101 citations · 264 across the 32 of their papers we have counts for
7 papers · 1 filter
A Data-driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation
Han Bao, Nam Dinh, Jeffrey Lane +1
Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal tra…
Validation and Uncertainty Quantification for Wall Boiling Closure Relations in Multiphase-CFD Solver
Yang Liu, Nam Dinh
The two-fluid model based Multiphase Computational Fluid Dynamics (MCFD) has been considered as one of the most promising tools to investigate two-phase flow and boiling system for…
Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results
Yang Liu, Nam Dinh, Yohei Sato +1
Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchan…
A Validation and Uncertainty Quantification Framework for Eulerian-Eulerian Two-Fluid-Model based Multiphase-CFD Solver. Part II: Applications
Yang Liu, Nam Dinh, Ralph Smith
This paper is the second part of a two-part series, which introduces and demonstrates a Validation and Uncertainty Quantification (VUQ) framework that serves two major purposes: i)…
A Validation and Uncertainty Quantification Framework for Eulerian-Eulerian Two-Fluid Model based Multiphase-CFD Solver. Part I: Methodology
Yang Liu, Nam Dinh, Ralph Smith
In this paper, a validation and uncertainty quantification (VUQ) framework for the Eulerian-Eulerian two-fluid-model based multiphase-computational fluid dynamics solver (MCFD) is…
Reynolds-Averaged Turbulence Modeling Using Type I and Type II Machine Learning Frameworks with Deep Learning
Chih-Wei Chang, Nam T. Dinh
Deep learning (DL)-based Reynolds stress with its capability to leverage values of large data can be used to close Reynolds-averaged Navier-Stoke (RANS) equations. Type I and Type…
Classification of Machine Learning Frameworks for Data-Driven Thermal Fluid Models
Chih-Wei Chang, Nam T. Dinh
Thermal fluid processes are inherently multi-physics and multi-scale, involving mass-momentum-energy transport phenomena. Thermal fluid simulation (TFS) is based on solving conserv…