Experimental and Numerical Study of Swirling ... - Solid Mechanics
Experimental and Numerical Study of Swirling ... - Solid Mechanics
Experimental and Numerical Study of Swirling ... - Solid Mechanics
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Experi imental <strong>and</strong> <strong>Numerical</strong> N Stud dy <strong>of</strong> <strong>Swirling</strong> g Flow in Scaveenging<br />
Processs<br />
for 2-Stroke<br />
Marin ne Diesel Engin nes<br />
Fig gure 6.18:<br />
Comp parison <strong>of</strong><br />
Expe erimental <strong>and</strong><br />
Num merical Results <strong>of</strong> f<br />
Norm malized RMS Va alues<br />
<strong>of</strong> Ax xial Velocity at z<br />
1 .<br />
Chapter 6<br />
do not distinguish between b turbuulence<br />
<strong>and</strong> unnsteadiness<br />
(GGyllenram<br />
et aal.,<br />
2008). The main reason r is RAANS<br />
assumptiion<br />
<strong>of</strong> all turrbulence<br />
beinng<br />
stochast tic <strong>and</strong> averag ging thus remmoves<br />
all unstteady<br />
motion (Leschziner MM.<br />
A., 2010 0). This (ensem mble averaginng)<br />
leads the ssolution<br />
to bee<br />
determined bby<br />
initial <strong>and</strong> a boundary conditions annd<br />
one can gett<br />
same solutioon<br />
repeatedly bby<br />
keeping g the same com mputer <strong>and</strong> innitial<br />
<strong>and</strong> boundary<br />
conditions<br />
(Gyllenram<br />
et al., 2008).The 2 UR RANS simulattion<br />
conducteed<br />
by (Spencer<br />
et al., 20009)<br />
could only o resolve th he ensemble oor<br />
phase averraged<br />
coherennce<br />
unsteadineess<br />
with sm mall fluctuating<br />
amplitude.<br />
Compa ared to eddy viscosity v basedd<br />
URANS, the<br />
RSM based simulations in<br />
several cases e.g. (Hir rai et al., 19888)<br />
(Benim et al., 2005) <strong>and</strong>d<br />
(Lübcke et aal.,<br />
2001) etc. e gives bett ter results. Thhe<br />
main reasson<br />
is a betteer<br />
treatment <strong>of</strong><br />
Reynold ds stresses by y solving a ttransport<br />
equuation<br />
for eacch<br />
componennt.<br />
Howeve er, for a better<br />
underst<strong>and</strong>iing<br />
<strong>and</strong> predicction<br />
<strong>of</strong> the fllow,<br />
turbulence<br />
modelin ng approache es are requireed<br />
where, connsidering<br />
the computationnal<br />
costs, a part <strong>of</strong> the tu urbulence specctrum<br />
is resolvved<br />
<strong>and</strong> the reest<br />
are modeleed<br />
(Hanjal lic K., 2005). Such S modelingg<br />
approaches include LES a<strong>and</strong><br />
hybrid LEES<br />
RANS etc. e<br />
In the current c simula ation cases, a steady state cconverged<br />
soluution<br />
could not<br />
be achieved<br />
with RN NG k <strong>and</strong>d<br />
RSM. This inndicated<br />
that the problem to<br />
be inhe erently unsteady<br />
in naturre<br />
<strong>and</strong> led too<br />
run URANNS<br />
simulationns.<br />
Figure 6.18 shows a comparison <strong>of</strong> normalizeed<br />
root meann<br />
square (RMS)<br />
values <strong>of</strong> o axial veloc city for experiimental<br />
data <strong>and</strong> the simuulation<br />
cases at<br />
position n z . 1<br />
It can be b seen that th he URANS simmulations<br />
<strong>of</strong> aall<br />
the current<br />
cases show aan<br />
unstead dy behavior. The RMS vaalues<br />
are almmost<br />
<strong>of</strong> the same order as<br />
experim mental data but b pr<strong>of</strong>iles are not in good agreemment<br />
with thhe<br />
162<br />
<strong>Numerical</strong> Modeling