Applications of machine learning in surge prediction for vehicle turbochargers - Hiroki Saito; Dai Kanzaki; Kazuo Yonekura (Machine Learning with Applications, 2024)
Surging in vehicle turbochargers is an important phenomenon that can damage the compressor and its peripheral equipment due to pressure fluctuations and vibration, so it is essential to understand the operating points where surging occurs. In this paper, we constructed a Neural Network (NN) that can predict these operating points, using as explanatory variables the geometry parameters of the vehicle turbocharger and one-dimensional predictions of the flow rates at surge. Our contribution is the use of machine learning to enable fast and low-cost prediction of surge points, which is usually only available through experiments or calculation-intensive Computational Fluid Dynamics (CFD). Evaluations conducted on the test data revealed that prediction accuracy was poor for some turbocharger geometries and operating conditions, and that this was associated with the relatively small data quantity included in the training data. Expanding the appropriate data offers some prospect of improving prediction accuracy.