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Building Simulation 2018, 11(5): 1029-1038 https://doi.org/10.1007/s12273-018-0453-9
Research Article | Issue | Published: 05 June 2018

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

Show Author's Information 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
Keywords:
contaminant source identification, inverse modeling, adjoint probability method, HVAC system
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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Abstract Full text About this article

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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About this article

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

Show Author's information 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

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.

Keywords: contaminant source identification, inverse modeling, adjoint probability method, HVAC system

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Publication history
Copyright
Acknowledgements

Publication history

Received: 21 February 2018
Revised: 02 May 2018
Accepted: 07 May 2018
Published: 05 June 2018
Issue date: October 2018

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Acknowledgements

The research presented in this paper was supported by the National Key Project of the Ministry of Science and Technology, China, on "Green Buildings and Building Industrialization" through Grant No. 2016YFC0700500, and the Fundamental Research Funds for the Central Universities of China (DUT16RC(3)047).

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