Robert Walker Youngblood

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.

By: L. Lin n, P. Athe n , P. Rouxelin n, M. Avramova n, A. Gupta n, R. Youngblood*, J. Lane*, N. Dinh n 

Contributors: L. Lin n, P. Athe n , P. Rouxelin n, M. Avramova n, A. Gupta n, R. Youngblood*, 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)
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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.

By: H. Bao*, N. Dinh n , J. Lane* & R. Youngblood*

Contributors: H. Bao*, N. Dinh n , J. Lane* & R. Youngblood*

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 Goals Color Wheel
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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.

By: B. Hanna  n, N. Dinh n , R. Youngblood* & I. Bolotnov n 

Contributors: B. Hanna  n, N. Dinh n , R. Youngblood* & 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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