Experimental and Numerical Study of Swirling ... - Solid Mechanics
Experimental and Numerical Study of Swirling ... - Solid Mechanics Experimental and Numerical Study of Swirling ... - Solid Mechanics
Experimental and Numerical Study of Swirling Flow in Scavenging Process for 2-Stroke Marine Diesel Engines Chapter 2 ‘Renormalization Group Theory’. The model has an additional term in the equation that improves the accuracy in terms of rapidly strained flows (Najafi et al., 2005). The model also accounts for the effect of swirl or rotation by modifying the turbulent viscosity, given in function form in equation (2.13) (ANSYS FLUENT, 2009). k t t f , , 0 s S c (2.13) Where t is the value of turbulent viscosity calculated without swirl using 0 equation (2.12), S c is the characteristic swirl number and s is swirl constant having different values based on whether the flow is strongly or mildly swirl dominated. In other attempts to account for streamline curvature involves modifications in the source term for equation by including the ‘gradient Richardson number’ which is the ratio of centrifugal force to a typical inertial force and in context of swirling flows, the extra mean rates of strain associated with a curved shear layer may be regarded as producing the effects of a centrifugal force on a displaced element (Sloan et al., 1986). In case of second order closure schemes, the predictions in general are better compared to two equation models in some cases but still their performance is not universal to different swirling flow regimes in experimental and industrial devices. One of the weaknesses is that neither the modeling of terms nor the numerical values of the model constants are fully established (Sloan et al., 1986). Further, the stress transport equations are numerically very sensitive and using higher order discretization schemes may sometimes destabilize the computations. Jawarneh et al. (2006) also used first-order upwind discretization schemes for the Reynolds stresses. However, confined swirling flow calculations performed by Strugess et al. (1985) demonstrate that the errors due to numerical diffusion can make a suitable turbulence model to produce bad results. Further, Craft et al. (2008) demonstrated different performance of same Reynolds stress model for a given case but using 3D elliptic and parabolic solvers. 30 Swirling Flows
Experi imental and Numerical N Stud dy of Swirling g Flow in Scaveenging Processs for 2-Stroke Marin ne Diesel Engin nes Fig gure 3.1: Scav venging Flow Tes st mod del (Andersen et al., a 2008). In this chapter c a descr ription of the eexperimental teest model of sccavenging flow is given and a different aspects of thhe design are discussed in detail. Overaall experimental setup is explained annd results fromm smoke visualization test aare presente ed. Setup arran ngements for thee Stereoscopic PParticle Image Velocimetry annd Laser Doppler Anemom metry measuremments are definned. A comparrison between th the real engi ine scavenging process and thee test model is ddiscussed. 3.1 Exper E rimmenta al Seetup p Scaveng ging Floww Test Moodel Chapter 3 In orde er to simplify the t problem aan experimenttal down-scalee and simplifieed model of the engine cylinder is ddeveloped whiich is analogoous to a straight cylinder/ pipe conn nected to a sswirl generatoor but havinng features likke movabl le piston, cylin nder head and guide vaness to divert thee flow enterinng the cyli inder at a desi ired angle etcc. (Figure 3.1). . The setup is developed ass a part of a Bachelors th hesis project bby Andersen eet al. (2008) cco-supervised bby the auth hor. The focu us in this studdy is on the chharacterizationn of in-cylindder confine ed swirling flo ow and at diffe ferent Reynoldds number. Thhe experimenttal results thus are not only signifficant for unnderstanding the in-cylindder swirling g flow durin ng scavengingg process buut also very useful for thhe fundam mental studies in turbulent cconfined swirling flows. 31 Experimental Setup
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<strong>Experimental</strong> <strong>and</strong> <strong>Numerical</strong> <strong>Study</strong> <strong>of</strong> <strong>Swirling</strong> Flow in Scavenging Process for 2-Stroke<br />
Marine Diesel Engines<br />
Chapter 2<br />
‘Renormalization Group Theory’. The model has an additional term in the<br />
equation that improves the accuracy in terms <strong>of</strong> rapidly strained flows<br />
(Najafi et al., 2005). The model also accounts for the effect <strong>of</strong> swirl or<br />
rotation by modifying the turbulent viscosity, given in function form in<br />
equation (2.13) (ANSYS FLUENT, 2009).<br />
k <br />
t t f , ,<br />
0 s S c <br />
(2.13)<br />
<br />
Where t is the value <strong>of</strong> turbulent viscosity calculated without swirl using<br />
0<br />
equation (2.12), S c is the characteristic swirl number <strong>and</strong> s is swirl<br />
constant having different values based on whether the flow is strongly or<br />
mildly swirl dominated.<br />
In other attempts to account for streamline curvature involves modifications<br />
in the source term for equation by including the ‘gradient Richardson<br />
number’ which is the ratio <strong>of</strong> centrifugal force to a typical inertial force <strong>and</strong><br />
in context <strong>of</strong> swirling flows, the extra mean rates <strong>of</strong> strain associated with a<br />
curved shear layer may be regarded as producing the effects <strong>of</strong> a centrifugal<br />
force on a displaced element (Sloan et al., 1986).<br />
In case <strong>of</strong> second order closure schemes, the predictions in general are better<br />
compared to two equation models in some cases but still their performance<br />
is not universal to different swirling flow regimes in experimental <strong>and</strong><br />
industrial devices. One <strong>of</strong> the weaknesses is that neither the modeling <strong>of</strong><br />
terms nor the numerical values <strong>of</strong> the model constants are fully established<br />
(Sloan et al., 1986). Further, the stress transport equations are numerically<br />
very sensitive <strong>and</strong> using higher order discretization schemes may sometimes<br />
destabilize the computations. Jawarneh et al. (2006) also used first-order<br />
upwind discretization schemes for the Reynolds stresses. However, confined<br />
swirling flow calculations performed by Strugess et al. (1985) demonstrate<br />
that the errors due to numerical diffusion can make a suitable turbulence<br />
model to produce bad results. Further, Craft et al. (2008) demonstrated<br />
different performance <strong>of</strong> same Reynolds stress model for a given case but<br />
using 3D elliptic <strong>and</strong> parabolic solvers.<br />
30<br />
<strong>Swirling</strong> Flows