Gandolfo Scialabba | Searcharxiv

arXiv2026

Direct Numerical Simulation of MILD Combustion: Mixing and Autoignition from Non-Premixed Streams

Moderate or intense low-oxygen dilution (MILD) combustion is achieved by strongly diluting and preheating the reactants through mixing with hot combustion products before ignition. To better understand how fuel/air/product mixing and interaction govern MILD combustion dynamics, a novel direct numerical simulation (DNS) dataset of a temporally evolving three-stream mixing layer consisting of fuel, air, and hot combustion products has been performed. In this configuration, both fuel-air and air-hot products mixing processes are considered with varying time scales, through four carefully designed DNS cases, to assess how their combined interaction controls ignition under MILD conditions. It is observed that the cases with higher dilution levels fall within the MILD combustion regime, whereas those with lower dilution correspond to non-MILD conditions. The results show that, as long as MILD conditions are observed, ignition is mainly driven by mixing with hot products. Flame index (FI) combined with chemical explosive mode analysis (CEMA) further identifies the local combustion mode: in MILD cases, ignition occurs predominantly through a premixed-autoignition mode, while in non-MILD scenarios, the premixed-deflagrative contribution to the heat release rate is more substantial. Conditional analysis of scalar dissipation rates shows that the combustion modes in MILD conditions are sensitive to mixing by both the fuel and hot products, whereas the combustion modes in non-MILD conditions are mainly influenced by the mixing of the fuel with the surrounding gases.

physics.flu-dyn↗

arXiv2022

Predictive data-driven model based on generative adversarial network for premixed turbulence-combustion regimes

Premixed flames exhibit different asymptotic regimes of interaction between heat release and turbulence depending on their respective length scales. At high Karlovitz number, the dilatation caused by heat release does not have any relevant effect on turbulent kinetic energy with respect to non-reacting flow, while at low Karlovitz number, the mean shear is a sink of turbulent kinetic energy, and counter-gradient transport is observed. This latter phenomenon is not well captured by closure models commonly used in Large Eddy Simulations that are based on gradient diffusion. The massive amount of data available from Direct Numerical Simulation (DNS) opens the possibility to develop data-driven models able to represent physical mechanisms and non-linear features present in both these regimes. In this work, the databases are formed by DNSs of two planar hydrogen/air flames at different Karlovitz numbers corresponding to the two asymptotic regimes. In this context, the Generative Adversarial Network (GAN) gives the possibility to successfully recognize and reconstruct both gradient and counter-gradient phenomena if trained with databases where both regimes are included. Two GAN models were first trained each for a specific Karlovitz number and tested using the same dataset in order to verify the capability of the models to learn the features of a single asymptotic regime and assess its accuracy. In both cases, the GAN models were able to reconstruct the Reynolds stress subfilter scales accurately. Later, the GAN was trained with a mixture of both datasets to create a model containing physical knowledge of both combustion regimes. This model was able to reconstruct the subfilter scales for both cases capturing the interaction between heat release and turbulence closely to the DNS as shown from the turbulent kinetic budget and barycentric maps.

physics.flu-dyn↗