Han Bao

Works (5)

Updated: July 22nd, 2024 08:05

2024 journal article

Evaluating the reliability of machine-learning-based predictions used in nuclear power plant instrumentation and control systems

RELIABILITY ENGINEERING & SYSTEM SAFETY, 250.

By: E. Chen*, H. Bao* & N. Dinh n 

author keywords: Machine learning; Reliability; Trustworthiness; Out -of -distribution detection
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7. Affordable and Clean Energy (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: July 17, 2024

2021 review

Uncertainty quantification and software risk analysis for digital twins in the nearly autonomous management and control systems: A review

[Review of ]. ANNALS OF NUCLEAR ENERGY, 160.

By: L. Lin n, H. Bao* & N. Dinh n 

author keywords: Digital twin; Autonomous control; Uncertainty quantification; Software risk analysis
TL;DR: This study selects and reviews relevant UQ techniques and software hazard and software risk analysis methods that may be suitable for DTs in the NAMAC system and evaluates the uncertainty of DTs and its impacts on the reactor digital instrumentation and control systems. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: July 6, 2021

2020 journal article

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

INTERNATIONAL JOURNAL OF MULTIPHASE FLOW, 135.

By: H. Bao*, J. Feng*, N. Dinh n  & H. Zhang *

author keywords: Machine learning; CFD; Two-phase flow; Interfacial forces; Coarse mesh
TL;DR: FSM can substantially improve the prediction of the coarse-mesh CFD model, regardless of the choice of interfacial closures, and it provides scalability and consistency across discontinuous flow regimes, demonstrating that data-driven methods can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: February 15, 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)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 4, 2019

2018 journal article

Safe reactor depressurization windows for BWR Mark I Station Blackout accident management strategy

ANNALS OF NUCLEAR ENERGY, 114, 518–529.

By: H. Bao n, H. Zhao *, H. Zhang *, L. Zou *, P. Sharpe * & N. Dinh n 

Contributors: H. Bao n, H. Zhao *, H. Zhang *, L. Zou *, P. Sharpe * & N. Dinh n 

author keywords: Station Blackout; BWR Mark I; GOTHIC; Depressurization windows; Containment venting
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

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