Journal of Computational Design and Engineering,
Journal Year:
2024,
Volume and Issue:
11(3), P. 72 - 90
Published: April 17, 2024
Abstract
This
analysis
considers
the
magnetized
third-grade
fluid
stream
and
microorganisms
over
a
non-linear
stretchy
cylinder.
The
radiation
impacts
are
taken
into
consideration.
effects
of
governing
flow
at
cylinder
represented
in
form
PDEs
employing
boundary
layer
approximations.
system
is
further
reduced
dimensionless
after
applying
similarity
transformations.
ODEs
solved
through
numerical
technique
bvp4c.
magnetism
on
liquid
extending
highlighted
graphs
numerically
tabular
form.
influence
variables
velocity
curve,
such
as
parameters,
second-grade
coefficients,
Reynolds
number,
illustrated
explored.
Suitable
ranges
for
parameters
$(
{1
\le
\eta
10,\
0.2
{{\alpha
}_1}
0.5,\
0
1.5,\
0.1
\beta
0.3,\
\gamma
1.6,\
0.05
M
0.15,\
0.5
\delta
2.0,\
0.7
Pr
1.3,\
Rd
0.4,0.1
\le}$
${e
0.4}
)$
chosen
depending
upon
convergence
method.
widths
momentum
layers
revealed
to
be
increasing
functions
curvature
parameter.
temperature
curve
declines
when
boosting
thermal
stratification,
Hartmann
number
while
up
parameters.
Journal of Computational Design and Engineering,
Journal Year:
2024,
Volume and Issue:
11(2), P. 146 - 160
Published: March 6, 2024
Abstract
The
industrial
sector
has
shown
a
growing
interest
in
hybrid
nanofluids
affected
by
magnetohydrodynamics
(MHD)
owing
to
their
wide
range
of
applications,
including
photovoltaic
water
heaters
and
scraped
surface
heat
exchangers.
main
purpose
this
study
is
look
at
how
entropy
created
nanofluid
${\rm{A{{l}_2}{{O}_3}{-}Cu}}$
mixed
with
${\rm{{{H}_2}O}}$
non-axisymmetric
stagnation
point
flow
Joule
heating
viscous
dissipation.
By
using
appropriate
non-similarity
transformations,
the
partial
differential
equations
(PDEs)
governing
boundary
layer
region
issue
are
transformed
into
set
non-linear
PDEs.
BVP4c
MATLAB
program,
which
uses
local
additional
truncation,
may
fix
problem.
velocity
profiles
both
directions
grow
when
values
${{\phi
}_2},\
M,\lambda
$,
A
parameters
increase.
temperature
profile
rises
as
$Ec$
lowers
}_2}$
M
obtained
numerical
findings
demonstrate
significant
impacts
on
transfer
rate
fluid
nanofluid.
When
concentration
nanoparticles
magnetic
parameter
heightened,
there
an
enhancement
seen
skin
friction
coefficient
decline
rate.
In
addition,
production
shows
increasing
tendency
function
M,$
$Br,$
while
demonstrating
decreasing
$\alpha
$.
Bejan
number
positive
correlation
$
but
negative
variables
$Br$.
Modern Physics Letters B,
Journal Year:
2024,
Volume and Issue:
unknown
Published: July 20, 2024
Industrial
applications
in
domains
such
as
warm
rolling,
crystal
development,
thermal
extrusion
and
optical
fiber
illustration
are
seeing
a
significant
increase.
These
specifically
focus
on
addressing
the
challenge
of
cylinder
motion
inside
fluid
environment.
Elevated
temperatures
may
affect
viscosity
conductivity
fluids.
Understanding
relationship
between
temperature
properties
fluids
is
crucial.
In
light
these
presumptions,
primary
goal
this
study
to
examine,
under
transverse
magnetic
field,
shape
factor,
velocity,
slip
conditions
viscous
dissipation,
how
temperature-dependent
could
enhance
heat
transfer
efficiency
performance
evolution
ternary
hybrid
nanofluid.
order
flow
fluctuations,
impact
nanoparticle
addition
improvements
transfer,
variable
Prandtl
number
also
included.
The
use
similarity
variables
converts
controlling
model
from
partial
differential
equations
(PDEs)
ordinary
(ODEs).
Mathematica’s
shooting
strategy
solves
ODEs
using
fourth-order
Runge–Kutta
(RK-IV)
method.
Numerical
calculations
were
done
after
setting
parameters
acquire
desired
results.
Analytical
data
provided
tables
graphs
for
convenient
usage.
results
showed
that
velocity
profile
increases
values
[Formula:
see
text],
Pr,
M,
Re
S
grow,
decreases
when
text]
decrease.
Re,
Pr
lower
profile,
whereas
Ec
raise
it.
skin
friction
steepens
S,
M
increase
relative
stretched
cylinder,
flattens
Nusselt
rises
decrease
with
text].
When
goes
3.0
6.2
nanofluid
brick-shaped
nanoparticles,
up
by
around
55.7%.
Numerical Heat Transfer Part A Applications,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 23
Published: May 10, 2024
This
analysis
uses
the
Levenberg-Marquardt
back
propagation
artificial
neural
networks
(LM-BP-ANNs)
approach
to
demonstrate
mathematical
strategy
of
for
simulation
MHD
Tangent
hyperbolic
nanofluid
(THNF)
flow
consisting
motile
microorganisms
through
a
vertically
extending
surface.
The
fluid
is
being
investigated
in
terms
exponential
heat
source/sink,
thermal
radiation,
and
magnetic
field.
modeled
equations
are
relegated
ordinary
system
differential
by
substituting
similarity
variables.
ND-solve
applied
numerically
handle
generate
dataset.
Several
activities,
including
testing,
verification,
training,
performed
creating
scheme
various
problems
using
reference
data
sets.
precision
LM-BP-ANNs
evaluated
mean
square
error,
curve
fitting
error
histogram,
regression
plot.
Furthermore,
graphs
used
analyze
parameters
concentration,
momentum,
energy
profiles.
It
has
been
observed
that
velocity
field
declines
as
grows
stronger.
THNF
model
energy,
mass,
momentum
tested,
authenticated,
trained
within
an
average
10−9.
accomplish
highest
accuracy,
with
target
date
absolute
values
10−4
10−5
range.
Open Physics,
Journal Year:
2024,
Volume and Issue:
22(1)
Published: Jan. 1, 2024
Abstract
Casson
fluids
containing
carbon
nanotubes
of
various
lengths
and
radii
on
a
moving
permeable
plate
reduce
friction
improve
equipment
efficiency.
They
flow
dynamics
to
heat
transfer,
particularly
in
electronic
cooling
exchangers.
The
core
objective
this
study
is
investigate
the
transmission
mechanism
identify
prerequisites
for
achieving
high
speeds
within
two-dimensional,
stable,
axisymmetric
boundary
layer.
This
considers
sodium
alginate-based
nanofluid
single/multi-wall
(SWCNTs/MWCNTs)
with
varying
length,
radius,
nonlinear
thermal
radiation
effects.
has
capacity
move
either
parallel
or
perpendicular
free
stream.
governing
partial
differential
equations
layer,
which
are
interconnected,
transformed
into
standard
equations.
These
then
numerically
solved
using
Runge–Kutta
fourth-order
scheme
incorporated
shooting
method.
research
analyses
graphically
displays
effects
factors
including
mass
suction,
nanoparticle
volume
fraction,
parameter,
radiation,
temperature
ratio.
Additionally,
comparison
made
between
present
result
previous
finding,
presented
tabular
format.
coefficient
skin
decreases
correlation
an
increase
fluid
parameters
Prandtl
number.
Heat
transfer
rate
variation
viscosity
while
it
increasing
In
addition,
demonstrates
that
MWCNT
significantly
higher
than
SWCNT
nanoparticles.
Thermal
ratio
rate,
whereas
fraction
parameter
enhance
over
shrinking
surface.