Computational Fluid Dynamics (CFD) is increasingly used to study a wide variety of complex Environmental Fluid Mechanics (EFM) processes, such as water flow and turbulent mixing of contaminants in rivers and estuaries and wind flow and air pollution dispersion in urban areas. However, the accuracy and reliability of CFD modeling and the correct use of CFD results can easily be compromised. In 2006, Jakeman et al. set out ten iterative steps of good disciplined model practice to develop purposeful, credible models from data and a priori knowledge, in consort with end-users, with every stage open to critical review and revision (Jakeman et al., 2006). This paper discusses the application of the ten-steps approach to CFD for EFM in three parts. In the first part, the existing best practice guidelines for CFD applications in this area are reviewed and positioned in the ten-steps framework. The second and third part present a retrospective analysis of two case studies in the light of the ten-steps approach: (1) contaminant dispersion due to transverse turbulent mixing in a shallow water flow and (2) coupled urban wind flow and indoor natural ventilation of the Amsterdam ArenA football stadium. It is shown that the existing best practice guidelines for CFD mainly focus on the last steps in the ten-steps framework. The reasons for this focus are outlined and the value of the additional - preceding - steps is discussed. The retrospective analysis of the case studies indicates that the ten-steps approach is very well applicable to CFD for EFM and that it provides a comprehensive framework that encompasses\ud and extends the existing best practice guidelines

Computational Fluid Dynamics (CFD) has consolidated as a tool to provide understanding and quantitative information regarding many complex environmental flows. The accuracy and reliability of CFD modelling results oftentimes come under scrutiny because of issues in the implementation of and input data for those simulations. Regarding the input data, if an approach based on the Reynolds-Averaged Navier-Stokes (RANS) equations is applied, the turbulent scalar fluxes are generally estimated by assuming the standard gradient diffusion hypothesis (SGDH), which requires the definition of the turbulent Schmidt number, Sc t (the ratio of momentum diffusivity to mass diffusivity in the turbulent flow). However, no universally-accepted values of this parameter have been established or, more importantly, methodologies for its computation have been provided. This paper firstly presents a review of previous studies about Sc t in environmental flows, involving both water and air systems. Secondly, three case studies are presented where the key role of a correct parameterization of the turbulent Schmidt number is pointed out. These include: (1) transverse mixing in a shallow water flow; (2) tracer transport in a contact tank; and (3) sediment transport in suspension. An overall picture on the use of the Schmidt number in CFD emerges from the paper.

No abstract

During the past two decades, hydraulic jumps have been investigated using Computational Fluid Dynamics (CFD). The second part of this two-part study is devoted to the state-of-the-art of the numerical simulation of the hydraulic jump. First, the most widely-used CFD approaches, namely the Reynolds-Averaged Navier–Stokes (RANS), the Large Eddy Simulation (LES), the Direct Numerical Simulation (DNS), the hybrid RANS-LES method Detached Eddy Simulation (DES), as well as the Smoothed Particle Hydrodynamics (SPH), are introduced pointing out their main characteristics also in the context of the best practices for CFD modeling of environmental flows. Second, the literature on numerical simulations of the hydraulic jump is presented and discussed. It was observed that the RANS modeling approach is able to provide accurate results for the mean flow variables, while high-fidelity methods, such as LES and DES, can properly reproduce turbulence quantities of the hydraulic jump. Although computationally very expensive, the first DNS on the hydraulic jump led to important findings about the structure of the hydraulic jump and scale effects. Similarly, application of the Lagrangian meshless SPH method provided interesting results, notwithstanding the lower research activity. At the end, despite the promising results still available, it is expected that with the increase in the computational capabilities, the RANS-based numerical studies of the hydraulic jump will approach the prototype scale problems, which are of great relevance for hydraulic engineers, while the application at this scale of the most advanced tools, such as LES and DNS, is still beyond expectations for the foreseeable future. Knowledge of the uncertainty associated with RANS modeling may allow the careful design of new hydraulic structures through the available CFD tools.

No abstract