Publication date: December 2017
Source:Atmospheric Environment, Volume 170
Author(s): George C. Efthimiou, Ivan V. Kovalets, Alexandros Venetsanos, Spyros Andronopoulos, Christos D. Argyropoulos, Konstantinos Kakosimos
An improved inverse modelling method to estimate the location and the emission rate of an unknown point stationary source of passive atmospheric pollutant in a complex urban geometry is incorporated in the Computational Fluid Dynamics code ADREA-HF and presented in this paper. The key improvement in relation to the previous version of the method lies in a two-step segregated approach. At first only the source coordinates are analysed using a correlation function of measured and calculated concentrations. In the second step the source rate is identified by minimizing a quadratic cost function. The validation of the new algorithm is performed by simulating the MUST wind tunnel experiment. A grid-independent flow field solution is firstly attained by applying successive refinements of the computational mesh and the final wind flow is validated against the measurements quantitatively and qualitatively. The old and new versions of the source term estimation method are tested on a coarse and a fine mesh. The new method appeared to be more robust, giving satisfactory estimations of source location and emission rate on both grids. The performance of the old version of the method varied between failure and success and appeared to be sensitive to the selection of model error magnitude that needs to be inserted in its quadratic cost function. The performance of the method depends also on the number and the placement of sensors constituting the measurement network. Of significant interest for the practical application of the method in urban settings is the number of concentration sensors required to obtain a "satisfactory" determination of the source. The probability of obtaining a satisfactory solution – according to specified criteria –by the new method has been assessed as function of the number of sensors that constitute the measurement network.
from #ORL-AlexandrosSfakianakis via ola Kala on Inoreader http://ift.tt/2xXr0bC
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