Industrial & Engineering Chemistry Research,
Journal Year:
2024,
Volume and Issue:
63(10), P. 4631 - 4646
Published: March 4, 2024
Empirical
correlations
for
bubble
diameter
and
velocity
are
incapable
of
predicting
the
local
behaviors
fairly
because
impact
hydrodynamics
on
bubbles
in
fluidized
beds.
Based
image
processing,
a
novel
identification
method
with
an
adaptive
threshold
was
proposed
to
distinguish
characterize
The
information
regarding
properties
can
thus
be
extracted
using
big
data
from
highly
resolved
simulations.
Accordingly,
deep
neural
network
trained
accurately
predict
properties,
where
inputs
were
determined
by
performing
correlation
analysis
random
forest
algorithm.
We
found
Reynolds
number,
voidage,
relative
coordinates
dominant
factors,
four-variable
choice
demonstrated
output
satisfactory
performance
velocity.
model
preliminarily
validated
coupling
EMMS
drag
into
CFD
codes,
which
showed
that
accuracy
coarse-grid
simulations
significantly
improved.
Biofuel Research Journal,
Journal Year:
2023,
Volume and Issue:
10(1), P. 1786 - 1809
Published: Feb. 28, 2023
Thermochemical
treatment
is
a
promising
technique
for
biomass
disposal
and
valorization.
Recently,
machine
learning
(ML)
has
been
extensively
used
to
predict
yields,
compositions,
properties
of
biochar,
bio-oil,
syngas,
aqueous
phases
produced
by
the
thermochemical
biomass.
ML
demonstrates
great
potential
aid
development
processes.
The
present
review
aims
1)
introduce
schemes
strategies
as
well
descriptors
input
output
features
in
processes;
2)
summarize
compare
up-to-date
research
both
ML-aided
wet
(hydrothermal
carbonization/liquefaction/gasification)
dry
(torrefaction/pyrolysis/gasification)
(i.e.,
predicting
oil/char/gas/aqueous
thermal
conversion
behavior
or
kinetics);
3)
identify
gaps
provide
guidance
future
studies
concerning
how
improve
predictive
performance,
increase
generalizability,
mechanistic
application
studies,
effectively
share
data
models
community.
processes
envisaged
be
greatly
accelerated
near
future.
Energies,
Journal Year:
2023,
Volume and Issue:
16(5), P. 2343 - 2343
Published: Feb. 28, 2023
Physics-informed
machine-learning
(PIML)
enables
the
integration
of
domain
knowledge
with
machine
learning
(ML)
algorithms,
which
results
in
higher
data
efficiency
and
more
stable
predictions.
This
provides
opportunities
for
augmenting—and
even
replacing—high-fidelity
numerical
simulations
complex
turbulent
flows,
are
often
expensive
due
to
requirement
high
temporal
spatial
resolution.
In
this
review,
we
(i)
provide
an
introduction
historical
perspective
ML
methods,
particular
neural
networks
(NN),
(ii)
examine
existing
PIML
applications
fluid
mechanics
problems,
especially
Reynolds
number
(iii)
demonstrate
utility
techniques
through
a
case
study,
(iv)
discuss
challenges
developing
mechanics.
Langmuir,
Journal Year:
2024,
Volume and Issue:
40(16), P. 8293 - 8326
Published: April 8, 2024
In
an
era
defined
by
insatiable
thirst
for
sustainable
energy
solutions,
responsible
water
management,
and
cutting-edge
lab-on-a-chip
diagnostics,
surface
wettability
plays
a
pivotal
role
in
these
fields.
The
seamless
integration
of
fundamental
research
the
following
demonstration
applications
on
groundbreaking
technologies
hinges
manipulating
fluid
through
wettability,
significantly
optimizing
performance,
enhancing
efficiency,
advancing
overall
sustainability.
This
Review
explores
behavior
liquids
when
they
engage
with
engineered
surfaces,
delving
into
far-reaching
implications
interactions
various
applications.
Specifically,
we
explore
wetting,
dissecting
it
three
distinctive
facets.
First,
delve
principles
that
underpin
wetting.
Next,
navigate
intricate
liquid–surface
interactions,
unraveling
complex
interplay
dynamics,
as
well
heat-
mass-transport
mechanisms.
Finally,
report
practical
realm,
where
scrutinize
myriad
everyday
processes
real-world
scenarios.
Energies,
Journal Year:
2023,
Volume and Issue:
16(3), P. 1500 - 1500
Published: Feb. 2, 2023
Heat
dissipation
in
high-heat
flux
micro-devices
has
become
a
pressing
issue.
One
of
the
most
effective
methods
for
removing
high
heat
load
is
boiling
transfer
microchannels.
A
novel
approach
to
flow
pattern
and
recognition
microchannels
provided
by
combination
image
machine
learning
techniques.
The
support
vector
method
texture
characteristics
successfully
recognizes
patterns.
To
determine
bubble
dynamics
behavior
micro-device,
features
are
combined
with
algorithms
applied
As
result,
relationship
between
evolution
established,
mechanism
revealed.
