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Phase-field simulations of the effect of temperature and interface for zirconiumδ-hydrides

Chinese Physics B 2024年第4期33卷 701-710页

作者：陈子航盛杰刘瑜施小明黄厚兵许可王越超武帅孙博刘海风宋海峰 School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Department of Physics University of Science and Technology BeijingBeijing 100083China

Hydride precipitation in zirconium cladding materials can damage their integrity and *** temperature and material defects have a significant effect on the dynamic growth of *** this study,we have developed a phasefield model based on the assumption of elastic behaviour within a specific temperature range(613 K-653 K).This model allows us to study the influence of temperature and interfacial effects on the morphology,stress,and average growth rate of zirconium *** results suggest that changes in temperature and interfacial energy influence the length-to-thickness ratio and average growth rate of the hydride *** ultimate determinant of hydride orientation is the loss of interfacial coherency,primarily induced by interfacial dislocation defects and quantifiable by the mismatch degree *** escalation in interfacial coherency loss leads to a transition of hydride growth from horizontal to vertical,accompanied by the onset of redirection ***,redirection occurs at a critical mismatch level,denoted as qc,and remains unaffected by variations in temperature and interfacial ***,this redirection leads to an increase in the maximum stress,which may influence the direction of hydride crack *** research highlights the importance of interfacial coherency and provides valuable insights into the morphology and growth kinetics of hydrides in zirconium alloys.

关键词： zirconium hydride phase-field method temperature effect mismatch degree

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FedDAA:a robust federated learning framework to protect privacy and defend against adversarial attack

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Frontiers of Computer science 2024年第2期18卷 107-122页

作者： Shiwei LU Ruihu LI Wenbin LIU Fundamentals Department Air Force Engineering UniversityXi’an 710051China Institute of Advanced Computational Science and Technology Guangzhou UniversityGuangzhou 510006China

Federated learning(FL)has emerged to break data-silo and protect clients’privacy in the field of artificial ***,deep leakage from gradient(DLG)attack can fully reconstruct clients’data from the submitted gradient,which threatens the fundamental privacy of *** cryptology and differential privacy prevent privacy leakage from gradient,they bring negative effect on communication overhead or model ***,the original distribution of local gradient has been changed in these schemes,which makes it difficult to defend against adversarial *** this paper,we propose a novel federated learning framework with model decomposition,aggregation and assembling(FedDAA),along with a training algorithm,to train federated model,where local gradient is decomposed into multiple blocks and sent to different proxy servers to complete *** bring better privacy protection performance to FedDAA,an indicator is designed based on image structural similarity to measure privacy leakage under DLG attack and an optimization method is given to protect privacy with the least proxy *** addition,we give defense schemes against adversarial attack in FedDAA and design an algorithm to verify the correctness of aggregated *** results demonstrate that FedDAA can reduce the structural similarity between the reconstructed image and the original image to 0.014 and remain model convergence accuracy as 0.952,thus having the best privacy protection performance and model training *** importantly,defense schemes against adversarial attack are compatible with privacy protection in FedDAA and the defense effects are not weaker than those in the traditional ***,verification algorithm of aggregation results brings about negligible overhead to FedDAA.

关键词： federated learning privacy protection adversarial attacks aggregated rule correctness verification

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Topological magnetoelectric response in ferromagnetic axion insulators

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National science Review 2024年第2期11卷 8-14页

作者： Yuhao Wan Jiayu Li Qihang Liu Department of Physics and Shenzhen Institute for Quantum Science and Engineering(SIQSE) Southern University of Science and Technology Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Guangdong Provincial Key Laboratory for Computational Science and Material Design Southern University of Science and Technology

The topological magnetoelectric effect （TME） is a hallmark response of the topological field theory,which provides a paradigm shift in the study of emergent topological ***,its direct observation is yet to be realized due to the demanding magnetic configuration required to gap all surface ***,we theoretically propose that axion insulators with a simple ferromagnetic configuration,such as the MnBi2Te4/（Bi2Te3）nfamily,provide an ideal playground to realize the *** the designed triangular prism geometry,all the surface states are magnetically *** a vertical electric field,the surface Hall currents give rise to a nearly half-quantized orbital moment,accompanied by a gapless chiral hinge mode circulating in ***,the orbital magnetization from the two topological origins can be easily distinguished by reversing the electric *** work paves the way for direct observation of the TME in realistic axion-insulator materials.

关键词： topological magnetoelectric effect axion insulator magnetic topological materials

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Necessities in the old jungle?: On Han’s analysis of the necessity of origin

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Asian Journal of Philosophy 2025年第1期4卷 1-12页

作者： Kim, Dongwoo The School of Digital Humanities and Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon South Korea

I shall discuss Han’s analysis of the necessity of origin theses. His analysis comes in two parts. The negative part argues that well-known Kripkean arguments leave an inferential gap, thus falling short of establishing the necessity of origin theses. The positive part contends that the gap can only be bridged by Aristotelian metaphysics of essence and causation. I shall critically examine both the negative and positive parts of Han’s analysis. © The Author(s) 2025.

关键词： CausaCon Essence Metaphysical necessity Natural necessity The necessity of origin

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Non-isothermal Crystallization and Thermal Transformation Behaviors of Plasma Sprayed Fe_(48)Cr_(15)Mo_(14)C_(15)B_(6)Y_(2)Amorphous Coating

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Journal of Wuhan University of technology(Materials science) 2024年第6期39卷 1397-1405页

作者： WANG Miqi ZHOU Zehua YI Yu ZHANG Xin SHI Fang Faculty of Mechanical and Material Engineering Huaiyin Institute of TechnologyHuai’an 223003China College of Materials Science and Engineering Hohai UniversityChangzhou 213200China Jiangsu Key Laboratory of Advanced Manufacturing Technology Huaiyin Institute of TechnologyHuai’an 223003China Research Laboratory of Computational Mechanics Huaiyin Institute of TechnologyHuai’an 223003China

The non-isothermal crystallization dynamic behavior and mechanism of plasma sprayed Fe_(48)Cr_(15)Mo_(14)C_(15)B_(6)Y_(2)coating were thoroughly *** phase transition and crystallization kinetics of the coating were elaborately investigated by differential scanning calorimetry(DSC),X-ray diffraction(XRD),scanning electron microscopy(SEM),and transmission electron microscopy(TEM).The findings reveal that the characteristic temperatures of the coating shift to an elevated temperature at a higher heating rate and the crystallization processes are thermally *** 3-rd step of crystallization processes is more susceptible to the continuously increased heating rate while the onset crystallization reaction is less sensitive to the continuously enhanced heating ***_(23)(C,B)_6 phase is inclined to precipitate than other crystal phases due to the substantial pre-generation ofα-*** onset nucleation and growth ofα-Fe crystals is tough due to a higher onset apparent activation ***,the transformation from Fe_(23)(C,B)_(6)to FeB is harder in comparison with the precipitation of other *** most parts of the three crystallization processes are dominated by three-dimensional diffusion model due to the fact that most values of local Avrami exponent are higher than 2.5.

关键词： plasma spraying non-isothermal crystallization phase transition diffusion mode

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Pretraining of attention-based deep learning potential model for molecular simulation

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npj computational Materials 2024年第1期10卷 2297-2304页

作者： Duo Zhang Hangrui Bi Fu-Zhi Dai Wanrun Jiang Xinzijian Liu Linfeng Zhang Han Wang AI for Science Institute Beijing 100080China DP Technology Beijing 100080China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijing 100871China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100094China HEDPS CAPTCollege of EngineeringPeking UniversityBeijing 100871China

Machine learning-assisted modeling of the inter-atomic potential energy surface(PES)is revolutionizing the field ofmolecular *** the accumulation of high-quality electronic structure data,a model that can be pretrained on all available data and finetuned on downstream tasks with a small additional effort would bring the field to a new *** we propose DPA-1,a Deep Potentialmodel with a gated attentionmechanism,which is highly effective for representing the conformation and chemical spaces of atomic systems and learning the *** tested DPA-1 on a number of systems and observed superior performance compared with existing *** pretrained on large-scale datasets containing 56 elements,DPA-1 can be successfully applied to various downstream tasks with a great improvement of sample ***,for different elements,the learned type embedding parameters form a spiral in the latent space and have a natural correspondence with their positions on the periodic table,showing interesting interpretability of the pretrained DPA-1 model.

关键词： potential representing positions

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Improved Engine Data Acquisition System and Interface to Enable Experimentation With Emerging Alternative Fuels in a Spark-Ignited Cooperative Fuel Research Engine

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Journal of Engineering for Gas Turbines and Power 2025年第7期147卷 071020页

作者： Loprete, Jason Dimosthenous, Christos Assanis, Dimitris Department of Mechanical Engineering Advanced Energy Research and Technology Center Stony Brook University Stony BrookNY11794 United States Institute for Advanced Computational Science Stony Brook University Stony BrookNY United States

In the face of new engine research challenges, legacy hardware and once state-of-the-art assumptions may become less relevant, especially those made due to the computational limitations of the hardware at the time of initial development. Such legacy elements can potentially limit the effectiveness of accurately describing key parameters or metrics during experimentation or postprocessing. As the behavior of novel fuels is also not always fully understood, this highlights the need for tools to guide the experimental design to avoid unexpected and potentially dangerous outcomes. This paper details the conversion of an existing data acquisition system for a spark-ignited single-cylinder cooperative fuel research (CFR) engine to handle a variety of different emerging alternative fuels. Modifications to the in-house legacy LabVIEW program and data acquisition system are detailed, with emphasis on providing additional tools to aid in the experimentation expediency and safety. The existing CFR testing facility is converted from a single liquid or single gaseous fuel capable system to a multifuel compatible system, complete with on-the-fly calculations of mass, energy, and volume fractions of existing fuel blends, increased low-speed sampling rates, and a calculating tool to command fuel flow set-points for a given blend fraction, equivalence ratio and airflow set-point. Additional factors, such as sensor selection and communication, are discussed with an emphasis on reducing experimental uncertainty. Overall, this work provides insights into possible adjustments and considerations when upgrading existing experimental setups to accommodate emerging alternative fuel use. Copyright © 2025 by ASME.

关键词： Alternative fuels

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Surge-protected mechanical amplifier of ion transport devised using a bipolar nanopore with broken symmetry

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Physical Review E 2025年第2期111卷 L023101-L023101页

作者： Arghyadeep Paul N. R. Aluru Department of Mechanical Science and Engineering Beckman Institute for Advanced Science and Technology University of Illinois at Urbana−Champaign Urbana Illinois 61801 United States Walker Department of Mechanical Engineering Oden Institute for Computational Engineering and Sciences The University of Texas at Austin Austin Texas 78712 United States

Electrokinetic ion transport under coupled external electric field and pressure drops has recently exhibited giant, but monotonous, mechanical amplifications of ionic current in some nanochannels. While such effects can be exploited to develop novel pressure-sensitive ionic circuits, they can also yield large undesirable current surges if the control parameter, i.e., the pressure drop, overshoots. To overcome this critical issue, we design a nanopore that displays a similar mechanical activation of ionic current, but up to a limited magnitude of pressure, after which current ceases to grow. We integrate a bipolar surface charge distribution with an asymmetric channel geometry in a nanopore to alter the effect of an applied pressure drop on the quasistatic distribution of ions in the pore, which directs how the ionic conductance varies with pressure, in a way that yields the desired current-pressure response. Via such steps, analyzed using numerical simulations, we devise a funnel-shaped nanopore with a negatively charged conical mouth and a positively charged elongated cylindrical stem, that acts as the intended “surge-protected” mechanical amplifier of ion transport. It yields a substantial pressure-driven growth in ionic current (by factors of ∼4) up to a limited intensity of pressure drop exerted (along a constant external electric field) toward the stem, beyond which current begins to fall. Besides exhibiting this hitherto undetected electrokinetic behavior, our study implements and paves the way to an innovative “function to design” approach in the field of electrokinetics that can help build improved nanofluidic devices with desired advanced ion transport functionalities.

关键词： Nanopores

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Optimizing solar radiation prediction: a novel SVM approach for renewable energy systems

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International Journal of Information technology (Singapore) 2025年 1-13页

作者： Basha, S. Sharief Rao, A. Nagaraja Department of Mathematics School of Advanced Science Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computational Intelligence School of Computer Science & Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India

The global transition toward renewable energy sources, driven by fossil fuel depletion and environmental imperatives, has positioned solar power as a crucial sustainable alternative. This study presents an advanced methodological framework for solar radiation (SR) prediction utilizing Support Vector Machine (SVM) techniques. The research analyzes comprehensive solar radiation data collected from India’s Vellore district throughout 2023, implementing a novel Support Vector Regression (SVR) approach with both polynomial and radial basis function (RBF) kernels. While conventional methodologies predominantly focus on minimizing training errors, our approach emphasizes reducing the generalization error bound to enhance predictive performance. The proposed model incorporates diverse meteorological parameters for SR prediction, demonstrating superior generalization capabilities compared to traditional approaches. Empirical results indicate that the SVR_rbf model significantly outperformed both SVR_poly and artificial neural network (ANN) models in SR prediction accuracy. The model's efficacy is validated through rigorous performance metrics: RMSE (10.3032), MAE (4.2728), MAPE (0.0152), and R² (0.9795). This research advances the understanding of machine learning applications in renewable energy forecasting, particularly for regions with limited meteorological data availability. Research findings enhance solar energy utilization and contribute to the transition towards sustainable energy. Future research directions may explore the integration of additional environmental parameters and the model’s adaptability across diverse geographical contexts. © Bharati Vidyapeeth's institute of Computer Applications and Management 2025.

关键词： Artificial neural network Predictive models Solar radiation Support vector regression

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