| Title |
Development of a CFD-Suitable Deep Neural Network Model for Laminar Burning Velocity |
| Authors |
Ambrutis, Andrius ; Povilaitis, Mantas |
| DOI |
10.3390/app12157460 |
| Full Text |
|
| Is Part of |
Applied Sciences.. Basel : MDPI AG. 2022, 12, 15, ARTN 7460, p. 115-133.. eISSN 2076-3417 |
| Keywords [eng] |
artificial neural network ; CFD ; hydrogen ; laminar burning velocity ; turbulent premixed combustion |
| Abstract [eng] |
Hydrogen is a valued resource for today’s industry. As a fuel, it produces large amounts of energy and creates water during the process, unlike most other polluting energy sources. However, the safe use of hydrogen requires reliable tools able to accurately predict combustion. This study presents the implementation of a deep neural network of laminar burning velocity of hydrogen into an open-source CFD solver flameFoam. DNN was developed based on a previously created larger DNN, which was too large for CFD applications since the calculations took around 40 times longer compared to the Malet correlation. Therefore, based on the original model, a faster, but still accurate, DNN was developed and implemented into flameFoam starting with version 0.10. The paper presents the adaptation of the original DNN into a CFD-applicable version and the initial test results of the CFD–DNN simulation. |
| Published |
Basel : MDPI AG |
| Type |
Journal article |
| Language |
Lithuanian |
| Publication date |
2022 |
