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-Reference-Cited by-同舟云学术

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

Published:2022-07 Issue: Volume:393 Page:111814
ISSN:0029-5493
Container-title:Nuclear Engineering and Design
language:en
Short-container-title:Nuclear Engineering and Design

Author:

Zhu Yangmo,Dinh Nam T.,Saini Nadish,Bolotnov Igor A.

Funder

Nuclear Energy University Program

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Mechanical Engineering,Waste Management and Disposal,Safety, Risk, Reliability and Quality,General Materials Science,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Reference32 articles.

1. Turbulence modelling for large volumetrically heated liquid pools;Dinh;Nucl. Eng. Des.,1997

2. Interface capturing simulations of droplet interaction with spacer grids under dffb conditions;Saini;Nucl. Eng. Des.,2020

3. Direct numerical simulation of reactor two-phase flows enabled by high-performance computing;Fang;Nucl. Eng. Des.,2018

4. Interface-resolved simulations of reactor flows;Fang;Nucl. Technol.,2019

5. Interface capturing simulations of bubble population effects in pwr subchannels;Cambareri;Nucl. Eng. Des.,2020

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Development of machine learning framework for interface force closures based on bubble tracking data;Nuclear Engineering and Design;2022-12

2. A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics;Nuclear Science and Engineering;2022-09-08