Principles and applications of probability-based inverse modeling method for finding indoor airborne contaminant sources

Abstract

Building indoor air quality (IAQ) has received growing attentions lately because of the extended time people spend indoors and the increasing reports of health problems related to poor indoor environments. Recent alarms to potential terrorist attacks with airborne chemical and biological agents (CBA) have further highlighted the research needs on building vulnerability and protection. To maintain a healthful and safe indoor environment, it is crucial to identify contaminant source locations, strengths, and release histories. Accurate and prompt identification of contaminant sources can ensure that the contaminant sources can be quickly removed and contaminated spaces can be effectively isolated and cleaned. This paper introduces a probability concept based prediction method -the adjoint probability method -that can track potential indoor airborne contaminant sources with limited sensor outputs. The paper describes the principles of the method and presents the general modeling algorithm and procedure that can be implemented with current computational fluid dynamics (CFD) or multi-zone airflow models. The study demonstrates the application of the method for identifying airborne pollutant source locations in two realistic indoor environments with few sensor measurement outputs. The numerical simulations verify the feasibility and accuracy of the method for indoor pollutant tracking applications, which forms a good foundation for developing an intelligent and integrated indoor environment management system that can promptly respond to indoor pollution episodes with effective detection, analysis, and control.

The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control

“…Because of the great effectiveness, Multi-zone method was widely used in many cases which can be found in Wu et al (2017) , Zhai and Liu (2008) , Lai et al (2008) , Ge et al (2011) and Zhai et al (2012) studies. Although the assumption of this model (to define a zone filled with fully mixed air and ignore the disturbance in airflow) reduces the accuracy of simulation, it provides an effective method to illustrate the airflow pattern in the building environment.…”

Decision analysis of emergency ventilation and evacuation strategies against suddenly released contaminant indoors by considering the uncertainty of source locations

“…Recent advancements in source identification studies make us optimistic that the use of an identification system can become a reality in the future [9][10][11][12][13][14][15]. As a realistic expectation, the results of the identification system may be best expressed in a probability form to take into account the uncertainties due to a variety of environmental disturbances.…”

“…Technically, this objective can be achieved by an online contaminant source identification system that identifies source characteristics (e.g., location, release time and strength) based on the measurements from a small network of contaminant sensors. Several ongoing preliminary studies are making progress in identifying the sources of airborne contaminant in indoor environments [9][10][11][12][13][14][15]. Although the current studies have resulted in methods to determine the source location, these methods cannot ensure that the source location can be correctly identified with complete certainty in wide range of real-world indoor environments where various environmental disturbances exist.…”

An advanced fire estimation model for decentralized building control

“…The method incorporating sensor data and physicalmodel output has been adopted in academic studies to invert the parameters with respect to the building environment, e.g., to identify contaminant sources (Zhai and Liu 2008;Zhai et al 2012;Zhang and Chen 2007), and to estimate heat transfer relevant parameters (Li et al 2013). The application of this method to determine fire source parameters enables sensor readings and model predictions to be mutually examined.…”