scholarly journals Near-Wall Determination of the Turbulent Prandtl Number Based on Experiments, Numerical Simulation and Analytical Models

2019 ◽  
Vol 41 (15-16) ◽  
pp. 1341-1353
Author(s):  
Helfried Steiner ◽  
Christoph Irrenfried ◽  
Günter Brenn
1981 ◽  
Vol 16 (1) ◽  
pp. 57-61
Author(s):  
M. A. Gol'dshtik ◽  
S. S. Kutateladze ◽  
A. M. Lifshits

2021 ◽  
Author(s):  
Michael Storchak ◽  
Thomas Stehle ◽  
Hans-Christian Möhring

Abstract Thermal properties of work materials, which depend significantly on the change in cutting temperature, have a considerable effect on thermal machining characteristics. Therefore, the thermal properties used for the numerical simulation of the cutting process should be determined depending on the cutting temperature. To determine the thermal properties of the work materials, a methodology and a software-implemented algorithm were developed for their calculation. This methodology is based on analytical models for the determination of tangential stress in the primary cutting zone. Based on this stress and experimentally or analytically determined cutting temperatures, thermal properties of the work material were calculated, namely the coefficient of the heat capacity as well as the coefficient of thermal conductivity. Three variants were provided for determining the tangential stress: based on the normal stress calculated using the Johnson-Cook constitutive equation, based on the experimentally determined cutting and thrust forces as well as by directly calculating the tangential stress. The thermal properties were determined using the example of three different materials: AISI 1045 and AISI 4140 steel as well as Ti10V2Fe3Al titanium alloy (Ti-1023). With the developed FE cutting model, the deviation between experimental and simulated temperature values ranged from approx. 7.5–14.4%.


2002 ◽  
Vol 124 (3) ◽  
pp. 485-498 ◽  
Author(s):  
Djamel Lakehal

The paper presents novel developments in the DNS-based, turbulence modeling strategy of Lakehal et al. developed for calculating jets in crossflow. The particular features of the model include: 1) dynamic coupling of the high-Re k−ε with a one-equation model resolving the near-wall viscosity-affected layer; 2) inclusion of the anisotropy of turbulent transport coefficients for all transport equations; 3) near-wall variation of the turbulent Prandtl number as a function of the local Reynolds number. Most of the important aspects of the proposed model are based on known DNS statistics of channel and boundary layer flows. The model is validated against experiments for the case of film cooling of a flat plate, where coolant air is injected from a row of streamwise inclined jets. Excellent results were obtained for this configuration as compared to earlier numerical investigations reported in the open literature. The model is then extended to calculate film cooling of a symmetrical turbine blade by a row of laterally injected jets for various blowing rates. Comparison of the calculated and measured wall-temperature distributions show that only with this anisotropy eddy-viscosity/diffusivity model can the spanwise spreading of the temperature field be well predicted and the strength of the secondary vortices reduced. Furthermore, results of additional calculations show that combining the anisotropy eddy viscosity model with the DNS-based relation for turbulent Prandtl number promotes the eddy diffusivity of heat vis-a`-vis that of momentum further, leading to an enhanced spanwise spreading of the jet. The performance of this new approach improves with increasing blowing rate.


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