UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A deep learning framework for hydrogen-fueled turbulent combustion simulation

An, J; Wang, H; Liu, B; Luo, KH; Qin, F; He, GQ; (2020) A deep learning framework for hydrogen-fueled turbulent combustion simulation. International Journal of Hydrogen Energy , 45 (35) pp. 17992-18000. 10.1016/j.ijhydene.2020.04.286.

[img] Text
Luo 2020 IJHE DL accepted.pdf - Accepted version
Access restricted to UCL open access staff until 10 June 2021.

Download (976kB)

Abstract

The high cost of high-resolution computational fluid/flame dynamics (CFD) has hindered its application in combustion related design, research and optimization. In this study, we propose a new framework for turbulent combustion simulation based on the deep learning approach. An optimized deep convolutional neural network (CNN) inspired by a U-Net architecture and inception module is designed for constructing the framework of the deep learning solver, named CFDNN. CFDNN is then trained on the simulation results of hydrogen combustion in a cavity with different inlet velocities. After training, CFDNN can not only accurately predict the flow and combustion fields within the range of the training set, but also shows an extrapolation ability for prediction outside the training set. The results from the CFDNN solver show excellent consistency with conventional CFD results in terms of both predicted spatial distributions and temporal dynamics. Meanwhile, two orders of magnitude of acceleration is achieved by using the CFDNN solver compared to a conventional CFD solver. The successful development of such a deep learning-based solver opens up new possibilities of low-cost, high-accuracy simulations, fast prototyping, design optimization and real-time control of combustion systems such as gas turbines and scramjets.

Type: Article
Title: A deep learning framework for hydrogen-fueled turbulent combustion simulation
DOI: 10.1016/j.ijhydene.2020.04.286
Publisher version: https://doi.org/10.1016/j.ijhydene.2020.04.286
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep learning, Convolutional neural network, Computational fluid dynamics, Turbulent combustion
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10106227
Downloads since deposit
1Download
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item