Gas distribution mapping for indoor environments based on laser absorption spectroscopy: Development of an improved tomographic algorithm

Abstract

Gas distribution mapping (GDM) is an important technology for the study of indoor environment, which can be used to evaluate the efficiency of environmental control system and identify pollutant sources. Most recent studies have implemented GDM through contact sensors or a sensor network, which is difficult to calibrate all the sensors and cover the whole space. In this study, we introduced the non-contact tunable diode laser absorption spectroscopy (TDLAS) technology for GDM in the indoor environment. An improved tomographic algorithm, namely Least Square with Tikhonov Regularization (LSTR), was proposed and compared with two available tomographic algorithms using four validated computational fluid dynamics (CFD) simulations. We also analyzed the effects of the laser emitter placements and optical path densities on the concentration field reconstruction quantitatively. The results showed that the LSTR method could reduce the average relative root mean square error (RRMSE) of tomography by 52%, and the laser emitter at the long edge middle (LEM) can achieve better tomographic performance. The degree of the concentration dispersion from the source would mainly impact the tomographic results: when the sector dispersion (SD) value of concentration distribution was about 2.3 times larger, the average RRMSE value would be decreased by about 40%. The intersection matrix with a higher path density achieved a more accurately reconstructed map due to its lower condition number. In addition, the optical path density was suggested to twice the number of grid cells considering the trade-off between scanning time and accuracy.