Supervised Clustering for Optimal Sub-model Selection in Reactor-Based Models

• Published in: Flow, turbulence and combustion Vol. 112; no. 3; pp. 931 - 955
• Main Authors: Péquin, Arthur, Iavarone, Salvatore, Malpica Galassi, Riccardo, Parente, Alessandro
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-03-2024; Springer Nature B.V
• Subjects: Algorithms; Automotive Engineering; Clustering; Combustion; Computer simulation; Data points; Direct numerical simulation; Domain names; Engineering; Engineering Fluid Dynamics; Engineering Thermodynamics; Fluid- and Aerodynamics; Heat and Mass Transfer; Large eddy simulation; Mathematical models; Numerical methods; Optimization; Partitioning; Premixed flames; Robustness (mathematics); Time; Turbulence; Large eddy simulation; Turbulence-chemistry interactions; Direct numerical simulation; Partially stirred reactor; Supervised partitioning algorithms
• ISSN: 1386-6184; 1573-1987
• DOI: 10.1007/s10494-023-00442-1

Reactor-based models are well-suited Turbulence-Chemistry Interactions, Sub-Grid Scale closures for Large Eddy Simulation (LES) due to their ability to account for finite-rate kinetics. The Partially Stirred Reactor (PaSR) model relies on the estimation of characteristic time scales to define the reacting fraction of each computational cell. However, chemistry develops a spectrum of intrinsic chemical time scales, leaving no clear consensus on the definition of a single representative scale. Nevertheless, in numerical codes, a single chemical time scale formulation is used on the whole physical domain despite local and complex phenomena. Through an a priori assessment on Direct Numerical Simulation (DNS) data of turbulent non-premixed combustion, the present work proposes a numerical method to locally select an optimal chemical time scale formulation that minimises the model error. Data points are grouped into clusters via supervised partitioning algorithms where the optimal formulation is attributed to each cluster by means of distances minimisation. Using a combination of partitioning procedures can further improve the reconstruction quality of the clustered solutions, up to 35% global errors reductions with respect to standard solutions. Existing data partitions are then tested on unseen data points, yielding great prediction capabilities. DNS data of a turbulent premixed flame are used to demonstrate that the methodology is also robust across combustion regimes. The present proof of concept shows suitable features to introduce systematic improvements for the PaSR combustion closure in LES.