Compressor map regression modelling based on partial least squares

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 extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely, the look-up table and artificial neural network. PLSO and PLSN are also compared to each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the artificial neural network, especially for the extrapolated prediction. At the same time, the computational time is also decreased sharply. Compared with PLSO, PLSN is characterized with higher prediction accuracy and shorter computational time than PLSO. It can be expected that PLSN can be time-saving and improve the accuracy of a thermodynamic model o...本研究采用两类偏最小二乘建模方法对压气机特性图进行预测：一类是以幂函数多项式作为基函数的偏最小二乘建模方法（PLSO），另一类是以三角函数多项式作为基函数的偏最小二乘建模方法（PLSN）。为验证PLSO与PLSN在典型插值预测及外推预测任务中的潜在性能，本研究将其与另外两种经典数据驱动建模方法——查表法与人工神经网络进行对比，同时也对PLSO与PLSN自身开展了性能比对。结果显示，相较于查表法与人工神经网络，PLSO与PLSN具备更优异的预测性能，尤其在外推预测场景中优势更为突出；同时二者的计算耗时也大幅降低。与PLSO相比，PLSN兼具更高的预测精度与更短的计算耗时。可以预期，PLSN能够有效节省计算资源并提升热力学模型的精度……