Abstract
Identification of potential contaminant sources in buildings is an important issue for indoor pollutant source control. In this paper, a method based on the characteristics matrix derived from the transport governing equation is proposed, and the procedure to identify the contaminant source is presented. Compared with the methods in the literature, the new method is more suitable for the identification of steady point contaminant sources because it only requires limited on-site concentration measurement data without historical information. As a demonstration case, a 2D room with a known flow field validated by the experiment in the literature is selected. A steady point source is presumed at a certain point and the concentration field is calculated by computational fluid dynamics (CFD). Then the concentration data at the specified sampling points are used to identify the source position. Without the measurement error, the method can work well when the concentration measurement data at only two sampling points are given. However, when concentration measurement errors are considered, sampling points need to be increased to improve the identification accuracy. For the simulated 2D case, nine sampling points are sufficient for acceptable accuracy when the relative measurement error is 10%. Effects of positions of the source and the sampling points, and the uncertainty of the flow field simulation on the identification results, as well as the limitation of the method are also discussed.
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References
Bastani A, Haghighat F, Kozinski JA (2012). Contaminant source identification within a building: Toward design of immune buildings. Building and Environment, 51: 320–329.
Cai H, Li X, Kong L, Ma X, Shao X (2012a). Rapid identification of single constant contaminant source by considering characteristics of real sensors. Journal of Central South University, 19: 593–599.
Cai H, Li X, Kong L, Shao X (2012b). An optimization method of sensor layout to improve source identification accuracy in the indoor environment. International Journal of Ventilation, 11: 155–170.
Fletcher CAJ (1988). Computational Techniques for Fluid Dynamics 1: Fundamental and General Techniques. Berlin: Springer.
Hwang J, Chou K, Yang C, Lin J, Chuang A, Tsao J, Chen C, Hu S (2012). Innovative approach to identify location of AMC source in cleanroom by inverse computational fluid dynamics modeling. In: Proceedings of Advanced Semiconductor Manufacturing Conference (ASMC 2012), New York, pp. 27–32.
Ito K, Kato S, Murakami S (2000). Model experiment of flow and temperature field in room for validating numerical simulation analysis of newly proposed ventilation effectiveness. Journal of Architecture, Planning and Environmental Engineering, 8: 49–56. (in Japanese)
Kim YM, Harrad S, Harrison RM (2001). Concentrations and sources of VOCs in urban domestic and public microenvironments. Environmental Science & Technology, 35: 997–1004.
Liu D, Zhao F, Wang H, Rank E (2013). History source identification of airborne pollutant dispersions in a slot ventilated building enclosure. International Journal of Thermal Sciences, 64: 81–92.
Liu X, Zhai Z (2007). Inverse modeling methods for indoor airborne pollutant tracking: Literature review and fundamentals. Indoor Air, 17: 419–438.
Liu X, Zhai Z (2008). Location identification for indoor instantaneous point contaminant source by probability-based inverse computational fluid dynamics modeling. Indoor Air, 18: 2–11.
Liu X, Zhai Z (2009). Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling. Building and Environment, 44: 1135–1143.
Minkowycz WJ, Sparrow EM, Murthy J (2006). Handbook of Numerical Heat Transfer, 2nd edn. Hoboken, NJ, USA: John Wiley & Sons.
Sreedharan P, Sohn M, Gadgil A, Nazaroff W (2006). Systems approach to evaluating sensor characteristics for real-time monitoring of high-risk indoor contaminant releases. Atmospheric Environment, 40: 3490–3502.
Sreedharan P, Sohn M, Nazaroff W, Gadgil A (2007). Influence of indoor transport and mixing time scales on the performance of sensor systems for characterizing contaminant releases. Atmospheric Environment, 41: 9530–9542.
Vukovic V, Srebric J (2007). Application of neural networks trained with multizone models for fast detection of contaminant source position in buildings. ASHRAE Transactions, 113(2): 154–162.
Xu Y, Zhang YP (2003). An improved mass transfer based model for analyzing VOC emissions from building materials. Atmospheric Environment, 37: 2497–2505.
Yang X, Chen Q, Zhang JS, Magee R, Zeng J, Shaw CY (2001). Numerical simulation of VOC emissions from dry materials. Building and Environment, 36: 1099–1107.
Zhai Z, Liu X, Wang H, Li Y, Liu J (2011). Experimental verification of tracking algorithm for dynamically-releasing single indoor contaminant. Building Simulation, 5: 5–14.
Zhang T, Chen Q (2007a). Identification of contaminant sources in enclosed spaces by a single sensor. Indoor Air, 17: 439–449.
Zhang T, Chen Q (2007b). Identification of contaminant sources in enclosed environments by inverse CFD modeling. Indoor Air, 17: 167–177.
Zhang T, Li H, Wang S (2012). Inversely tracking indoor airborne particles to locate their release sources. Atmospheric Environment, 55: 328–338.
Zhang Y, Li X, Wang X, Deng W, Qian K (2006). Spatial flow influence factor: A novel concept for indoor air pollutant control. Science in China: Series E Technological Sciences, 49: 115–128.
Zhang Z, Zhai Z, Zhang W, Chen Q (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.
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Wang, X., Tao, W., Lu, Y. et al. A method to identify the point source of indoor gaseous contaminant based on limited on-site steady concentration measurements. Build. Simul. 6, 395–402 (2013). https://doi.org/10.1007/s12273-013-0127-6
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DOI: https://doi.org/10.1007/s12273-013-0127-6