Abstract
Information on concentration of contaminants is important for management of indoor air quality. Recently, data assimilation techniques are used in order to accurately estimate location and intensity of contamination source in addition to concentration field. In this study, the variational continuous assimilation (VCA) method, which was originally developed in meteorological simulations, was applied to estimates of indoor air quality. The method modifies the governing equations of computational fluid dynamics (CFD) model by adding a correction term which reduces the error between original CFD calculation and observed data. In the mass conservation equation, the correction term can be assumed to be a pseudo source term. The validity of VCA method was confirmed by numerical experiments for two-dimensional steady-state calculation with the following procedures: (i) “true” concentration field was produced by CFD calculations with “true” concentration source; (ii) “pseudo-observation” data were extracted from “true” concentration field; (iii) the VCA method was applied to “false” CFD calculations without contamination source to produce “corrected” concentration field using “pseudo-observation” data; (iv) “corrected” concentration field and contamination source were compared with “true” dataset. The numerical experiments revealed the following findings: the VCA method can identify the area where the contamination source was located; and the VCA method can also reduce errors between “true” and CFD-calculated concentration field although the peak concentration was not well-estimated. These results suggest that the VCA method is a utilizable method to estimate concentration field, and location and intensity of contamination source.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Chen X, Li A, Gao R (2012). Effects of near-wall heat source on particle deposition. Building Simulation, 5: 371–382.
Derber JC (1989). A variational continuous assimilation technique. Monthly Weather Review, 117: 2437–2446.
Cai H, Li X, Kong L, Ma X, Shao X (2012). Rapid identification of single constant source by considering characteristics of real sensors. Journal of Central South University, 19: 593–599.
Cai H, Li X, Chen Z, Wang M (2014). Rapid identification of multiple constantly-released contaminant sources in indoor environments with unknown release time. Building and Environment, 81: 7–19.
Kato S, Kajii Y, Itokazu R, Hirokawa J, Koda S, Kinjo Y (2004). Transport of atmospheric carbon monoxide, ozone, and hydrocarbons from Chinese coast to Okinawa island in the Western Pacific during winter. Atmospheric Environment, 38: 2975–2981.
Kato S, Pochanart P, Hirokawa J, Kajii Y, Akimoto H, Ozaki Y, Obi K, Katsuno T, Streets DG, Minko NP (2002). The influence of Siberian forest fires on carbon monoxide concentrations at Happo, Japan. Atmospheric Environment, 36: 385–390.
Keats A, Yee E, Lien FS (2007). Bayesian inference for source determination with applications to a complex urban environment. Atmospheric Environment, 41: 465–479.
Kondo A, Nakagawa H, Kaga A, Inoue Y (2010). Understanding of flow and scalar fields by combining measured data and CFD. ASHRAE Transactions, 116(2): 318–328.
Kovalets IV, Andronopoulos S, Venetsanos AG, Bartzis JG (2011). Identification of strength and location of stationary point source of atmospheric pollutant in urban conditions using computational fluid dynamics model. Mathematics and Computers in Simulation, 82: 244–257.
Le Dim et FX, Talagrand O (1986). Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 38A: 97–110.
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 ZJ (2009). Prompt tracking of indoor airborne contaminant source location with probability-based inverse multi-zone modeling. Building and Environment, 44: 1135–1143.
Matsuo T, Kondo A, Shimadera H, Inoue Y (2014). Source estimation of indoor contamination with variational continuous assimilation method. In: Proceedings of 25th International Symposium on Transport Phenomena (ISTP-25), Krabi, Thailand.
Navon IM (1998). Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography. Dynamics of Atmospheres and Oceans, 27: 55–79.
Neupauer RM, Wilson JL (2005). Backward probability model using multiple observations of contamination to identify groundwater contamination sources at the Massachusetts Military Reservation. Water Resources Research, 41(2): 1–14.
Patankar SV (1980). Numerical Heat Transfer and Fluid Flow. Washington, DC: Hemisphere Publishing Corporation.
Toth Z, Peña M (2007). Data assimilation and numerical forecasting with imperfect models: The mapping paradigm. Physica D, 230: 146–158.
Tung YC, Hu SC, Xu T, Wang RH (2010). Influence of ventilation arrangements on particle removal in industrial cleanrooms with various tool coverage. Building Simulation, 3: 3–13.
Wang X, Tao W, Lu Y, Wang F (2013). A method to identify the point source of indoor gaseous contaminant based on limited on-site steady concentration measurements. Building Simulation, 6: 395–402.
Zhang T, Chen Q (2007a). Identification of contaminant sources in enclosed environments by inverse CFD modeling. Indoor Air, 17: 167–177.
Zhang T, Chen Q (2007b). Identification of contaminant sources in enclosed spaces by a single sensor. Indoor Air, 17: 439–449.
Zhang T, Li H, Wang S (2012). Inversely tracking indoor airborne particles to locate their release sources. Atmospheric Environment, 55: 328–338.
Zou X, Navon IM, LeDemit FX (1992). An optimal nudging data assimilation scheme using parameter estimation. Quarterly Journal of the Royal Meteorological Society, 118: 1163–1186.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Matsuo, T., Kondo, A., Shimadera, H. et al. Estimation of indoor contamination source location by using variational continuous assimilation method. Build. Simul. 8, 443–452 (2015). https://doi.org/10.1007/s12273-015-0221-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12273-015-0221-z