Works (3)
Updated: July 5th, 2023 15:22
2021 journal article
Digital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system
ANNALS OF NUCLEAR ENERGY, 166.
Contributors: L. Lin n, P. Athe n , P. Rouxelin n, M. Avramova n, A. Gupta n, *, J. Lane*, N. Dinh n
author keywords: autonomous control; digital twin; diagnosis; prognosis
TL;DR:
This study refines a NAMAC system for making reasonable recommendations during complex loss-of-flow scenarios with a validated Experimental Breeder Reactor II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects.
(via Semantic Scholar)
open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
9. Industry, Innovation and Infrastructure
(Web of Science)
12. Responsible Consumption and Production
(Web of Science)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: November 1, 2021
2019 journal article
A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
NUCLEAR ENGINEERING AND DESIGN, 349, 27–45.
author keywords: Coarse mesh; Error estimation; System-level modeling and simulation; Machine learning; Physical feature
TL;DR:
A data-driven framework is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size and constitutive correlations for low-f fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 4, 2019
2019 journal article
Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD)
PROGRESS IN NUCLEAR ENERGY, 118.
Contributors: B. Hanna n, N. Dinh n , * & I. Bolotnov n
author keywords: Coarse grid (mesh); CFD; Machine learning; Discretization error; Big data; Artificial neural network; Random forest; Data-driven
TL;DR:
The proposed CG-CFD method has proven capable of correcting the coarse-grid results and obtaining reasonable predictions for new cases and shows potential for a good predictive capability.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: January 21, 2020
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