Predicting the thermal behaviour of engine oils using artificial neural networks

Abstract

A multi-layer neural network (NN) was developed to analyse experimental boiling data obtained for five engine oils at bulk temperatures ranging from 40 to 150 °C and heat fluxes ranging from 30 to 400 kW/m2. The inputs to the NN were the oil chemical composition (nine elements) along with the wall superheats for different oil bulk temperatures and the NN output was the corresponding heat fluxes. The developed NN model predicted boiling curves that are in close agreement with experimental data ( R2 ≈ 1). The NN was then trained on the experimental data of four of these oils and allowed to predict the boiling behaviour of the fifth. The NN, therefore, demonstrated its ability to predict the boiling characteristics of untested oils, provided their chemical compositions fall within the range of the training data. In addition, for oils with constituents outside the considered range, the NN was able to predict their boiling characteristics at all bulk temperatures when trained on a sample of experimental measurement. The success of such a technique provides researchers and developers of oils with the thermal performance of oils (based on chemical constituents) without testing or with very limited measurements. This, as a result, has the advantages of saving experimental time and cost of oil testing.