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Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant.
Zhiqiang John Zhai1, Xiang Liu1, Haidong Wang1
11Department of Civil, Environmental and Architectural Engineering, University of Colorado, UCB 428, ECOT 441, Boulder, CO 80309 USA.
Building Simulation
|March 29, 2020
Summary
This study presents advanced inverse modeling to pinpoint continuous indoor air quality contaminant sources. The method accurately identifies dynamic source locations, crucial for effective air quality management in various environments.
Area of Science:
- Environmental Science
- Chemical Engineering
- Public Health
Background:
- Precise identification of indoor air quality (IAQ) contaminant sources is vital for effective IAQ management.
- Existing methods are limited in locating continuous or dynamic contaminant releases.
- Understanding source information aids in controlling airborne infections, fire smoke, and chemical pollutants.
Purpose of the Study:
- To advance probability-based inverse modeling for identifying continuously releasing contaminant sources.
- To develop algorithms for promptly locating dynamic sources with known release times in IAQ events.
- To validate the developed algorithms in realistic indoor environments.
Main Methods:
- Formulation of a suite of inverse modeling algorithms for dynamic source identification.
- Application of probability-based inverse modeling principles.
- Verification through field experiments in a multi-room apartment and a hospital ward.
Main Results:
- The developed algorithms demonstrated prompt and accurate identification of continuous contaminant source locations.
- Successful validation in diverse experimental settings, including a hospital ward with historical relevance (SARS outbreak).
- The method proved effective for tackling more realistic, continuous contaminant release scenarios.
Conclusions:
- The advanced inverse modeling algorithms are effective for rapidly and accurately locating continuous contaminant sources.
- This approach significantly enhances capabilities for indoor air quality management.
- The findings have implications for mitigating risks associated with airborne contaminants in various settings.
