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Dinh, Nam T.
2525
results:
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1
Data-Driven RANS Turbulence Closures for Forced Convection ..:
Iskhakov, Arsen S.
;
Tai, Cheng-Kai
;
Bolotnov, Igor A.
...
Nuclear Technology. 210 (2023) 7 - p. 1167-1184 , 2023
Link:
https://doi.org/10.1080/..
?
2
Machine learning from RANS and LES to inform coarse grid si..:
Iskhakov, Arsen S.
;
Dinh, Nam T.
;
Leite, Victor Coppo
.
Progress in Nuclear Energy. 163 (2023) - p. 104809 , 2023
Link:
https://doi.org/10.1016/..
?
3
Data-Driven High-to-Low for Coarse Grid System Thermal Hydr..:
Iskhakov, Arsen S.
;
Coppo Leite, Victor
;
Merzari, Elia
.
Nuclear Science and Engineering. 198 (2023) 7 - p. 1426-1438 , 2023
Link:
https://doi.org/10.1080/..
?
4
Direct Numerical Simulation of Low and Unitary Prandtl Numb..:
Tai, Cheng-Kai
;
Nguyen, Tri
;
Iskhakov, Arsen S.
...
Nuclear Technology. 210 (2023) 7 - p. 1097-1118 , 2023
Link:
https://doi.org/10.1080/..
?
5
An adaptive knowledge-based data-driven approach for turbul..:
Zhu, Yangmo
;
Dinh, Nam T.
;
Saini, Nadish
.
Nuclear Engineering and Design. 393 (2022) - p. 111814 , 2022
Link:
https://doi.org/10.1016/..
?
6
Development and assessment of a reactor system prognosis mo..:
Gurgen, Anil
;
Dinh, Nam T.
Nuclear Engineering and Design. 398 (2022) - p. 111976 , 2022
Link:
https://doi.org/10.1016/..
?
7
A Perspective on Data-Driven Coarse Grid Modeling for Syste..:
Iskhakov, Arsen S.
;
Tai, Cheng-Kai
;
Bolotnov, Igor A.
.
Nuclear Science and Engineering. 197 (2022) 10 - p. 2527-2542 , 2022
Link:
https://doi.org/10.1080/..
?
8
Integration of neural networks with numerical solution of P..:
Iskhakov, Arsen S.
;
Dinh, Nam T.
;
Chen, Edward
Physics Letters A. 406 (2021) - p. 127456 , 2021
Link:
https://doi.org/10.1016/..
?
9
Reynolds-Averaged Turbulence Modeling Using Deep Learning w..:
Chang, Chih-Wei
;
Fang, Jun
;
Dinh, Nam T.
Nuclear Science and Engineering. 194 (2020) 8-9 - p. 650-664 , 2020
Link:
https://doi.org/10.1080/..
?
10
An Application of ASP in Nuclear Engineering: Explaining th..:
HANNA, BOTROS N.
;
TRIEU, LY LY T
;
SON, TRAN C.
.
Theory and Practice of Logic Programming. 20 (2020) 6 - p. 926-941 , 2020
Link:
https://doi.org/10.1017/..
?
11
Machine-learning based error prediction approach for coarse..:
Hanna, Botros N.
;
Dinh, Nam T.
;
Youngblood, Robert W.
.
Progress in Nuclear Energy. 118 (2020) - p. 103140 , 2020
Link:
https://doi.org/10.1016/..
?
12
A data-driven framework for error estimation and mesh-model..:
Bao, Han
;
Dinh, Nam T.
;
Lane, Jeffrey W.
.
Nuclear Engineering and Design. 349 (2019) - p. 27-45 , 2019
Link:
https://doi.org/10.1016/..
?
13
Classification of machine learning frameworks for data-driv..:
Chang, Chih-Wei
;
Dinh, Nam T.
International Journal of Thermal Sciences. 135 (2019) - p. 559-579 , 2019
Link:
https://doi.org/10.1016/..
?
14
Validation and uncertainty quantification of multiphase-CFD..:
Liu, Yang
;
Sun, Xiaodong
;
Dinh, Nam T.
Nuclear Engineering and Design. 354 (2019) - p. 110200 , 2019
Link:
https://doi.org/10.1016/..
?
15
Uncertainty quantification of two-phase flow and boiling he..:
Liu, Yang
;
Dinh, Nam T.
;
Smith, Ralph C.
.
International Journal of Heat and Mass Transfer. 138 (2019) - p. 1096-1116 , 2019
Link:
https://doi.org/10.1016/..
1-15