While quantitative flow data are subjected to qualitative analysis by their "appearance", it has recently been suggested that quantitative analysis can be performed also from qualitative experiments. In this paper, we propose a method to quantitatively analyze and visualize spatiotemporal data acquired from the tuft method, which is regarded as lacking quantitativity and objectivity. We propose a method to obtain the temporal division and spatial division through time by machine learning methods using stochastic model. By applying this method to the actual data, we succeeded in extracting the temporal and spatial pattern and showed that the proposed method has certain validity.
The gas phase area in journal bearing is associated directly with bearing characteristics. Therefore, gas phase area observation have an ongoing by many researchers. This paper describes a new visualization method which can visualize oil film distribution and gas phase area on journal bearing. The distribution of oil supplied from two sites is visualized by two color oil, the blended color and gas phase area are measured by RGB representation. Moreover, the measured gas phase area compared to one by traditional visualization method. In this study, oil flow of wedge side under flooded lubrication, oil whip and starved lubrication condition are visualized. As results, it is found that the distribution of oil film under oil whip and starved lubrication condition have been changed by changing the amount of supply oil. Moreover, gas phase area by the new observation method agree rather well with a result of traditional experiment. Therefore, it was found that the new visualization method by using two color oil and RGB representation is possible to observe both gas phase area and blended oil color in journal bearing.