Abstract
In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and an extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely the look-up table and artificial neural network (ANN). PLSO and PLSN are also compared with each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the ANN, especially for the extrapolated prediction. The computational time is also decreased sharply. Compared with PLSO, PLSN is characterized by a higher prediction accuracy and shorter computational time than PLSO. It is expected that PLSN could save computational time and also improve the accuracy of a thermodynamic model of a diesel engine.
Highlights
The turbocharger is a vital component of the modern diesel engine
The results show that, when compared with a look-up table, the back-propagation neural network (BPNN) and the traditional partial least squares (PLS) method (PLSO), PLSN achieves the best prediction accuracy while having the shortest computational time
It is interesting to find that the PLS with the trigonometric function polynomial form as the basis function has an advantage over that with the polynomial form as the basis function, especially in computational time, which is reduced by an order of magnitude
Summary
The turbocharger is a vital component of the modern diesel engine. It allows the engine to increase its power density through the downsizing concept [1,2,3] while simultaneously decreasing fuel consumption. The most popular methodology is look-up tables [4], which use the experimental compressor mass flow rate and efficiency maps. To improve the accuracy of the compressor’s prediction, in [6], the elliptical curve fitting method was introduced to map the fitting process. Computational fluid dynamics (CFD) simulations were applied to estimate the compressor map numerically in several studies [16,17,18,19] This method has been proven to have a relatively high accuracy, but it is usually quite time-consuming, which makes it impossible to predict the compressor map in an available time frame. The results show that, when compared with a look-up table, the BPNN and the traditional PLS method (PLSO), PLSN achieves the best prediction accuracy while having the shortest computational time.
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