Investigating the Reynolds number dependency of the scalar transfer to a wall using a stochastic turbulence model

PAMM ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Marten Klein ◽  
Heiko Schmidt
Keyword(s):  
Reynolds Number ◽  
Turbulence Model ◽  
Reynolds Number Dependency

scholarly journals Turbulence model performance for ventilation components pressure losses

2021 ◽  
Author(s):  
Karsten Tawackolian ◽  
Martin Kriegel
Keyword(s):  
Reynolds Number ◽  
Turbulence Model ◽  
Pressure Loss ◽  
Low Reynolds Number ◽  
Turbulence Models ◽  
Reynolds Numbers ◽  
Test Case ◽  
Pressure Losses ◽  
Loss Coefficients ◽  
Near Wall

AbstractThis study looks to find a suitable turbulence model for calculating pressure losses of ventilation components. In building ventilation, the most relevant Reynolds number range is between 3×104 and 6×105, depending on the duct dimensions and airflow rates. Pressure loss coefficients can increase considerably for some components at Reynolds numbers below 2×105. An initial survey of popular turbulence models was conducted for a selected test case of a bend with such a strong Reynolds number dependence. Most of the turbulence models failed in reproducing this dependence and predicted curve progressions that were too flat and only applicable for higher Reynolds numbers. Viscous effects near walls played an important role in the present simulations. In turbulence modelling, near-wall damping functions are used to account for this influence. A model that implements near-wall modelling is the lag elliptic blending k-ε model. This model gave reasonable predictions for pressure loss coefficients at lower Reynolds numbers. Another example is the low Reynolds number k-ε turbulence model of Wilcox (LRN). The modification uses damping functions and was initially developed for simulating profiles such as aircraft wings. It has not been widely used for internal flows such as air duct flows. Based on selected reference cases, the three closure coefficients of the LRN model were adapted in this work to simulate ventilation components. Improved predictions were obtained with new coefficients (LRNM model). This underlined that low Reynolds number effects are relevant in ventilation ductworks and give first insights for suitable turbulence models for this application. Both the lag elliptic blending model and the modified LRNM model predicted the pressure losses relatively well for the test case where the other tested models failed.

Improved form of the low Reynolds number k−ɛ turbulence model

1975 ◽  
Vol 18 (3) ◽  
pp. 309 ◽  
Author(s):  
Gilbert H. Hoffman
Keyword(s):  
Reynolds Number ◽  
Turbulence Model ◽  
Low Reynolds Number ◽  
Low Reynolds

scholarly journals Discussion on improved method of turbulence model for supercritical water flow and heat transfer

2020 ◽  
Vol 24 (5 Part A) ◽  
pp. 2729-2741
Author(s):  
Zhenchuan Wang ◽  
Guoli Qi ◽  
Meijun Li
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Turbulence Model ◽  
Buoyancy Effect ◽  
Improved Method ◽  
Production Term ◽  
Flow And Heat Transfer ◽  
Effect Model

The turbulence model fails in supercritical fluid-flow and heat transfer simulation, owing to the drastic change of thermal properties. The inappropriate buoyancy effect model and the improper turbulent Prandtl number model are several of these factors lead to the original low-Reynolds number turbulence model unable to predict the wall temperature for vertically heated tubes under the deteriorate heat transfer conditions. This paper proposed a simplified improved method to modify the turbulence model, using the generalized gradient diffusion hypothesis approximation model for the production term of the turbulent kinetic energy due to the buoyancy effect, using a turbulence Prandtl number model for the turbulent thermal diffusivity instead of the constant number. A better agreement was accomplished by the improved turbulence model compared with the experimental data. The main reason for the over-predicted wall temperature by the original turbulence model is the misuse of the buoyancy effect model. In the improved model, the production term of the turbulent kinetic energy is much higher than the results calculated by the original turbulence model, especially in the boundary-layer. A more accurate model for the production term of the turbulent kinetic energy is the main direction of further modification for the low Reynolds number turbulence model.

The Simulation Study of Turbulence Models for Conjugate Heat Transfer Analysis of a High Pressure Air-Cooled Gas Turbine

2010 ◽  
Author(s):  
Zhenfeng Wang ◽  
Peigang Yan ◽  
Hongfei Tang ◽  
Hongyan Huang ◽  
Wanjin Han
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Gas Turbine ◽  
Turbulence Model ◽  
Conjugate Heat Transfer ◽  
Turbulence Models ◽  
Turbulence Characteristic ◽  
The Heat Transfer Coefficient

The different turbulence models are adopted to simulate NASA-MarkII high pressure air-cooled gas turbine. The experimental work condition is Run 5411. The paper researches that the effect of different turbulence models for the flow and heat transfer characteristics of turbine. The turbulence models include: the laminar turbulence model, high Reynolds number k-ε turbulence model, low Reynolds number turbulence model (k-ω standard format, k-ω-SST and k-ω-SST-γ-θ) and B-L algebra turbulence model which is adopted by the compiled code. The results show that the different turbulence models can give good flow characteristics results of turbine, but the heat transfer characteristics results are different. Comparing to the experimental results, k-ω-SST-θ-γ turbulence model results are more accurate and can simulate accurately the flow and heat transfer characteristics of turbine with transition flow characteristics. But k-ω-SST-γ-θ turbulence model overestimates the turbulence kinetic energy of blade local region and makes the heat transfer coefficient higher. It causes that local region temperature is higher. The results of B-L algebra turbulence model show that the results of B-L model are accurate besides it has 4% temperature error in the transition region. As to the other turbulence models, the results show that all turbulence models can simulate the temperature distribution on the blade pressure surface except the laminar turbulence model underestimates the heat transfer coefficient of turbulence flow region. On the blade suction surface with transition flow characteristics, high Reynolds number k-ε turbulence model overestimates the heat transfer coefficient and causes the blade surface temperature is high about 90K than the experimental result. Low Reynolds number k-ω standard format and k-ω-SST turbulence models also overestimate the blade surface temperature value. So it can draw a conclusion that the unreasonable choice of turbulence models can cause biggish errors for conjugate heat transfer problem of turbine. The combination of k-ω-SST-γ-θ model and B-L algebra model can get more accurate turbine thermal environment results. In addition, in order to obtain the affect of different turbulence models for gas turbine conjugate heat transfer problem. The different turbulence models are adopted to simulate the different computation mesh domains (First case and Second case). As to each cooling passages, the first case gives the wall heat transfer coefficient of each cooling passages and the second case considers the conjugate heat transfer course between the cooling passages and blade. It can draw a conclusion that the application of heat transfer coefficient on the wall of each cooling passages avoids the accumulative error. So, for the turbine vane geometry models with complex cooling passages or holes, the choice of turbulence models and the analysis of different mesh domains are important. At last, different turbulence characteristic boundary conditions of turbine inner-cooling passages are given and K-ω-SST-γ-θ turbulence model is adopted in order to obtain the effect of turbulence characteristic boundary conditions for the conjugate heat transfer computation results. The results show that the turbulence characteristic boundary conditions of turbine inner-cooling passages have a great effect on the conjugate heat transfer results of high pressure gas turbine.

Research on improvements of low Reynolds number turbulence model based on flow around automobiles

2020 ◽  
Vol 78 (11) ◽  
pp. 674-695
Author(s):  
Yi Yang ◽  
Guanglin Qiang ◽  
Zhen Chen ◽  
Zhengqi Gu ◽  
Yong Zhang
Keyword(s):  
Reynolds Number ◽  
Turbulence Model ◽  
Low Reynolds Number ◽  
Model Based ◽  
Low Reynolds

scholarly journals Blockage Correction and Reynolds Number Dependency of an Alpine Skier: A Comparison Between Two Closed-Section Wind Tunnels

Proceedings ◽  
2020 ◽  
Vol 49 (1) ◽  
pp. 19
Author(s):  
Ola Elfmark ◽  
Robert Reid ◽  
Lars Morten Bardal
Keyword(s):  
Reynolds Number ◽  
High Speed ◽  
Absolute Error ◽  
Reynolds Numbers ◽  
Correction Method ◽  
Wind Tunnels ◽  
Alpine Skier ◽  
Blockage Correction ◽  
Reynolds Number Dependency ◽  
The Impact

The purpose of this study was to investigate the impact of blockage effect and Reynolds Number dependency by comparing measurements of an alpine skier in standardized positions between two wind tunnels with varying blockage ratios and speed ranges. The results indicated significant blockage effects which need to be corrected for accurate comparison between tunnels, or for generalization to performance in the field. Using an optimized blockage constant, Maskell’s blockage correction method improved the mean absolute error between the two wind tunnels from 7.7% to 2.2%. At lower Reynolds Numbers (<8 × 105, or approximately 25 m/s in this case), skier drag changed significantly with Reynolds Number, indicating the importance of testing at competition specific wind speeds. However, at Reynolds Numbers above 8 × 105, skier drag remained relatively constant for the tested positions. This may be advantageous when testing athletes from high speed sports since testing at slightly lower speeds may not only be safer, but may also allow the athlete to reliably maintain difficult positions during measurements.

Aerodynamic Loss Characteristics of a Turbine Blade With Trailing Edge Coolant Ejection: Part 1—Effect of Cut-Back Length, Spanwise Rib Spacing, Free-Stream Reynolds Number, and Chordwise Rib Length on Discharge Coefficients

2000 ◽  
Vol 123 (2) ◽  
pp. 238-248 ◽  
Author(s):  
Oguz Uzol ◽  
Cengiz Camci ◽  
Boris Glezer
Keyword(s):  
Reynolds Number ◽  
Mass Flow Rate ◽  
Flow Rate ◽  
Discharge Coefficient ◽  
Free Stream ◽  
Cooling System ◽  
Trailing Edge ◽  
Discharge Coefficients ◽  
Reynolds Number Dependency ◽  
Spanwise Rib

The internal fluid mechanics losses generated between the blade plenum chamber and a reference point located just downstream of the trailing edge are investigated for a turbine blade trailing edge cooling system. The discharge coefficient Cd is presented as a function of the free-stream Reynolds number, cut-back length, spanwise rib spacing, and chordwise rib length. The results are presented in a wide range of coolant to free-stream mass flow rate ratios. The losses from the cooling system show strong free-stream Reynolds number dependency, especially at low ejection rates, when they are correlated against the coolant to free-stream pressure ratio. However, when Cd is correlated against a coolant to free-stream mass flow rate ratio, the Reynolds number dependency is eliminated. The current data clearly show that internal viscous losses due to varying rib lengths do not differ significantly. The interaction of the external wall jet in the cutback region with the free-stream fluid is also a strong contributor to the losses. Since the discharge coefficients do not have Reynolds number dependency at high ejection rates, Cd experiments can be performed at a low free-stream Reynolds number. Running a discharge coefficient experiment at low Reynolds number (or even in still air) will sufficiently define the high blowing rate portion of the curve. This approach is extremely time efficient and economical in finding the worst possible Cd value for a given trailing edge coolant system.

Sign in / Sign up

Export Citation Format

Share Document