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High-Performance in N-Type Pbte Thermoelectric Materials by Defects Management and Extraordinary Role of Cd Alloying

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SSRN 2024年

作者： Chen, Zhiyu Li, Mancang Chen, Zhang Wang, Yu Zhou, Daijie Huang, Xueliang Zhang, Xinhu Guo, Rui Liu, Xianbo Wang, Zhengshang Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China Sichuan Research Center of New Materials Chengdu610000 China

The conventional multiple isovalent alloying is proved to be effective for enhancing n-type PbTe thermoelectrics. However, the introduction of various elements undeniably leads to potential stability concerns during material application. Here, we propose a straightforward strategy that leverages Cd alloying and defect management to enhance the TE performance of n-type PbTe. The Cd doping facilitates band gap widening, resulting in increased effective mass and Seebeck coefficient, especially at elevated temperatures due to the improved Cd solubility. Furthermore, the introduction of excess Pb atoms serves to occupy intrinsic Pb vacancies, consequently enhancing carrier mobility. As a result, the maximum power factor of Cd-alloyed Pb1.01Te samples reaches 31.5 μW cm-1 K-2, signifying a remarkable improvement of 69.3% in comparison to pristine PbTe. Additionally, the enlarged band gap induced by Cd alloying also effectively mitigates bipolar diffusion, combining with the enhanced phonon scattering due to CdTe secondary phase, yields a 34.3% decrease in thermal conductivity. By harnessing the multifaceted effects of Cd alloying and defect management, the ZT value of Pb1.01Te-2%Cd samples reaches 1.35 at 782 K. The present findings provide a novel perspective on the versatile utilization of single isovalent element alloying and the optimization of performance across other TE materials. © 2024, The Authors. All rights reserved.

关键词： Energy gap

Experimental Investigation on Natural Circulation Characteristics of Core Make-Up Tank Under Rolling Conditions 29

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Experimental Investigation on Natural Circulation Characteri...

2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Wang, Shuguang Zhang, Jinlei Shen, Ya'ou Liu, Xiaobo Ding, Shuhua Liu, Yusheng Li, Dongyang Tang, Jilin Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment Harbin Engineering University Harbin China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Nuclear and Radiation Safety Center Beijing China

ISBN: (纸本)9780791886502

In the floating nuclear power plant, Core Make-up Tank (CMT) is one of the passive safety facilities, has been widely used in nuclear engineering. Under emergency conditions, natural circulation occurs and consequent thermal stratification appears in CMT system. However, rolling motion will directly affect the natural circulation and destroy the formation of thermal stratification, so there will be uncertain and unknown change of the safety injection capacity of CMT. Based on above analysis, this work carried out experiments under rolling conditions to reveal natural circulation and thermal stratification characteristics in CMT. A temperature detecting matrix formed by thermocouples is deployed in CMT. It is concluded that thermal stratification distribution is affected by the rolling conditions. Rolling conditions increase the thickness of thermal stratification and reduce the temperature gradient. The CMT natural circulation has two stages, the influence of rolling motion on the average flow in the first stage of natural circulation is very weak, but rolling motion will significantly affect the second stage of natural circulation and advance the decline time of direct vessel injection (DVI) flow rate. The transition time of natural circulation stage and the flow change in the second stage of natural circulation are related to the thermal stratification in CMT. The impact of rolling conditions should not be ignored when evaluating the performance of CMT. © 2022 by ASME.

关键词： Safety engineering

Deep learning from three-dimensional Lithium-ion battery multiphysics model Part II:Convolutional neural network and long short-term memory integration

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Energy and AI 2024年第3期17卷 569-578页

作者： Yiheng Pang Anqi Dong Yun Wang Zhiqiang Niu Renewable Energy Resources Lab(RERL) Department of Mechanical and Aerospace EngineeringUniversity of CaliforniaIrvineCaliforniaUSA92697-3975 College of Engineering School of System Design and Intelligent ManufacturingSouthern University of Science and TechnologyChina Department of Aeronautical and Automotive Engineering Loughborough UniversityUnited KingdomLE113TU

Lithium-ion (Li-ion) batteries have emerged as a cornerstone of electric vehicles (EVs), enabling the road transportation towards net zero. The success of electric vehicles largely hinges on the battery performance and safety. It is challenging to test and predict battery performance and safety issues by conventional methods, which are usually time-consuming and expensive, involving significant human and measurement errors. To enable the quick estimation of battery performance and safety, we developed three data-driven machine learning (ML) models, namely a convolutional neural network (CNN), a long short-term memory (LSTM), and a CNN-LSTM to predict battery discharge curves and local maximum temperature (hot spot) under various operating conditions. The developed ML models mitigated data scarcity by employing a three-dimensional multi-physics Li-ion battery model to generate enormous and diverse high-quality data. It was found the CNN-LSTM model outperforms the others and achieved high accuracy of 98.68% to learn discharge curves and battery maximum temperature, owing to the integration of spatial and sequential feature extraction. The battery safety can be improved by comparing the predicted maximum battery temperature against safe temperature threshold. The proposed data development and data-driven ML models are of great potential to provide digital tools for engineering high-performance and safe EVs.

关键词： Li-ion batteries CNN LSTM Integration Performance Hot spot

Effect of AU Ion Irradiation on Microstructure, Mechanical, and Lead-Bismuth Eutectic Corrosion Properties of Zirconia Coatings with Different Y2o3 Content

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SSRN 2024年

作者： Zhu, Changda Qu, Guofeng Zhou, Mingyang Qiu, Xi Gao, Yang Long, Ziyao Yang, Jijun Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

The yttria-stabilized zirconia (YSZ) coatings with 4 ~ 6 mol.% Y2O3 content was irradiated by 6 MeV Au ions with a fluence 6.0[[EQUATION]]1015 ions/cm2. The cubic to monoclinic transformation of ZrO2 is is identified in the Au-irradiated samples. The cavities are observed in the YSZ layer while larger size cavities are found in the 6YSZ layer. LBE corrosion results show that irradiation improves the adhesion and corrosion resistance of the coating due to the fact that irradiation releases the stresses in the coating. Overall, the 4YSZ coating showed the best corrosion resistance and irradiation resistance in the case of heavy ion irradiation. © 2024, The Authors. All rights reserved.

关键词： Yttria stabilized zirconia

Relaxation and Crystallization of Amorphous Fecraltimo Coatings: Effects on Mechanical Properties and Lead-Bismuth Eutectic Corrosion Resistance

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SSRN 2024年

作者： Yang, Jian Zhou, Mingyang Yang, Jijun Key Laboratory of Radiation Physics and Technology of Ministry of Education Institute of Nuclear Science and Technology Sichuan University Chengdu610064 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610213 China

The microstructure evolution of amorphous FeCrAlTiMo coatings during annealing process, and the effects of structural relaxation and crystallization on its mechanical properties and LBE corrosion resistance are systematically investigated. With increasing annealing temperatures, three coatings with different microstructure, including relaxation and crystallization, could be obtained. The structural relaxation is beneficial to improve the LBE corrosion resistance of the coating, but it is accompanied by the loss of mechanical properties (interface bonding strength and wear resistance). The crystalline coating with multi-phase microstructure of BCC, FCC and HCP ( © 2024, The Authors. All rights reserved.

关键词： Corrosion resistance

Research on Fine Modeling of Dispersion Fuel Plate Based on Lattice Method

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SSRN 2023年

作者： Chen, Yuqing Liu, Pengfei Li, Song Lou, Lei Li, Ang College of Nuclear Science and Technology Naval University of Engineering Wuhan China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China

For Double Heterogeneous(DH) systems, the analysis shows that the lattice modeling method has a great advantage in efficiency compared to the stochastic explicit modeling method, but the Lattice Modeling Method with Single-Particle Universe(LMMSU) has difficulties in calculating the problem of DH system with multiple particle sizes. In this paper, a Lattice Modeling Method with Multiple -Particles Universe(LMMMU) is proposed . The LMMMU has a computational error of less than 30 pcm when dealing with particle-dispersed fuel systems and still less than 50 pcm when dealing with particle-dispersed burnable poison systems. The memory footprint is reduced to 5.21% and the computation time is reduced to 1.25% compared to the stochastic explicit modeling method. The LMMMU also shows good compatibility when dealing with the problem of filling by two types of dispersed particles. Comprehensive analysis shows that the LMMMU is better than LMMSU in terms of applicability, efficiency, and accuracy. © 2023, The Authors. All rights reserved.

关键词： Efficiency

DeepRank: Test Case Prioritization for Deep Neural Networks 35

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DeepRank: Test Case Prioritization for Deep Neural Networks

35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Li, Wei Zhang, Zhiyi Jian, Yifan Liu, Chen Huang, Zhiqiu Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Marxism Yangzhou University Yangzhou China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China

Deep neural networks (DNNs) have been widely used in safety-critical fields such as autonomous driving and medical diagnosis. However, DNNs are easily disturbed to make wrong decisions, which may lead to loss of life or property. Therefore, it is vital to test DNN adequately. In practice, to reveal the incorrect behavior of DNN and improve its robustness, testers usually need massive labeled data to test and optimize DNN. However, labeling test inputs to detect the correctness of DNN predictions is an expensive and time-consuming task that even affects the efficiency of DNN testing. To relieve the labeling-cost problem, we propose DeepRank, a test case prioritization technique based on cross-entropy loss. The key idea of DeepRank is that the higher the loss value of a test case relative to the DNN, the more likely it is to be mispredicted and the more conducive it is to improve the robustness of the DNN through retraining. Therefore, the cross-entropy loss value can be used for test case prioritization. We experimentally validate our approach on two datasets and three DNNs models. The experimental results demonstrate that DeepRank is significantly better than existing test case prioritization methods regarding fault-revealing capability and retraining effectiveness. © 2023 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Deep neural networks

Effect of Fission Defects on Tensile Strength of U3si2 Σ5(210) Grain Boundary from First-Principles Calculations

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SSRN 2023年

作者： Zhao, Jiajun Sun, Dan Xi, Liu Chen, Ping Zhao, Jijun Wang, Yuanyuan Dalian University of Technology Liaoning Dalian China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Sichuan Chengdu China

U3Si2 is regarded as a promising accident tolerant fuel (ATF) to replace the commercial fuel UO2;however, U3Si2 grain boundary (GB) embrittlement caused by irradiation-induced defect segregation remains to be clarified. In this work, U3Si2 Σ5(210) symmetrically tilt GB is taken as representative to elucidate the individual effect of xenon (Xe) and vacancy on the tensile strength and failure of GB using first-principles calculations. Compared with the predicted segregation energies of defects at the most energetically favourable positions of GB, Si vacancy (VSi) has much stronger preference to segregate to GB than that of Xe substitution on Si sublattice (XeSi). Moreover, the strengthening/embrittlement potency of GB with single vacancy/Xe is evaluated using the first-principles-based uniaxial tensile test. Although both VSi and XeSi yield a weakening effect on the strength of U3Si2 Σ5(210) GB, such defective GBs exhibit significantly stronger interface strengths compared to the corresponding defects segregated to UO2 Σ3(111) GB. The underlying mechanism of strength change of U3Si2 GB is discussed in terms of charge analysis. Our results can provide fundamental understanding on the mechanical behavior of irradiated GB from an atomic perspective. © 2023, The Authors. All rights reserved.

关键词： Grain boundaries

Study on hydrogen stratification and erosion process in Chinese SMR containment under severe accident scenario

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International Journal of Advanced Nuclear reactor design and technology 2020年 2卷 43-51页

作者： Fan, Yang Songwei, Li Wei, Zeng Zhifang, Qiu Jian, Deng Science and Technology on Reactor System Design Technology Laboratory NPIC China Design and Research Sub-institute NPIC China

During postulated severe accident, large amount of hydrogen-vapor mixture will be released into the containment and inflammable gas mixture may form. Once the gas will be ignited, the generated pressure loads can jeopardize the integrity of the internal structures. As the local accumulation of high content hydrogen related much to the stratification and erosion mechanism, it is therefore of interest whether a stratification of gas layers will form within small containment. In this study, a stratification/erosion experiment is performed and the criteria under which the steady stratification will be destroyed by steam jet discussed. Then the simulation method is validated by the experimental data. At last, several severe accident scenarios for Chinese SMR performed and one of them is adopted for containment hydrogen CFD analysis. It is shown that high content of hydrogen will form in the upper part of the containment, and clear gas stratification will form, such stratification will be enhanced due to the condensation effect of steel containment wall. © 2020

关键词： Hydrogen