Augmenting Simulations of Airflow around Buildings

نویسندگان

  • Didier G. Vernay
  • Benny Raphael
چکیده

Computational fluid-dynamics (CFD) simulations have become an important tool for the 10 assessment of airflow in urban areas. However, large discrepancies may appear when simulated 11 predictions are compared with field measurements because of the complexity of airflow behaviour 12 around buildings and difficulties in defining correct sets of parameter values, including those for inlet 13 conditions. Inlet conditions of the CFD model are difficult to estimate and often the values employed 14 do not represent real conditions. In this paper, a model-based data-interpretation framework is proposed 15 in order to integrate knowledge obtained through CFD simulations with those obtained from field 16 measurements carried out in the urban canopy layer (UCL). In this framework, probability-based inlet 17 conditions of the CFD simulation are identified with measurements taken in the UCL. The framework 18 is built on the error-domain model falsification approach that has been developed for the identification 19 of other complex systems. System identification of physics-based models is a challenging task because 20 of the presence of errors in models as well as measurements. This paper presents a methodology to 21 estimate modelling errors. Furthermore, error-domain model falsification has been adapted for the 22 application of airflow modelling around buildings in order to accommodate the time variability of 23 atmospheric conditions. As a case study, the framework is tested and validated for the predictions of 24 airflow around an experimental facility of the Future Cities Laboratory, called “BubbleZERO”. Results 25 show that the framework is capable of narrowing down parameter-value sets from over five hundred to 26 a few having possible inlet conditions for the selected case-study. Thus the case-study illustrates an 27 approach to identifying time-varying inlet conditions and predicting wind characteristics at locations 28 where there are no sensors. 29

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تاریخ انتشار 2016