Variable Surface Roughness Modeling for Skin Friction Estimation

2014 ◽  
Author(s):  
Shivaji Medida ◽  
James D. Baeder ◽  
Nikhil Nigam ◽  
Peter Chen
Keyword(s):  
Surface Roughness ◽  
Skin Friction ◽  
Variable Surface ◽  
Friction Estimation

Turbulent Boundary Layer Subjected to a Sudden Change in Surface Roughness and Temperature

1999 ◽  
Author(s):  
João Henrique D. Guimarães ◽  
Sergio J. F. dos Santos ◽  
Jian Su ◽  
Atila P. Silva Freire
Keyword(s):  
Boundary Layer ◽  
Surface Roughness ◽  
Turbulent Boundary Layer ◽  
Skin Friction ◽  
Sudden Change ◽  
Stanton Number ◽  
Temperature Profiles ◽  
Internal Layers ◽  
Mean Velocity ◽  
Wall Boundary Conditions

Abstract In present work, the dynamic and thermal behaviour of flows that develop over surfaces that simultaneously present a sudden change in surface roughness and temperature are discussed. In particular, the work is concerned with the physical validation of a newly proposed formulation for the near wall temperature profile. The theory uses the concept of the displacement in origin, together with some asymptotic arguments, to propose a new expression for the logarithmic region of the turbulent boundary layer. The new expressions are, therefore, of universal applicability, being independent of the type of rough surface considered. The present formulation may be used to give wall boundary conditions for two-equation differential models. The theoretical results are validated with experimental data obtained for flows that develop over flat surfaces with sudden changes in surface roughness and in temperature conditions. Measurements of mean velocity and of mean temperature are presented. A reduction of the data provides an estimate of the skin-friction coefficient, the Stanton number, the displacement in origin for both the velocity and the temperature profiles, and the thickness of the internal layers for the velocity and temperature profiles. The skin-friction co-efficient was calculated based on the chart method of Perry and Joubert (J.F.M., 17, 193–211, 1963) and on a balance of the integral momentum equation. The same chart method was used for the evaluation of the Stanton number and the displacement in origin.

scholarly journals Influence of Support Vector Regression (SVR) on Cryogenic Face Milling

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Rao M. C. Karthik ◽  
Rashmi L. Malghan ◽  
Fuat Kara ◽  
Arunkumar Shettigar ◽  
Shrikantha S. Rao ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Comparative Analysis ◽  
Support Vector ◽  
Depth Of Cut ◽  
Convergence Factor ◽  
Learning Capability ◽  
Variable Surface ◽  
Relationship Model ◽  
Optimal Target ◽  
Independent Variable

The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, “one parametric approach” was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is “multiple linear regression.” Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%–8.43%), (BNN: 2.36%–5.88%), (SVR: 1.04%–3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error.

Nanofluid flow past an impulsively started vertical plate with variable surface temperature

2016 ◽  
Vol 26 (1) ◽  
pp. 328-347 ◽  
Author(s):  
Rajesh Vemula ◽  
A J Chamkha ◽  
Mallesh M. P.
Keyword(s):  
Nusselt Number ◽  
Friction Coefficient ◽  
Skin Friction ◽  
Vertical Plate ◽  
Aluminium Oxide ◽  
Skin Friction Coefficient ◽  
Viscous Drag ◽  
Content Type ◽  
Variable Surface ◽  
Variable Surface Temperature

Purpose – The purpose of this paper is to focus on the numerical modelling of transient natural convection flow of an incompressible viscous nanofluid past an impulsively started semi-infinite vertical plate with variable surface temperature. Design/methodology/approach – The problem is governed by the coupled non-linear partial differential equations with appropriate boundary conditions. A robust, well-tested, Crank-Nicolson type of implicit finite-difference method, which is unconditionally stable and convergent, is used to solve the governing non-linear set of partial differential equations. Findings – The local and average values of the skin-friction coefficient (viscous drag) and the average Nusselt number (the rate of heat transfer) decreased, while the local Nusselt number increased for all nanofluids, namely, aluminium oxide-water, copper-water, titanium oxide-water and silver-water with an increase in the temperature exponent m. Selecting aluminium oxide as the dispersing nanoparticles leads to the maximum average Nusselt number (the rate of heat transfer), while choosing silver as the dispersing nanoparticles leads to the minimum local Nusselt number compared to the other nanofluids for all values of the temperature exponent m. Also, choosing silver as the dispersing nanoparticles leads to the minimum skin-friction coefficient (viscous drag), while selecting aluminium oxide as the dispersing nanoparticles leads to the maximum skin-friction coefficient (viscous drag) for all values of the temperature exponent m. Research limitations/implications – The Brinkman model for dynamic viscosity and Maxwell-Garnett model for thermal conductivity are employed. The governing boundary layer equations are written according to The Tiwari-Das nanofluid model. A range of nanofluids containing nanoparticles of aluminium oxide, copper, titanium oxide and silver with nanoparticle volume fraction range less than or equal to 0.04 are considered. Practical implications – The present simulations are relevant to nanomaterials thermal flow processing in the chemical engineering and metallurgy industries. This study also provides an important benchmark for further simulations of nanofluid dynamic transport phenomena of relevance to materials processing, with alternative computational algorithms (e.g. finite element methods). Originality/value – This paper is relatively original and illustrates the influence of variable surface temperature on transient natural convection flow of a viscous incompressible nanofluid and heat transfer from an impulsively started semi-infinite vertical plate.

Predicting Skin Friction and Heat Transfer for Turbulent Flow Over Real Gas-Turbine Surface Roughness Using the Discrete-Element Method

2003 ◽  
Author(s):  
Stephen T. McClain ◽  
B. Keith Hodge ◽  
Jeffrey P. Bons
Keyword(s):  
Heat Transfer ◽  
Surface Roughness ◽  
Wind Tunnel ◽  
Discrete Element Method ◽  
Skin Friction ◽  
Gas Turbine ◽  
Discrete Element ◽  
Real Gas ◽  
Roughness Elements ◽  
Element Method

The discrete-element method considers the total aerodynamic drag on a rough surface to be the sum of shear drag on the flat part of the surface and the form drag on the individual roughness elements. The total heat transfer from a rough surface is the sum of convection through the fluid on the flat part of the surface and the convection from each of the roughness elements. The discrete-element method has been widely used and validated for predicting heat transfer and skin friction for rough surfaces composed of sparse, ordered, and deterministic elements. Real gas-turbine surface roughness is different from surfaces with sparse, ordered, and deterministic roughness elements. Modifications made to the discrete-element roughness method to extend the validation to real gas-turbine surface roughness are detailed. Two rough surfaces found on high-hour gas-turbine blades were characterized using a Taylor-Hobson Form Talysurf Series 2 profilometer. Two rough surfaces and two elliptical-analog surfaces were generated for wind-tunnel testing using a three-dimensional printer. The printed surfaces were scaled to maintain similar boundary-layer thickness to roughness height ratio in the wind tunnel as found in gas-turbine operation. The results of the wind tunnel skin friction and Stanton number measurements and the discrete-element method predictions for each of the four surfaces are presented and discussed. The discrete-element predictions made considering the gas-turbine roughness modifications are within 7% of the experimentally-measured skin friction coefficients and are within 16% of the experimentally-measured Stanton numbers.

A Wall Function for Calculating the Skin Friction With Surface Roughness

2004 ◽  
Author(s):  
Aamir Shabbir ◽  
Mark G. Turner
Keyword(s):  
Surface Roughness ◽  
Skin Friction ◽  
Solid Surfaces ◽  
Explicit Calculation ◽  
Wall Function ◽  
Compressor Cascade ◽  
Wall Functions ◽  
Cfd Applications ◽  
Flow Variables ◽  
Linear Compressor Cascade

Wall functions are used in CFD to provide skin friction values and hence the boundary conditions for flow variables along solid surfaces. In this paper the effect of surface roughness on skin friction is incorporated in a wall function approach which uses Spalding’s formula. The use of Spalding’s formula extends the method to a wider range of wall distance than the logarithmic friction law. For CFD applications the results are then re-formulated for explicit calculation throught the use of an additional variable — the ratio of the surface roughness height to the distance from the solid surface. For a given computational grid this information is readily available in a CFD calculation. This methodology is applied to a linear compressor cascade that has been experimentally measured for different roughness values. The comparison of the simulation followed the same trends as the experiment, but with less overall effect.

Influence of Machining Strategies on Surface Roughness in Ball End Milling of Inclined Surfaces

2012 ◽  
Vol 488-489 ◽  
pp. 836-840 ◽  
Author(s):  
S. Shajari ◽  
M.H. Sadeghi ◽  
H. Hassanpour ◽  
B. Jabbaripour
Keyword(s):  
Surface Roughness ◽  
Tool Path ◽  
End Milling ◽  
Cutting Speed ◽  
Machining Parameters ◽  
Scallop Height ◽  
Inclined Surfaces ◽  
Milling Operation ◽  
Variable Surface ◽  
Machining Strategies

Inclined surfaces are commonly used in the aerospace and die/mold industries. For machining this kind of surfaces, many aspects have to be considered as machinability considerations including milling strategies, machining parameters and etc. In machining, achieving better quality is challenging task. Various tool-path strategies during milling operation leads to variable surface roughness on machined samples. The objective of this study is to analyze different machining strategies in 3-axis milling of a typical curved geometry part. The machining parameters used in this study, are cutting speed, feedrate and stepover. This paper also presents an approach to develop a mathematical model for measuring Scallop height size and distribution for different machining strategies to show that Scallop height size has direct relation with Surface roughness measurements in each strategy. Finally the optimized strategy based on the results was determined.

A Critical Assessment of Reynolds Analogy for Turbine Flows

2005 ◽  
Vol 127 (5) ◽  
pp. 472-485 ◽  
Author(s):  
J. Bons
Keyword(s):  
Heat Transfer ◽  
Surface Roughness ◽  
Reynolds Number ◽  
Pressure Gradient ◽  
Skin Friction ◽  
Adverse Pressure Gradient ◽  
Design Parameters ◽  
Freestream Turbulence ◽  
Reynolds Analogy ◽  
Pressure Drag

The application of Reynolds analogy 2St/cf≅1 for turbine flows is critically evaluated using experimental data collected in a low-speed wind tunnel. Independent measurements of St and cf over a wide variety of test conditions permit assessments of the variation of the Reynolds analogy factor (i.e., 2St/cf) with Reynolds number, freestream pressure gradient, surface roughness, and freestream turbulence. While the factor is fairly independent of Reynolds number, it increases with positive (adverse) pressure gradient and decreases with negative (favorable) pressure gradient. This variation can be traced directly to the governing equations for momentum and energy which dictate a more direct influence of pressure gradient on wall shear than on energy (heat) transfer. Surface roughness introduces a large pressure drag component to the net skin friction measurement without a corresponding mechanism for a comparable increase in heat transfer. Accordingly, the Reynolds analogy factor decreases dramatically with surface roughness (by as much as 50% as roughness elements become more prominent). Freestream turbulence has the opposite effect of increasing heat transfer more than skin friction, thus the Reynolds analogy factor increases with turbulence level (by up to 35% at a level of 11% freestream turbulence). Physical mechanisms responsible for the observed variations are offered in each case. Finally, synergies resulting from the combinations of pressure gradient and freestream turbulence with surface roughness are evaluated. With this added insight, the Reynolds analogy remains a useful tool for qualitative assessments of complex turbine flows where both heat load management and aerodynamic efficiency are critical design parameters.

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