Application of Reynolds flux modeling in CFD simulation of 45∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ}$$\end{document} inclined negatively buoyant jets

Predicting mixing parameters and mean concentration field of inclined negatively buoyant jets (INBJs) appropriately, has been a long-lasting challenge in numerical modeling of this type of jet flows. To address this challenge, 45∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ}$$\end{document} inclined dense jets were simulated in OpenFOAM hiring three different approaches as Boussinesq-based and non-Boussinesq-based ones. The first approach was conducted by using a two-equation turbulence model, realizable k–ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document} (RKE), in correlation with the standard gradient diffusion hypothesis (SGDH). In the second one, as the pure non-Boussinesq-based approach, turbulent scalar flux modeling (SFM), as an implicit second-moment closure-based approach, for the first time, was combined with the SSG turbulence model. Setting the model coefficients was conducted for both the SSG and SFM given that the calibration is a mutually coupled problem. The best agreement with the experimental data was found in approach two, where the return point dilution ratio, Sr/Fr, shows an increase about the 70%, 32%, 86% over their counterparts in approach one, the large eddy simulation (LES), and the standard k–ϵ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\epsilon$$\end{document}, respectively. Quantitative comparisons for bulk parameters (geometrical and mixing) confirms that the SFM approach stands out with ∼4%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 4\%$$\end{document} average error while the corresponding value is ∼19%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 19\%$$\end{document} for the SGDH approach. These significant improvements over the prediction of mixing parameters can be perceived as the main benefit of the hired SFM approach. Evaluating the turbulent scalar flux vector was performed for both approaches, and it was shown that the SFM is more coordinated with the physics of the flow, compared to the SGDH, concerning the evolutionary behavior of the flow moving from the upstream to the downstream captured by the SFM. Regarding the LES simulations hiring high-performance computers and large quantities of grid numbers, the SFM can be regarded as the best RANS option to model the INBJs. This is the case, specifically, for the mixing parameters.