Systematic assessment of data-driven approaches for wall heat transfer modelling for LES in IC engines using DNS data

Data-driven (DD) methods offer a promising pathway towards novel modelling solutions in fluid flow and heat transfer. In this study, we investigate the application of DD neural network (NN) methods on wall heat transfer modelling in the context of wall-modelled large-eddy simulation (WMLES) in engines, focusing on the systematic evaluation of criteria for the successful DD model generation. High-fidelity input data for model training and testing is generated by spatial filtering of DNS and wall-resolved LES fields in several engine and engine-like configurations. The NN-based models are constructed using different input data and wall-adjacent cell schemes, while cell size and network complexity are also varied. The evaluated NN-based models demonstrate improved performance with respect to classical wall functions, indicating promising potential for engineering applications. In particular, better modelling results were obtained with the inclusions of a wall-normal cell Reynolds number and of data from the second wall-normal cell. Such a two-cell input format appears to offer a good compromise between performance and complexity. Both the present NN models and literature reference approaches generally perform better in unburned regions than in burned ones. In near-wall regions with flame fronts, we present an analysis dividing samples into “unburned”, “burned”, and “flame boundary” zones exposing different characteristics and a varying degree of modelling difficulty.