首页|New Data from Ministry of Education Illuminate Findings in Machine Learning (Mec hanical Degradations of Fe-c Alloys Induced By Stress Corrosion In Supercritical Co2 Environments: a Study Based On Molecular Dynamics Simulation and ...)
New Data from Ministry of Education Illuminate Findings in Machine Learning (Mec hanical Degradations of Fe-c Alloys Induced By Stress Corrosion In Supercritical Co2 Environments: a Study Based On Molecular Dynamics Simulation and ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting originating in Changsha, People’s Repub lic of China, by NewsRx journalists, research stated, “Under supercritical carbo n dioxide (SCO2) conditions, stress corrosion cracking (SCC) of steel can signif icantly degrade their mechanical properties and shorten their service life. Howe ver, few studies have been focused on predicting such property degradation.”
ChangshaPeople’s Republic of ChinaAsiaAlloysCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysicsMinistry of Education
NETL
NSTL
