Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

On the use of particle image velocimetry (PIV) data for the validation of Reynolds averaged Navier-Stokes (RANS) simulations during the intake process of a spark ignition direct injection (SIDI) engine

Computational fluid dynamics (CFD) simulations of the in-cylinder flow field are widely used in the design of internal combustion engines (ICEs) and must be validated against experimental measurements to enable a robust predictive capability. Such validation is complicated by the presence of both large-scale cycle-to-cycle variations and small-scale turbulent fluctuations in experimental measurements of in-cylinder flow fields. Reynolds averaged Navier-Stokes (RANS) simulations provide overall flow structures with acceptable accuracy and affordable computational cost for widespread industrial applications. Due to the nature of averaging physical parameters in RANS, its validation against experimental results obtained by particle image velocimetry (PIV) requires consideration of how best to average or filter the measured turbulent flows. In this paper, PIV measurements on the cross-tumble plane were recorded every five crank angle degrees for [Formula: see text] cycles during the intake process of a motored, optically accessible spark ignition direct injection (SIDI) engine. Several methods including ensemble averaging, speed-based averaging and low-order proper orthogonal decomposition (POD) reconstruction were applied to remove the fluctuations from experimental PIV vector fields and thus enable comparison to RANS simulations. Quantitative comparison metrics were used to evaluate the performances of each method in representing the intake jet. Recommendations are made on how to provide a fair validation between measured data and simulation results in highly fluctuating flow fields such as the engine intake jet.

Scale-resolving Simulations of Steady and Pulsatile Flow Through Healthy and Stenotic Heart Valves

Blood-flow downstream of stenotic and healthy aortic valves exhibits intermittent random fluctuations in the velocity field which are associated with turbulence. Such flows warrant the use of computationally demanding scale-resolving models. The aim of this work was to compute and quantify this turbulent flow in healthy and stenotic heart valves for steady and pulsatile flow conditions. Large Eddy Simulations (LES) and Reynolds-Averaged Navier-Stokes (RANS) simulations were used to compute the flow field at inlet Reynolds numbers of 2700 and 5400 for valves with an opening area of 70 mm^2 and 175 mm^2, and their projected orifice-plate type counterparts. Power spectra, and turbulent kinetic energy were quantified on the centerline. Projected geometries exhibited an increased pressure-drop (>90%), and elevated turbulent kinetic energy levels (>150%). Turbulence production was an order of magnitude higher in stenotic heart valves compared to healthy valves. Pulsatile flow stabilizes flow in the acceleration phase, whereas onset of deceleration triggered (healthy valve) or amplified (stenotic valve) turbulence. Simplification of the aortic valve by projecting the orifice area should be avoided in computational fluid dynamics. RANS simulations may be used to predict the transvalvular pressure-drop, but scale-resolving models are recommended when detailed information of the flow field is required.

Application of Scale-Resolving Simulations and Hybrid Models for Contraction-Expansion Pipe Flows

Contractions and expansions are commonly found in various piping systems including flow control in the oil and gas industry. They impose complex flow characteristics such as flow recirculation, boundary layer separation and unsteady re-attachment. Computational Fluid Dynamics (CFD) using RANS simulations can offer general information about the time-averaged flow properties in expansion and contraction geometries including the pressure drop across the fitting. However, they generally fail to provide details of turbulent flow such as shedding of vortices and high turbulent intensities which are observed in experimental data at the expansion and contraction regions. Large Eddy Simulations (LES) can resolve a turbulence spectrum by filtering Navir-Stokes equations over the computational cells. In this study, LES is utilized to examine a sudden-contraction and expansion pipe flow. Furthermore, Stress-Blended Eddy Simulations (SBES) as a hybrid LES-RANS model is employed for comparison. All of these Scale-Resolving Simulations (SRS) are examined against the experimental data and compared to commonly used RANS simulations. Various flow parameters are examined at different locations for a 50.8 mm pipe which is suddenly reduced to a 25.4 mm pipe and then suddenly expands to the original size, and highlights of each model are presented. The details of the turbulent flow in these geometries are critical to many applications such as particle-laden flows and this investigation would provide insight into the appropriate flow modeling in the expansion and contraction geometries.