4 papers · 1 filter
Prediction of Critical Heat Flux in Rod Bundles Using Tube-Based Hybrid Machine Learning Models in CTF
Aidan Furlong, Robert Salko, Xingang Zhao +1
The prediction of critical heat flux (CHF) using machine learning (ML) approaches has become a highly active research activity in recent years, the goal of which is to build models…
A Three-Stage Bayesian Transfer Learning Framework to Improve Predictions in Data-Scarce Domains
Aidan Furlong, Robert Salko, Xingang Zhao +1
The use of ML in engineering has grown steadily to support a wide array of applications. Among these methods, deep neural networks have been widely adopted due to their performance…
Development and Deployment of Hybrid ML Models for Critical Heat Flux Prediction in Annulus Geometries
Aidan Furlong, Xingang Zhao, Robert Salko +1
Accurate prediction of critical heat flux (CHF) is an essential component of safety analysis in pressurized and boiling water reactors. To support reliable prediction of this quant…
Physics-Based Hybrid Machine Learning for Critical Heat Flux Prediction with Uncertainty Quantification
Aidan Furlong, Xingang Zhao, Robert Salko +1
Critical heat flux is a key quantity in boiling system modeling due to its impact on heat transfer and component temperature and performance. This study investigates the developmen…