Experimental Visualization and Quantitative Analysis of Contaminant Dispersion under Different Smoke Injection Time in a Cleanroom

Contaminant dispersion in environments plays a critical role in maintaining air quality and ensuring product integrity in sensitive industries such as pharmaceuticals and biomedical manufacturing. This study presents an experimental investigation into the influence of contaminant release time on the dispersion behavior and temporal evolution of airborne pollutants within a cleanroom. The contaminant was released from a glycerin-based artificial smoke generator in center of the room, representing realistic operational scenarios encountered in cleanroom. High-resolution video recordings were captured to document the dispersion process. A Python-based video-processing framework was developed to analyze the recorded videos and quantitatively extract dispersion characteristics. The analysis focused on key metrics including dispersion patterns, spatial spreading rates, and the time required for contaminant dilution and clearance. Frame-by-frame processing techniques were employed to track the evolution of the contaminants and assess differences in dispersion dynamics arising from variations in release time. The results demonstrate that the contaminant release time significantly affects both the dispersion pathway and removal time. Although longer release time lead to stronger and more persistent initial dispersion fronts, the long-term evolution of the contaminant field is primarily governed by the room airflow structure. These results highlight the effectiveness of the ventilation system in diluting and redistributing airborne contaminants, while also emphasizing the critical role of injection duration in determining the transient spreading dynamics and front propagation characteristics. Furthermore, the proposed video-based analysis approach offers a flexible and cost-effective tool for experimental assessment of contaminant dispersion in controlled indoor environments.

کلیدواژه ها:

Cleanroom ، Contaminant dispersion ، HVAC ، Python ، Smoke visualization