This study compares the microstructural evolution,dynamic recrystallization(DRX)behavior,tensile properties,and age-hardenability between the newly developed high-speed-extrudable BA56 alloy and those of the widely re...This study compares the microstructural evolution,dynamic recrystallization(DRX)behavior,tensile properties,and age-hardenability between the newly developed high-speed-extrudable BA56 alloy and those of the widely recognized AZ31 alloy in industry.Unlike the AZ31 alloy,which retains partially unrecrystallized grains,the high-speed-extruded BA56 alloy demonstrates a coarser but entirely recrystallized and more homogeneous microstructure.The fine-grained structure and abundant Mg_(3)Bi_(2) particles in the BA56 extrusion billet significantly enhance its DRX behavior,thus enabling rapid and complete recrystallization during extrusion.The BA56 alloy contains band-like fragmented Mg_(3)Bi_(2) particles and numerous fine Mg_(3)Bi_(2) particles distributed throughout the material,in contrast to the sparse Al_(8)Mn_(5) particles in the AZ31 alloy.These features contribute to superior mechanical properties of the BA56 alloy,which achieves tensile yield and ultimate tensile strengths of 205 and 292 MPa,respectively,compared to 196 and 270 MPa for the AZ31 alloy.The superior strength of the BA56 alloy,even with its coarser grain size,can be explained by its elevated Hall-Petch constant and the strengthening contribution from the fine Mg_(3)Bi_(2) particles.Additionally,the BA56 alloy demonstrates significant age-hardenability,achieving a 22%enhancement in hardness following T5 aging,attributed to the precipitation of nanoscale Mg_(3)Bi_(2) and Mg_(17)Al_(12) phases.By contrast,the AZ31 alloy shows minimal hardening due to the absence of precipitate formation during aging.These findings suggest that the BA56 alloy is a promising candidate for the production of extruded Mg components requiring a combination of high productivity,superior mechanical performance,and wide-ranging process adaptability.展开更多
Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical mod...Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).展开更多
Renewable energy resources,including geothermal,are crucial for sustainable environmental management and climate change mitigation,offering clean,reliable,and low-emission alternatives to fossil fuels that reduce gree...Renewable energy resources,including geothermal,are crucial for sustainable environmental management and climate change mitigation,offering clean,reliable,and low-emission alternatives to fossil fuels that reduce greenhouse gases and support ecological balance.In this study,geographic information system(GIS)predictive analysis was employed to explore geothermal prospects,promoting environmental sustainability by reducing the dependence on fossil energy resources.Spatial and statistical analysis including the attribute correlation analysis was used to evaluate the relationship between exploration data and geothermal energy resources represented by hot springs.The weighted sum model was then used to develop geothermal predictive maps while the accuracy of prediction was determined using the receiver operating characteristic/area under curve(ROC/AUC)analysis.Based on the attribute correlation analysis,exploration data relating to geological structures,host rock(Asu River Group)and sedimentary contacts were the most critical parameters for mapping geothermal resources.These parameters were characterized by a statistical association of 0.52,0.48,and 0.46 with the known geothermal occurrences.Spatial data integration reveals the central part of the study location as the most prospective zone for geothermal occurrences.This zone occupies 14.76%of the study location.Accuracy assessment using the ROC/AUC analysis suggests an efficiency of 81.5%for the weight sum model.GIS-based multi-criteria analysis improves the identification and evaluation of geothermal resources,leading to better decision-making.展开更多
基金supported by the National Research Foundation of Korea(NRF)grants funded by the Korea government(MSIT)(Nos.RS-2024–00351052 and RS-2024–00450561).
文摘This study compares the microstructural evolution,dynamic recrystallization(DRX)behavior,tensile properties,and age-hardenability between the newly developed high-speed-extrudable BA56 alloy and those of the widely recognized AZ31 alloy in industry.Unlike the AZ31 alloy,which retains partially unrecrystallized grains,the high-speed-extruded BA56 alloy demonstrates a coarser but entirely recrystallized and more homogeneous microstructure.The fine-grained structure and abundant Mg_(3)Bi_(2) particles in the BA56 extrusion billet significantly enhance its DRX behavior,thus enabling rapid and complete recrystallization during extrusion.The BA56 alloy contains band-like fragmented Mg_(3)Bi_(2) particles and numerous fine Mg_(3)Bi_(2) particles distributed throughout the material,in contrast to the sparse Al_(8)Mn_(5) particles in the AZ31 alloy.These features contribute to superior mechanical properties of the BA56 alloy,which achieves tensile yield and ultimate tensile strengths of 205 and 292 MPa,respectively,compared to 196 and 270 MPa for the AZ31 alloy.The superior strength of the BA56 alloy,even with its coarser grain size,can be explained by its elevated Hall-Petch constant and the strengthening contribution from the fine Mg_(3)Bi_(2) particles.Additionally,the BA56 alloy demonstrates significant age-hardenability,achieving a 22%enhancement in hardness following T5 aging,attributed to the precipitation of nanoscale Mg_(3)Bi_(2) and Mg_(17)Al_(12) phases.By contrast,the AZ31 alloy shows minimal hardening due to the absence of precipitate formation during aging.These findings suggest that the BA56 alloy is a promising candidate for the production of extruded Mg components requiring a combination of high productivity,superior mechanical performance,and wide-ranging process adaptability.
基金funding from NSERC Alliance Grant ALLRP 576858e22 in partnership with Rocscience Inc.
文摘Accurate estimation of rockfall trajectories is essential for mitigation of rockfall hazards.Nowadays,Doppler radar technologies can measure rockfall trajectories with centimeter resolution.Calibrating a numerical model to fit these measured trajectories,i.e.back analysis,often involves manual trial-anderror processes and subjective goodness-of-fit criteria.Here,we propose a framework that uses the chi-square statistic to quantify the misfit between modeled and measured rockfall trajectories.The framework can also quantify the uncertainty bounds on the best-fit model parameters.The approach is validated using field data from an Australian copper mine under two scenarios.(1)We perform an unconstrained back-analysis where the initial position and velocity of the rock,in addition to the coefficients of restitution(COR),are free variables.This scenario yields a normal COR Rn?0.866±0.109 and tangential COR R_(t)=0.29±0.151 with 68%confidence.(2)We perform a constrained back-analysis using predetermined initial position and velocity of the rock,which further constrains Rn to 0.8±0.014 and Rt to 0.39±0.065.Both scenarios show a higher uncertainty in Rt than in Rn.We also demonstrate the adaptability of the back-analysis framework to two-dimensional(2D)rockfall modeling using the same data.To the best of our knowledge,this is the first quantitative goodness-of-fit metric for trajectorybased rockfall back analysis that supports the estimation of inherent uncertainty.The simplicity of the metric lends itself to robust model optimization of rockfall back-analysis and can be adapted to other model assumptions(e.g.rigid-body mechanics)and metrics(e.g.velocity or energy).
文摘Renewable energy resources,including geothermal,are crucial for sustainable environmental management and climate change mitigation,offering clean,reliable,and low-emission alternatives to fossil fuels that reduce greenhouse gases and support ecological balance.In this study,geographic information system(GIS)predictive analysis was employed to explore geothermal prospects,promoting environmental sustainability by reducing the dependence on fossil energy resources.Spatial and statistical analysis including the attribute correlation analysis was used to evaluate the relationship between exploration data and geothermal energy resources represented by hot springs.The weighted sum model was then used to develop geothermal predictive maps while the accuracy of prediction was determined using the receiver operating characteristic/area under curve(ROC/AUC)analysis.Based on the attribute correlation analysis,exploration data relating to geological structures,host rock(Asu River Group)and sedimentary contacts were the most critical parameters for mapping geothermal resources.These parameters were characterized by a statistical association of 0.52,0.48,and 0.46 with the known geothermal occurrences.Spatial data integration reveals the central part of the study location as the most prospective zone for geothermal occurrences.This zone occupies 14.76%of the study location.Accuracy assessment using the ROC/AUC analysis suggests an efficiency of 81.5%for the weight sum model.GIS-based multi-criteria analysis improves the identification and evaluation of geothermal resources,leading to better decision-making.
