Results in Engineering,
Год журнала:
2024,
Номер
23, С. 102477 - 102477
Опубликована: Июнь 28, 2024
Temperature
and
liquid
flow
monitoring
are
required
in
many
industrial
applications
order
to
maximize
machine
performance.
When
exploring
polymers,
fabricating
synthetic
films
transparent
materials,
manufacturing
metal-based
equipment,
frictional
forces
thermal
rates
need
be
controlled.
The
significance
of
the
study
prominent
characteristics
Diffusion-Thermo,
Quadratic
radiation
absorption
over
a
permeable
stretching
geometry
discussed.
Similarity
transformations
reduced
boundary
layer
partial
differential
an
ODE.
acquired
system
ODEs
is
solved
numerical-manner
using
MATLAB
bvp4c.
distributions
f
'(η),
θ(η)
φ(η)
described
graphical
aspect,
with
specified
governed
parameters.
As
Diffusion
thermo
parameter
raises,
temperature
field
enhances.
velocity
enhance
concentration
decreases.
A
rise
causes
raise
buoyancy,
which
turn
raises
rate,
that
this
reflected
profiles.
temperatures
nearer
porous
lowers
concentration.
Rising
ratio
θw
values
were
accompanied
by
rising
temperatures.
enhancing
when
improving
Biot
parameter.
skin-friction
heightened
raising
'n'
values.
Nusselt
number
declined,
Df.
Sherwood
enhanced
'Sc'.
An
incorporation
thermo,
effects
nano-Carreau
across
Wedge
Cone
novelty.
boundary-layer
wedge
gets
made
growingly
significant,
not
long
ago,
owing
its
practical
applications,
including
those
medical,
bioengineering,
crude
oil
extraction,
nuclear
power
plants,
polymer
extrusion
process,
production
plastic
sheet,
metal
spinning,
friction
drag
ship,
electronic
gadgets,
etc.
This
will
extended
incorporating
micro-polar,
tangent
hyperbolic,
Sutterby,
distinct
non-Newtonian
fluids.
Results in Engineering,
Год журнала:
2024,
Номер
22, С. 102209 - 102209
Опубликована: Май 7, 2024
Artificial
neural
networks
(ANNs)
have
brought
a
huge
transformation
to
the
machine
learning
regime
by
furnishing
unparalleled
abilities
for
modeling
intricate
phenomena
and
coping
with
variety
of
challenges.
In
oneiric
field
ANNs,
essential
approach
training
ANNs
is
backpropagation.
However,
optimization
crucial
when
handling
complex
fluid
flow
phenomenon
through
The
current
article
implements
Levenberg
Marquardt
Technique
ANN
backpropagation
(LMT-BP-ANN)
investigate
radiative
hybrid
nanofluid
(RHNF)
addressing
Cattaneo-Christov
flux
system
past
stretching
sheet
(SS).
Fe2O3
single-walled
carbon
nanotubes
(SWCNT)
multi-walled
(MWCNT)
considered.
base
ethylene
glycol.
Physical
parameters
nanofluidic
system,
including
magnetic
parameter
M,
solid
volume
fractions
φ1
φ2,
velocity
slip
λ,
relaxation
time
Ω,
Biot
number
ϒ,
thermal
radiation
Gr
are
evaluated
appropriate
variations
that
effectively
accomplish
dynamics
model.
These
utilized
create
dataset
LMT-BP-ANNs
using
renowned
deterministic
Adam's
numerical
technique
in
Mathematica
software.
MATLAB
software,
70%
data
kept
preparing,
15%
checking,
validating
network.
performance
plot,
regression
graphs,
error
histograms
constructed
demonstrate
suggested
LMT-BP
network
scheme's
high
efficiency,
efficacy,
exactness.
When
performs,
mean
squared
(MSE)
order
10-11
observed.
observed
R
value
1.
As
estimate
grows,
declines,
although
profile
shows
increasing
behavior.
increases,
drops.
Key
findings
this
study
anticipated
noteworthy
bearing
on
sectors
require
local
refrigeration
heating
contravention.
