Measurement and Simulation of Turbulent Mixing in a Jet in Crossflow

Abstract

Computational fluid dynamics (CFD) has an important role in current research. While large eddy simulations (LES) are now common practice in academia, Reynolds-averaged Navier–Stokes (RANS) simulations are still very common in the industry. Using RANS allows faster simulations, however, the choice of the turbulence model has a bigger impact on the results. An important influence of the turbulence modeling is the description of turbulent mixing. Experience has shown that often inaccurate simulations of combustion processes originate from an inadequate description of the mixing field. A simple turbulent flow and mixing configuration of major theoretical and practical importance is the jet in crossflow (JIC). Due to its good fuel-air mixing capability over a small distance, JIC is favored by gas turbine manufacturers. As the design of the mixing process is the key to creating stable low NOx combustion systems, reliable predictive tools and detailed understanding of this basic system are still demanded. Therefore, the current study has re-investigated the JIC configuration under engine relevant conditions both experimentally and numerically using the most sophisticated tools available today. The combination of planar particle image velocimetry and laser induced fluorescence was used to measure the turbulent velocity and concentration fields as well as to determine the correlations of the Reynolds stress tensor ui′uj′¯ and the Reynolds flux vector ui′c′¯. Boundary conditions were determined using laser Doppler velocimetry. The comparisons between the measurements and simulation using RANS and LES showed that the mean velocity field was well described using the SST turbulence model. However, the Reynolds stresses and more so, the Reynolds fluxes deviate substantially from the measured data. The systematic variation of the turbulent Schmidt number reveals the limited influence of this parameter indicating that the basic modeling is amiss. The results of the LES simulation using the standard Smagorinsky model were found to provide much better agreement with the experiments also in the description of turbulent mixing.

3D CFD simulations of trailing suction hopper dredger plume mixing: Comparison with field measurements

“…In LES the larger turbulent scales which possess the majority of the turbulent energy are solved on the grid and the small turbulent scales (smaller than the grid size) are modelled by a turbulence model. Many LES simulations of non buoyant JICF have been published, see for example Wegner et al (2004), Ziefle and Kleiser (2009), Galeazzo et al (2011). Numerical LES simulations of buoyant JICF are more scarce however; we found one example with almost no initial buoyancy (Ri ¼ 0:01) (Yuan and Street, 1998) and one example without initial momentum (Ri ¼ 1) (Devenish et al, 2010).…”

Influence of important near field processes on the source term of suspended sediments from a dredging plume caused by a trailing suction hopper dredger: the effect of dredging speed, propeller, overflow location and pulsing

“…Therefore process based CFD (Computational Fluid Dynamics) simulations and experiments are necessary to better understand how surface plumes are generated. Many CFD simulations of non buoyant JICF have been published in the past, see for example [19][20][21][22][23][24][25][26], and many more]. Numerical CFD simulations of buoyant JICF are more scarce however.…”

Assessment of a Gas Turbine NOx Reduction Potential Based on a Spatiotemporal Unmixedness Parameter

“…These approaches are of a general nature and do not consider mixing processes downstream of the flame front. The mixing processes have been investigated considering experiments [21,22] and simulations [23][24][25]. Models [26,27] showed an exponential unmixedness decay over the residence time.…”