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A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics

Journal Article · · Nuclear Science and Engineering
 [1];  [1];  [1];  [1]
  1. North Carolina State University, Department of Nuclear Engineering, Campus Box 7909, Raleigh, North Carolina 27695
+ Show Author Affiliations

Not provided.

Research Organization:
US Department of Energy (USDOE), Washington, DC (United States). Office of Nuclear Energy (NE)
Sponsoring Organization:
USDOE
OSTI ID:
1982414
Journal Information:
Nuclear Science and Engineering, Journal Name: Nuclear Science and Engineering; ISSN 0029-5639
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English

References (73)

Multiscale Computational Fluid Dynamics journal August 2019
Turbulent Flows book July 2012
Direct numerical simulation of reactor two-phase flows enabled by high-performance computing journal April 2018
Quantification of model uncertainty in RANS simulations: A review journal July 2019
Three-dimensional flow model development for thermal mixing and stratification modeling in reactor system transients analyses journal April 2019
Validation and uncertainty quantification of multiphase-CFD solvers: A data-driven Bayesian framework supported by high-resolution experiments journal December 2019
A review of current progress in multiscale simulations for fluid flow and heat transfer problems: The frameworks, coupling techniques and future perspectives journal July 2019
A general strategy for designing seamless multiscale methods journal August 2009
Equation-Free, Coarse-Grained Multiscale Computation: Enabling Mocroscopic Simulators to Perform System-Level Analysis journal January 2003
Adaptive Mesh and Algorithm Refinement Using Direct Simulation Monte Carlo journal September 1999
Multigrid Methods for Elliptic Problems: A Review journal May 1986
Machine-learning for turbulence and heat-flux model development: A review of challenges associated with distinct physical phenomena and progress to date journal June 2022
A CFD four parameter heat transfer turbulence model for engineering applications in heavy liquid metals journal February 2014
Data-driven scalar-flux model development with application to jet in cross flow journal February 2020
Experiments in nearly homogenous turbulent shear flow with a uniform mean temperature gradient. Part 1 journal March 1981
Analysis of Turbulent Scalar Flux Models for a Discrete Hole Film Cooling Flow journal October 2015
Turbulent Scalar Mixing in a Skewed Jet in Crossflow: Experiments and Modeling journal November 2016
Numerical simulation of scalar dispersion downstream of a square obstacle using gradient-transport type models journal May 2009
Transport of Passive Scalars in a Turbulent Channel Flow book January 1989
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Application of a new K-tau model to near wall turbulent flows journal February 1992
Investigations of data-driven closure for subgrid-scale stress in large-eddy simulation journal December 2018
Subgrid-scale model for large-eddy simulation of isotropic turbulent flows using an artificial neural network journal December 2019
Data-driven deconvolution for large eddy simulations of Kraichnan turbulence journal December 2018
Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence journal January 2020
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
  • Wang, Rui; Kashinath, Karthik; Mustafa, Mustafa
  • KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining https://doi.org/10.1145/3394486.3403198
conference August 2020
Predictive large-eddy-simulation wall modeling via physics-informed neural networks journal March 2019
An adaptive knowledge-based data-driven approach for turbulence modeling using ensemble learning technique under complex flow configuration: 3D PWR sub-channel with DNS data journal July 2022
Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks journal April 2022
Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach journal February 2020
Integration of neural networks with numerical solution of PDEs for closure models development journal August 2021
A framework to develop data-driven turbulence models for flows with organised unsteadiness journal April 2019
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance journal October 2016
Machine learning strategies for systems with invariance properties journal August 2016
Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations journal September 2020
Neural network models for the anisotropic Reynolds stress tensor in turbulent channel flow journal December 2019
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks journal April 2019
The development of algebraic stress models using a novel evolutionary algorithm journal December 2017
Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines journal March 2019
RANS turbulence model development using CFD-driven machine learning journal June 2020
Applying Machine Learnt Explicit Algebraic Stress and Scalar Flux Models to a Fundamental Trailing Edge Slot journal September 2018
On the generality of tensor basis neural networks for turbulent scalar flux modeling journal November 2021
An artificial intelligence-based method to efficiently bring CFD to building simulation journal December 2017
A paradigm for data-driven predictive modeling using field inversion and machine learning journal January 2016
Using field inversion to quantify functional errors in turbulence closures journal April 2016
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data journal March 2017
Prediction of Reynolds stresses in high-Mach-number turbulent boundary layers using physics-informed machine learning journal December 2018
A Priori Assessment of Prediction Confidence for Data-Driven Turbulence Modeling journal March 2017
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework journal July 2018
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach journal November 2016
A Bayesian Calibration–Prediction Method for Reducing Model-Form Uncertainties with Application in RANS Simulations journal March 2016
Using deep learning to explore local physical similarity for global-scale bridging in thermal-hydraulic simulation journal November 2020
A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation journal August 2019
Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning journal October 2020
Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data journal February 2021
Machine-learning based error prediction approach for coarse-grid Computational Fluid Dynamics (CG-CFD) journal January 2020
Deep neural networks for data-driven LES closure models journal December 2019
Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence journal May 2021
Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned journal April 2019
Turbulent scalar flux in inclined jets in crossflow: counter gradient transport and deep learning modelling journal November 2020
Analysis on numerical stability and convergence of Reynolds averaged Navier–Stokes simulations from the perspective of coupling modes journal January 2022
Deep learning of turbulent scalar mixing journal December 2019
A review of uncertainty quantification in deep learning: Techniques, applications and challenges journal December 2021
Verification of RELAP5-3D code in natural circulation loop as function of the initial water inventory journal November 2017
Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work journal January 2013
Classification of machine learning frameworks for data-driven thermal fluid models journal January 2019
Data driven methodology for model selection in flow pattern prediction journal November 2019
Development and evaluation of data-driven modeling for bubble size in turbulent air-water bubbly flows using artificial multi-layer neural networks journal February 2020
Automatic detection of the onset of film boiling using convolutional neural networks and Bayesian statistics journal May 2019
Data-driven modeling for boiling heat transfer: Using deep neural networks and high-fidelity simulation results journal November 2018
Critical flow prediction using simplified cascade fuzzy neural networks journal February 2020
Prediction of the minimum film boiling temperature using artificial neural network journal July 2020
Efficient Double-Tee Junction Mixing Assessment by Machine Learning journal January 2020

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