International Journal of Energy Research,
Journal Year:
2022,
Volume and Issue:
46(13), P. 19242 - 19257
Published: April 23, 2022
Hybrid
nanofluids
are
gaining
popularity
owing
to
the
synergistic
effects
of
nanoparticles,
which
provide
them
with
better
heat
transfer
capabilities
than
base
fluids
and
normal
nanofluids.
The
thermophysical
characteristics
hybrid
critical
in
shaping
transmission
properties.
As
a
result,
before
using
qualities
industrial
applications,
an
in-depth
investigation
properties
is
required.
In
this
paper,
metamodel
framework
constructed
forecast
effect
nanofluid
temperature
concentration
on
numerous
parameters
Fe3O4-coated
MWCNT
Evolutionary
gene
expression
programming
(GEP)
adaptive
neural
fuzzy
inference
system
(ANFIS)
were
employed
develop
prediction
models.
model
was
trained
70%
datasets,
remaining
15%
used
for
testing
validation.
A
variety
statistical
measurements
Taylor's
diagrams
assess
proposed
Pearson's
correlation
coefficient
(R),
determination
(R2)
regression
index,
error
evaluated
root
mean
squared
(RMSE).
model's
comprehensive
assessment
additionally
includes
modern
efficiency
indices
such
as
Kling-Gupta
(KGE)
Nash-Sutcliffe
(NSCE).
models
demonstrated
impressive
capabilities.
However,
GEP
(R
>
0.9825,
R2
0.9654,
RMSE
=
0.7929,
KGE
0.9188,
NSCE
0.9566)
outperformed
ANFIS
0.9601,
0.9218,
1.495,
0.8015,
0.8745)
majority
findings.
generated
robust
enough
replace
repetitive
expensive
lab
procedures
required
measure
Highlights
Predictions
AI-based
performed
well
GEP-based
prognostic
validated
compared
Taylor
Energy & Fuels,
Journal Year:
2022,
Volume and Issue:
36(13), P. 6626 - 6658
Published: June 13, 2022
Nanofluids
have
gained
significant
popularity
in
the
field
of
sustainable
and
renewable
energy
systems.
The
heat
transfer
capacity
working
fluid
has
a
huge
impact
on
efficiency
system.
addition
small
amount
high
thermal
conductivity
solid
nanoparticles
to
base
improves
transfer.
Even
though
large
research
data
is
available
literature,
some
results
are
contradictory.
Many
influencing
factors,
as
well
nonlinearity
refutations,
make
nanofluid
highly
challenging
obstruct
its
potentially
valuable
uses.
On
other
hand,
data-driven
machine
learning
techniques
would
be
very
useful
for
forecasting
thermophysical
features
rate,
identifying
most
influential
assessing
efficiencies
different
primary
aim
this
review
study
look
at
applications
employed
nanofluid-based
system,
reveal
new
developments
research.
A
variety
modern
algorithms
studies
systems
examined,
along
with
their
advantages
disadvantages.
Artificial
neural
networks-based
model
prediction
using
contemporary
commercial
software
simple
develop
popular.
prognostic
may
further
improved
by
combining
marine
predator
algorithm,
genetic
swarm
intelligence
optimization,
intelligent
optimization
approaches.
In
well-known
networks
fuzzy-
gene-based
techniques,
newer
ensemble
such
Boosted
regression
K-means,
K-nearest
neighbor
(KNN),
CatBoost,
XGBoost
gaining
due
architectures
adaptabilities
diverse
types.
regularly
used
fuzzy-based
mostly
black-box
methods,
user
having
little
or
no
understanding
how
they
function.
This
reason
concern,
ethical
artificial
required.
International Journal of Extreme Manufacturing,
Journal Year:
2023,
Volume and Issue:
5(4), P. 042011 - 042011
Published: Aug. 29, 2023
Abstract
Grinding
is
a
crucial
process
in
machining
workpieces
because
it
plays
vital
role
achieving
the
desired
precision
and
surface
quality.
However,
significant
technical
challenge
grinding
potential
increase
temperature
due
to
high
specific
energy,
which
can
lead
thermal
damage.
Therefore,
ensuring
control
over
integrity
of
during
becomes
critical
concern.
This
necessitates
development
field
models
that
consider
various
parameters,
such
as
workpiece
materials,
wheels,
cooling
methods,
media,
guide
industrial
production.
study
thoroughly
analyzes
summarizes
models.
First,
theory
investigated,
classifying
into
traditional
based
on
continuous
belt
heat
source
those
discrete
source,
depending
whether
uniform
continuous.
Through
this
examination,
more
accurate
model
closely
aligns
with
practical
conditions
derived.
Subsequently,
are
summarized,
including
for
distribution,
energy
distribution
proportional
coefficient,
convective
transfer
coefficient.
comprehensive
research,
most
widely
recognized,
utilized,
each
category
identified.
The
application
these
reviewed,
shedding
light
governing
laws
dictate
influence
arc
zone
field.
Finally,
considering
current
issues
temperature,
future
research
directions
proposed.
aim
provide
theoretical
guidance
support
predicting
improving
integrity.
Chinese Journal of Mechanical Engineering,
Journal Year:
2023,
Volume and Issue:
36(1)
Published: Jan. 30, 2023
Abstract
Nanofluid
minimum
quantity
lubrication
(NMQL)
is
a
green
processing
technology.
Cottonseed
oil
suitable
as
base
because
of
excellent
performance,
low
freezing
temperature,
and
high
yield.
Al
2
O
3
nanoparticles
improve
not
only
the
heat
transfer
capacity
but
also
performance.
The
physical
chemical
properties
nanofluid
change
when
are
added.
However,
effects
concentration
on
performance
remain
unknown.
Furthermore,
mechanisms
interaction
between
cottonseed
unclear.
In
this
research,
prepared
by
adding
different
mass
concentrations
(0,
0.2%,
0.5%,
1%,
1.5%,
2%
wt)
to
during
(MQL)
milling
45
steel.
tribological
with
at
tool/workpiece
interface
studied
through
macro-evaluation
parameters
(milling
force,
specific
energy)
micro-evaluation
(surface
roughness,
micro
morphology,
contact
angle).
result
show
that
energy
(114
J/mm
),
roughness
value
lowest
(1.63
μm)
0.5
wt%.
surfaces
chip
workpiece
smoothest,
angle
lowest,
indicating
best
under
This
research
investigates
intercoupling
oil,
acquires
optimal
receive
satisfactory
properties.
