This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined wi...This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined with optical microscopy and electron backscatter diffraction(EBSD)analysis demonstrate that increasing undercooling(ΔT)can induce a consistent sequence of microstructural transitions:coarse dendrites,fine equiaxed grains(first refinement),oriented fine dendrites,and fine equiaxed grains(second refinement).Two distinct grain refinement events are identified,with critical undercooling thresholds(ΔT)dependent on composition:increasing Cu content increases the critical undercoolingΔT*required for the second refinement(Cu55Ni45:227 K;Cu60Ni40:217 K;Ni65Cu35:200 K).The BCT(Bridgman Crystal Growth)model quantitatively elucidates this behavior,revealing a shift from solute-diffusion-dominated growth at low undercooling to thermally dominated diffusion at high undercooling(ΔT).Crucially,refined grains at high undercooling exhibit smaller sizes(10μm)and higher uniformity than those at low undercooling(20μm).These findings provide fundamental insights into non-equilibrium solidification mechanisms and establish a foundation for designing high-performance Ni-Cu alloys via deep undercooling processing.展开更多
To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station ...To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station without massive raw data exchange.However,heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning.Dropping out vehicles with limited resources confines the training data.As a result,the accuracy and applicability of federated learning models will be reduced.To mitigate the straggler effect and improve performance of federated learning,we propose a reconfigurable intelligent surface(RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV.Furthermore,we optimize the phase shift of RIS to achieve a more reliable communication environment.In addition,we define vehicular competence to measure both vehicular trustworthiness and resources.Based on the vehicular competence,the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence.The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.展开更多
基金Funded by the Central Government-Guided Local Development Fund Project(No.YDZJSX2025D042)the Key R&D Program of Shanxi Province(No.202202150401018)+1 种基金the Basic Research Program of Shanxi Province(No.20210302124220)the State Key Laboratory of CAD/CG of Zhejiang University(No.A2325)。
文摘This study systematically investigated the microstructural evolution of binary Ni-Cu alloys(Cu55Ni45,Cu60Ni40,and Ni65Cu35)under deep undercooling conditions.The controlled rapid solidification experiments combined with optical microscopy and electron backscatter diffraction(EBSD)analysis demonstrate that increasing undercooling(ΔT)can induce a consistent sequence of microstructural transitions:coarse dendrites,fine equiaxed grains(first refinement),oriented fine dendrites,and fine equiaxed grains(second refinement).Two distinct grain refinement events are identified,with critical undercooling thresholds(ΔT)dependent on composition:increasing Cu content increases the critical undercoolingΔT*required for the second refinement(Cu55Ni45:227 K;Cu60Ni40:217 K;Ni65Cu35:200 K).The BCT(Bridgman Crystal Growth)model quantitatively elucidates this behavior,revealing a shift from solute-diffusion-dominated growth at low undercooling to thermally dominated diffusion at high undercooling(ΔT).Crucially,refined grains at high undercooling exhibit smaller sizes(10μm)and higher uniformity than those at low undercooling(20μm).These findings provide fundamental insights into non-equilibrium solidification mechanisms and establish a foundation for designing high-performance Ni-Cu alloys via deep undercooling processing.
基金supported in part by the Fundamental Research Funds for the Central Universities (2022JBQY004)the Beijing Natural Science Foundation L211013+4 种基金the Basic Research Program under Grant JCKY2022XXXX145the National Natural Science Foundation of China (No. 62221001,62201030)the Science and Technology Research and Development Plan of China Railway Co., Ltd (No. K2022G018)the project of CHN Energy Shuohuang Railway under Grant SHTL-2332the China Postdoctoral Science Foundation 2021TQ0028,2021M700369
文摘To protect vehicular privacy and speed up the execution of tasks,federated learning is introduced in the Internet of Vehicles(IoV)where users execute model training locally and upload local models to the base station without massive raw data exchange.However,heterogeneous computing and communication resources of vehicles cause straggler effect which weakens the reliability of federated learning.Dropping out vehicles with limited resources confines the training data.As a result,the accuracy and applicability of federated learning models will be reduced.To mitigate the straggler effect and improve performance of federated learning,we propose a reconfigurable intelligent surface(RIS)-assisted federated learning framework to enhance the communication reliability for parameter transmission in the IoV.Furthermore,we optimize the phase shift of RIS to achieve a more reliable communication environment.In addition,we define vehicular competence to measure both vehicular trustworthiness and resources.Based on the vehicular competence,the straggler effect is mitigated where training tasks of computing stragglers are offloaded to surrounding vehicles with high competence.The experiment results verify that our proposed framework can improve the reliability of federated learning in terms of computing and communication in the IoV.
