Data from: A prediction model of compressor with variable geometry diffuser based on elliptic equation and Partial Least Squares

In order to fulfill more and more extensive intake air flow range of diesel engine, variable geometry compressor (VGC) is introduced into turbocharged diesel engine. However, due to the variable diffuser vanes angle (DVA), the prediction for the performance of VGC becomes more difficult than normal compressor. In the present study, a prediction model comprised of elliptical equation and PLS (Partial Least Squares) model was proposed to predict the performance of VGC. The speed lines of pressure ratio map and efficiency map with elliptical equation were fitted, and the coefficients of elliptical equation was introduced into PLS model to build the polynomial relationship between the coefficients and relative speed, DVA. And further, the maximal order of polynomical was detailed investigated to reduce the number of sub-coefficients and acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority in compressor performance prediction, the prediction results of this model were compared with those of look-up table and BPNN. The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than look-up table and BPNN under the same condition in the VGA performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

为满足柴油机日益宽泛的进气流量需求，可变几何截面压气机（variable geometry compressor, VGC）被应用于涡轮增压柴油机中。然而由于可调扩压器叶片角（variable diffuser vanes angle, DVA）的存在，可变几何截面压气机的性能预测难度远高于常规压气机。本研究提出了一种结合椭圆方程与偏最小二乘（Partial Least Squares, PLS）模型的预测方法，用于预测可变几何截面压气机的性能。研究中先通过椭圆方程拟合压比特性图与效率特性图的等转速线，随后将椭圆方程的系数引入偏最小二乘模型，建立系数与相对转速、可调扩压器叶片角之间的多项式关联关系。此外，本研究还对多项式的最高阶次进行了详细探究，在保证可接受拟合精度的同时，有效减少了子系数的数量。随后利用样本数据对所提预测模型进行了验证，并将该模型的预测结果与查表法和反向传播神经网络（Back Propagation Neural Network, BPNN）的结果进行对比，以凸显其在压气机性能预测中的优势。验证与对比结果表明，所提新模型的预测精度符合要求，且在相同工况下，其性能优于查表法与反向传播神经网络。此外，该新型预测模型为可变几何截面压气机性能预测提供了一种新颖且有效的解决方案，未来可用于提升涡轮增压柴油机热力学模型的预测精度。