Seldom could metals and alloys maintain excellent properties in cryogenic condition, such as the ductility, owing to the restrained dislocation motion.However, a face-centered-cubic(FCC) CoCrFeNi highentropy alloy(HEA...Seldom could metals and alloys maintain excellent properties in cryogenic condition, such as the ductility, owing to the restrained dislocation motion.However, a face-centered-cubic(FCC) CoCrFeNi highentropy alloy(HEA) with great ductility is investigated under the cryogenic environment. The tensile strength of this alloy can reach a maximum at 1,251±10 MPa, and the strain to failure can stay at as large as 62% at the liquid helium temperature. We ascribe the high strength and ductility to the low stacking fault energy at extremely low temperatures,which facilitates the activation of deformation twinning.Moreover, the FCC→HCP(hexagonal close-packed) transition and serration lead to the sudden decline of ductility below 77 K. The dynamical modeling and analysis of serrations at 4.2 and 20 K verify the unstable state due to the FCC→HCP transition. The deformation twinning together with phase transformation at liquid helium temperature produces an adequate strain-hardening rate that sustains the stable plastic flow at high stresses, resulting in the serration feature.展开更多
In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation...In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.展开更多
基金supported in part by the Nationa Natural Science Foundation of China (51471025, 51671020, 51471024 and 11771407)the Department of Energy (DOE), Office of Fossil Energy, National Energy Technology Laboratory (DE-FE-0011194)+1 种基金the support from the US Army Research Office project (W911NF-13-1-0438)the support from the National Science Foundation (DMR-1611180 and 1809640)
文摘Seldom could metals and alloys maintain excellent properties in cryogenic condition, such as the ductility, owing to the restrained dislocation motion.However, a face-centered-cubic(FCC) CoCrFeNi highentropy alloy(HEA) with great ductility is investigated under the cryogenic environment. The tensile strength of this alloy can reach a maximum at 1,251±10 MPa, and the strain to failure can stay at as large as 62% at the liquid helium temperature. We ascribe the high strength and ductility to the low stacking fault energy at extremely low temperatures,which facilitates the activation of deformation twinning.Moreover, the FCC→HCP(hexagonal close-packed) transition and serration lead to the sudden decline of ductility below 77 K. The dynamical modeling and analysis of serrations at 4.2 and 20 K verify the unstable state due to the FCC→HCP transition. The deformation twinning together with phase transformation at liquid helium temperature produces an adequate strain-hardening rate that sustains the stable plastic flow at high stresses, resulting in the serration feature.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2018YFF0300104 and 2017YFC0209804)the National Natural Science Foundation of China (Grant No. 11421101)Beijing Academy of Artifical Intelligence (BAAI)
文摘In this paper, the model output machine learning (MOML) method is proposed for simulating weather consultation, which can improve the forecast results of numerical weather prediction (NWP). During weather consultation, the forecasters obtain the final results by combining the observations with the NWP results and giving opinions based on their experience. It is obvious that using a suitable post-processing algorithm for simulating weather consultation is an interesting and important topic. MOML is a post-processing method based on machine learning, which matches NWP forecasts against observations through a regression function. By adopting different feature engineering of datasets and training periods, the observational and model data can be processed into the corresponding training set and test set. The MOML regression function uses an existing machine learning algorithm with the processed dataset to revise the output of NWP models combined with the observations, so as to improve the results of weather forecasts. To test the new approach for grid temperature forecasts, the 2-m surface air temperature in the Beijing area from the ECMWF model is used. MOML with different feature engineering is compared against the ECMWF model and modified model output statistics (MOS) method. MOML shows a better numerical performance than the ECMWF model and MOS, especially for winter. The results of MOML with a linear algorithm, running training period, and dataset using spatial interpolation ideas, are better than others when the forecast time is within a few days. The results of MOML with the Random Forest algorithm, year-round training period, and dataset containing surrounding gridpoint information, are better when the forecast time is longer.
