A Data-Driven Approach to Refine the Partially Stirred Reactor Closure for Turbulent Premixed Flames

Flow Turbulence and Combustion, vol. 115, pp. 1235–1260

Abstract

Abstract Accurately predicting turbulent combustion processes is fundamental for optimizing efficiency, reducing pollutant emissions, and ensuring operational safety in combustion systems. To this purpose, computational fluid dynamics (CFD) simulations are widely employed. In particular, large eddy simulations (LES) balance prediction accuracy with computational efficiency by resolving only the most energy-containing scales of turbulence and rely on modeling the turbulence-chemistry interactions (TCI) occurring at the smallest scales. Among the existing closures, the partially stirred reactor (PaSR) model incorporates finite-rate chemistry and estimates a cell reacting fraction based on the local Damköhler number to account for the subfilter-scale TCI. Although widely validated in CFD computations, the PaSR model was found limited by the way it computes the cell reacting fraction. To tackle this point, our study proposes a machine learning (ML) enhanced partially stirred reactor model for LES. A fully connected neural network is trained on direct numerical simulation (DNS) data of turbulent premixed jet flames to compute a correction coefficient for the cell reacting fraction. Maintaining the original model shape, this ML-enhanced closure aims at bridging the gap between physics-based models and advanced data-driven techniques. The proposed formulation not only improves the prediction accuracy of quantities of interest such as the heat release rate but also features computational feasibility and generalisation capabilities over a large range of LES grid refinement. This demonstrates the significant potential of ML-aided TCI closures in future applications of combustion engineering.

Authors 6

1. Lorenzo Piu corresponding

   Université Libre de Bruxelles

   Affiliation as printed

   Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, 1050, Brussels, Belgium

   Brussels Institute for Thermal Energy (BRITE), 1050, Brussels, Belgium

2. Arthur Péquin

   Université Libre de Bruxelles

   Affiliation as printed

   Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, 1050, Brussels, Belgium

   Brussels Institute for Thermal Energy (BRITE), 1050, Brussels, Belgium

3. Rodolfo S. M. Freitas

   Queen Mary University of London

   Affiliation as printed

   School of Engineering and Materials Science, Queen Mary University of London, London, E1 4NS, UK

4. Salvatore Iavarone

   Centre National de la Recherche Scientifique · Université Libre de Bruxelles · Université Paris-Saclay · CentraleSupélec · Laboratoire d'Énergétique Moléculaire et Macroscopique, Combustion

   Affiliation as printed

   Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, 1050, Brussels, Belgium

   Brussels Institute for Thermal Energy (BRITE), 1050, Brussels, Belgium

   EM2C laboratory, CentraleSupélec, CNRS, Université Paris-Saclay, 91190, Gif-sur-Yvette, France

5. Heinz G. Pitsch Aachen Institut für Technische Verbrennung

   RWTH Aachen University

   Affiliation as printed

   Institut für Technische Verbrennung, RWTH Aachen University, 52062, Aachen, Germany

6. Alessandro Parente corresponding

   Université Libre de Bruxelles

   Affiliation as printed

   Aero-Thermo-Mechanics Laboratory, École Polytechnique de Bruxelles, Université Libre de Bruxelles, 1050, Brussels, Belgium

   Brussels Institute for Thermal Energy (BRITE), 1050, Brussels, Belgium

   WEL Research Institute, 1300, Wavre, Belgium