An Attempt for Detecting Transonic Buffet Signature via Unsteady-Data Mining

摘要

The transonic buffet degrades the aerodynamic performance of the aircraft during cruise. It is a phenomenon that should be avoided absolutely as it may lead to accidents. However, the mechanism of occurrence has yet to be elucidated. To understand this phenomenon, large-scale unsteady data is accumulated using computational fluid dynamics. In contrast, data mining of time series data such as unsteady data is a topic of the future in that field. In this study, we attempted mining unsteady data with capacity exceeding Tera's order. As a result, the behavior of the physical quantity is suggested to be different from the data just before the transonic buffet occurs. Based on this result, we visualized the data over time, and found that the characteristic change of the viscosity distribution of the blade surface can be seen. This should be a clue to elucidate this phenomenon.