First attempt for sign detection to clarify transonic buffet via unsteady-data mining

摘要

Transonic buffet reduces the aerodynamic performance of aircraft at cruise conditions; it is a phenomenon to absolutely avoidrnbecause it can lead to accidents. However, the mechanism of occurrence has not yet been elucidated. To understand the phenomena,rnlarge scale unsteady data have been accumulated by using computational fluid dynamics now. In contrast, data mining for time seriesrndata such as unsteady that is a future topic in that field. In this study, we tried mining on unsteady data with capacity exceeding Tera’srnorder. As a consequence, it has been suggested that the behavior of the physical quantity differs from the data immediately before therntransonic buffet occurred. In response to this result, we have found that characteristic changes in the viscosity distribution of the wingrnsurface can be seen as a result of visualizing the data around the time. This is expected to be a clue to elucidate the phenomenon.