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Citation |
Ngae P, Kouichi H, Kumar P, Feiz AA, Chpoun A. Q. J. Roy. Meteorol. Soc. 2019; 145(720): 967-981. |
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Copyright |
(Copyright © 2019, John Wiley and Sons) |
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DOI |
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PMID |
unavailable |
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Abstract |
The aim of this study is to optimize sensor networks for fast deployment in order to reconstruct an unknown source of intentional or accidental release in local urban topography. In such emergency circumstances, only the meteorological conditions are available in real time and the network deployed must be efficient enough regardless of a source's position and intensity. To determine the optimal positions to be instrumented by the sensors, an adequate cost function is defined based on the renormalization inversion method. This function, named the entropic criterion, quantifies the amount of information contained in a network of the sensors to estimate the intensity and the location of an unknown source. The optimal design is approached as combinatorial optimization (NP-Hard) and a stochastic algorithm (simulated annealing, SA) is employed to solve this problem. The computation is performed by coupling the CFD adjoint fields in an urban environment, the renormalization algorithm and the SA. The optimization is evaluated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment for the reconstruction of a continuous point release in an idealized urban geometry using optimal networks of sizes 10 and 13 sensors. The process is achieved successfully and the results showed that the reduction of an original network of 40 sensors to one third (13) and one quarter (10) does not degrade the performance of this network. Also, a comparison of the optimal design efficiency based on apriori information and without apriori information about the source showed that the present entropic criterion leads to network design and performance that can accurately retrieve an unknown emission source in an urban environment. Language: en |
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Keywords |
CFD; monitoring network optimization; renormalized inversion; simulated annealing; source term estimation |