Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Янв. 31, 2025
This
work
aims
to
simulate
the
impacts
of
exothermic
reaction
and
Soret-Dufour
numbers
on
double
diffusion
Nano
Enhanced
Phase
Change
Materials
(NEPCM)
inside
a
porous
annulus.
The
complex
rectangular
annulus
contains
two
ellipses
triangles
walls'
vertical
sides.
proposals
closed
domains
during
heat/mass
transfer
NEPCM
can
be
used
in
energy
savings,
cooling
electronic
devices,
heat
exchangers.
fractional-time
derivative
governing
systems
is
solved
numerically
based
ISPH
method.
artificial
neural
network
(ANN)
combined
with
results
predict
average
Nusselt
number
Nu¯
Sherwood
Sh¯
.
main
objective
establishing
ANN
model
this
investigation
create
reliable
predictive
instrument
capable
estimating
values
described
dimensionless
Frank-Kamenetskii
(Fk
=
0-1),
Darcy
(Da
10-2-10-5),
Dufour
(Du
0-0.1),
buoyancy
ratio
(N
-
2
5),
Rayleigh
(Ra
103-106),
Lewis
(Le
1-20),
Soret
(Sr
0-0.2),
fusion
temperature
(θf
0.05-0.9),
fractional
order
parameter
(α
0.9-1)
thermosolutal
convection
suspension.
overall
transition
as
well
velocity
field
are
dramatically
enhanced
when
Ra
N
were
boosted.
time
helps
reach
steady
state
less
instants.
phase
change
material
(PCM)
always
changed
distribution
changes
controlled
by
temperature.
struggled
nanofluid
flow
at
lower
number.
promising
factor
enhancing
distributions
an
As
result,
may
applied
various
engineering
industrial
fields
because
it
significant
terms
improving
transmission
material.
introduced
precise
agreement
prediction
actual
Then,
present
accurately
estimate
values.
The
study
of
Casson
fluid
in
cavities
is
relevant
different
fields
like
biomedical
simulations,
chemical
processing,
lubrication,
reactor
design,
and
microfluidic
devices,
to
mention
just
a
few.
Therefore,
it
remains
always
topic
great
interest
for
researchers
explore
the
flow
field
aspects
both
theoretical
experimental
frames.
Owing
such
motivation,
we
offered
an
integration
neural
networks
with
finite
element
method
free
convective
thermal
partially
heated
square
cavity
rooted
rectangular
heat
source.
source
uniformly
heated,
bottom
wall
taken
non-uniform
heating.
right
left
walls
are
engaged
cold.
top
considered
adiabatic.
equations
mathematically
modeled
solved
by
using
hybrid
meshed
based
method.
Nusselt
number
along
predicted
AI-based
network
model.
performance
constructed
model
tested
through
regression
coefficients
mean
error.
artificial
(ANN)
appears
be
well-trained
capable
reliably
forecasting
transfer
this
system,
on
close
match
between
ANN
predictions
real
data
number.
It
found
that
horizontal
vertical
velocities
significantly
increase
as
Rayleigh
rises,
indicating
more
intense
flow.
Furthermore,
rises
higher
numbers.
Processes,
Год журнала:
2025,
Номер
13(4), С. 1055 - 1055
Опубликована: Апрель 1, 2025
This
research
investigates
the
impact
of
second-order
slip
conditions,
Stefan
flow,
and
convective
boundary
constraints
on
stagnation-point
flow
couple
stress
nanofluids
over
a
solid
sphere.
The
nanofluid
density
is
expressed
as
nonlinear
function
temperature,
while
diffusion-thermo
effect,
chemical
reaction,
thermal
radiation
are
incorporated
through
linear
models.
governing
equations
transformed
using
appropriate
non-similar
transformations
solved
numerically
via
finite
difference
method
(FDM).
Key
physical
parameters,
including
heat
transfer
rate,
analyzed
in
relation
to
Dufour
number,
velocity,
parameters
an
artificial
neural
network
(ANN)
framework.
Furthermore,
response
surface
methodology
(RSM)
employed
optimize
skin
friction,
transfer,
mass
by
considering
influence
radiation,
slip,
reaction
rate.
Results
indicate
that
velocity
enhances
behavior
reducing
temperature
concentration
distributions.
Additionally,
increase
number
leads
higher
profiles,
ultimately
lowering
overall
ANN-based
predictive
model
exhibits
high
accuracy
with
minimal
errors,
offering
robust
tool
for
analyzing
optimizing
transport
characteristics
nanofluids.
