Irradiance modeling of tubular ultraviolet light bulbs - Li, Peiyang; Koziel, Jacek A.; Walz, William B.; Yedilbayev, Bauyrzhan; Ramirez, Brett C. (Frontiers in Built Environment, 2024)
Clean indoor air is crucial for human lives in residential and workplace settings, including food safety and supply chains. Since the COVID-19 outbreak, disinfecting air has become vital for the public as the SARS-CoV-2 virus and other infectious diseases transmit via inhalable aerosols. Ultraviolet (UV) light is known to be effective in disinfecting air. However, it is still challenging to properly design practical UV applications with common light design software. Visible light analysis software, AGi32, assists architectural applications. AGi32 utilizes digitized luminaire IES files to model light intensity on user-designed geometry in common far-field uses. IES files are based on far-field photometry to model light intensity at different distances and angles from the source. The common application of IES files is to model visible light intensities; however, IES files generated for UV light are still rarely available. Due to the increasing need for surface and air disinfection, modeling UV light intensity using IES files may be beneficial for designing and evaluating systems containing UV light bulbs in near-field applications. There is limited information regarding the accuracy of UV modeling in AGi32 software using IES files. Therefore, the research objectives were to (1) create IES files of a UV-C germicidal lamp (254 nm) and a visible fluorescent lamp with almost identical metrics; (2) compare the IES files output with physical UV measurements inside and outside of an air duct; (3) develop correlations between light intensities of visible and UV light bulbs. Linear correlations were observed when comparing UV irradiance and visible illuminance for both measured and modeled data. The results indicated high variability between the measured and modeled light data, signifying the importance of further investigation of potential error sources and improving the accuracy of modeling.