Heliyon,
Год журнала:
2022,
Номер
8(11), С. e11373 - e11373
Опубликована: Ноя. 1, 2022
In
this
paper,
the
thermal
conductivity
(knf)
of
cerium
oxide/ethylene
glycol
nanofluid
is
extracted
for
different
temperatures
(T
=
25,
30,
35,
40,
45,
and
50
°C)
volume
fraction
nanoparticles
(φ=
0,
0.25,
0.5,
0.75,
1,
1.5,
2
2.5%)
then
knf
predicted
by
two
methods
including
Artificial
Neural
Network
(ANN)
fitting
method.
For
both
methods,
results
have
been
presented
compared.
The
experiments
showed
that
with
increasing
φ
temperature,
ratio
(TCR)
increases.
It
was
also
observed
when
are
performed
at
high
temperatures,
rate
increase
in
much
higher
than
change
same
amount
low
temperatures.
An
ANN
7
neurons
has
a
correlation
coefficient
very
close
to
1
proves
outputs
compatible
experimental
results.
Also,
it
can
be
seen
could
predict
behavior
more
accurately.
Biomimetics,
Год журнала:
2023,
Номер
8(8), С. 574 - 574
Опубликована: Дек. 1, 2023
Evolutionary
algorithms
are
a
large
class
of
optimization
techniques
inspired
by
the
ideas
natural
selection,
and
can
be
employed
to
address
challenging
problems.
These
iteratively
evolve
populations
using
crossover,
which
combines
genetic
information
from
two
parent
solutions,
mutation,
adds
random
changes.
This
iterative
process
tends
produce
effective
solutions.
Inspired
this,
current
study
presents
results
thermal
variation
on
surface
wetted
wavy
fin
algorithm
in
context
parameter
estimation
for
artificial
neural
network
models.
The
physical
features
convective
radiative
heat
transfer
during
wet
conditions
also
considered
develop
model.
highly
nonlinear
governing
ordinary
differential
equation
proposed
problem
is
transmuted
into
dimensionless
equation.
graphical
outcomes
aspects
profile
demonstrated
specific
non-dimensional
variables.
primary
observation
decrease
temperature
with
rise
parameters
convective-conductive
parameters.
implemented
offers
powerful
technique
that
effectively
tune
network,
leading
an
enhanced
predictive
accuracy
convergence
numerically
obtained
solution.
Symmetry,
Год журнала:
2023,
Номер
15(8), С. 1601 - 1601
Опубликована: Авг. 18, 2023
The
impact
of
convection
and
radiation
on
the
thermal
distribution
wavy
porous
fin
is
examined
in
present
study.
A
hybrid
model
that
combines
differential
evolution
(DE)
algorithm
with
an
artificial
neural
network
(ANN)
proposed
for
predicting
heat
transfer
fin.
equation
representing
variation
reduced
to
its
dimensionless
arrangement
numerically
solved
using
Rung,
e-Kutta-Fehlberg’s
fourth-fifth
order
method
(RKF-45).
study
demonstrates
effectiveness
this
model,
results
indicate
approach
outperforms
ANN
parameters
obtained
through
grid
search
(GS),
showcasing
superiority
DE-ANN
terms
accuracy
performance.
This
research
highlights
potential
utilizing
DE
improved
predictive
modeling
sector.
originality
it
addresses
problem
by
optimizing
selection
algorithm.
