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Study of the Impact of Operation Distance of Outdoor Portable Generators under Different Weather Conditions

Published

Author(s)

Leon Wang, Steven Emmerich, Cheng-Chun Lin

Abstract

The U.S. Centers for Disease Control and Prevention (CDC) has reported that up to half of non-fatal monoxide (CO) poisoning incidents during the hurricane seasons in 2004 and 2005 involved generators operated outdoors but within seven feet of the home. Current guidance for safe operating distances of generators is often neither specific nor consistent. A study was conducted to examine the impact of distance of gasoline-powered portable electric generators on indoor CO exposure. The study was based on computer simulations of CO transport outdoors and subsequently into a generic two-story house. This paper presents the simulation results when using the CONTAM indoor air quality model coupled with a large eddy simulation (LES) computational fluid dynamics (CFD) model, Fire Dynamics Simulator (FDS), to predict CO concentrations near and within the home. FDS was validated against the experimental data of contaminant dispersion around a building model in a wind tunnel. A parametric study was then conducted for the two-story house to consider the effects on indoor CO levels of generator location, distance, exhaust temperature and speed, and weather conditions. It was found that in most cases, to reduce CO levels for the house and conditions modeled in this study, it was more effective to point the generator exhaust away from the house and position the generator at a distance of more than 4.6 m.
Citation
Indoor and Built Environment
Volume
23
Issue
8

Keywords

Generator, carbon monoxide, CONTAM, computational fluid dynamics, exposure, indoor air quality, health, multizone airflow model, simulation

Citation

Wang, L. , Emmerich, S. and Lin, C. (2014), Study of the Impact of Operation Distance of Outdoor Portable Generators under Different Weather Conditions, Indoor and Built Environment, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=910891 (Accessed April 25, 2024)
Created November 12, 2014, Updated October 12, 2021