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Reynolds Number K (reynold + number_k)

Distribution by Scientific Domains
Chemistry40%

Kinds of Reynolds Number K

  • low Reynold number k
    		•

    Selected Abstracts

    Prediction of jet flows in the supersonic nozzle and diffuser

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 10-11 2005
    Yi Liu
    Abstract The authors' recently-developed code for a needle-free powdered vaccine delivery device, the epidermal powdered inject system (EPI), is summarized in this paper. The behaviour of supersonic jet flows, which accelerate micron sized powdered vaccines to penetrate human skin or mucosal tissue, is therefore of great importance. A well-established modified implicit flux vector splitting (MIFVS) solver for the Navier,Stokes equations is extended to study numerically the transient supersonic jet flows of interest. A low Reynolds number k,, turbulence model, with the compressibility effect considered, is integrated into MIFVS solver to predict the turbulent structures and interactions with inherent shock systems. The results for the NASA validation case NPARC, Venturi and contoured shock tube (CST) of the EPI system are discussed. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Low Reynolds number k,, model for near-wall flow

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 4 2005
    M. M. Rahman
    Abstract A wall-distance free k,, turbulence model is developed that accounts for the near-wall and low Reynolds number effects emanating from the physical requirements. The model coefficients/functions depend non-linearly on both the strain rate and vorticity invariants. Included diffusion terms and modified C,(1,2) coefficients amplify the level of dissipation in non-equilibrium flow regions, thus reducing the kinetic energy and length scale magnitudes to improve prediction of adverse pressure gradient flows, involving flow separation and reattachment. The model is validated against a few flow cases, yielding predictions in good agreement with the direct numerical simulation (DNS) and experimental data. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Computation of turbulent free-surface flows around modern ships

    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, Issue 4 2003
    Tingqiu Li
    Abstract This paper presents the calculated results for three classes of typical modern ships in modelling of ship-generated waves. Simulations of turbulent free-surface flows around ships are performed in a numerical water tank, based on the FINFLO-RANS SHIP solver developed at Helsinki University of Technology. The Reynolds-averaged Navier,Stokes (RANS) equations with the artificial compressibility and the non-linear free-surface boundary conditions are discretized by means of a cell-centred finite-volume scheme. The convergence performance is improved with the multigrid method. A free surface is tracked using a moving mesh technology, in which the non-linear free-surface boundary conditions are given on the actual location of the free surface. Test cases recommended are a container ship, a US Navy combatant and a tanker. The calculated results are compared with the experimental data available in the literature in terms of the wave profiles, wave pattern, and turbulent flow fields for two turbulence models, Chien's low Reynolds number k,,model and Baldwin,Lomax's model. Furthermore, the convergence performance, the grid refinement study and the effect of turbulence models on the waves have been investigated. Additionally, comparison of two types of the dynamic free-surface boundary conditions is made. Copyright © 2003 John Wiley& Sons, Ltd. [source]

    CFD modeling of heat transfer in turbulent pipe flows

    AICHE JOURNAL, Issue 9 2000
    S. S. Thakre
    Twelve versions of low Reynolds number k-, and two low Reynolds number Reynolds stress turbulence models for heat transfer were analyzed comparatively. Predictions of the mean axial temperature, the radial and axial turbulent heat fluxes, and the effect of Prandtl number on Nusselt number were compared with the experimental data. The model by Lai and So from the k-, group and Lai and So from the Reynolds stress group had the best overall predictive ability for heat transfer in turbulent pipe flow. The Lai and So model was attributed to its success in the predictions of flow parameters such as mean axial velocity, turbulent kinetic energy, eddy diffusivity, and the overall energy dissipation rate. The k-, models performed relatively better than the Reynolds stress models for predicting the mean axial temperature and the Nusselt number. This qualitative and quantitative study found the need for more sophisticated near-wall experimental measurements and the accuracy of the dissipation (of turbulent energy) and the pressure-scrambling models. [source]

    A low reynolds number k-, modelling of turbulent pipe flow: Flow pattern and energy balance

    THE CANADIAN JOURNAL OF CHEMICAL ENGINEERING, Issue 2 2001
    Shirish S. Thakre
    Abstract The present paper addresses a comparative analysis of twelve different versions of low Reynolds number k -, turbulence models. The predictive capability of the models have been tested on the basis of the flow patterns and energy balance. Numerical simulations were performed at the Reynolds numbers of 7400, 22 000 and 500 000. The predicted mean axial velocity and turbulent kinetic energy were compared with the experimental data of Durst et al. (1995) and Schildknecht et al.(1979) for the Reynolds number of 7400 and 22000 respectively. The overall energy balance was established at three Reynolds numbers of 7400, 22 000 and 500000. A comparison of all the models has been predicted. On décrit dans cet article une analyse comparative de douze versions différentes de modèles de turbulence à faibles nombres de Reynolds k -,. La capacité de prédiction de ces modèles a été testée d'après les profils d'écoulement et le bilan énergétique. Des simulations numériques ont été réalisées à des nombres de Reynolds de 7400, 22 000 et 500 000. La vitesse axiale et l'énergie cinétique turbulente moyennes prédites ont été comparées aux données expérimentales de Durst et al. (1995) et Schildknecht et al. (1979) pour les nombres de Reynolds de 7400 et 22 000, respectivement. Le bilan énergétique global a été établi pour les trois nombres de Reynolds. Une comparaison de tous les modèles a été effectuée. [source]