AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Building Simulation Article
Article Link
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article

Inverse tracking of an airborne pollutant source location in a residential apartment by joint simulation of CFD and a multizone model

Yibing Hu1, Haidong Wang1( ), Jiajia Cheng2, Enbo Wang1
School of Environment and Architecture, University of Shanghai for Science and Technology, 516 Jungong Rd., Shanghai, China
Tianhua Architecture Planning & Engineering Co. Ltd. 27F, 1800 West Zhongshan Rd., Shanghai, China
Show Author Information

Abstract

Prompt identification of an indoor air pollutant source location is important for the safety of building residents in gaseous contaminant leakage incidents. Using the computational fluid dynamics (CFD) method in such inverse modeling is time consuming, especially for naturally ventilated residential buildings, which have multiple rooms and require consideration both indoor and outdoor environments. This paper compares the results of the pollutant source location identification and the simulation time based on two different inverse modeling methods: the CFD method and joint modeling of the multizone and CFD methods, to discuss the consumption of the computing time, as well as the accuracy of the location identification result. An instantaneous airborne pollutant source is assumed in a typical residential apartment that utilizes natural ventilation. A CFD model with the computational domain of the whole apartment and surrounding environment is built, for which the adjoint probability method is applied to simulate the source location probability from limited sensor readings. Meanwhile, a multizone model of the apartment is built to simulate and identify the room in which the source is located using the adjoint probability method. The CFD method is applied to the identified room afterwards to identify the exact location of the source within that room. The joint simulation of CFD and the multizone model is verified by a scaled model experiment of the apartment. It is found that the joint simulation method can significantly reduce the computing time and provides a good alternative for real-time inverse tracking of the indoor airborne pollutant.

Keywords

adjoint probability methodCFDmultizone modelindoor airborne pollutantjoint simulation

References

 
H Cai, X Li, Z Chen, M Wang (2014). Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time. Building and Environment, 81: 7-19.
 
Y Chen, J Wen (2010). Comparison of sensor systems designed using multizone, zonal, and CFD data for protection of indoor environments. Building and Environment, 45: 1061-1071.
 
D Cornacchiulo, AC Bagtzoglou, J Atmadja (2002). Hydrologic inverse on using marching-jury backward beam equation and quasi reversibility methods. In: Proceedings of the 15th ASCE Engineering Mechanics Conference, New York, USA.
 
DG Eliades, MP Michaelides, CG Panayiotou, MM Polycarpou (2013). Security-oriented sensor placement in intelligent buildings. Building and Environment, 63: 114-121.
 
M Ferry (2002). New features of MIGAL solver. In: Proceedings of the International PHOENICS Users Conference, Luxembourg.
 
Y Gao, Q Chen (2003). Coupling of a multi-zone airflow analysis program with a computational fluid dynamics program for indoor air quality studies. In: Proceedings of the 4th International Symposium on HVAC, Beijing, China.
 
Standard Indian (1987). IS: 875 (Part-3), Code of Practice for the Design Loads (other than earthquake) for Buildings and Structures—Wind Loads. New Delhi: Bureau of Indian Standards.
 
NE Klepeis, WC Nelson, WR Ott, JP Robinson, AM Tsang, P Switzer, JV Behar, SC Hern, WH Engelmann (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal Exposure of Exposure Science & Environmental Epidemiology, 11: 231-252.
 
X Li, J Chen (2008). Evolution of contaminant distribution at steady airflow field with an arbitrary initial condition in ventilated space. Atmospheric Environment, 42: 6775-6784.
 
X Li, X Shao, X Ma, Y Zhang, H Cai (2011). A numerical method to determine the steady state distribution of passive contaminant in generic ventilation systems. Journal of Hazardous Materials, 192: 139-149.
 
R Lin (2003). Identification of groundwater contamination sources using probabilities conditioned on measured concentrations. PhD Thesis, University of Virginia, USA.
 
X Liu, Z Zhai (2007). Inverse modeling methods for indoor airborne pollutant tracking: literature review and fundamentals. Indoor Air, 17: 419-438.
 
X Liu (2008). Identification of indoor airborne contaminant sources with probability-based inverse modeling methods. PhD Thesis, University of Colorado, USA.
 
X Liu, Z Zhai (2008). Location identification for indoor instantaneous point contaminant source by probability-based inverse Computational Fluid Dynamics modeling. Indoor Air, 18: 2-11.
 
X Liu, Z Zhai (2009). Protecting a whole building from critical indoor contamination with optimal sensor network design and source identification methods. Building and Environment, 44: 2276-2283.
 
PS Mahar, B Datta (2000). Identification of pollution sources in transient groundwater systems. Water Resources Management, 14: 209-227.
 
RM Neupauer, JL Wilson (1999). Adjoint method for obtaining backward-in-time location and travel time probabilities of a conservative groundwater contaminant. Water Resources Research, 35: 3389-3398.
 
RM Neupauer, JL Wilson (2001). Adjoint-derived location and travel time probabilities for a multidimensional groundwater system. Water Resources Research, 37: 1657-1668.
 
RM Neupauer, JL Wilson (2002). Backward probabilistic model of groundwater contamination in non-uniform and transient flow. Advances in Water Resources, 25: 733-746.
 
X Shao, X Li, H Ma (2016). Identification of constant contaminant sources in a test chamber with real sensors. Indoor and Built Environment, 25: 997-1010.
 
X Shao, K Wang, X Li (2019). Rapid prediction of the transient effect of the initial contaminant condition using a limited number of sensors. Indoor and Built Environment, 28: 322-334.
 
DB Spalding (1972). A novel finite difference formulation for differential expressions involving both first and second derivatives. International Journal for Numerical Methods in Engineering, 4: 551-559.
 
V Vukovic, J Srebric (2007). Application of neural networks trained with multizone models for fast detection of pollutant source position in buildings. ASHRAE Transactions, 113(2): 154-162.
 
L Wallace (1996). Indoor particles: A review. Journal of the Air & Waste Management Association, 46: 98-126.
 
L Wang, WS Dols, Q Chen (2010). Using CFD capabilities of CONTAM 3.0 for simulating airflow and contaminant transport in and around buildings. HVAC&R Research, 16: 749-763.
 
H Wang, S Lu, J Cheng, Z Zhai (2017). Inverse modeling of indoor instantaneous airborne contaminant source location with adjoint probability-based method under dynamic airflow field. Building and Environment, 117: 178-190.
 
J Wang, T Zhang, S Wang, F Battaglia (2018). Numerical investigation of single-sided natural ventilation driven by buoyancy and wind through variable window configurations. Energy and Buildings, 168: 147-164.
 
Y Wei, H Zhou, T Zhang, S Wang (2017). Inverse identification of multiple temporal sources releasing the same tracer gaseous pollutant. Building and Environment, 118: 184-195.
 
E Yee (2012). Probability theory as logic: Data assimilation for multiple source reconstruction. Pure and Applied Geophysics, 169: 499-517.
 
L Zeng, J Gao, B Du, R Zhang, X Zhang (2018). Probability-based inverse characterization of the instantaneous pollutant source within a ventilation system. Building and Environment, 143: 378-389.
 
Z Zhai, X Liu, H Wang, Y Li, J Liu (2012). Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant. Building Simulation, 5: 5-14.
 
Z Zhai, X Liu (2017). Sensitivity analysis of the probability-based inverse modeling method for indoor contaminant tracking. International Journal of Low-Carbon Technologies, 12: 75-83.
 
T Zhang, Q Chen (2007). Identification of contaminant sources in enclosed spaces by a single sensor. Indoor Air, 17: 439-449.
 
Z Zhang, W Zhang, ZJ Zhai, QY Chen (2007). Evaluation of various turbulence models in predicting airflow and turbulence in enclosed environments by CFD: Part 2—Comparison with experimental data from literature. HVAC&R Research, 13: 871-886.
 
T Zhang, H Li, S Wang (2012). Inversely tracking indoor airborne particles to locate their release sources. Atmospheric Environment, 55: 328-338.
 
T Zhang, S Yin, S Wang (2013). An inverse method based on CFD to quantify the temporal release rate of a continuously released pollutant source. Atmospheric Environment, 77: 62-77.
Building Simulation
Volume 12 Issue 4,
August 2019
Pages 605-616
DOI: 10.1007/s12273-019-0547-z
Cite this article:
Hu Y, Wang H, Cheng J, et al. Inverse tracking of an airborne pollutant source location in a residential apartment by joint simulation of CFD and a multizone model. Building Simulation, 2019, 12(4): 605-616. https://doi.org/10.1007/s12273-019-0547-z

527

Views

14

Crossref

N/A

Web of Science

14

Scopus

0

CSCD

Altmetrics
Received: 29 September 2018
Revised: 11 March 2019
Accepted: 25 March 2019
Published: 14 June 2019
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature
Return