Turbulence closure modeling with machine learning: a foundational physics perspective
New Journal of Physics,
Journal Year:
2024,
Volume and Issue:
26(7), P. 071201 - 071201
Published: July 1, 2024
Abstract
Turbulence
closure
modeling
using
machine
learning
(ML)
is
at
an
early
crossroads.
The
extraordinary
success
of
ML
in
a
variety
challenging
fields
had
given
rise
to
expectation
similar
transformative
advances
the
area
turbulence
modeling.
However,
by
most
accounts,
current
rate
progress
toward
accurate
and
predictive
ML-RANS
(Reynolds
Averaged
Navier–Stokes)
models
has
been
very
slow.
Upon
retrospection,
absence
rapid
can
be
attributed
two
factors:
underestimation
intricacies
overestimation
ML’s
ability
capture
all
features
without
employing
targeted
strategies.
To
pave
way
for
more
meaningful
closures
tailored
address
nuances
turbulence,
this
article
seeks
review
foundational
flow
physics
assess
challenges
context
data-driven
approaches.
Revisiting
analogies
with
statistical
mechanics
stochastic
systems,
key
physical
complexities
mathematical
limitations
are
explicated.
It
noted
that
approaches
do
not
systematically
inherent
approach
or
inadequacies
forms
expressions.
study
underscores
drawbacks
supervised
learning-based
stresses
importance
discerning
framework.
As
methods
evolve
(which
happening
pace)
our
understanding
phenomenon
improves,
inferences
expressed
here
should
suitably
modified.
Language: Английский
Adaptive scale resolving for turbulent jets using data assimilation augmented Reynolds-averaged simulations
Physics of Fluids,
Journal Year:
2024,
Volume and Issue:
36(10)
Published: Oct. 1, 2024
This
study
proposes
a
turbulence
model
called
the
PaSAS–Re
which
has
low
computational
cost.
The
proposed
can
predict
time-averaged
flow
accurately
and
fluctuation
field
for
turbulent
jets.
A
data
assimilation
that
mean
distribution
in
free
jets
wall
is
used
as
parent
model.
scale-adaptive
simulation
(SAS)
source
term
added
to
equip
it
with
ability
achieve
behavior
like
large-eddy
simulation.
However,
SAS
approach
cannot
switch
scale-resolving
mode
if
flow,
such
jet,
does
not
exhibit
sufficiently
strong
instability.
Therefore,
partially
averaged
Navier–Stokes
(PANS)
this
generate
necessary
instabilities.
PANS
converts
modeled
kinetic
energy
k
into
resolved
fluctuation,
beneficial
activating
tested
on
impinging
using
coarse
meshes
highlight
its
results
of
velocity
show
best
performance
achieved
fk
=
0.8.
effects
approach,
vortex
stretching
term,
prediction
are
analyzed
found
be
important
predicting
generating
dynamic
behavior.
Finally,
simulations
conducted
further
verification
application
suitable
fluctuations.
engineering
obtain
Language: Английский