Inferring heat release dynamics from particle image velocimetry fields using physics-informed neural networks

This study investigates the inference of heat release fields from time-averaged and time-resolved particle image velocimetry data using physics-informed neural networks. The method assimilates density fields using a continuity equation, from which heat release fields are calculated a posteriori using an enthalpy equation. The methodology is applied to data of a laminar, premixed methane V-flame of 1.53 kW thermal power and an equivalence ratio of 0.73 that is forced acoustically in the unsteady cases. Validation for the steady case is provided by comparing the assimilated density fields to fields obtained from the particle image velocimetry seeding concentration and by comparing the inferred heat release distribution to Abel de-convoluted OH* chemiluminescence images. The identified unsteady heat release rate fields are validated against global heat release readings from an OH*-filtered photomultiplier tube. This validation data is also transferred into a flame transfer function. The results indicate that dynamics of laminar, premixed flames can be identified solely from velocity data and detailed insights can be gained without any measurement of the heat release rate.