriodicalIF":1.5,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-10DOI: 10.1109/TSM.2025.3642343
Linyu Wei;Jueping Cai
The defect of the lighting-emitting diode (LED) chip is inevitable in the manufacturing process, which makes it necessary to classify the defective LED-chips with a robust inspection system to guarantee high production efficiency. Recently, convolutional neural networks (CNN) have attracted considerable attention in defect classification. With the miniaturization of chip size, it is difficult to recognize the defective chip using the traditional deep CNN, which obtains the large receptive field of the last layer so that the spatial details are ignored and small defects cannot be detected. To address this issue, we propose a multi-scale content-aware enhancement dual-branch CNN for LED-chip defect classification, which is a shallow network with a strong cross-layer feature extraction ability. Aiming at recognizing different sizes of the defect and filtering the noise, a multi-scale content-aware enhancement module is proposed to highlight the important features and inhibit the noise with three different receptive fields, which is beneficial for the detailed and semantic information extraction. Furthermore, a joint loss is adopted to improve the classification ability and facilitate the recognition of difficult samples. Experiments show that the proposed model achieves high recognition accuracy of 95.258% with a low model complexity, which is superior to state-of-the-art methods.
{"title":"Multi-Scale Content-Aware Enhancement Dual-Branch CNN for LED-Chip Defect Classification","authors":"Linyu Wei;Jueping Cai","doi":"10.1109/TSM.2025.3642343","DOIUrl":"https://doi.org/10.1109/TSM.2025.3642343","url":null,"abstract":"The defect of the lighting-emitting diode (LED) chip is inevitable in the manufacturing process, which makes it necessary to classify the defective LED-chips with a robust inspection system to guarantee high production efficiency. Recently, convolutional neural networks (CNN) have attracted considerable attention in defect classification. With the miniaturization of chip size, it is difficult to recognize the defective chip using the traditional deep CNN, which obtains the large receptive field of the last layer so that the spatial details are ignored and small defects cannot be detected. To address this issue, we propose a multi-scale content-aware enhancement dual-branch CNN for LED-chip defect classification, which is a shallow network with a strong cross-layer feature extraction ability. Aiming at recognizing different sizes of the defect and filtering the noise, a multi-scale content-aware enhancement module is proposed to highlight the important features and inhibit the noise with three different receptive fields, which is beneficial for the detailed and semantic information extraction. Furthermore, a joint loss is adopted to improve the classification ability and facilitate the recognition of difficult samples. Experiments show that the proposed model achieves high recognition accuracy of 95.258% with a low model complexity, which is superior to state-of-the-art methods.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 1","pages":"62-73"},"PeriodicalIF":2.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/TPS.2025.3637112
Xinghe Fu;Jingqi Bu
Coreless winding topologies are a pivotal design element in a diverse range of electromagnetic devices, from high-precision actuators to high-energy pulsed-power systems. These configurations are essential for achieving high power density and rapid dynamic response, with conventional topologies including rectangular, skewed, diamond, and hexagonal. To further reduce material consumption, improve magnetic flux utilization, and enhance electromagnetic performance, this article proposes a novel elliptical winding topology, with a focus on its implementation in coreless brushed permanent magnet dc motors (CBPMDCMs). The structural design of the motor and elliptical winding is first introduced. Detailed analytical models for back EMF, electromagnetic, and mechanical characteristics are then developed and validated through 3-D finite element analysis (3-D FEA). A comparative study is subsequently conducted between the elliptical winding and conventional windings under identical performance requirements. The results demonstrate that the elliptical winding achieves higher flux utilization, lower copper consumption, improved efficiency, and stronger short-term overload capability, while maintaining competitive electromagnetic and mechanical performance.
{"title":"A Novel Elliptical Coreless Winding Topology for Enhanced Electromagnetic Performance","authors":"Xinghe Fu;Jingqi Bu","doi":"10.1109/TPS.2025.3637112","DOIUrl":"https://doi.org/10.1109/TPS.2025.3637112","url":null,"abstract":"Coreless winding topologies are a pivotal design element in a diverse range of electromagnetic devices, from high-precision actuators to high-energy pulsed-power systems. These configurations are essential for achieving high power density and rapid dynamic response, with conventional topologies including rectangular, skewed, diamond, and hexagonal. To further reduce material consumption, improve magnetic flux utilization, and enhance electromagnetic performance, this article proposes a novel elliptical winding topology, with a focus on its implementation in coreless brushed permanent magnet dc motors (CBPMDCMs). The structural design of the motor and elliptical winding is first introduced. Detailed analytical models for back EMF, electromagnetic, and mechanical characteristics are then developed and validated through 3-D finite element analysis (3-D FEA). A comparative study is subsequently conducted between the elliptical winding and conventional windings under identical performance requirements. The results demonstrate that the elliptical winding achieves higher flux utilization, lower copper consumption, improved efficiency, and stronger short-term overload capability, while maintaining competitive electromagnetic and mechanical performance.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"54 1","pages":"306-313"},"PeriodicalIF":1.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, the effect of an ac-driven pin-to-pin gliding arc discharge (GAD) on premixed NH3/O2 combustion is experimentally investigated. The discharge substantially improves flame stability and extends the lean flammability limit from $varphi =0.55$ to 0.30. Meanwhile, an approximately 80% reduction in NO emissions is achieved under plasma-assisted conditions. With increasing oxygen flow rate, the discharge undergoes a transition from glow to spark types, which promotes the ignition of lean mixtures. Optical emission spectroscopy (OES) identifies the presence of OH${}^{ast }$ , NH${}^{ast }$ , and N${}_{2}^{ast } $ species only when plasma participates in burning, while the intensity of NH${}_{2}^{ast } $ emission is markedly increased. As the equivalence ratio increases, OH${}^{ast }$ emission decreases, whereas NH${}^{ast }$ , NH${}_{2}^{ast } $ , and N${}_{2}^{ast } $ emissions are strengthened, implying enhanced NH3 dissociation induced by the plasma. Based on these results, we propose a set of DeNOx reaction pathways involving plasma-generated NHx radicals.
{"title":"Effect of Pin-to-Pin Gliding Arc Plasma on NOx Suppression in Premixed Ammonia/Oxygen Combustion","authors":"Qin-Kun Yu;Yu-Long Niu;Shou-Zhe Li;Xiaoqiong Wen;Yong Li;Daoman Han;Cheng Zhou","doi":"10.1109/TPS.2025.3638163","DOIUrl":"https://doi.org/10.1109/TPS.2025.3638163","url":null,"abstract":"In this work, the effect of an ac-driven pin-to-pin gliding arc discharge (GAD) on premixed NH<sub>3</sub>/O<sub>2</sub> combustion is experimentally investigated. The discharge substantially improves flame stability and extends the lean flammability limit from <inline-formula> <tex-math>$varphi =0.55$ </tex-math></inline-formula> to 0.30. Meanwhile, an approximately 80% reduction in NO emissions is achieved under plasma-assisted conditions. With increasing oxygen flow rate, the discharge undergoes a transition from glow to spark types, which promotes the ignition of lean mixtures. Optical emission spectroscopy (OES) identifies the presence of OH<inline-formula> <tex-math>${}^{ast }$ </tex-math></inline-formula>, NH<inline-formula> <tex-math>${}^{ast }$ </tex-math></inline-formula>, and N<inline-formula> <tex-math>${}_{2}^{ast } $ </tex-math></inline-formula> species only when plasma participates in burning, while the intensity of NH<inline-formula> <tex-math>${}_{2}^{ast } $ </tex-math></inline-formula> emission is markedly increased. As the equivalence ratio increases, OH<inline-formula> <tex-math>${}^{ast }$ </tex-math></inline-formula> emission decreases, whereas NH<inline-formula> <tex-math>${}^{ast }$ </tex-math></inline-formula>, NH<inline-formula> <tex-math>${}_{2}^{ast } $ </tex-math></inline-formula>, and N<inline-formula> <tex-math>${}_{2}^{ast } $ </tex-math></inline-formula> emissions are strengthened, implying enhanced NH<sub>3</sub> dissociation induced by the plasma. Based on these results, we propose a set of DeNO<italic><sub>x</sub></i> reaction pathways involving plasma-generated NH<italic><sub>x</sub></i> radicals.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"54 1","pages":"261-269"},"PeriodicalIF":1.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neutral beam injection (NBI) is a key auxiliary heating technology used in magnetic confinement fusion devices. With the scale up of the device, the requirement of beam energy is higher. A negative ion source-based neutral beam injection (NNBI) system is an inevitable choice, but the NNBI system presents significant engineering complexity and technical challenges. To investigate and master core NNBI technologies, an NNBI test facility is currently being developed under the Comprehensive Research Facility for Fusion Technology (CRAFT) in China. The initial operational targets for the CRAFT NNBI system are to achieve a beam with energies in the range of 200400 keV, the neutral beam power of 2 MW, and the pulse duration of 100 s. In the negative ion source, the beam divergence is one of the important parameters that determines the pulse duration and energy of the beam. A large beam divergence will cause heavy thermal load on the electrode grids and additional heat load on the beamline components. These can cause the breakdown of grids and interrupt the acceleration process. The accelerator beam optics design for the CRAFT NNBI dual-driver negative ion source is based on IBSimu. Two diagnostic methods, beam emission spectroscopy (BES) and secondary electron emission (SEE), are adopted to analyze the optical characteristics of beam in the experiments. The experimental results verify the simulation results calculated by IBSimu, confirming the limitations and reliability of the simulation program.
{"title":"Benchmark of the Beam Optics Simulation Against the Beam Acceleration Experiments of a Dual-Driver Radio Frequency Negative Ion Source for Fusion","authors":"Jiahao Cheng;Yuwen Yang;Qinglong Cui;Zhengkun Cao;Yao Qin;Yongjian Xu;Lizhen Liang;Jianglong Wei","doi":"10.1109/TPS.2025.3638740","DOIUrl":"https://doi.org/10.1109/TPS.2025.3638740","url":null,"abstract":"Neutral beam injection (NBI) is a key auxiliary heating technology used in magnetic confinement fusion devices. With the scale up of the device, the requirement of beam energy is higher. A negative ion source-based neutral beam injection (NNBI) system is an inevitable choice, but the NNBI system presents significant engineering complexity and technical challenges. To investigate and master core NNBI technologies, an NNBI test facility is currently being developed under the Comprehensive Research Facility for Fusion Technology (CRAFT) in China. The initial operational targets for the CRAFT NNBI system are to achieve a beam with energies in the range of 200400 keV, the neutral beam power of 2 MW, and the pulse duration of 100 s. In the negative ion source, the beam divergence is one of the important parameters that determines the pulse duration and energy of the beam. A large beam divergence will cause heavy thermal load on the electrode grids and additional heat load on the beamline components. These can cause the breakdown of grids and interrupt the acceleration process. The accelerator beam optics design for the CRAFT NNBI dual-driver negative ion source is based on IBSimu. Two diagnostic methods, beam emission spectroscopy (BES) and secondary electron emission (SEE), are adopted to analyze the optical characteristics of beam in the experiments. The experimental results verify the simulation results calculated by IBSimu, confirming the limitations and reliability of the simulation program.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"54 1","pages":"290-296"},"PeriodicalIF":1.5,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145957906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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