
Theoretical and Applied Mechanics Letters, Journal Year: 2024, Volume and Issue: unknown, P. 100564 - 100564
Published: Dec. 1, 2024
Language: Английский
Theoretical and Applied Mechanics Letters, Journal Year: 2024, Volume and Issue: unknown, P. 100564 - 100564
Published: Dec. 1, 2024
Language: Английский
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
Citations
8Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(6)
Published: June 1, 2024
Complex turbulent flows with large-scale instabilities and coherent structures pose challenges to both traditional data-driven Reynolds-averaged Navier–Stokes methods. The difficulty arises due the strong flow-dependence (the non-universality) of unsteady structures, which translates poor generalizability models. It is well-accepted that dynamically active reside in larger scales, while smaller scales turbulence exhibit more “universal” (generalizable) characteristics. In such flows, it prudent separate treatment flow-dependent aspects from universal features field. Scale resolving simulations (SRS), as partially averaged (PANS) method, seek resolve motion model only stochastic features. Such an approach requires development scale-sensitive closures not allow for but also appropriate dependence on cut-off length scale. objectives this work are (i) establish physical characteristics dependent turbulence; (ii) develop a procedure subfilter stress neural network at different cut-offs using high-fidelity data; (iii) examine optimal incorporation consistent posteriori use. scale-dependent closure physics analysis performed context PANS approach, technique can be extended other SRS benchmark “flow past periodic hills” case considered proof concept. self-similarity parameters incorporating identified. study demonstrates when data suitably normalized, machine learning based indeed insensitive
Language: Английский
Citations
2Physica Scripta, Journal Year: 2024, Volume and Issue: 99(8), P. 085207 - 085207
Published: May 13, 2024
Abstract
Cylindrical
structures
find
usage
in
many
engineering
applications
including
tethered
oil
drums
and
engine
canisters
slung
beneath
helicopters
flight.
The
motion
of
air
around
such
circular
cylindrical
the
helicopter
presents
interesting
phenomena
flow
separation,
wakes
turbulence.
physics
these
are
enshrined
continuity
equation
Navier–Stokes
equations.
Therefore,
their
studies
not
only
important
mathematics
physics,
but
they
also
required
for
efficient
operations.
In
practice,
reduced-order
models
operations
that
take
aerodynamics
loads
utilized
stability
analysis,
flight
certification
pilot
training
because
prohibitive
cost
experimentation
computational
analyses
configurations.
However,
there
is
a
dearth
realistic
analytical
finite
cylinder
flows
problem.
Classical
potential
theory
provides
an
avenue
to
develop
models,
extant
gaps
its
predictions
significantly
preclude
applications.
Attempting
bridge
gaps,
this
article
introduces
refined
which
governing
equations
boundary
conditions
satisfied.
Viscous
effects,
fluctuations
mean
three-dimensional
effects
incorporated.
For
characterization,
employed
on
incompressible
over
impulsively
started
Reynolds
numbers
non-dimensional
times
range
Language: Английский
Citations
0Theoretical and Applied Mechanics Letters, Journal Year: 2024, Volume and Issue: unknown, P. 100564 - 100564
Published: Dec. 1, 2024
Language: Английский
Citations
0