Login / Signup

Numerical model of the irradiance field surrounding a UV disinfection robot.

Ludovic De MatteisMichael F CullinanConor McGinn
Published in: Biomedical physics & engineering express (2022)
Objective. New technologies, including robots comprising germ-killing UV lamps, are increasingly being used to decontaminate hospitals and prevent the spread of COVID-19 and other superbugs. Existing approaches for modelling the irradiance field surrounding mobile UV disinfection robots are limited by their inability to capture the physics of their bespoke geometrical configurations and do not account for reflections. The goal of this research was to extend current models to address these limitations and to subsequently verify these models using empirically collected data. Approach. Two distinct parametric models were developed to describe a multi-lamp robotic UV system and adapted to incorporate the effects of irradiance amplification from the device's reflectors. The first model was derived from electromagnetic wave theory while the second was derived from conservation of energy and diffusion methods. Both models were tuned using data from empirical testing of an existing UV robot, and then validated using an independent set of measurements from the same device. Results. For each parameter, predictions made using the conservation of energy method were found to closely approximate the empirical data, offering more accurate estimates of the 3D irradiance field than the electromagnetic wave theory model. Significance. The versatility of the proposed method ensures that it can be easily adapted to different embodiments, providing a systematic way for researchers to develop accurate numerical models of custom UV robots, which may be used to inform deployment and/or to improve the accuracy of virtual simulation.
Keyphrases
  • electronic health record
  • drinking water
  • sars cov
  • coronavirus disease
  • aqueous solution
  • big data
  • high resolution
  • healthcare
  • high frequency
  • mass spectrometry
  • data analysis
  • machine learning
  • minimally invasive