首页|Data on Machine Learning Discussed by Researchers at Chinese Academy of Sciences (Machine Learning Informed Visco-plastic Model for the Cyclic Relaxation of 316h Stainless Steel At 550 c)
Data on Machine Learning Discussed by Researchers at Chinese Academy of Sciences (Machine Learning Informed Visco-plastic Model for the Cyclic Relaxation of 316h Stainless Steel At 550 c)
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NETL
NSTL
Elsevier
Investigators discuss new findings in Machine Learning. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Among the structural alloys for this fast reactor, 316H stainless steel has emerged as a promising candidate. Because the operating temperature of Sodium-cooled reactor is specifically designed to be 550 degrees C, this operating temperature triggers material inelastic behavior depends more on the coupling of fatigue and creep, which complicates the constitutive model.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Youth Innovation Promotion Association CAS. Our news editors obtained a quote from the research from the Chinese Academy of Sciences, “By introducing static recovery terms, previous studies could capture some experimental features, but failed to describe the interaction by fatigue and creep. In this work, in order to describe the fatigue and creep during cyclic relaxation of 316H stainless steel at 550 degrees C, we propose a modified visco-plastic constitutive model within the framework of unified Chaboche model. In the proposed model, the parameters related to the static recovery items are coupled, and thus cannot be identified from experiments using the traditional trial and error. To address this issue, we employed the Bayesian approach to identify these parameters. The parameter identification involves two steps: (ⅰ) con-structing a Gaussian Process surrogate model using data generated from the finite element method, and (ⅱ) obtaining the value of parameters through Markov Chain Monte Carlo sampling under the Bayesian framework. The proposed procedure, is demonstrated by the using experi-mental results of 316H stainless steel at 550 degrees C. Under the coupling of fatiguecreep, the material exhibits a cyclic-dependent accelerated stress relaxation before reaching the saturated stage and a steady state of relaxed stress after a long holding time. These mechanical responses are well predicted by the proposed model.”
BeijingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningStainless SteelChinese Academy of Sciences
NETL
NSTL
Elsevier
