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Accurate prediction of turbulent flows remains a challenging task despite considerable work in this area and the acceptance of CFD as a design tool. The quality of the CFD calculations of the flows in engineering applications strongly depends on the proper prediction of turbulence phenomena. Investigations of flow instability, heat transfer, skin friction, secondary flows, flow separation, and reattachment effects demand a reliable modelling and simulation of the turbulence, reliable methods, accurate programming, and robust working practices. The current scientific status of simulation of turbulent flows as well as some advances in computational techniques and practical applications of turbulence research is reviewed and considered in the book.
This volume contains contributions to the BRITE-EURAM 3rd Framework Programme ETMA and extended articles of the TMA-Workshop. It focusses on turbulence modelling techniques suitable to use in typical flow configurations, with emphasis on compressibility effects and inherent unsteadiness. These methodologies are applied to the Navier-Stokes equations, involving various turbulence modelling levels from algebraic to RSM. Basic turbulent flows in aeronautics are considered; mixing layers, wall-flows (flat-plate, backward-facing step, ramp, bump), and more complex configurations (bump, aerofoil). A critical assessment of the turbulence modelling performances is offered, based on previous results and on the experimental data-base of this research programme. The ETMA results figure in the data-base constituted by all partners and organized by INRIA
The report presents an overview of jet noise computation utilizing the computational fluid dynamic solution of the turbulent jet flow field. The jet flow solution obtained with an appropriate turbulence model provides the turbulence characteristics needed for the computation of jet mixing noise. A brief account of turbulence models that are relevant for the jet noise computation is presented. The jet flow solutions that have been directly used to calculate jet noise are first reviewed. Then, the turbulent jet flow studies that compute the turbulence characteristics that may be used for noise calculations are summarized. In particular, flow solutions obtained with the k-e model, algebraic Reynolds stress model, and Reynolds stress transport equation model are reviewed. Since, the small scale jet mixing noise predictions can be improved by utilizing anisotropic turbulence characteristics, turbulence models that can provide the Reynolds stress components must now be considered for jet flow computations. In this regard, algebraic stress models and Reynolds stress transport models are good candidates. Reynolds stress transport models involve more modeling and computational effort and time compared to algebraic stress models. Hence, it is recommended that an algebraic Reynolds stress model (ASM) be implemented in flow solvers to compute the Reynolds stress components.Nallasamy, N.Glenn Research CenterTURBULENCE MODELS; AERODYNAMIC NOISE; COMPUTATIONAL FLUID DYNAMICS; JET AIRCRAFT NOISE; NOISE PREDICTION; FLOW DISTRIBUTION; TURBULENT JETS; REYNOLDS STRESS; STRESS ANALYSIS; MATHEMATICAL MODELS; ANISOTROPY
Comparisons were made between experiment and theory to assess the capability of turbulent mixing models to predict the fluid flow-properties in the mixing region of both shear layers and jets. Jets exiting into both moving and quiescent streams were investigated. Attention was centered on two turbulence models: (i) k epsilon two and (ii) k omega prime. The same numerical flow field code was utilized with both turbulence models thus allowing a direct comparison of the turbulence models without fear of difference in the numerics masking the results. Results showed significant errors can be made when utilizing these models for prediction of shear flows of interest. The flow structure for these shear flows is in no way accounted for by the models and hence poor predictions result. It is felt the basic vortex structure will have to be modeled before significant improvement in the modeling will occur. (Author).