Prediction of developing turbulent ow in 90◦-curved ducts using linear and non-linear low-Re k– models

نویسندگان

  • M. Raisee
  • H. Alemi
  • H. Iacovides
چکیده

This paper reports the outcome of applying two di erent low-Reynolds-number eddy-viscosity models to resolve the complex three-dimensional motion that arises in turbulent ows in ducts with 90◦ bends. For the modelling of turbulence, the Launder and Sharma low-Re k– model and a recently produced variant of the cubic non-linear low-Re k– model have been employed. In this paper, developing turbulent ow through two di erent 90◦ bends is examined: a square bend, and a rectangular bend with an aspect ratio of 6. The numerical results indicate that for the bend of square cross-section the curvature induces a strong secondary ow, while for the rectangular cross-section the secondary motion is con ned to the corner regions. For both curved ducts, the secondary motion persists downstream of the bend and eventually slowly disappears. For the bend of square cross-section, comparisons indicate that both turbulence models can produce reasonable predictions. For the bend of rectangular cross-section, for which a wider range of data is available, while both turbulence models produce satisfactory predictions of the mean ow eld, the non-linear k– model returns superior predictions of the turbulence eld and also of the pressure and friction coe cients. Copyright ? 2006 John Wiley & Sons, Ltd.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

پیش‌بینی جریان و انتقال حرارت در کانال‌های ریب‌دار سه بعدی توسط مدل‌های ?-K خطی و غیرخطی

The present paper deals with the prediction of three-dimensional fluid flow and heat transfer in rib-roughened ducts of square cross-section. Such flows are of direct relevance to the internal cooling system of modern gas turbine blades. The main objective is to assess how a recently developed variant of a cubic non-linear model (proposed by Craft et al. (1999)), that has been shown to produce ...

متن کامل

کاربرد مدل های k-? خطی و غیر خطی در پیش بینی جریان و انتقال حرارت جا به جائی در کانال های با موانع منفصل

Roughness elements or turbulence promoters have been widely used to enhance heat transfer in cooling passages of modern gas turbine blades. Although such ribs substantially enhance heat transfer, the heat transfer coefficient is reduced immediately at corner downstream of each rib, creating hot spots. To remove such hot spots some of the ribs can be detached from the channel walls. In this pape...

متن کامل

Assessment of Turbulent Models in Computation of Strongly Curved Open Channel Flows

Several rigorous turbulent models have been developed in the past years and it can be seen that more research is needed to reach a better understanding of their generality and precision by verifying their applications for distinct hydraulic phenomena; under certain assumptions. This survey evaluates the performance of Standard k-ε, Realizable k-ε, RNG k-ε, k-ω and RSM models in predicting flow ...

متن کامل

Prediction of toxicity of aliphatic carboxylic acids using adaptive neuro-fuzzy inference system

Toxicity of 38 aliphatic carboxylic acids was studied using non-linear quantitative structure-toxicityrelationship (QSTR) models. The adaptive neuro-fuzzy inference system (ANFIS) was used to construct thenonlinear QSTR models in all stages of study. Two ANFIS models were developed based upon differentsubsets of descriptors. The first one used log ow K and LUMO E as inputs and had good predicti...

متن کامل

Investigating Financial Crisis Prediction Power using Neural Network and Non-Linear Genetic Algorithm

Bankruptcy is an event with strong impacts on management, shareholders, employees, creditors, customers and other stakeholders, so as bankruptcy challenges the country both socially and economically. Therefore, correct prediction of bankruptcy is of high importance in the financial world. This research intends to investigate financial crisis prediction power using models based on Neural Network...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2006