activity
20192022
most citedDeep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

3 citations · 9 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC2022

Systems-theoretic Hazard Analysis of Digital Human-System Interface Relevant to Reactor Trip

Edward Chen, Han Bao, Tate Shorthill +2

Human-system interface is one of the key advanced design features applied to modern digital instrumentation and control systems of nuclear power plants. The conventional design is…

physics.flu-dyn20203 cited

Deep Learning Interfacial Momentum Closures in Coarse-Mesh CFD Two-Phase Flow Simulation Using Validation Data

Han Bao, Jinyong Feng, Nam Dinh +1

Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (C…

eess.SY20203 cited

A Redundancy-Guided Approach for the Hazard Analysis of Digital Instrumentation and Control Systems in Advanced Nuclear Power Plants

Tate Shorthill, Han Bao, Hongbin Zhang +1

Digital instrumentation and control (I&C) upgrades are a vital research area for nuclear industry. Despite their performance benefits, deployment of digital I&C in nuclear power pl…

cs.LG20201 cited

Using Deep Learning to Explore Local Physical Similarity for Global-scale Bridging in Thermal-hydraulic Simulation

Han Bao, Nam Dinh, Linyu Lin +3

Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond th…

physics.comp-ph20192 cited

Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep Learning

Han Bao, Jinyong Feng, Nam Dinh +1

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven ap…