SAM-ML: Integrating data-driven closure with nuclear system code SAM for improved modeling capability

Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning

(2022) Yifei Guan et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

(2022) Yang Liu et al.   NUCLEAR ENGINEERING AND DESIGN

Parameter identification and state estimation for nuclear reactor operation digital twin

(2022) Helin Gong et al.   ANNALS OF NUCLEAR ENERGY

An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

(2022) Helin Gong et al.   ANNALS OF NUCLEAR ENERGY

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

(2021) Linyu Lin et al.   ANNALS OF NUCLEAR ENERGY

Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments

(2021) Yang Liu et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

On the generality of tensor basis neural networks for turbulent scalar flux modeling

(2021) Pedro M. Milani et al.   INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER

Towards improving the predictive capability of computer simulations by integrating inverse Uncertainty Quantification and quantitative validation with Bayesian hypothesis testing

(2021) Ziyu Xie et al.   NUCLEAR ENGINEERING AND DESIGN

A comprehensive survey of inverse uncertainty quantification of physical model parameters in nuclear system thermal–hydraulics codes

(2021) Xu Wu et al.   NUCLEAR ENGINEERING AND DESIGN

A status review on the thermal stratification modeling methods for Sodium-cooled Fast Reactors

(2020) Zeyun Wu et al.   PROGRESS IN NUCLEAR ENERGY

A RELAP5-3D/LSTM model for the analysis of drywell cooling fan failure

(2020) D.P. Guillen et al.   PROGRESS IN NUCLEAR ENERGY

Three-dimensional flow model development for thermal mixing and stratification modeling in reactor system transients analyses

(2019) Rui Hu   NUCLEAR ENGINEERING AND DESIGN

Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments

(2019) Yang Liu et al.   NUCLEAR ENGINEERING AND DESIGN

Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach

(2019) Yang Liu et al.   INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER

A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation

(2019) Han Bao et al.   NUCLEAR ENGINEERING AND DESIGN

Integrated framework for model assessment and advanced uncertainty quantification of nuclear computer codes under Bayesian statistics

(2019) Majdi I. Radaideh et al.   RELIABILITY ENGINEERING & SYSTEM SAFETY

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Deep neural networks for data-driven LES closure models

(2019) Andrea Beck et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

(2018) Xu Wu et al.   NUCLEAR ENGINEERING AND DESIGN

Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework

(2018) Jin-Long Wu et al.   Physical Review Fluids

Turbulence Modeling in the Age of Data

(2018) Karthik Duraisamy et al.   Annual Review of Fluid Mechanics

Data-driven modeling for boiling heat transfer: Using deep neural networks and high-fidelity simulation results

(2018) Yang Liu et al.   APPLIED THERMAL ENGINEERING

Validation and Uncertainty Quantification for Wall Boiling Closure Relations in Multiphase-CFD Solver

(2018) Yang Liu et al.   NUCLEAR SCIENCE AND ENGINEERING

A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling

(2017) Jin-Long Wu et al.   FLOW TURBULENCE AND COMBUSTION

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes

(2016) Allison Lewis et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

(2016) Julia Ling et al.   JOURNAL OF FLUID MECHANICS

Stratified flow-induced air-ingress accident assessment of the GAMMA code in HTGRs

(2011) Hyung Gon Jin et al.   NUCLEAR ENGINEERING AND DESIGN