Large Eddy Simulations (LES) of turbulent reacting flows carried out with detailed kinetic mechanisms have a key role for the discovery of the physical and chemical processes occurring in combustion systems, and are essential for the development of efficient, stable, and non-pollutant technologies. Nevertheless, these simulations require a large amount of computational resources, making their utilization for large-scale systems, such as industrial burners and gas turbines, impractical. In this work, we combine state-of-the-art machine learning algorithms and model reduction methods to deliver a fully automated strategy for performing LES with adaptive chemistry. This strategy is based on the Sample-Partitioning Adaptive Chemistry (SPARC) algorithmic procedure, which consists of four steps: the generation of a training dataset, its partitioning in clusters, the generation of a set of reduced chemical mechanisms specifically tailored to each cluster and, lastly, the numerical simulation of the case of interest with adaptive chemistry enabled by an on-the-fly classification of every grid point. The SPARC approach has already been demonstrated to substantially reduce the computational effort of reactive flows simulations. However a non-negligible level of user interventions is needed, upon which the method's success critically depend. Therefore, with the goal of boosting the performance of this workflow and minimise the user-specified degrees of freedom, we plug in and exploit the Local Principal Component Analysis augmented with an automated Bayesian-optimised search for optimal clustering solutions, and the Computational Singular Perturbation method with an additional layer of automation based on the Tangential Stretching Rate for minimally-sized reduced mechanisms. We employ a cheap and easy-to-generate 1-dimensional-flames training database and we demonstrate the efficiency, accuracy and robustness of this strategy with an application to LES of the Adelaide Jet in Hot Coflow (AJHC) burner, a turbulent reacting flow exhibiting intense turbulence-chemistry interactions.

Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows / Amaduzzi, R.; D'Alessio, G.; Pagani, P.; Cuoci, A.; Malpica Galassi, R.; Parente, A.. - In: COMBUSTION AND FLAME. - ISSN 0010-2180. - 259:(2024). [10.1016/j.combustflame.2023.113136]

Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows

Malpica Galassi R.;
2024

Abstract

Large Eddy Simulations (LES) of turbulent reacting flows carried out with detailed kinetic mechanisms have a key role for the discovery of the physical and chemical processes occurring in combustion systems, and are essential for the development of efficient, stable, and non-pollutant technologies. Nevertheless, these simulations require a large amount of computational resources, making their utilization for large-scale systems, such as industrial burners and gas turbines, impractical. In this work, we combine state-of-the-art machine learning algorithms and model reduction methods to deliver a fully automated strategy for performing LES with adaptive chemistry. This strategy is based on the Sample-Partitioning Adaptive Chemistry (SPARC) algorithmic procedure, which consists of four steps: the generation of a training dataset, its partitioning in clusters, the generation of a set of reduced chemical mechanisms specifically tailored to each cluster and, lastly, the numerical simulation of the case of interest with adaptive chemistry enabled by an on-the-fly classification of every grid point. The SPARC approach has already been demonstrated to substantially reduce the computational effort of reactive flows simulations. However a non-negligible level of user interventions is needed, upon which the method's success critically depend. Therefore, with the goal of boosting the performance of this workflow and minimise the user-specified degrees of freedom, we plug in and exploit the Local Principal Component Analysis augmented with an automated Bayesian-optimised search for optimal clustering solutions, and the Computational Singular Perturbation method with an additional layer of automation based on the Tangential Stretching Rate for minimally-sized reduced mechanisms. We employ a cheap and easy-to-generate 1-dimensional-flames training database and we demonstrate the efficiency, accuracy and robustness of this strategy with an application to LES of the Adelaide Jet in Hot Coflow (AJHC) burner, a turbulent reacting flow exhibiting intense turbulence-chemistry interactions.
2024
Adaptive chemistry; Large eddy simulation; Machine learning; Moderate or intense low-oxygen dilution; Turbulent flame
01 Pubblicazione su rivista::01a Articolo in rivista
Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows / Amaduzzi, R.; D'Alessio, G.; Pagani, P.; Cuoci, A.; Malpica Galassi, R.; Parente, A.. - In: COMBUSTION AND FLAME. - ISSN 0010-2180. - 259:(2024). [10.1016/j.combustflame.2023.113136]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1706270
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