Work

An iterative machine-learning framework for RANS turbulence modeling

Year: 2021

Type: article

Source: International Journal of Heat and Fluid Flow

DOI: 10.1016/j.ijheatfluidflow.2021.108822

Authors: Weishuo Liu, Jian Fang, Stefano Rolfo, Charles Moulinec, David R Emerson

Institutions: Beihang University, Science and Technology Facilities Council, Daresbury Laboratory

Language: English

Topic: Fluid Dynamics and Turbulent Flows

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Newton Fund, UK Research and Innovation, National Natural Science Foundation of China, Engineering and Physical Sciences Research Council

Awards: A holistic framework for hybrid modelling of solid-liquid flows, G2422316862, UK Turbulence Consortium, UK Turbulence Consortium, G7386642793, Addressing Challenges Through Effective Utilisation of High Performance Computing - a case for the UK Consortium on Turbulent Reacting Flows (UKCTRF), Nano-Engineered Flow Technologies: Simulation for Design across Scale and Phase, "HiLeMMS": High-Level Mesoscale Modelling System

OpenAlex ID: W3165367918

FWCI: 4.442

Cites: 73

Cited by: 53

Related to: 10