Data-Driven Prediction of Unsteady Vortex Phenomena in a Conical Diffuser

Abstract

The application of machine learning to solve engineering problems is in extremely high demand. This article proposes a tool that employs machine learning algorithms for predicting the frequency response of an unsteady vortex phenomenon, the precessing vortex core (PVC), occurring in a conical diffuser behind a radial swirler. The model input parameters are the two components of the time-averaged velocity profile at the cone diffuser inlet. An empirical database was obtained using a fully automated experiment. The database associates multiple inlet velocity profiles with pressure pulsations measured in the cone diffuser, which are caused by the PVC in the swirling flow. In total, over 103 different flow regimes were measured by varying the swirl number and the cone angle of the diffuser. Pressure pulsations induced by the PVC were detected using two pressure fluctuations sensors residing on opposite sides of the conical diffuser. A classifier was constructed using the Linear Support Vector Classification (Linear SVC) model and the experimental data. The classifier based on the average velocity profiles at the cone diffuser inlet allows one to predict the emergence of the PVC with high accuracy (99%). By training a regression artificial neural network, the frequency response of the flow was predicted with an error of no more than 1.01 and 5.4% for the frequency and power of pressure pulsations, respectively.