Visual Causal Analysis of the Impact of Temperature Heterogeneity on Airflow Convection
Jia-Ming Zhou, Wen-Lin Chu, and Jun-Ye Luo *Corresponding author: Bo-Lin JianAbstract
This study introduces a new color schlieren system that can directly observe temperature through image color and simultaneously capture temperature changes using an infrared handheld thermal imager for verification. We applied two dehazing methods to the Schlieren images to enhance airflow details: the Dark Channel Prior (DCP) and the Non-Local Image Dehazing Algorithm (NLIDA). Our results demonstrate that NLIDA excels in enhancing the colors and details of the airflow, whereas DCP proves more accurate in analyzing the correlation between airflow and temperature. Analysis across four color spaces (RGB, HSV, Lab*, YUV) reveals that DCP-processed images exhibit a high correlation with temperature in multiple channels (correlation coefficient absolute value > 0.8). Furthermore, we employed a causal decomposition algorithm to investigate the relationship between airflow and temperature changes on Schlieren image colors. The causal analysis confirms that thermal variations influence schlieren formation, identifying temperature changes as a critical factor in schlieren image production. Finally, we developed an interactive Graphical User Interface (GUI) to visualize the results of the causal relationship analysis, facilitating intuitive data interpretation.Keywords
- Color Schlieren Technique
- Dark Channel Prior (DCP)
- Non-Local Image Dehazing Algorithm (NLIDA)
- Clustering Analysis
- Causal Analysis
Sample Results
