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Home Archives Officers Future meetings American Society of Thermal and Fluids Engineering

ISSN Online: 2379-1748

ISBN Print: 978-1-56700-483-0 (Flash Drive)

ISBN Online: 978-1-56700-482-3

4th Thermal and Fluids Engineering Conference
April, 14–17, 2019 , Las Vegas, NV, USA

TURBULENCE MODELING OF BOUNDARY LAYERS SUBJECT TO VERY STRONG FAVORABLE PRESSURE GRADIENT (FPG) WITH PASSIVE SCALAR TRANSPORT

Get access pages 1819-1833
DOI: 10.1615/TFEC2019.tfl.028426

Abstract

Turbulent boundary layers subject to severe acceleration or strong favorable pressure gradient (FPG) are of fundamental and technological importance. Scientifically, they elicit great interest from the points of view of scaling laws, the complex interaction between the outer and inner regions, and the quasi-laminarization phenomenon. Many flows of industrial and technological applications are subject to strong acceleration such as convergent ducts, turbines blades and nozzles. Our recent numerical predictions (J. Fluid Mech., vol. 775, pp. 189-200, 2015) of turbulent boundary layers subject to very strong FPG with high spatial/temporal resolution, i.e. Direct Numerical Simulation (DNS), have shown a meaningful weakening of the Reynolds shear stresses with an evident logarithmic behavior. In the present study, assessment of three different turbulence models (Shear Stress Transport, k-ω and Spalart-Allmaras, henceforth SST, k-ω and SA, respectively) in Reynolds-averaged Navier-Stokes (RANS) simulations is performed. The main objective is to evaluate the ability of popular turbulence models in capturing the characteristic features present during the quasi-laminarization phenomenon in highly accelerating turbulent boundary layers. Favorable pressure gradient is prescribed by a top converging surface (sink flow) with an approximately constant acceleration parameter of K = 4.0 × 10-6. Furthermore, the quasi-laminarization effect on the temperature field is also examined by solving the energy equation and assuming the temperature as a passive scalar. Validation of RANS results is carried out by means of a large DNS dataset.
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