Case Studies in Thermal Engineering,
Год журнала:
2022,
Номер
40, С. 102448 - 102448
Опубликована: Сен. 24, 2022
The
thermal
performance
of
a
flat
plate
solar
collector
using
MWCNT
+
Fe3O4/Water
hybrid
nanofluids
was
examined
in
this
research.
tested
different
nanofluid
concentrations
and
flow
rates
an
arid
environment.
A
significant
enhancement
coefficient
heat
transfer
(26.3%)
with
marginal
loss
on
pressure
drop
due
to
friction
factor
(18.9%).
data
collected
during
experimental
testing
utilized
develop
novel
prediction
models
for
efficient
transfer,
Nusselt's
number,
factor,
efficiency.
modern
ensemble
machine
learning
techniques
Boosted
Regression
Tree
(BRT)
Extreme
Gradient
Boosting
(XGBoost)
were
used
prognostic
each
parameter.
battery
statistical
methods
Taylor's
graphs
compare
the
these
two
ML
techniques.
value
R2
BRT-based
0.9619
-
0.9994
0.9914
0.9997
XGBoost-based
models.
mean
squared
error
quite
low
all
(0.000081
9.11),
while
absolute
percentage
negligible
from
0.0025
0.3114.
comprehensive
analysis
model
complemented
improved
comparison
paradigm,
reveal
superiority
XGBoost
over
BRT.
Energy & Fuels,
Год журнала:
2022,
Номер
36(13), С. 6626 - 6658
Опубликована: Июнь 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.
