The microstructure evolutions and mechanical properties of a heterogeneous Mg88Y8Zn4（in at.%） alloy during multi-pass equal channel angular pressing（ECAP） were systematically investigated in this work.The results ...The microstructure evolutions and mechanical properties of a heterogeneous Mg88Y8Zn4（in at.%） alloy during multi-pass equal channel angular pressing（ECAP） were systematically investigated in this work.The results show that four phases,i.e.α-Mg,18 R long period stacking ordered（LPSO） phase,Mg24Y5 and Y-rich phase,are present in cast alloy.During ECAP,dynamic recrystallization（DRX） occurs and the diameter of DRXedα-Mg grains decreases to 0.8 μm.Moreover,precipitation of lamellar 14 H LPSO structure is developed withinα-Mg phase.Both the refinement of α-Mg grains and precipitation of 14 H LPSO contribute to the increase in micro-hardness from 98 HV to 135 HV for α-Mg.In addition,a simplified model describing the evolution of 18 R LPSO phase is established,which illustrates that 18 R undergoes a four-step morphological evolution with increasing strains during ECAP,i.e.original lath → bent lath → cracked lath → smaller particles.Compression test results indicate that the alloy has been markedly strengthened after multi-pass ECAP,and the main reason for the significantly enhanced mechanical properties could be ascribed to the DRXed α-Mg grains,newly precipitated 14 H lamellas,18 R kinking and refined 18 R particles.展开更多
The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the...The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the existing climate models suffer from constraints imposed by computational resources and model complexity,leading to outputs with coarse spatio-temporal resolution.Current meteorological super-resolution techniques predominantly focus on singledimensional(spatial or temporal)enhancements,failing to effectively reconstruct dynamic spatio-temporal coupled features.To address these limitations,this study proposes a Spatio-Temporal Multi-Scale Residual Network(ST-MSRN),which integrates a Multi-Scale Residual Feature Block(MSRFB)with a Channel Stacking Mechanism.The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns,while the integrated Efficient Multiscale Attention(EMA)module adaptively weights features based on spatio-temporal heterogeneity.Experimental results demonstrate:(1)Successful upscaling from 1.5°spatial/3-day temporal to 0.25°/daily resolution;(2)Superior performance over traditional methods(spline/nearest-neighbor interpolation)and mainstream deep learning methods,with marked improvements in key indicators such as structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)for temperature and precipitation data,while the mean absolute error(MAE)and mean squared error(MSE)have been significantly reduced.This work establishes a new paradigm for Earth system data enhancement,particularly advancing extreme weather early warning systems through physics-aware deep learning architectures.展开更多
基金the financial support from the Natural Science Foundation of Jiangsu Province(No.BK20160869)the Nantong Science and Technology Project(No.GY12015009)+1 种基金the Fundamental Research Funds for the Central Universities(No.2015B01314)the National Natural Science Foundation of China(No.51501039)
文摘The microstructure evolutions and mechanical properties of a heterogeneous Mg88Y8Zn4（in at.%） alloy during multi-pass equal channel angular pressing（ECAP） were systematically investigated in this work.The results show that four phases,i.e.α-Mg,18 R long period stacking ordered（LPSO） phase,Mg24Y5 and Y-rich phase,are present in cast alloy.During ECAP,dynamic recrystallization（DRX） occurs and the diameter of DRXedα-Mg grains decreases to 0.8 μm.Moreover,precipitation of lamellar 14 H LPSO structure is developed withinα-Mg phase.Both the refinement of α-Mg grains and precipitation of 14 H LPSO contribute to the increase in micro-hardness from 98 HV to 135 HV for α-Mg.In addition,a simplified model describing the evolution of 18 R LPSO phase is established,which illustrates that 18 R undergoes a four-step morphological evolution with increasing strains during ECAP,i.e.original lath → bent lath → cracked lath → smaller particles.Compression test results indicate that the alloy has been markedly strengthened after multi-pass ECAP,and the main reason for the significantly enhanced mechanical properties could be ascribed to the DRXed α-Mg grains,newly precipitated 14 H lamellas,18 R kinking and refined 18 R particles.
基金supported by the National Key Research and Development Program of China,(No.2024YFC3013100)the Joint Research Project for Meteorological Capacity Improvement(No.24NLTSZ007)China Meteorological Administration(CMA)Youth Innovation Team(CMA2024QN06).
文摘The application of Al and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings,as well as the analysis of climate trends.However,the existing climate models suffer from constraints imposed by computational resources and model complexity,leading to outputs with coarse spatio-temporal resolution.Current meteorological super-resolution techniques predominantly focus on singledimensional(spatial or temporal)enhancements,failing to effectively reconstruct dynamic spatio-temporal coupled features.To address these limitations,this study proposes a Spatio-Temporal Multi-Scale Residual Network(ST-MSRN),which integrates a Multi-Scale Residual Feature Block(MSRFB)with a Channel Stacking Mechanism.The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns,while the integrated Efficient Multiscale Attention(EMA)module adaptively weights features based on spatio-temporal heterogeneity.Experimental results demonstrate:(1)Successful upscaling from 1.5°spatial/3-day temporal to 0.25°/daily resolution;(2)Superior performance over traditional methods(spline/nearest-neighbor interpolation)and mainstream deep learning methods,with marked improvements in key indicators such as structural similarity(SSIM)and peak signal-to-noise ratio(PSNR)for temperature and precipitation data,while the mean absolute error(MAE)and mean squared error(MSE)have been significantly reduced.This work establishes a new paradigm for Earth system data enhancement,particularly advancing extreme weather early warning systems through physics-aware deep learning architectures.
