A Robust Turbulent Combustion Closure model via Deep Operator Network

In this research, we introduced a novel technique called Deep Operator Network (DeepONet) to assess closure terms for turbulence and chemical source terms in the Sydney turbulent non-premixed flames. To achieve this, we utilized temperature, major species, and velocity point measurements to develop closures for momentum and thermo-chemical transport. The DeepONet method was trained on three different flame conditions: Sydney Flames 57, 59, and 80, and its performance was validated on an additional flame, Flame 103.

The results of our study demonstrate that our DeepONet approach effectively captures the mass fraction of species and the axial and radial velocity components, in comparison to experimental statistics. Furthermore, DeepONet successfully reconstructs closure terms related to turbulence and scalar transport, including the averaged chemical source terms for species.