For turbocharged engine design, manufacturer-provided turbocharger maps are
typically used in simulation analysis to understand key engine performance
metrics. Each data point in the turbocharger map is generated by physically
testing the hardware or through CFD analysis—both of which are time-consuming
and expensive. As such, only a modest set of data can be generated, and each
data map must be interpolated and extrapolated to create a smooth surface, which
can then be used for engine simulation analysis.
In this article, five different machine learning algorithms are described and
compared to experimental data for the prediction of Cummins Turbo Technologies
(CTT) fixed geometry turbines within and outside of the experimental data range.
The results were validated against xxx-provided test data. The results
demonstrate that the Bayesian neural networks performed the best, realizing a
0.5%–1% error band. In addition, it is extrapolatable when suitable manually
created extra data points are incorporated within the dataset at low and high
turbine speeds.
