Speeding up turbulent reactive flow simulation via a deep artificial neural network: A methodology study
| Autor: | Yi Ouyang, Kevin Van Geem, Laurien Vandewalle, Maarten R. Dobbelaere, Pieter Plehiers, Guy B. Marin, Geraldine Heynderickx, Lin Chen |
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| Jazyk: | angličtina |
| Předmět: |
Artificial Neural Network
PARALLEL CHEMICAL-REACTIONS Technology and Engineering Computer science General Chemical Engineering Monte Carlo method Turbulence-Chemistry Interaction Industrial and Manufacturing Engineering Field (computer science) PROBABILITY DENSITY-FUNCTION PDF METHODS Environmental Chemistry Sub-Grid Effect ALGORITHM Simulation Turbulent Reactive Flow Simulation Coupling Artificial neural network Turbulence General Chemistry DIFFERENTIAL-EQUATIONS Lagrangian PDF Method Power (physics) MODEL Workflow Flow (mathematics) REACTORS |
| Zdroj: | CHEMICAL ENGINEERING JOURNAL |
| ISSN: | 1385-8947 1873-3212 |
| DOI: | 10.1016/j.cej.2021.132442 |
| Popis: | Turbulent reactive flow simulation often requires accounting for turbulence-chemistry interactions and the sub-grid phenomena. Their complexity leads to a trade-off between computational efficiency on one hand and computational accuracy on the other. We attempt to bridge this gap by coupling the power of machine learning with the turbulent reactive flow simulation, specifically in the form of a deep artificial neural network. The Lagrangian Monte Carlo method is chosen as a demonstration case as it is one of the most accurate models for turbulent reactive flow simulation, but also one of the most time-consuming. The workflow consists of training data generation, deep neural network construction, and implementation in ANSYS-Fluent, followed by an evaluation of model accuracy and efficiency, which results in an order of magnitude faster simulation without loss of accuracy thanks to our data-driven deep neural network. This approach can be of universal relevance in speeding up time-consuming models in the field of reactive flow simulation. |
| Databáze: | OpenAIRE |
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