AN ENHANCED SAMPLE-PARTITIONING ADAPTIVE REDUCED CHEMISTRY METHOD WITH A-PRIORI ERROR ESTIMATION
(2023) 12th Mediterranean Combustion Symposium — Location: Luxor, Egypt (23.January.2023)
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- Abstract
- The use of detailed chemistry in reactor-based combustion closure models is becoming an essential requirement in CFD simulations of complex reacting flows, such as the Moderate or Intense Low-oxygen Dilution (MILD) combustion regime, which is characterized by a strong coupling between turbulent mixing and chemical kinetics. However, detailed kinetics mechanisms often require to solve hundreds of ordinary differential equations per computational cell, causing the need for massive computational resources. The Sample-Partitioning Adaptive Reduced Chemistry (SPARC) methodology, demonstrated to be effective in the speed-up of the chemical step of such costly simulations. This methodology couples adaptive chemistry and machine learning: in a pre-processing step, a library of locally-reduced mechanisms, associated to clusters of similar thermo-chemical states, is built; at run-time, each computational cell is classified as belonging to a specific cluster and the associated reduced mechanism is employed. In this paper, we present an enhanced version of SPARC which improves a number of aspects of the original methodology. In particular, we focus on the automatic selection of the reduction target species, and on the a-priori error estimation of the reduced mechanisms. The impact on CFD, both in terms of speed-up and accuracy improvements, is evaluated and reported for the Adelaide jet-in-hot coflow (AJHC) burner.
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Pietro Pagani, & et al. (2023). AN ENHANCED SAMPLE-PARTITIONING ADAPTIVE REDUCED CHEMISTRY METHOD WITH A-PRIORI ERROR ESTIMATION. 12th Mediterranean Combustion Symposium, Luxor, Egypt. https://hdl.handle.net/2078.5/101042