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Highlights • Introduces the "perturbation method", which calculates turbulence using the large-eddy simulation technique. • Sets a new paradigm for deriving turbulence statistics from ADCP data. • The method yields a high level of accuracy in replicating observational turbulence data. • Introduces a new framework for testing turbulence closure schemes. Abstract Turbulence in the ocean dominates the vertical movement of heat and salt, as well as chemical and biological particulates. The modelling of turbulence is therefore essential to forecast the strength of the biological pump, for example, in which CO2 is drawn out of the atmosphere and trapped in the deep ocean. Obtaining observations of turbulence is an expensive process and the modelling of turbulence still remains an open problem. Using state-of-the-art 3D hydrodynamic models, such as Large-Eddy Simulation and Direct Numerical Simulation, to understand turbulence driven by mean flow is a popular method. However in this approach, the turbulence creates its own mean flow contribution which, in some applications, results in an undesirable divergence from the prescribed mean flow. Here, the perturbation method is introduced. This technique ensures zero divergence to the prescribed mean flow. Results reveal the high level of accuracy this approach has in replicating the observed turbulent field when using ADCP mean current data to prescribe the model mean flow. It is envisaged that the user-friendly nature of this method will enable non-specialists to derive turbulence data when turbulence profilers are not a tractable resource. This modelling approach also sets a rigid framework for the testing of turbulence closure schemes. [ABSTRACT FROM AUTHOR]

Reynolds-Averaged Navier-Stokes (RANS) simulation has been demonstrated to be a powerful and efficient approach for conducting numerical assessment of the hydraulic performance of disinfection systems for water treatment at a much lower cost than physical experiments. Recently, large eddy simulation (LES) has been introduced for the first time as a potentially more accurate alternative to RANS for predicting hydraulic performance of disinfection systems such as baffled contactors (Kim et al., 2010, "Large Eddy Simulation of Flow and Tracer Transport in Multichamber Ozone Contactors," J. Environ. Eng., 136, pp. 22-31). This gives rise to the need to carefully assess RANS and LES in order to understand under which flow characteristics LES should be recommended instead o f the less computationally intensive RANS for predicting hydraulic performance of a disinfection system. To that extent, this manuscript presents results from RANS and LES simulations of flow and tracer transport in a laboratory-scale column contactor and a laboratory-scale baffled contactor. Flow fields, residence time distributions, and characteristic residence times are analyzed. LES is shown to be a more reliable strategy than RANS in simulating tracer transport in column contactors due to its ability to better predict the spatial transition to turbulence characterizing the flow. However, in baffled contactors where such transition does not occur and the flow is characterized by a quasi-steady short circuiting jet and dead zones, RANS performs on par with LES. [ABSTRACT FROM AUTHOR]

Abstract: In the last two decades, Computational Fluid Dynamics (CFD) has shown great potential as a powerful and cost-efficient tool to troubleshoot existing disinfection contactors and improve future designs for water treatment industry. However, numerous challenges in the simulation of water disinfection processes still remain. This review summarizes past CFD studies of the hydraulic and associated disinfection efficiency of disinfection contactors. Hydraulic efficiency studies based on flow and tracer transport simulation were found to be the most common and successful. Challenges existing in flow and disinfection simulation are identified and discussed. These challenges can be overcome via advanced turbulent simulation approaches, such as Large Eddy Simulation and multi-phase resolving simulations. Although turbulence-chemistry interaction is found to be the most challenging problem for proper representation of the reaction system and inactivation kinetics, solutions to this challenge can be overshadowed unless errors induced by unresolved unsteady flow and multi-phase flow are reduced sufficiently. [Copyright &y& Elsevier]

Abstract: Large-eddy simulations (LES) of wind-driven shallow water flows with Langmuir turbulence have been conducted and scalar transport and surface scalar transfer dynamics analyzed. In these flows, the largest scales of the Langmuir turbulence consist of full-depth Langmuir circulation (LC), parallel downwind-elongated, counter-rotating vortices acting as a secondary structure to the mean flow. Langmuir turbulence is generated by the interaction of the wind-driven shear current with the Stokes drift velocity induced by surface gravity waves. In the absence of resolved surface waves, the Langmuir turbulence-generating mechanism is parameterized via the well-known Craik–Leibovich vortex force (Craik and Leibovich, 1976) appearing in the momentum equation. Simulations do not resolve surface waves, thus the top of the domain is taken as a non-deforming, free-slip, wind shear-driven surface. LES guided by the full-depth LC field measurements of Gargett and Wells (2007) shows that Langmuir turbulence plays a major role in determining scalar transport throughout the entire water column and scalar transfer at the surface. Langmuir turbulence affects scalar transport and its surface transfer through (1) full-depth, large-scale LC and (2) near-surface, small-scale eddies. Two key parameters controlling the extent of these two mechanisms are (1)the ratio , where is the dominant wavelength of the surface waves generating the turbulence and H is the mean water column depth and (2)the turbulent Langmuir number, La t , which is inversely proportional to wave forcing relative to wind forcing. Results from simulations with varying combinations of and La t are analyzed in order to understand the effect of these two parameters on scalar dynamics. [Copyright &y& Elsevier]