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Research Article

Identifying decaying contaminant source location in building HVAC system using the adjoint probability method

Zhiqiang (John) Zhai1,2( )Qi Jin1
Department of Civil, Environmental and Architectural Engineering, University of Colorado at Boulder, Boulder, CO 80309, USA
Faculty of Architectural Engineering, Dalian University of Technology, Dalian, Liaoning 116023, China
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Abstract

Building heating, ventilation and air-conditioning (HVAC) system can be potential contaminant emission source. Released contaminants from the mechanical system are transported through the HVAC system and thus impact indoor air quality (IAQ). Effective control and improvement measures require accurate identification and prompt removal of contaminant sources from the HVAC system so as to eliminate the unfavourable influence on the IAQ. This paper studies the application of the adjoint probability method for identifying a dynamic (decaying) contaminant source in building HVAC system. A limited number of contaminant sensors are used to detect contaminant concentration variations at certain locations of the HVAC ductwork. Using the sensor inputs, the research is able to trace back and find the source location. A multi-zone airflow model, CONTAM, is employed to obtain a steady state airflow field for the studied building with detailed duct network, upon which the adjoint probability based inverse tracking method is applied. The study reveals that the adjoint probability method can effectively identify the decaying contaminant source location in building HVAC system with few properly located contaminant concentration sensors.

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Building Simulation
Pages 1029-1038

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Cite this article:
Zhai Z(, Jin Q. Identifying decaying contaminant source location in building HVAC system using the adjoint probability method. Building Simulation, 2018, 11(5): 1029-1038. https://doi.org/10.1007/s12273-018-0453-9

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Received: 21 February 2018
Revised: 02 May 2018
Accepted: 07 May 2018
Published: 05 June 2018
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018