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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

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