An enhanced temperature field inversion model by POD-BPNN-GA method for a 3D wing with limited sensors
International Communications in Heat and Mass Transfer,
Journal Year:
2025,
Volume and Issue:
164, P. 108778 - 108778
Published: March 5, 2025
Language: Английский
Internal flow characteristics of a tank with multiple jet inflows and liquid sloshing
Junwen Liang,
No information about this author
Xinuo Tu,
No information about this author
Z.T. Liang
No information about this author
et al.
Physics of Fluids,
Journal Year:
2025,
Volume and Issue:
37(4)
Published: April 1, 2025
This
paper
conducted
numerical
investigations
into
the
flow
field
characteristics
of
a
multi-inlet
tank,
focusing
on
coupled
effects
jet
inflows
and
liquid
sloshing.
Turbulent
swirling
was
numerically
investigated
using
Reynolds
Stress
Model,
combined
with
Volume
Fluid
method
Adaptive
Mesh
Refinement
technique
for
accurate
free
surface
capturing.
The
vorticity
structure
identified
Q-criterion.
Numerical
simulations
were
validated
against
experimental
data,
confirming
reliability
accuracy
model.
A
systematic
parametric
study
to
investigate
inlet
pipe
diameter,
inflow
rate,
tank
immersed
depth.
Four
points
fitting
curves
established
describe
relationships
between
maximum
velocity
these
parameters.
results
indicated
that
distribution
uniformity
mostly
affected
by
diameter.
Additionally,
various
filling
levels
analyzed
sloshing
induced
surge
motion.
probability
index
significantly
at
an
depth
0.8
m.
hybrid
neural
network
framework
integrating
Proper
Orthogonal
Decomposition
(POD)
Long
Short-Term
Memory
(LSTM)
networks
developed
predict
field.
POD
employed
extract
dominant
modes,
while
corresponding
temporal
coefficients
fed
LSTM
prediction.
reconstructed
fields
demonstrated
effectiveness
POD-LSTM
model
in
accurately
predicting
evolution
field,
as
confirmed
comparisons
simulated
results.
Language: Английский
Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information
Ping Wang,
No information about this author
Guangzhong Hu,
No information about this author
Wenli Hu
No information about this author
et al.
Aerospace,
Journal Year:
2024,
Volume and Issue:
11(11), P. 871 - 871
Published: Oct. 24, 2024
The
rapid
reconstruction
of
the
internal
flow
field
within
pressure
vessel
equipment
based
on
features
from
limited
detection
points
was
significant
value
for
online
monitoring
and
construction
a
digital
twin.
This
paper
proposed
surrogate
model
that
combined
Proper
Orthogonal
Decomposition
(POD)
with
deep
learning
to
capture
dynamic
mapping
relationship
between
sensor
point
information
global
state
during
operation,
enabling
temperature
velocity
field.
Using
POD,
order
tested
reduced
by
99.75%,
99.13%,
effectively
decreasing
dimensionality
Our
analysis
revealed
first
modal
coefficient
snapshot
data,
after
decomposition,
had
higher
energy
proportion
compared
along
more
pronounced
marginal
effect.
indicates
modes
need
be
retained
achieve
total
proportion.
By
constructing
CSSA-BP
represent
coefficients
fields
data
collected
points,
comparison
made
BP
method
in
reconstructing
shell-and-tube
heat
exchanger.
yielded
maximum
mean
squared
error
(MSE)
9.84
reconstructed
field,
absolute
(MAE)
1.85.
For
MSE
0.0135
MAE
0.0728.
errors
were
4.85%,
3.65%,
4.29%,
respectively.
17.72%,
11.30%,
16.79%,
indicating
established
this
study
has
high
accuracy.
Conventional
CFD
simulation
methods
require
several
hours,
whereas
here
can
rapidly
reconstruct
1
min
training
is
completed,
significantly
reducing
time.
work
provides
new
quickly
obtaining
under
offering
reference
development
twins
equipment.
Language: Английский