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Snapshot of coupled HOQ simulations with a DC and two machine learning models with (ML) and without the source term correction (ML*) according to Eq. (3).

Snapshot of coupled HOQ simulations with a DC and two machine learning models with (ML) and without the source term correction (ML*) according to Eq. (3).

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Fig. 1. Schematic view of the SWQ burner and the numerical subdomain...
Fig. 5. Snapshot of coupled HOQ simulations with a DC and two machine...
Fig. 6. Comparison of the wall heat flux obtained for coupled HOQ...
Fig. 7. 2D contours of the temperature for the SWQ configuration...
+3 Fig. 8. 2D contours of the normalized local HRR (top) and CO mass...
Application of dense neural networks for manifold-based modeling of flame-wall interactions
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  • Aug 2023
Artifical neural networks (ANNs) are universal approximators capable of learning any correlation between arbitrary input data with corresponding outputs, which can also be exploited to represent a low-dimensional chemistry manifold in the field of combustion. In this work, a procedure is developed to simulate a premixed methane-air flame undergoing...
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... snapshot of the coupled HOQ simulation with (ML) and without the source term correction (ML*) is shown together with the DC result in Fig. 5. The ML* model predicts small, non-zero temperature (and progress variable) source terms in the preheat zone of the flame, which accumulates to an unphysical temperature increase over the simulation runtime. At the beginning of the simulation, ML* underpredicts the flame propagation speed by approximately 30%, which later turns to an ...

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