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Numerical Heat Transfer, Part A: Applications
An International Journal of Computation and Methodology
Volume 70, 2016 - Issue 8
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Original Articles

Large eddy simulation of fluid injection under transcritical and supercritical conditions

, &
Pages 870-886 | Received 25 Feb 2016, Accepted 27 May 2016, Published online: 20 Sep 2016
 

ABSTRACT

When a cryogenic fluid initially at a subcritical temperature is injected into a supercritical environment, it will experience a process across a pseudo-boiling point, at which the specific heat reaches its maximum value under the corresponding pressure. Large eddy simulation (LES) is conducted to explore the effects of pseudo-vaporization phenomenon around the pseudo-critical temperature on fluid jet evolution. To highlight the pseudo-vaporization effect, a cryogenic nitrogen jet with different injection temperatures, which correspond to transcritical and supercritical conditions, respectively, is injected into a chamber with same supercritical conditions. All of the thermophysical and transport properties are determined directly from fundamental theories combined with a real fluid equation of state. It is found that when the fluid transits through the pseudo-boiling point, the constant-pressure specific heat reaches a local maximum, while the thermal conductivity and viscosity become minimum. The condition-averaged constant-pressure specific heat suggests that the pseudo-boiling point has the effect of increasing the density gradients. Vorticity and Q-criterion analysis reveals that high-temperature injection facilitates the mixing of jet fluid with ambient gas. Also, the high-temperature injection of supercritical fluid can earlier transit into the full developed region.

Nomenclature

=

slope of the linear regression

EOS=

equation of state

et=

total energy

H=

energy flux

=

sub-grid scale energy fluxes

LES=

large eddy simulation

Lρ=

half-width

P=

pressure

Pc=

critical pressure

R=

universal gas constant

=

sub-grid scale strain-rate tensor

SRK=

Soave–Redlich–Kwong

T=

temperature

Tc=

critical pressure

Tinj=

inject temperature

T=

environment temperature

t=

time

θH=

HWHM spreading angle

ρinj=

inject density

ρc=

critical density

ρ=

environment density

ρ=

density gradient

Δ=

filter width

ω=

acentric factor

τij=

sheer stress

σij=

viscous work

Nomenclature

=

slope of the linear regression

EOS=

equation of state

et=

total energy

H=

energy flux

=

sub-grid scale energy fluxes

LES=

large eddy simulation

Lρ=

half-width

P=

pressure

Pc=

critical pressure

R=

universal gas constant

=

sub-grid scale strain-rate tensor

SRK=

Soave–Redlich–Kwong

T=

temperature

Tc=

critical pressure

Tinj=

inject temperature

T=

environment temperature

t=

time

θH=

HWHM spreading angle

ρinj=

inject density

ρc=

critical density

ρ=

environment density

ρ=

density gradient

Δ=

filter width

ω=

acentric factor

τij=

sheer stress

σij=

viscous work

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