Neural Network Modeling of CuO/Au Hybrid Nanofluid Thermal Performance with Slip Effects for Advanced Process Applications
Jyothi Kotike,
Omprakash Beedalannagari,
Leelavathi Rekapalli
и другие.
Processes,
Год журнала:
2025,
Номер
13(2), С. 516 - 516
Опубликована: Фев. 12, 2025
This
study
explores
transient
magnetohydrodynamic
(MHD)
heat
and
mass
transfer
in
the
flow
of
a
hybrid
nanofluid
over
stretching
surface,
considering
both
steady
unsteady
scenarios.
The
investigation
incorporates
chemical
reactions,
slip
boundary
conditions,
effects
thermal
radiation.
nanofluid,
composed
copper
oxide
(CuO)
gold
(Au)
nanoparticles
water-based
fluid,
demonstrates
enhanced
performance
compared
with
base
fluids.
Key
findings
reveal
that
higher
nanoparticle
concentrations
significantly
improve
transfer,
highlighting
potential
nanofluids
advanced
management
applications.
Additionally,
machine
learning
models
effectively
predict
characteristics
high
accuracy
(R2
=
0.99),
showcasing
their
effectiveness
complementing
traditional
numerical
methods.
These
contribute
to
understanding
complex
systems
highlight
utility
emerging
computational
tools
for
analysis.
Язык: Английский
Machine learning-based predictive modeling and optimization: Artificial neural network-genetic algorithm vs. response surface methodology for black soldier fly (Hermetia illucens) farm waste fermentation.
Biochemical Engineering Journal,
Год журнала:
2025,
Номер
unknown, С. 109685 - 109685
Опубликована: Фев. 1, 2025
Язык: Английский