101 citations · 264 across the 32 of their papers we have counts for
15 papers · 1 filter
Interface Capturing Flow Boiling Simulations in a Compact Heat Exchanger
Anna Iskhakova, Yoshiyuki Kondo, Koichi Tanimoto +2
High-fidelity flow boiling simulations are conducted in a vertical minichannel with offset strip fins (OSF) using R113 as a working fluid. Finite-element code PHASTA coupled with l…
Direct Numerical Simulation of Low and Unitary Prandtl Number Fluids in Reactor Downcomer Geometry
Cheng-Kai Tai, Tri Nguyen, Arsen S. Iskhakov +3
Buoyancy effect on low-flow condition convective heat transfer of non-conventional coolants, such as liquid metal and molten salts, is a crucial safety factor to advanced reactors…
Data-driven Hi2Lo for Coarse-grid System Thermal Hydraulic Modeling
Arsen S. Iskhakov, Nam T. Dinh, Victor Coppo Leite +1
Traditional 1D system thermal hydraulic analysis has been widely applied in nuclear industry for licensing purposes due to its numerical efficiency. However, such codes are inheren…
Review of Physics-based and Data-driven Multiscale Simulation Methods for Computational Fluid Dynamics and Nuclear Thermal Hydraulics
Arsen S. Iskhakov, Nam T. Dinh
Modeling of fluid flows requires corresponding adequate and effective approaches that would account for multiscale nature of the considered physics. Despite the tremendous growth o…
Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments
Yang Liu, Dewei Wang, Xiaodong Sun +2
In this paper, we develop a machine learning-based Bayesian approach to inversely quantify and reduce the uncertainties of the two-fluid model-based multiphase computational fluid…
Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations. Part II
Arsen S. Iskhakov, Nam T. Dinh
The work is a continuation of a paper by Iskhakov A.S. and Dinh N.T. "Physics-integrated machine learning: embedding a neural network in the Navier-Stokes equations". Part I // arX…
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…
A Data-driven Approach for Turbulence Modeling
Yangmo Zhu, Nam Dinh
Data-driven turbulence modeling is a newly emerged research area in thermal hydraulics simulation of nuclear power plant (NPP). The most common CFD method used in NPP thermal hydra…
Development of a Data-driven Turbulence Model for 3d Thermal Stratification Simulation during Reactor Transients
Yangmo Zhu, Nam Dinh, Rui Hu +1
SAM, a plant-level system analysis tool for advanced reactors (SFR, LFR, MSR/FHR) is under development at Argonne. As a modern system code, SAM aims to improve the predictions of 3…
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…
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…
Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning
Botros N Hanna, Nam T. Dinh, Robert W. Youngblood +1
Despite the progress in high performance computing, Computational Fluid Dynamics (CFD) simulations are still computationally expensive for many practical engineering applications s…