Industrial & Engineering Chemistry Research,
Год журнала:
2022,
Номер
61(24), С. 8530 - 8541
Опубликована: Март 25, 2022
The
modeling
of
flow
and
transport
in
porous
media
is
the
utmost
importance
many
chemical
engineering
applications,
including
catalytic
reactors,
batteries,
CO2
storage.
aim
this
study
to
test
use
fully
connected
(FCNN)
convolutional
neural
networks
(CNN)
for
prediction
crucial
properties
systems:
permeability
filtration
rate.
data-driven
models
are
trained
on
a
dataset
computational
fluid
dynamics
(CFD)
simulations.
To
end,
geometries
created
silico
by
discrete
element
method,
rigorous
setup
CFD
simulations
presented.
have
as
input
both
geometrical
operating
conditions
features
so
that
they
could
find
application
multiscale
modeling,
optimization
problems,
in-line
control.
average
error
lower
than
2.5%,
rate
5%
all
models.
These
results
achieved
with
at
least
∼100
Computational Geosciences,
Год журнала:
2023,
Номер
27(2), С. 245 - 262
Опубликована: Янв. 31, 2023
Abstract
In
recent
years,
convolutional
neural
networks
(CNNs)
have
experienced
an
increasing
interest
in
their
ability
to
perform
a
fast
approximation
of
effective
hydrodynamic
parameters
porous
media
research
and
applications.
This
paper
presents
novel
methodology
for
permeability
prediction
from
micro-CT
scans
geological
rock
samples.
The
training
data
set
CNNs
dedicated
consists
labels
that
are
typically
generated
by
classical
lattice
Boltzmann
methods
(LBM)
simulate
the
flow
through
pore
space
segmented
image
data.
We
instead
direct
numerical
simulation
(DNS)
solving
stationary
Stokes
equation
efficient
distributed-parallel
manner.
As
such,
we
circumvent
convergence
issues
LBM
frequently
observed
on
complex
geometries,
therefore,
improve
generality
accuracy
our
set.
Using
DNS-computed
permeabilities,
physics-informed
CNN
(PhyCNN)
is
trained
additionally
providing
tailored
characteristic
quantity
space.
More
precisely,
exploiting
connection
problems
graph
representation
space,
additional
information
about
confined
structures
provided
network
terms
maximum
value,
which
key
innovative
component
workflow.
robustness
this
approach
reflected
very
high
accuracy,
variety
sandstone
samples
archetypal
formations.
Advances in Water Resources,
Год журнала:
2024,
Номер
188, С. 104695 - 104695
Опубликована: Апрель 9, 2024
Digital
rock
physics
is
at
the
forefront
of
characterizing
porous
media,
leveraging
advanced
tomographic
imaging
and
numerical
simulations
to
extract
key
properties
like
permeability.
However,
fully
capturing
heterogeneity
natural
rocks
necessitates
increasingly
larger
sample
volumes,
presenting
a
significant
challenge.
Direct
these
scales
become
either
prohibitively
expensive
or
computationally
unfeasible
due
limitations
in
resolution
field
view
(FOV).
This
issue
particularly
pronounced
carbonate
rocks,
known
for
their
complex,
multiscale
pore
structures,
which
exacerbate
resolution-FOV
tradeoff.
To
address
this,
we
introduce
machine
learning
strategy
that
merges
data
from
various
resolutions
with
3D
convolutional
neural
network
(CNN)
model.
approach
innovative
its
ability
identify
cross-scale
correlations,
thereby
enabling
estimation
transport
volumes—properties
are
difficult
simulate
directly—using
trainable
proxies.
The
integration
deep
allows
accurate
permeability
predictions
beyond
those
feasible
traditional
direct
simulation
methods.
By
employing
transfer
across
different
during
training
phase,
our
model
achieves
robust
performance,
an
R²
exceeding
0.96
when
evaluated
on
diverse
lower-resolution
domains
FOVs.
Notably,
this
method
significantly
enhances
computational
efficiency,
reducing
time
by
orders
magnitude.
Originally
developed
intricate
structures
shows
promise
application
wide
range
offering
viable
solution
longstanding
tradeoff
between
FOV
digital
physics.
Physical Review Fluids,
Год журнала:
2022,
Номер
7(7)
Опубликована: Июль 12, 2022
Predicting
the
pore
flow
velocity
directly
from
sub-sampled
structure
is
an
ill-conditioned
problem.
Inspired
by
multi-grid
methods
for
solving
systems
of
linear
equations,
we
use
fields
simulated
on
coarse
meshes
to
remedy
such
ill-conditioning.
This
leads
a
super-resolution-assisted
geometry-to-velocity
mapping
porous
media.
Fundamental Research,
Год журнала:
2022,
Номер
3(3), С. 409 - 421
Опубликована: Янв. 5, 2022
Gas
transport
mechanisms
can
be
categorized
into
viscous
flow
and
mass
diffusion,
both
of
which
may
coexist
in
a
porous
media
with
multiscale
pore
sizes.
To
determine
the
dominant
mechanism
its
contribution
to
gas
capacity,
diffusion
processes
are
analyzed
single
nanoscale
pores
via
theoretical
method,
simulated
3D
nanoporous
pore-scale
lattice
Boltzmann
methods.
The
apparent
permeability
from
diffusivity
estimated.
A
dimensionless
parameter,
i.e.,
diffusion-flow
ratio,
is
proposed
evaluate
mechanism,
function
permeability,
diffusivity,
bulk
dynamic
viscosity,
working
pressure.
results
show
that
increases
by
approximately
two
orders
magnitude
when
average
Knudsen
number
(Knavg)
or
(Kn)
0.1
10.
Under
same
conditions,
increment
only
one
order
magnitude.
When
Kn
<
0.01,
has
lower
bound
(i.e.,
absolute
permeability).
>
10,
an
upper
diffusivity).
for
0.01
100,
where
maximum
ratio
less
than
one.
In
media,
relies
heavily
on
Knavg
structural
parameters.
For
throat
diameter
3
nm,
=
0.2
critical
point,
above
dominant;
otherwise,
dominant.
As
3.4,
overwhelming,
reaching
∼4.
