Browsing by Subject "turbulence modeling"
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Item Computational fluid dynamics (CFD) simulations of aerosol in a u-shaped steam generator tube(2009-05-15) Longmire, PamelaTo quantify primary side aerosol retention, an Eulerian/Lagrangian approach was used to investigate aerosol transport in a compressible, turbulent, adiabatic, internal, wall-bounded flow. The ARTIST experimental project (Phase I) served as the physical model replicated for numerical simulation. Realizable k-? and standard k-? turbulence models were selected from the computational fluid dynamics (CFD) code, FLUENT, to provide the Eulerian description of the gaseous phase. Flow field simulation results exhibited: a) onset of weak secondary flow accelerated at bend entrance towards the inner wall; b) flow separation zone development on the convex wall that persisted from the point of onset; c) centrifugal force concentrated high velocity flow in the direction of the concave wall; d) formation of vortices throughout the flow domain resulted from rotational (Dean-type) flow; e) weakened secondary flow assisted the formation of twin vortices in the outflow cross section; and f) perturbations induced by the bend influenced flow recovery several pipe diameters upstream of the bend. These observations were consistent with those of previous investigators. The Lagrangian discrete random walk model, with and without turbulent dispersion, simulated the dispersed phase behavior, incorrectly. Accurate deposition predictions in wall-bounded flow require modification of the Eddy Impaction Model (EIM). Thus, to circumvent shortcomings of the EIM, the Lagrangian time scale was changed to a wall function and the root-mean-square (RMS) fluctuating velocities were modified to account for the strong anisotropic nature of flow in the immediate vicinity of the wall (boundary layer). Subsequent computed trajectories suggest a precision that ranges from 0.1% to 0.7%, statistical sampling error. The aerodynamic mass median diameter (AMMD) at the inlet (5.5 ?m) was consistent with the ARTIST experimental findings. The geometric standard deviation (GSD) varied depending on the scenario evaluated but ranged from 1.61 to 3.2. At the outlet, the computed AMMD (1.9 ?m) had GSD between 1.12 and 2.76. Decontamination factors (DF), computed based on deposition from trajectory calculations, were just over 3.5 for the bend and 4.4 at the outlet. Computed DFs were consistent with expert elicitation cited in NUREG-1150 for aerosol retention in steam generators.Item PANS method of turbulence: simulation of high and low Reynolds number flows past a circular cylinder(Texas A&M University, 2006-04-12) Lakshmipathy, SunilThe objective of the study is to investigate the capability of PANS (Partially Averaged Navier-Stokes Simulation) model over a wide range of Reynolds numbers and flow physics. In this regard, numerical simulations of turbulent flow past a circular cylinder are performed at ReD 140,000 and ReD 3900 using the PANS model. The high Reynolds number PANS results are compared with experimental results from Cantwell and Coles, Large Eddy Simulation results from Breuer, and Detached Eddy Simulation results from Travin et al. Low Reynolds number PANS results are compared with experimental results from Ong and Wallace and Large Eddy Simulation results from Breuer. The effects of the various PANS parameters (fk, fε, σku, σεu) on the ability to capture turbulence physics at various Reynolds numbers are studied. It is confirmed, as previously predicted from theoretical considerations that: (i) for high Reynolds number flow fε = 1 and σku = σk ?? fk2 / fε are most appropriate; and (ii) for low Reynolds number flow fε = fk and σku = σk are most suitable. These choices for the parameters stem from the fact that there is no clear separation of scales between the energy scales and the dissipation scales at low Reynolds number unlike in the high Reynolds number where there is a clear separation of scales between the energy containing scales and the dissipation scales. Also, in both cases it is found that decreasing fk leads to improved accuracy in predicting the flow statistics.