Zobrazeno 1 - 10
of 68
pro vyhledávání: '"and Govindarajan Muralidharan"'
Autor:
Jian Peng, Rishi Pillai, Marie Romedenne, Bruce A. Pint, Govindarajan Muralidharan, J. Allen Haynes, Dongwon Shin
Publikováno v:
npj Materials Degradation, Vol 5, Iss 1, Pp 1-8 (2021)
Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to p
Externí odkaz:
https://doaj.org/article/87f65d909fea40b68565da846e7fd0e6
A Technique for the Quantitative Characterization of Weld Microstructure and Application to Mo Welds
Autor:
Noah M. Kohlhorst, Kevin M. Faraone, Roger G. Miller, Govindarajan Muralidharan, George B. Ulrich, Ji-Cheng Zhao
Publikováno v:
Metallurgical and Materials Transactions B. 54:1434-1448
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85ed116ecc76f4010a05ed4b51c1ed34
https://doi.org/10.1007/s11663-023-02771-y
https://doi.org/10.1007/s11663-023-02771-y
Autor:
Govindarajan Muralidharan, Jian Peng, Bruce A. Pint, Rishi Pillai, Marie Romedenne, James A Haynes, Dongwon Shin
Publikováno v:
Oxidation of Metals. 97:51-76
Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce developme
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::676ae6b6f8f24428d9c8506b41c6d0e6
https://doi.org/10.1007/s11085-021-10076-1
https://doi.org/10.1007/s11085-021-10076-1
Publikováno v:
Scripta Materialia. 182:62-67
A new technique to calculate spatial variations of grain shape, grain size, and grain boundary curvature in the fusion zone (FZ) of refractory metal alloys has been developed. This technique was applied to quantitatively evaluate changes in microstru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::baf826aba1cb612cb6678ee99085396c
https://doi.org/10.1016/j.scriptamat.2020.02.026
https://doi.org/10.1016/j.scriptamat.2020.02.026
Autor:
Eric Gingrich, Dean Pierce, Gerald Byrd, Katherine Sebeck, Vamshi Korivi, Govindarajan Muralidharan, Hsin Wang, James Torres, Artem Trofimov, James Haynes, Michael Tess
Publikováno v:
SAE Technical Paper Series.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ab49c0ce106cad004c03f7b0ae01046
https://doi.org/10.4271/2022-01-0599
https://doi.org/10.4271/2022-01-0599
Autor:
Grigoreta M. Stoica, Luc L. Dessieux, Alexandru D. Stoica, Sven C. Vogel, Govindarajan Muralidharan, Balasubramaniam Radhakrishnan, Sarma B. Gorti, Ke An, Dong Ma, Xun-Li Wang
Publikováno v:
Quantum Beam Science, Vol 2, Iss 3, p 17 (2018)
The time-of-flight neutron diffraction data collected in-situ on Oak Ridge National Laboratory’s (ORNL, Oak Ridge, TN, USA) VULCAN and Los Alamos National Laboratory’s (LANL, Los Alamos, NM, USA) High-Pressure-Preferred-Orientation (HIPPO) diffra
Externí odkaz:
https://doaj.org/article/b72f4870bd6c4d62acb28a13db52da7c
Autor:
Bruce A. Pint, Govindarajan Muralidharan, Marie Romedenne, Rishi Pillai, Dongwon Shin, J. Allen Haynes, Jian Peng
Publikováno v:
npj Materials Degradation, Vol 5, Iss 1, Pp 1-8 (2021)
Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e0ba6fdae2680fe6e87e4a56741f103
https://doi.org/10.1038/s41529-021-00188-z
https://doi.org/10.1038/s41529-021-00188-z