Using Machine Learning to Predict Synthetic Fuel Spray Penetration from Limited Experimental Data Without Computational Fluid Dynamics

Abstract

AbstractMachine Learning (ML) is increasingly used to predict fuel spray characteristics, but ML conventionally requires large datasets for training. There is a problem of limited training data in the field of synthetic fuel sprays. One solution is to reproduce experimental results using Computational Fluid Dynamics (CFD) and then to augment or replace experimental data with more abundant CFD output data. However, this approach can obscure the relationship of the neural network to the training data by introducing new factors, such as CFD grid design, turbulence model, near-wall treatment, and the particle tracking approach. This paper argues that CFD can be eliminated as a data augmentation tool in favour of a systematic treatment of the uncertainty in the neural network training. Confidence intervals are calculated for neural network outputs, and these encompass both (1) uncertainty due to errors in experimental measurements of the neural networks’ training data and (2) uncertainty due to under-training the neural networks with limited experimental data. This approach potentially improves the usefulness of artificial neural networks for predicting the behaviour of sprays in engineering applications. Confidence intervals represent a more rigorous and trustworthy measure of the reliability of a neural network’s predictions than a conventional validation exercise. Furthermore, when data are limited, the best use of all available data is to improve the training of a neural network and our confidence in that training, rather than to reserve data for ad-hoc testing, which exercise can only at best approximate a confidence interval.