Localization and characterization of intermittent pollutant source in buildings with ventilation systems: Development an
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Localization and characterization of intermittent pollutant source in buildings with ventilation systems: Development and validation of an inverse model
1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China 2. School of Mechanical Engineering, Tongji University, Shanghai 200092, China 3. Department of Civil and Mineral Engineering, University of Toronto, Toronto, Canada
Abstract
Keywords
Terrorist attacks through building ventilation systems are becoming an increasing concern. In case pollutants are intentionally released in a building with mechanical ventilation systems, it is critical to localize the source and characterize its releasing curve. Previous inverse modeling studies have adopted the adjoint probability method to identify the source location and used the Tikhonov regularization method to determine the source releasing profile, but the selection of the prediction model and determination of the regularization parameter remain challenging. These limitations can affect the identification accuracy and prolong the computational time required. To address the difficulties in solving the inverse problems, this work proposed a Markov-chain-oriented inverse approach to identify the temporal release rate and location of a pollutant source in buildings with ventilation systems and validated it in an experimental chamber. In the modified Markov chain, the source term was discrete by each time step, and the pollutant distribution was directly calculated with no iterations. The forward Markov chain was reversed to characterize the intermittently releasing profile by introducing the Tikhonov regularization method, while the regularized parameter was determined by an automatic iterative discrepancy method. The source location was further estimated by adopting the Bayes inference. With chamber experiments, the effectiveness of the proposed inverse model was validated, and the impact of the sensor performance, quantity and placement, as well as pollutant releasing curves on the identification accuracy of the source intensity was explicitly discussed. Results showed that the inverse model can identify the intermittent releasing rate efficiently and promptly, and the identification error for pollutant releasing curves with complex waveforms is about 20%.
intermittent source,
Introduction
Terrorist attacks through building ventilation systems are becoming an increasing concern (Zeng et al. 2018b). In case hazardous chemical weapon agents (CWA) are deliberately released in a mechanically ventilated building, fast localization of the source and characterization of its release rate can help minimizing the attack consequences. The source location determines proper operation modes of emergency ventilation and facilitates optimizing the evacuation route of building occupants (Cai et al. 2010; Zeng et al. 2018b). Meanwhile, E-mail: [email protected]
inverse identification, Markov chain, regularization parameter, ventilation system
Article History Received: 22 June 2020 Revis
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