Bernoulli wavelet analysis of mixed convective magnetohydrodynamic boundary layer flow of Casson nanofluid over inclined stretching sheet with entropy generation
Multiscale and Multidisciplinary Modeling Experiments and Design,
Год журнала:
2025,
Номер
8(3)
Опубликована: Фев. 21, 2025
Язык: Английский
Stagnation point flow of γ-Al2O3 nanoparticles with suspension of blood and ethylene glycol materials: Thermal optimization through nonlinear radiative effects
Journal of Radiation Research and Applied Sciences,
Год журнала:
2025,
Номер
18(2), С. 101484 - 101484
Опубликована: Апрель 4, 2025
Язык: Английский
MHD ternary (Ag–CuO–SWCNT) blood-based Jeffrey nanofluid flow with surface catalyzed reaction
AIP Advances,
Год журнала:
2025,
Номер
15(4)
Опубликована: Апрель 1, 2025
The
present
study
aims
to
investigate
the
effects
of
MHD
non-Newtonian
Jeffrey
ternary
hybrid
nanofluid
flow
over
a
porous
moving
wedge
with
surface-catalyzed
homogeneous–heterogeneous
chemical
reactions.
To
analyze
energy
transmission
rate,
this
considers
prominent
nanoparticles
silver
(Ag),
cupric
oxide
(CuO)
and
single-walled
carbon
nanotube
(SWCNT)
suspended
in
blood,
which
serves
as
base
fluid.
In
fluid
problem,
momentum,
energy,
concentration,
mass
diffusion
are
inspected
under
influence
magnetic
field,
thermal
radiation,
activation
binary
reactions,
thermophoresis,
Brownian
motion.
is
significant
due
its
potential
improve
heat
transfer,
catalysis,
efficiency,
biomedical
applications.
model
mathematically,
system
partial
differential
equations
(PDEs)
formulated
subsequently
transformed
into
non-dimensional
ordinary
using
suitable
similarity
variables.
shooting
technique
implemented
MATLAB
obtain
numerical
solutions
for
dragging
force
(Cfx),
rate
(Nux),
transport
Shx,
fluxes
ShA
ShB.
This
reveals
that
an
increase
medium
parameter
(Kp)
reduces
velocity
profile,
while
(λ1)
enhances
it.
volume
fraction
parameters
(φAg,
φCuO,
φSWCNT),
motion
(Nb)
thermophoresis
(Nt)
contribute
temperature.
concludes
(Ag
+
CuO
SWCNT/Blood)
exhibits
superior
transfer
capabilities
it
achieves
7.79%
higher
than
(CuO
SWCNT/Blood),
10.76%
(SWCNT/Blood)
11.31%
blood.
Язык: Английский
Optimization of heat and mass transfer in exothermic reactive fluids using advanced numerical methods and ANN models
ZAMM ‐ Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik,
Год журнала:
2025,
Номер
105(5)
Опубликована: Апрель 19, 2025
Abstract
Artificial
intelligence
(AI)
has
emerged
as
a
transformative
tool
in
fluid
flow
modeling,
offering
enhanced
simulation
accuracy,
optimization,
and
system
performance.
This
study
investigates
the
mixed
convective
of
Jeffery
over
slendering
sheet,
incorporating
effects
thermal
radiation,
heat
generation,
Joule
heating,
chemical
reactions.
The
governing
partial
differential
equations
(PDEs)
are
transformed
into
nonlinear
ordinary
(ODEs)
solved
using
bvp4c
solver
MATLAB.
To
optimize
artificial
neural
networks
(ANNs)
backpropagation
(BPNNs)
employed,
leveraging
Levenberg–Marquardt
algorithm
(LMA)
for
training
validation.
dataset
is
partitioned
training,
testing,
validation,
with
performance
evaluated
mean
squared
error
(MSE),
curve‐fitting
graphs,
histograms.
results
demonstrate
high
MSE
values
consistently
range
validating
robustness
ANN‐LM
LMA‐BPNN
frameworks.
Furthermore,
physical
parameters
on
momentum,
thermal,
concentration
boundary
layers
examined
detail.
Heat
generation
found
to
enhance
temperature
profile,
thickening
layer,
while
variable
thickness
parameter
improves
skin
friction,
heat,
mass
transfer.
Conversely,
higher
Schmidt
numbers
reduce
profile
due
limited
diffusivity.
quantitative
qualitative
outcomes
thoroughly
analyzed,
benchmarked
against
existing
literature,
showing
close
alignment.
provides
valuable
insights
influence
key
behavior
establishes
robust
AI‐driven
framework
future
research
dynamics.
Язык: Английский