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Results for “eddying model”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

State of the art in subgrid scale modeling

The present state of the art in subgrid scale modeling for large eddy simulation is reviewed. It is found that the eddy viscosity models are sufficient for simulating homogeneous flows but are probably insufficient for inhomogeneous shear flows. One- and two-equation subgrid scale models probably will not be of great value in subgrid scale modeling, and full Reynolds stress models may be required. In the latter, the convection and diffusion terms are expected to be of greater importance than they are in time-average modeling, but the pressure redistribution terms may be of less importance. A scale similarity model may provide an alternative.

Ferziger, J. H.↗
NASA NTRS1985

Numerical study of the response of a compressible, turbulent boundary layer to a short region of surface curvature

Calculations were performed to investigate the supersonic flow of a turbulent boundary layer over short regions of concave surface curvature. Upstream of each curved surface the freestream Mach number was 2.9. Three different constant radii of curvature models were investigated to cover a range of curvatures and turning angles. The numerical technique solved the full, Reynolds-averaged Navier-Stokes equations using two different turbulence models: the algebraic eddy viscosity model due to Baldwin and Lomax (1978), and the one-equation model to Rubesin (1976). The calculations were compared with the experimental data of Taylor and Smits (1984), and the agreement was very satisfactory, especially for the computations using the one-equation model.

Degani, D.↗
NASA NTRS1987

Calculation of wall and free turbulent-shear flows at supersonic speeds

Supersonic turbulent flows are simulated numerically by solving the Reynolds-averaged full Navier-Stokes equations by an implicit finite-volume method. This flux-vector-split upwind scheme uses approximate factorization followed by line-Gauss-Seidel relaxations. The effects of turbulence are modeled by two eddy viscosity models. High-Reynolds-number form of the k-epsilon model is coupled with a wall-function to avoid excessive refinement of the grid in the low-Reynolds number regions. The k-epsilon equations are solved by the explicit-implicit MacCormack method. The algebraic Baldwin-Lomax model is also being used as an inexpensive alternative for the cases which do not experience massive separation. Several examples of two-dimensional solutions are given to illustrate both wall and free turbulent shear flows which include fluid dynamic phenomena, such as shocks, boundary layers, shear layers, wakes, separations and recirculations. The results compared with experimental data show good agreeent.

Baysal, O.↗
NASA NTRS2005

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

MCKissick, Burnell T.↗