A multi-fidelity framework for the estimation of the turbulent Schmidt number in the simulation of atmospheric dispersion. (November 2020)

• Record Type: Journal Article
• Authors: Longo, Riccardo; Bellemans, Aurélie; Derudi, Marco; Parente, Alessandro
• Is Part Of: Building and environment. Volume 185(2020)
• Publication Date: 2020-11
• Subjects: Urban environment -- Atmospheric boundary layer -- Turbulent Schmidt number -- Multi-fidelity framework -- Data-driven -- Principal component analysis; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2020.107066
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: A multi-fidelity framework is presented to accurately predict the turbulent Schmidt number, S c t, with applications to atmospheric dispersion modelling. According to the literature and experimental evidence, different physical correlations can be traced for S c t, relating this quantity to various turbulent parameters. The objective is to derive a reliable formulation for S c t that can be used in various test cases and combined with several turbulence models in the context of Reynolds-averaged Navier-Stokes (RANS) simulations. To achieve that, high-fidelity data are obtained with a delayed Detached Eddy Simulation (dDES) and used in a correlation study to analyze the inter-dependencies of S c t with important turbulent variables. A first data-driven model for S c t is proposed by calibrating the data to the semi-empirical relation by Reynolds. A second model is presented using the results of a correlation study in combination with Principal Component Analysis (PCA). Both data-driven models were verified with the RANS simulation of the Cedval A1-5 case, and 2 additional dispersion cases: the Cedval B1-1 array of building, and the empty street canyon from the CODASC database. The resulting S c t formulation is able to significantly improve the accuracy of the concentration field compared to standard RANS approaches. Furthermore, the validity of the new formulation is demonstrated in combination with several turbulence models. Highlights: A novel multi-fidelity framework is presented to accurately predict Sct . The inter-dependencies of Sct are analyzed starting from high-fidelity data. Both empirical relations and Principal Component Analysis are employed. The local variability of Sct is kept also with standard models. An improvement of the accuracy of the concentration results is achieved.