Ann.
Acad.
Rom.
Sci.
Ser.
Math.
Appl.
ISSN
2066-6594
Vol.
18,
No.
4/2026
OPTIMAL
DISTRIBUTED
CONTROL
OF
SECOND-GRADE
FLUID
FLOWS
THROUGH
A
POROUS
MEDIUM
∗
Evgenii
S.
Baranovskii
†
With
profound
respect,
this
work
is
dedicated
to
Professor
Constantin
Fetecau
for
his
80th
birthday
DOI 10.56082/annalsarscimath.2026.4.205
Abstract
This
paper
deals
with
optimal
distributed
control
of
a
strongly
nonlinear
system
modeling
the
steady-state
flow
of
a
variable-viscosity
second-grade
fluid
through
a
porous
medium.
We
consider
the
internal
flow
within
a
bounded
Lipschitz
domain
(of
dimension
two
or
three)
under
the
no-slip
boundary
condition,
assuming
that
the
external
force
serves
as
the
control
parameter.
The
problem
is
rigorously
formulated
in
both
classical
and
weak
settings.
Our
main
result
establishes
the
existence
of
an
optimal
weak
solution
by
applying
a
known
solvability
principle
for
degenerate
nonlinear
operator
equations
involving
weakly
and
completely
continuous
mappings
in
Sobolev
spaces.
In
addition,
we
analyze
the
associated
value
function,
proving
its
lower
semicon-
tinuity
with
respect
to
the
one-sided
Hausdorff
distance,
and
present
a
necessary
condition
for
the
existence
of
a
classical
solution
to
the
considered
flow
problem.
Keywords:
second-grade
fluid,
porous
media,
optimization
problem,
distributed
control,
classical
solution,
weak
solution,
existence
theorem,
value
function,
energy
discrepancy
functional.
MSC:
49J20,
76D55,
76A05.
∗
Accepted
for
publication
on
August
04,
2026
†
esbaranovskii@gmail.com
,
Department
of
Applied
Mathematics,
Informatics
and
Mechanics,
Voronezh
State
University,
Russia
205
Optimal
distributed
control
of
second-grade
fluid
flows
206
1
Introduction
and
problem
statement
As
is
known,
the
classical
Navier–Stokes
equations
do
not
account
for
the
shear-dependent
viscosity
and
viscoelastic
effects
observed
in
many
indus-
trial
and
biological
fluids,
which
are
commonly
referred
to
as
complex
or
non-Newtonian
fluids
[3,
16,
40].
Given
the
wide
variety
of
microscopic
mechanisms
responsible
for
non-Newtonian
behavior,
numerous
constitu-
tive
models
have
been
proposed
in
the
scientific
literature.
Among
them,
second-grade
fluids
stand
out
as
a
prominent
family.
They
belong
to
the
class
of
differential-type
fluids
of
complexity
2
and
play
an
important
role
in
the
theory
of
viscoelastic
media
[19,21–23,29,38].
For
the
mathematical
description
of
steady-state
flows
of
a
second-grade
fluid
with
a
constant
density
through
a
porous
medium,
the
following
system
can
be
used:
(
y
·
∇
)
y
−
∇
·
S
+
∇
p
+
κ
y
+
β
|
y
|
y
=
f,
∇
·
y
=
0
,
S
=
η
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
α
D
(
y
)
W
(
y
)
−
α
W
(
y
)
D
(
y
)
,
(1)
where
•
y
=
y
(
x
)
is
the
velocity
field
at
a
point
x
∈
R
n
,
n
=
2
,
3;
•
S
=
S
(
x
)
is
the
extra
stress
tensor;
•
p
=
p
(
x
)
is
the
pressure;
•
κ
is
the
Darcy
resistance
coefficient,
κ
≥
0;
•
β
is
the
Forchheimer
resistance
coefficient,
β
≥
0;
•
f
=
f
(
x
)
is
the
external
force
density;
•
D
(
y
)
is
the
rate-of-strain
tensor,
D
(
y
)
=
∇
y
+
(
∇
y
)
/
2;
•
W
(
y
)
is
the
vorticity
tensor,
W
(
y
)
=
∇
y
−
(
∇
y
)
/
2;
•
∇
stands
for
the
gradient
with
respect
to
the
spatial
variables
x
1
,
.
.
.
,
x
n
;
•
η
is
the
viscosity
coefficient,
η
≥
0;
•
α
is
a
material
modulus
characterizing
the
elastic
properties
of
the
fluid,
α
≥
0.
E.S.
Baranovskii
207
System
(1)
is
normalized
with
respect
to
the
constant
fluid
density,
so
that
the
density
does
not
appear
explicitly.
Moreover,
in
what
follows,
we
suppose
that
the
contributions
of
α
D
(
y
)
W
(
y
)
and
α
W
(
y
)
D
(
y
),
which
involve
products
of
first-order
spatial
derivatives
of
the
velocity,
are
negligi-
ble
relative
to
the
other
terms
appearing
in
the
third
equation
of
(1),
and
may
consequently
be
omitted.
This
reduction
is
analogous
to
a
first-order
linearization
when
all
terms
of
order
higher
than
one
are
discarded.
Sub-
stitution
of
the
simplified
extra
stress
tensor
into
the
momentum
equation
leads
to
the
following
system:
(
y
·
∇
)
y
−
∇
·
η
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
∇
p
+
κ
y
+
β
|
y
|
y
=
f,
∇
·
y
=
0
,
(2)
which
is
commonly
regarded
as
a
suitable
model
for
the
flow
of
viscous
fluids
containing
polymer
additives,
in
particular
aqueous
solutions
of
polyacry-
lamide
and
polyethylene
oxide
[1,34].
It
should
be
noted
that
even
for
this
simplified
formulation,
the
issue
of
existence
and
uniqueness
of
solutions
is
nontrivial.
The
principal
difficulty
arises
from
the
term
∇
·
[
α
(
y
·
∇
)
D
(
y
)],
which
is
a
third-order
differential
operator,
whereas
the
viscous
term
∇
·
[
η
D
(
y
)]
is
merely
the
Laplacian.
A
possible
approach
to
overcoming
this
obstacle
is
based
on
a
modified
Faedo–Galerkin
scheme
involving
a
specially
constructed
basis
of
eigenfunc-
tions
[6].
The
mathematical
analysis
of
the
governing
equations
is
of
fundamental
importance
for
predicting
flow
behavior
and
for
the
design
of
optimal
flow
configurations
in
various
technological
contexts.
Following
the
pioneering
works
of
Oskolkov
[31,32],
a
substantial
number
of
studies
have
been
devoted
to
diverse
aspects
of
the
well-posedness
of
the
second-grade
fluid
model
and
its
modifications
[9,13,15,17,18,24,33,35].
A
discussion
of
some
open
problems
can
be
found
in
the
monograph
[16].
The
present
paper
is
concerned
with
an
optimal
control
problem
for
the
equations
describing
the
internal
flow
of
second-grade
fluid
through
a
porous
medium
in
a
bounded
domain
Ω
⊂
R
n
.
In
contrast
to
the
majority
of
earlier
contributions,
which
treat
the
vis-
cosity
as
constant,
we
consider
a
nonlocal
variable-viscosity
formulation
.
More
precisely,
the
viscosity
is
assumed
to
depend
on
the
shear
rate
accord-
ing
to
η
=
η
(
|
M
D
(
y
)
|
),
where
M
is
a
prescribed
linear
operator
such
that
Optimal
distributed
control
of
second-grade
fluid
flows
208
M
D
is
frame-indifferent
[37].
Under
this
hypothesis,
(2)
assumes
the
form
(
y
·
∇
)
y
−
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
∇
p
+
κ
y
+
β
|
y
|
y
=
f,
∇
·
y
=
0
.
(3)
We
highlight
five
important
particular
cases
of
the
above
system
in
Ta-
ble
1.
Table
1:
Some
particular
cases
of
system
(3)
Special
case
Model
for
fluid
flow
M
is
the
zero
operator,
α
=
0,
κ
=
0,
and
β
=
0
The
standard
Navier–Stokes
system
for
New-
tonian
fluids
in
the
absence
of
a
porous
ma-
trix
[8,14,27,36]
M
is
the
identity
oper-
ator,
α
=
0,
κ
=
0,
and
β
=
0
The
flow
model
for
incompressible
nonlin-
early
viscous
fluid
in
the
absence
of
a
porous
matrix
[10,28]
M
is
the
zero
operator,
α
>
0,
κ
=
0,
and
β
=
0
The
flow
model
for
an
incompressible
fluid
with
polymer
additives
in
the
absence
of
a
porous
matrix
[1,34]
M
is
the
zero
operator,
α
>
0,
κ
>
0,
and
β
>
0
The
model
for
flows
of
an
incompressible
fluid
with
polymer
additives
through
a
porous
medium
[11]
M
is
an
averaging
oper-
ator
(e.g.,
a
convolution
with
a
smooth
kernel),
α
=
0,
κ
=
0,
and
β
=
0
The
nonlocal
model
for
fluid
flow,
where
the
viscosity
at
a
material
point
x
is
influenced
by
the
shear
rate
in
a
neighborhood
of
that
point
[20]
For
the
purpose
of
the
subsequent
analysis,
it
is
convenient
to
eliminate
the
pressure
p
from
(3).
To
this
end,
we
employ
the
Leray
projector
π
L
,
which
is
defined
via
the
Leray–Helmholtz
decomposition
of
a
vector
field
into
its
divergence-free
part
and
its
gradient
part
(see,
e.g.,
[14,
Chap.
IV]).
Application
of
π
L
to
both
sides
of
the
first
equation
in
(3)
yields
π
L
(
y
·
∇
)
y
−
π
L
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
κ
π
L
y
+
βπ
L
(
|
y
|
y
)
=
π
L
f,
∇
·
y
=
0
.
(4)
Because
π
L
y
=
y
in
the
case
of
an
incompressible
fluid
flow
with
the
E.S.
Baranovskii
209
no-penetration
boundary
condition,
system
(4)
reduces
to
π
L
(
y
·
∇
)
y
−
π
L
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
κ
y
+
βπ
L
(
|
y
|
y
)
=
π
L
f,
∇
·
y
=
0
.
(5)
Note
that
if
a
vector
function
y
satisfies
(5),
then
there
exists
a
pres-
sure
field
p
such
that
the
pair
(
y,
p
)
satisfies
(3).
Therefore
the
subsequent
analysis
will
be
focused
on
(5).
Since
our
primary
objective
is
to
investigate
the
problem
of
optimal
distributed
control,
we
introduce
into
the
first
equation
of
(5)
a
control
operator
C
,
which
maps
the
control
input
u
into
the
corresponding
external
force
term.
As
a
result,
we
have
π
L
(
y
·
∇
)
y
−
π
L
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
+
κ
y
+
βπ
L
(
|
y
|
y
)
=
π
L
C
u,
∇
·
y
=
0
.
(6)
It
is
assumed
that
the
operator
C
is
linear;
for
example,
it
may
be
the
identity
or
a
smoothing
operator.
We
impose
the
no-slip
condition
on
the
boundary
of
the
flow
domain:
y
=
0
on
∂
Ω
(7)
and
suppose
that
the
control
function
u
satisfies
the
following
constraint:
u
∈
U
,
(8)
where
U
is
a
specified
set
of
admissible
controls.
The
optimal
control
problem
is
stated
as
follows:
Find
a
velocity
field
y
and
a
control
u
that
minimize
a
given
cost
func-
tional
J
subject
to
the
constraints
(6)
–
(8)
.
Symbolically,
we
write
J
(
y,
u
)
→
min
subject
to
(6)–(8)
.
(9)
A
rigorous
formulation
of
problem
(9)
in
an
appropriate
functional-
analytic
framework,
including
the
notions
of
classical
and
weak
solutions,
will
be
provided
in
Section
4.
This
paper
continues
the
work
[12],
where
the
special
case
of
model
(6)
without
a
porous
matrix
(i.e.,
κ
=
0
and
β
=
0)
and
C
=
Id
is
studied
un-
der
the
Navier
slip
boundary
condition.
Our
main
aim
is
to
establish
the
Optimal
distributed
control
of
second-grade
fluid
flows
210
solvability
of
problem
(9)
in
the
class
of
weak
solutions
without
imposing
a
monotonicity
assumption
on
the
viscosity
function
η
(cf.
[10]).
The
proof
relies
on
a
solvability
result
(see
Proposition
2)
for
a
class
of
degenerate
nonlinear
operator
equations
involving
weakly
continuous
and
completely
continuous
mappings
in
Sobolev
spaces.
A
further
contribution
of
this
work,
inspired
by
the
approaches
devel-
oped
in
[10],
is
the
introduction
of
the
value
function
V
J
=
V
J
(
U
),
which
captures
the
dependence
of
the
optimal
value
of
the
cost
functional
J
on
the
admissible
control
set.
We
prove
that
V
J
is
lower
semicontinuous
with
re-
spect
to
the
one-sided
Hausdorff
distance.
Moreover,
a
necessary
condition
for
the
existence
of
classical
solutions
to
the
control
system
(6)–(8)
is
de-
rived
in
terms
of
the
value
function
associated
with
the
energy
discrepancy
functional.
The
rest
of
the
paper
is
organized
as
follows.
In
Section
2,
we
present
the
notation,
function
spaces,
and
auxiliary
results
that
will
be
used
throughout.
Section
3
describes
the
main
assumptions
on
the
model
data
and
discusses
several
examples.
Section
4
provides
the
functional
setting
of
the
problem
and
introduces
the
value
function.
Finally,
Section
5
states
the
main
results
of
the
work
(Theorem
11)
and
contains
their
proof.
2
Preliminaries
2.1
Basic
notations
and
function
spaces
By
Z
we
denote
the
set
of
all
integers,
and
let
Z
+
:=
{
m
∈
Z
:
m
>
0
}
.
For
vectors
a,
b
∈
R
n
and
matrices
F
,
G
∈
R
n
×
n
,
we
define
the
inner
prod-
ucts:
a
·
b
:=
n
i
=1
a
i
b
i
,
F
:
G
:=
n
i,j
=1
F
ij
G
ij
.
The
corresponding
Euclidean
norms
are
given
by
|
a
|
:=
√
a
·
a,
|
F
|
:=
√
F
:
F
.
For
Banach
spaces
E
1
and
E
2
,
the
space
of
all
bounded
linear
operators
from
E
1
into
E
2
is
denoted
by
L
(
E
1
,
E
2
).
For
brevity,
L
(
E
)
:=
L
(
E,
E
).
Given
subsets
U
and
W
of
a
Banach
space
E
,
the
one-sided
Hausdorff
distance
from
U
to
W
is
defined
as
d
E
(
U,
W
)
:=
sup
u
∈
U
inf
w
∈
W
u
−
w
E
.
E.S.
Baranovskii
211
Strong
and
weak
convergence
in
a
Banach
space
are
denoted
by
→
and
w
−
→
,
respectively.
We
will
write
unif
.
−
−
−
→
to
indicate
uniform
convergence.
Continuous
and
compact
embeddings
are
denoted
by
→
and
→
→
,
re-
spectively.
Let
Ω
be
a
bounded
domain
in
R
n
,
n
=
2
,
3,
with
locally
Lipschitz
bound-
ary
∂
Ω.
We
shall
employ
the
standard
Lebesgue
spaces
L
q
(Ω),
for
q
≥
1,
and
the
Sobolev
spaces
H
m
(Ω)
:=
W
m,
2
(Ω)
with
m
∈
Z
+
.
For
their
definitions
and
fundamental
properties,
we
refer
to
[16,30].
For
vector-
and
matrix-valued
functions,
we
introduce
the
following
no-
tation:
L
q
(Ω)
:=
L
q
(Ω)
n
,
L
q
(Ω)
:=
L
q
(Ω)
n
×
n
,
H
m
(Ω)
:=
H
m
(Ω)
n
,
H
m
(Ω)
:=
H
m
(Ω)
n
×
n
,
C
(
Ω)
:=
C
(
Ω)
n
,
C
(
Ω)
:=
C
(
Ω)
n
×
n
.
Let
m
∈
Z
+
.
We
define
the
following
spaces:
V
0
(Ω)
:=
{
φ
:
Ω
→
R
n
:
φ
∈
C
∞
(Ω)
and
supp
φ
⊂
Ω
}
,
V
σ
(Ω)
:=
{
φ
:
Ω
→
R
n
:
φ
∈
C
∞
(Ω)
and
∇
·
φ
=
0
in
Ω
}
,
V
0
,σ
(Ω)
:=
V
0
(Ω)
∩
V
σ
(Ω)
,
H
m
0
,σ
(Ω)
:=
the
closure
of
the
set
V
0
,σ
(Ω)
in
H
m
(Ω)
,
H
−
m
0
,σ
(Ω)
:=
the
dual
space
of
H
m
0
,σ
(Ω)
.
The
space
H
m
0
,σ
(Ω)
is
a
Hilbert
space
when
equipped
with
the
stan-
dard
inner
product
(
·
,
·
)
H
m
(Ω)
.
However,
for
the
study
of
problem
(9),
it
is
preferable
to
use
the
following
inner
product
and
norm
on
H
1
0
,σ
(Ω):
(
y
1
,
y
2
)
H
1
0
,σ
(Ω)
:=
Ω
D
(
y
1
)
:
D
(
y
2
)
d
x,
y
H
1
0
,σ
(Ω)
:=
(
y,
y
)
H
1
0
,σ
(Ω)
.
In
view
of
Korn’s
inequality,
this
inner
product
(
·
,
·
)
H
1
0
,σ
(Ω)
is
well
defined
and
the
associated
Euclidean
norm
·
H
1
0
,σ
(Ω)
is
equivalent
to
the
standard
H
1
-norm
(see,
e.g.,
[28,
Chap.
I]).
We
also
recall
the
compact
embedding
H
1
(Ω)
→
→
L
p
(Ω)
,
∀
p
∈
[1
,
6)
,
which,
in
particular,
implies
H
1
0
,σ
(Ω)
→
→
L
p
(Ω)
,
∀
p
∈
[1
,
6)
.
(10)
Optimal
distributed
control
of
second-grade
fluid
flows
212
2.2
Auxiliary
results
Proposition
1
(Continuity
of
the
Nemytskii
operator
in
Lebesgue
spaces)
.
Let
U
be
a
bounded
domain
in
the
space
R
n
.
Suppose
ϕ
:
U
×
R
→
R
is
a
function
such
that
•
there
exist
constants
p
1
≥
1
,
p
2
≥
1
,
b
>
0
,
and
a
function
a
∈
L
p
2
(
U
)
such
that
|
ϕ
(
x,
s
)
|
≤
a
(
x
)
+
b
|
s
|
p
1
/p
2
,
for
any
s
∈
R
and
almost
every
x
∈
U
;
•
the
function
ϕ
(
·
,
s
):
U
→
R
is
measurable
for
any
s
∈
R
;
•
the
function
ϕ
(
x,
·
):
R
→
R
is
continuous
for
almost
every
x
∈
U
.
Then
the
superposition
operator
Λ
ϕ
defined
by
Λ
ϕ
[
w
](
·
)
:=
ϕ
(
·
,
w
(
·
))
,
∀
w
∈
L
p
1
(
U
)
,
is
continuous
and
bounded
as
a
mapping
from
L
p
1
(
U
)
into
L
p
2
(
U
)
.
A
proof
of
this
statement
can
be
found
in
the
book
[26,
Chap.
I,
§
2].
The
following
abstract
existence
result,
which
is
analogous
to
Proposi-
tion
1
of
[12],
will
play
a
key
role
in
the
present
paper.
It
is
designed
for
a
degenerate
operator
equation
in
which
the
principal
(dissipative)
part
A
and
its
degenerate
perturbation
Q
act
in
different
Sobolev
spaces,
reflecting
the
mismatch
between
the
regularity
levels
of
the
terms
in
this
equation.
Proposition
2
(Abstract
existence
result)
.
Let
A
be
an
operator
from
H
1
0
,σ
(Ω)
into
H
−
1
0
,σ
(Ω)
,
let
E
A
be
the
functional
defined
by
E
A
:
H
1
0
,σ
(Ω)
→
R
,
E
A
(
y
)
:=
A
(
y
)
,
y
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
,
and
let
Q
be
an
operator
from
H
1
0
,σ
(Ω)
into
H
−
3
0
,σ
(Ω)
.
Suppose
that
•
the
operator
A
is
weakly
continuous,
i.e.,
the
convergence
y
m
w
−
→
y
in
H
1
0
,σ
(Ω)
implies
that
A
(
y
m
)
w
−
→
A
(
y
)
in
H
−
1
0
,σ
(Ω)
as
m
→
∞
;
•
the
inequality
E
A
(
y
)
≥
c
A
y
2
H
1
0
,σ
(Ω)
holds
for
all
y
∈
H
1
0
,σ
(Ω)
with
a
positive
constant
c
A
;
•
the
functional
E
A
is
lower
semicontinuous
with
respect
to
weak
conver-
gence
in
H
1
0
,σ
(Ω)
;
E.S.
Baranovskii
213
•
the
operator
Q
is
completely
continuous,
i.e.,
the
convergence
y
m
w
−
→
y
in
H
1
0
,σ
(Ω)
implies
that
Q
(
y
m
)
→
Q
(
y
)
in
H
−
3
0
,σ
(Ω)
as
m
→
∞
;
•
the
equality
Q
(
v
)
,
v
H
−
3
0
,σ
(Ω)
×
H
3
0
,σ
(Ω)
=
0
holds
for
all
v
∈
H
3
0
,σ
(Ω)
.
Then,
for
any
h
∈
H
−
1
0
,σ
(Ω)
,
the
following
equation
A
(
y
)
,
v
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
+
Q
(
y
)
,
v
H
−
3
0
,σ
(Ω)
×
H
3
0
,σ
(Ω)
=
h,
v
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
,
∀
v
∈
H
3
0
,σ
(Ω)
,
has
at
least
one
solution
y
h
∈
H
1
0
,σ
(Ω)
such
that
A
(
y
h
)
,
y
h
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
≤
h,
y
h
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
.
Notably,
this
existence
result
can
be
extended
to
the
case
of
models
incorporating
various
slip
boundary
conditions
[2,4,5,7].
3
Main
assumptions
and
motivating
examples
3.1
Assumptions
on
the
model
data
Throughout
the
paper,
we
assume
that
the
following
seven
hypotheses
are
satisfied:
(H.1)
Ω
is
a
bounded
Lipschitz
domain
in
R
n
,
n
=
2
,
3;
(H.2)
α
≥
0,
κ
≥
0,
and
β
≥
0;
(H.3)
η
:
[0
,
+
∞
)
→
[0
,
+
∞
)
is
a
continuous
function
and
there
exist
con-
stants
η
min
and
η
max
such
that
0
<
η
min
≤
η
(
s
)
≤
η
max
for
every
s
∈
[0
,
+
∞
);
(H.4)
M
is
a
linear
operator
from
L
2
(Ω)
to
itself
such
that,
for
any
se-
quence
{
y
m
}
∞
m
=1
⊂
H
1
0
,σ
(Ω),
the
convergence
y
m
w
−
→
y
in
H
1
0
,σ
(Ω)
im-
plies
M
D
(
y
m
)
→
M
D
(
y
)
in
L
2
(Ω)
as
m
→
∞
;
(H.5)
U
is
a
bounded,
sequentially
weakly
closed
subset
of
L
2
(Ω);
(H.6)
J
:
H
1
0
,σ
(Ω)
×
L
2
(Ω)
→
R
is
a
lower
semicontinuous
functional
with
respect
to
weak
convergence
in
H
1
0
,σ
(Ω)
×
L
2
(Ω);
(H.7)
C
is
a
bounded
linear
operator
from
L
2
(Ω)
to
itself.
Optimal
distributed
control
of
second-grade
fluid
flows
214
3.2
Examples
of
cost
functionals
Here,
we
give
two
examples
of
cost
functionals
satisfying
condition
(H.6):
1.
The
functional
J
1
(
y,
u
)
:=
(1
−
χ
)
y
−
y
0
2
L
2
(Ω)
+
χ
u
2
L
2
(Ω)
,
where
y
0
is
a
given
vector
function
describing
the
desired
velocity
field
in
the
domain
Ω,
while
χ
is
a
numerical
parameter,
0
≤
χ
≤
1.
2.
The
functional
J
2
(
y,
u
)
:=
−
y
−
y
1
2
L
2
(Ω)
+
u
2
L
2
(Ω)
,
where
y
1
is
an
unfavourable
velocity
field,
i.e.,
a
velocity
field
whose
occurrence
in
the
domain
Ω
is
undesirable.
In
the
first
example,
we
consider
a
“compromise”
cost
functional,
where
the
cost
of
the
control
u
is
penalized.
In
some
applications,
however,
the
cost
of
control
is
immaterial,
whereas
the
state
y
is
of
primary
importance.
Mathematically,
this
means
that
the
functional
either
does
not
depend
ex-
plicitly
on
u
,
or
the
parameter
χ
tends
to
zero.
Such
problems
are
referred
to
as
rigid
control
problems
(see
[25,
Chap.
1,
Sect.
5]).
Note
that,
although
the
first
term
of
J
2
has
a
negative
sign,
it
is
actually
continuous
with
respect
to
weak
convergence
in
H
1
0
,σ
(Ω)
due
to
the
compact
embedding
(10)
with
p
=
2.
Therefore,
J
2
satisfies
condition
(H.6).
3.3
The
averaging
operator
as
an
exemplary
case
of
M
A
natural
example
of
an
operator
satisfying
condition
(H.4)
is
the
averaging
operator
M
ρ
defined
by
M
ρ
K
(
x
)
:=
R
n
ρ
(
x
−
x
)
K
(
x
)
d
x
,
∀
K
∈
L
2
(Ω)
,
where
K
(
x
)
:=
K
(
x
)
if
x
∈
Ω
,
O
if
x
∈
R
n
\
Ω
,
the
symbol
O
denotes
the
zero
matrix,
and
ρ
:
R
n
→
R
is
a
radially
sym-
metric
smooth
function
with
compact
support
such
that
R
n
ρ
(
x
)
d
x
=
1
.
From
a
physical
perspective,
the
function
ρ
may
be
interpreted
as
an
isotropic
influence
function
[20].
As
established
by
Vorotnikov
[39],
M
ρ
D
is
frame-indifferent,
which
confirms
the
physical
consistency
of
the
nonlocal
rheological
model
with
M
:=
M
ρ
.
E.S.
Baranovskii
215
Lemma
3
(Uniform
convergence
property)
.
Let
{
y
m
}
∞
m
=1
be
a
sequence
such
that
y
m
w
−
→
y
0
in
H
1
0
,σ
(Ω)
.
Then
M
ρ
D
(
y
m
)
unif
.
−
−
−
→
M
ρ
D
(
y
0
)
on
Ω
as
m
→
∞
.
(11)
Proof.
First,
we
observe
that
y
m
→
y
0
in
L
2
(Ω)
as
m
→
∞
,
due
to
(10)
with
p
=
2.
Therefore,
we
have
lim
m
→∞
y
m
−
y
0
L
2
(Ω)
=
0
.
(12)
Further,
using
integration
by
parts,
we
obtain
[
M
ρ
D
(
ψ
)]
ij
(
x
)
=
1
2
Ω
ρ
(
x
−
x
)
∂ψ
i
(
x
)
∂x
j
+
∂ψ
j
(
x
)
∂x
i
d
x
=
−
1
2
Ω
∂
[
ρ
(
x
−
x
)]
∂x
j
ψ
i
(
x
)
+
∂
[
ρ
(
x
−
x
)]
∂x
i
ψ
j
(
x
)
d
x
,
(13)
for
any
x
∈
Ω
and
ψ
∈
H
1
0
,σ
(Ω).
Using
the
Cauchy–Schwarz
inequality,
we
derive
from
(13)
the
following
estimate
M
ρ
D
(
ψ
)
C
(
Ω)
≤
sup

∂ρ
∂x
s
:
x
∈
R
n
,
s
=
1
,
.
.
.
,
n
meas(Ω)
ψ
L
2
(Ω)
.
Setting
ψ
=
y
m
−
y
0
in
the
above
estimate,
we
get
M
ρ
D
(
y
m
−
y
0
)
C
(
Ω)
≤
sup

∂ρ
∂x
s
:
x
∈
R
n
,
s
=
1
,
.
.
.
,
n
×
meas(Ω)
y
m
−
y
0
L
2
(Ω)
.
The
last
inequality,
together
with
(12),
yields
that
lim
m
→∞
M
ρ
D
(
y
m
−
y
0
)
C
(
Ω)
=
0
,
and
hence
(11)
holds.
Thus,
Lemma
3
is
proved.
The
above
lemma
immediately
yields
the
following
proposition.
Proposition
4
(Weak-to-strong
continuity
property)
.
Condition
(H.4)
holds
for
the
operator
M
:=
M
ρ
.
Optimal
distributed
control
of
second-grade
fluid
flows
216
4
Classical
and
weak
solutions
and
the
value
function
We
now
proceed
to
formulate
problem
(9)
within
the
framework
of
Sobolev
spaces.
As
a
preliminary
step,
we
introduce
the
concept
of
a
classical
solu-
tion,
which
requires
the
existence
of
all
derivatives
appearing
in
the
govern-
ing
equations.
Definition
5
(Classical
solution)
.
We
shall
say
that
(
y,
u
)
is
a
classical
solution
of
the
control
system
(6)–(8)
if
y
∈
C
3
(Ω)
∩
C
(
Ω),
u
∈
U
,
and
the
pair
(
y,
u
)
satisfies
(6)
and
(7).
The
set
of
all
classical
solutions
to
(6)–(8)
will
be
denoted
by
S
cl
(
U
).
Proposition
6
(Integral
identity
for
classical
solutions)
.
If
a
pair
(
y,
u
)
is
a
classical
solution
of
(6)
–
(8)
,
then
−
n
i
=1
Ω
y
i
y
·
∂
v
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
:
D
(
v
)
d
x
−
α
n
i
=1
Ω
y
i
D
(
y
)
:
∂
D
(
v
)
∂x
i
d
x
+
κ
Ω
y
·
v
d
x
+
β
Ω
y
|
y
|
·
v
d
x
=
Ω
C
u
·
v
d
x,
(14)
for
every
v
∈
H
2
0
,σ
(Ω)
.
Proof.
Let
v
be
an
arbitrary
function
from
H
2
0
,σ
(Ω).
Taking
the
inner
prod-
uct
of
the
first
equation
in
(6)
with
v
and
integrating
over
Ω,
we
obtain
Ω
π
L
(
y
·
∇
)
y
·
v
d
x

=:Int
1
−
Ω
π
L
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)

·
v
d
x

=:Int
2
+
κ
Ω
y
·
v
d
x
+
β
Ω
π
L
(
|
y
|
y
)
·
v
d
x

=:Int
3
=
Ω
(
π
L
C
u
)
·
v
d
x

=:Int
4
.
(15)
E.S.
Baranovskii
217
Since
(
a,
v
)
L
2
(Ω)
=
(
π
L
a,
v
)
L
2
(Ω)
for
any
a
∈
L
2
(Ω),
we
have
Int
1
=
Ω
(
y
·
∇
)
y
·
v
d
x,
Int
2
=
Ω
∇
·
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)

·
v
d
x,
Int
3
=
Ω
|
y
|
y
·
v
d
x,
(16)
Int
4
=
Ω
C
u
·
v
d
x.
(17)
Applying
integration
by
parts,
using
the
incompressibility
condition
and
the
no-slip
boundary
condition,
we
find
Int
1
=
−
n
i
=1
Ω
y
i
y
·
∂
v
∂x
i
d
x
(18)
and
Int
2
=
−
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
∇
v
d
x.
(19)
Taking
into
account
the
symmetry
of
the
following
matrix
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
,
one
readily
verifies
that
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
∇
v
d
x
=
1
2
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
∇
v
d
x
+
1
2
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
(
∇
v
)
d
x
=
1
2
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
∇
v
+
(
∇
v
)
d
x
=
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
D
(
v
)
d
x.
(20)
Combining
(19)
and
(20)
yields
Int
2
=
−
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
+
α
(
y
·
∇
)
D
(
y
)
:
D
(
v
)
d
x.
(21)
Optimal
distributed
control
of
second-grade
fluid
flows
218
Substitution
of
(16)–(18)
and
(21)
into
(15)
leads
to
−
n
i
=1
Ω
y
i
y
·
∂
v
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
:
D
(
v
)
d
x
+
α
Ω
(
y
·
∇
)
D
(
y
)
:
D
(
v
)
d
x

=:Int
5
+
κ
Ω
y
·
v
d
x
+
β
Ω
|
y
|
y
·
v
d
x
=
Ω
C
u
·
v
d
x.
(22)
Finally,
integrating
the
term
Int
5
by
parts,
we
obtain
(14)
from
(22).
Thus,
Proposition
6
is
proved.
Proposition
7
(Energy
balance
equation)
.
If
a
pair
(
y,
u
)
is
a
classical
solution
of
(6)
–
(8)
,
then
Ω
η
(
|
M
D
(
y
)
|
)
|
D
(
y
)
|
2
d
x
+
κ
Ω
|
y
|
2
d
x
+
β
Ω
|
y
|
3
d
x
=
Ω
C
u
·
y
d
x.
(23)
Proof.
Taking
v
=
y
in
(14),
we
get
−
n
i
=1
Ω
y
i
y
·
∂
y
∂x
i
d
x

=:Int
6
+
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
:
D
(
y
)
d
x
−
α
n
i
=1
Ω
y
i
D
(
y
)
:
∂
D
(
y
)
∂x
i
d
x

=:Int
7
+
κ
Ω
y
·
y
d
x
+
β
Ω
|
y
|
y
·
y
d
x
=
Ω
C
u
·
y
d
x.
(24)
A
straightforward
integration
by
parts
gives
Int
6
=
1
2
n
i
=1
Ω
y
i
∂
|
y
|
2
∂x
i
d
x
=
−
1
2
Ω
(
∇
·
y
)
|
y
|
2
d
x
=
0
E.S.
Baranovskii
219
and
Int
7
=
1
2
n
i
=1
Ω
y
i
∂
|
D
(
y
)
|
2
∂x
i
d
x
=
−
1
2
Ω
(
∇
·
y
)
|
D
(
y
)
|
2
d
x
=
0
.
Consequently,
(24)
reduces
to
(23),
which
completes
the
proof.
Since
classical
solutions
may
not
exist
for
arbitrary
controls
u
∈
U
,
we
introduce
the
following
weak
formulation,
which
is
directly
motivated
by
Propositions
6
and
7.
Definition
8
(Weak
solution)
.
We
shall
say
that
(
y,
u
)
is
a
weak
solution
of
the
control
system
(6)–(8)
if
•
(
y,
u
)
∈
H
1
0
,σ
(Ω)
×
U
;
•
equality
(14)
holds
for
any
test
function
v
∈
H
3
0
,σ
(Ω);
•
the
energy
inequality
holds:
Ω
η
(
|
M
D
(
y
)
|
)
|
D
(
y
)
|
2
d
x
+
κ
Ω
|
y
|
2
d
x
+
β
Ω
|
y
|
3
d
x
≤
Ω
C
u
·
y
d
x.
(25)
The
set
of
all
weak
solutions
to
(6)–(8)
will
be
denoted
by
S
weak
(
U
).
From
the
definitions
it
follows
that
the
following
inclusion
holds:
S
cl
(
U
)
⊂
S
weak
(
U
)
.
Definition
9
(Optimal
weak
solution)
.
A
pair
(
y
∗
,
u
∗
)
is
called
an
optimal
weak
solution
to
problem
(9)
if
(
y
∗
,
u
∗
)
belongs
to
the
set
S
weak
(
U
)
and
J
(
y
∗
,
u
∗
)
=
inf
J
(
y,
u
)
:
(
y,
u
)
∈
S
weak
(
U
)
.
(26)
The
set
of
optimal
weak
solutions
to
(9)
will
be
denoted
by
S
opt
weak
(
U
).
Optimal
distributed
control
of
second-grade
fluid
flows
220
The
notion
of
an
optimal
classical
solution
can
be
defined
analogously.
To
address
situations
in
which
the
admissible
control
set
may
vary,
we
introduce
the
function
V
J
.
The
class
of
all
bounded,
sequentially
weakly
closed
subsets
of
L
2
(Ω)
is
denoted
by
P
L
2
(Ω)
.
By
definition,
we
set
V
J
:
P
L
2
(Ω)
→
R
∪
{−∞}
,
V
J
(
U
)
:=
inf
J
(
y,
u
)
:
(
y,
u
)
∈
S
weak
(
U
)
.
Note
that
any
closed,
convex,
bounded
subset
of
L
2
(Ω)
belongs
to
P
L
2
(Ω)
.
Definition
10
(Value
function)
.
The
function
V
J
is
called
the
value
function
of
problem
(9).
Clearly,
if
(
y
∗
,
u
∗
)
is
a
pair
belonging
to
S
opt
weak
(
U
),
then
V
J
(
U
)
=
J
(
y
∗
,
u
∗
).
5
Main
results
and
their
proof
Let
us
introduce
the
energy
discrepancy
functional
J
d
,
which
measures
the
gap
in
the
energy
balance,
by
J
d
(
y,
u
)
:=
Ω
C
u
·
y
d
x
−
Ω
η
(
|
M
D
(
y
)
|
)
|
D
(
y
)
|
2
d
x
−
κ
Ω
|
y
|
2
d
x
−
β
Ω
|
y
|
3
d
x.
(27)
Indeed,
for
a
classical
solution,
the
energy
balance
(Proposition
7)
implies
the
equality
J
d
=
0.
On
the
other
hand,
for
a
weak
solution,
J
d
≥
0,
which
may
be
interpreted
as
the
energy
defect
associated
with
the
lack
of
regularity,
analogous
to
the
turbulent
energy
dissipation
defect
in
non-smooth
flows
with
small-scale
motions.
The
findings
of
this
paper
are
summarized
in
the
following
theorem.
Theorem
11
(Main
results)
.
Under
conditions
(H.1)–(H.7),
the
following
statements
hold.
(a)
Problem
(9)
has
at
least
one
optimal
weak
solution.
(b)
The
value
function
V
J
is
lower
semicontinuous
in
the
following
sense:
if
U
m
∈
P
L
2
(Ω)
,
for
any
m
∈
Z
+
∪
{
0
}
,
and
lim
m
→∞
d
L
2
(Ω)
(
U
m
,
U
0
)
=
0
,
(28)
then
V
J
(
U
0
)
≤
lim
inf
m
→∞
V
J
(
U
m
)
.
E.S.
Baranovskii
221
(c)
If
the
set
of
classical
solutions
S
cl
(
U
)
is
nonempty,
then
V
J
d
(
U
)
=
0
.
Proof.
We
begin
by
proving
assertion
(a).
The
proof
is
divided
into
four
steps.
Step
1:
Weak
solvability.
We
first
establish
that
S
weak
(
U
)
=
∅
.
Fix
an
arbitrary
vector
function
u
∈
U
.
To
apply
Proposition
2,
we
define
the
concrete
realizations
of
the
abstract
operators
A
and
Q
as
follows:
A
:
H
1
0
,σ
(Ω)
→
H
−
1
0
,σ
(Ω)
,
A
(
y
)
,
ω
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
:=
Ω
η
(
|
M
D
(
y
)
|
)
D
(
y
)
:
D
(
ω
)
d
x
+
κ
Ω
y
·
ω
d
x
+
β
Ω
|
y
|
y
·
ω
d
x
and
Q
:
H
1
0
,σ
(Ω)
→
H
−
3
0
,σ
(Ω)
,
Q
(
y
)
,
v
H
−
3
0
,σ
(Ω)
×
H
3
0
,σ
(Ω)
:=
−
n
i
=1
Ω
y
i
y
·
∂
v
∂x
i
d
x
−
α
n
i
=1
Ω
y
i
D
(
y
)
:
∂
D
(
v
)
∂x
i
d
x,
where
y
∈
H
1
0
,σ
(Ω),
ω
∈
H
1
0
,σ
(Ω),
and
v
∈
H
3
0
,σ
(Ω).
Moreover,
we
define
the
functional
u
∈
H
−
1
0
,σ
(Ω)
by
u,
ω
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
:=
Ω
C
u
·
ω
d
x.
With
these
operators
at
hand,
equation
(14)
can
be
rewritten
in
the
abstract
form
A
(
y
)
,
v
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
+
Q
(
y
)
,
v
H
−
3
0
,σ
(Ω)
×
H
3
0
,σ
(Ω)
=
u,
v
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
,
(29)
to
be
satisfied
for
all
v
∈
H
3
0
,σ
(Ω).
By
using
conditions
(H.3)
and
(H.4),
the
compact
embedding
(10)
with
p
=
4,
and
the
following
relations:
A
(
y
)
,
y
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
≥
η
min
y
2
H
1
0
,σ
(Ω)
,
∀
y
∈
H
1
0
,σ
(Ω)
,
Q
(
v
)
,
v
H
−
3
0
,σ
(Ω)
×
H
3
0
,σ
(Ω)
=
0
,
∀
v
∈
H
3
0
,σ
(Ω)
,
Optimal
distributed
control
of
second-grade
fluid
flows
222
one
verifies
that
all
the
assumptions
of
Proposition
2
are
satisfied.
Appli-
cation
of
this
proposition
to
problem
(29)
yields
the
existence
of
a
solution
y
u
∈
H
1
0
,σ
(Ω)
satisfying
A
(
y
u
)
,
y
u
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
≤
u,
y
u
H
−
1
0
,σ
(Ω)
×
H
1
0
,σ
(Ω)
.
The
pair
(
y
u
,
u
)
is
then
a
weak
solution
of
(6)–(8),
so
that
S
weak
(
U
)
=
∅
.
Step
2:
Weak
closedness
.
We
now
prove
that
S
weak
(
U
)
is
sequentially
weakly
closed
in
H
1
0
,σ
(Ω)
×
L
2
(Ω).
Consider
a
sequence
{
(
y
m
,
u
m
)
}
∞
m
=1
such
that
(
y
m
,
u
m
)
∈
S
weak
(
U
),
for
m
∈
Z
+
,
and
suppose
that
y
m
w
−
→
y
0
in
H
1
0
,σ
(Ω)
as
m
→
∞
,
(30)
u
m
w
−
→
u
0
in
L
2
(Ω)
as
m
→
∞
.
(31)
We
must
show
the
inclusion
(
y
0
,
u
0
)
∈
S
weak
(
U
).
Since
each
(
y
m
,
u
m
)
is
a
weak
solution,
the
identity
−
n
i
=1
Ω
y
mi
y
m
·
∂
φ
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
m
)
|
)
D
(
y
m
)
:
D
(
φ
)
d
x
−
α
n
i
=1
Ω
y
mi
D
(
y
m
)
:
∂
D
(
φ
)
∂x
i
d
x
+
κ
Ω
y
m
·
φ
d
x
+
β
Ω
y
m
|
y
m
|
·
φ
d
x
=
Ω
C
u
m
·
φ
d
x,
∀
m
∈
Z
+
,
(32)
holds
for
every
φ
∈
V
0
,σ
(Ω),
together
with
the
energy
inequality
Ω
η
(
|
M
D
(
y
m
)
|
)
|
D
(
y
m
)
|
2
d
x
+
κ
Ω
|
y
m
|
2
d
x
+
β
Ω
|
y
m
|
3
d
x
≤
Ω
C
u
m
·
y
m
d
x,
∀
m
∈
Z
+
.
(33)
From
(30)
and
condition
(H.4),
we
infer
that
D
(
y
m
)
w
−
→
D
(
y
0
)
in
L
2
(Ω)
as
m
→
∞
,
(34)
M
D
(
y
m
)
→
M
D
(
y
0
)
in
L
2
(Ω)
as
m
→
∞
.
(35)
By
virtue
of
Proposition
1
and
condition
(H.3),
we
deduce
from
(35)
that
η
(
|
M
D
(
y
m
)
|
)
→
η
(
|
M
D
(
y
0
)
|
)
in
L
2
(Ω)
as
m
→
∞
,
(36)
η
(
|
M
D
(
y
m
)
|
)
→
η
(
|
M
D
(
y
0
)
|
)
in
L
2
(Ω)
as
m
→
∞
.
(37)
E.S.
Baranovskii
223
Furthermore,
using
the
compact
embedding
(10)
with
p
=
4,
we
obtain
y
m
→
y
0
in
L
4
(Ω)
as
m
→
∞
.
(38)
Passing
to
the
limit
m
→
∞
in
(32)
with
the
help
of
(31),
(34),
(36),
and
(38),
we
obtain
−
n
i
=1
Ω
y
0
i
y
0
·
∂
φ
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
0
)
|
)
D
(
y
0
)
:
D
(
φ
)
d
x
−
α
n
i
=1
Ω
y
0
i
D
(
y
0
)
:
∂
D
(
φ
)
∂x
i
d
x
+
κ
Ω
y
0
·
φ
d
x
+
β
Ω
|
y
0
|
y
0
·
φ
d
x
=
Ω
C
u
0
·
φ
d
x.
Since
V
0
,σ
(Ω)
is
dense
in
H
3
0
,σ
(Ω),
the
above
equality
holds
for
arbitrary
test
function
v
∈
H
3
0
,σ
(Ω):
−
n
i
=1
Ω
y
0
i
y
0
·
∂
v
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
0
)
|
)
D
(
y
0
)
:
D
(
v
)
d
x
−
α
n
i
=1
Ω
y
0
i
D
(
y
0
)
:
∂
D
(
v
)
∂x
i
d
x
+
κ
Ω
y
0
·
v
d
x
+
β
Ω
|
y
0
|
y
0
·
v
d
x
=
Ω
C
u
0
·
v
d
x.
(39)
Moreover,
from
(34),
(37),
and
the
boundedness
of
the
function
η
(see
condition
(H.3)),
we
derive
η
(
|
M
D
(
y
m
)
|
)
D
(
y
m
)
w
−
→
η
(
|
M
D
(
y
0
)
|
)
D
(
y
0
)
in
L
2
(Ω)
,
which,
combined
with
(33)
and
(38),
implies
Ω
η
(
|
M
D
(
y
0
)
|
)
|
D
(
y
0
)
|
2
d
x
≤
lim
inf
m
→∞
Ω
η
(
|
M
D
(
y
m
)
|
)
|
D
(
y
m
)
|
2
d
x
≤
lim
inf
m
→∞
Ω
C
u
m
·
y
m
d
x
−
κ
Ω
|
y
m
|
2
d
x
−
β
Ω
|
y
m
|
3
d
x
=
Ω
C
u
0
·
y
0
d
x
−
κ
Ω
|
y
0
|
2
d
x
−
β
Ω
|
y
0
|
3
d
x.
(40)
Optimal
distributed
control
of
second-grade
fluid
flows
224
Because
{
u
m
}
∞
m
=1
⊂
U
and
U
is
sequentially
weakly
closed
in
L
2
(Ω),
it
follows
from
(31)
that
u
0
∈
U
.
(41)
Combining
(39)–(41),
we
conclude
that
(
y
0
,
u
0
)
is
a
weak
solution
of
problem
(6)–(8),
i.e.,
(
y
0
,
u
0
)
∈
S
weak
(
U
).
Step
3:
Boundedness
.
We
next
establish
that
the
set
S
weak
(
U
)
is
bounded
in
H
1
0
,σ
(Ω)
×
L
2
(Ω).
Consider
the
projection
operator
Pr
H
1
0
,σ
(Ω)
defined
by
Pr
H
1
0
,σ
(Ω)
:
H
1
0
,σ
(Ω)
×
L
2
(Ω)
→
H
1
0
,σ
(Ω)
,
Pr
H
1
0
,σ
(Ω)
(
y,
u
)
:=
y.
Since
U
is
bounded
in
L
2
(Ω),
it
suffices
to
prove
that
Pr
H
1
0
,σ
(Ω)
S
weak
(
U
)
is
a
bounded
set
in
H
1
0
,σ
(Ω).
From
the
energy
inequality
(25),
we
deduce
that
Ω
η
(
|
M
D
(
y
)
|
)
|
D
(
y
)
|
2
d
x
+
κ
Ω
|
y
|
2
d
x
+
β
Ω
|
y
|
3
d
x
≤
sup
u
∈U
Ω
C
u
·
y
d
x
≤
C
L
(
L
2
(Ω))
sup
u
∈U
u
L
2
(Ω)
y
L
2
(Ω)
,
(42)
for
any
y
∈
Pr
H
1
0
,σ
(Ω)
S
weak
(
U
).
On
the
other
hand,
condition
(H.3)
yields
η
min
y
2
H
1
0
,σ
(Ω)
≤
Ω
η
(
|
M
D
(
y
)
|
)
|
D
(
y
)
|
2
d
x.
(43)
Combining
(42)
and
(43),
we
obtain
η
min
y
2
H
1
0
,σ
(Ω)
≤
C
L
(
L
2
(Ω))
sup
u
∈U
u
L
2
(Ω)
y
L
2
(Ω)
.
(44)
Let
Id
denote
the
embedding
operator
of
H
1
0
,σ
(Ω)
into
L
2
(Ω).
Then
y
L
2
(Ω)
≤
Id
L
(
H
1
0
,σ
(Ω)
,
L
2
(Ω))
y
H
1
0
,σ
(Ω)
.
(45)
From
(44)
and
(45)
we
derive
the
estimate
y
H
1
0
,σ
(Ω)
≤
1
η
min
C
L
(
L
2
(Ω))
Id
L
(
H
1
0
,σ
(Ω)
,
L
2
(Ω))
sup
u
∈U
u
L
2
(Ω)
.
(46)
E.S.
Baranovskii
225
Since
y
is
arbitrary,
this
establishes
the
boundedness
of
Pr
H
1
0
,σ
(Ω)
S
weak
(
U
)
in
H
1
0
,σ
(Ω).
Step
4:
Existence
of
optimal
weak
solutions
.
With
the
results
of
the
previous
three
steps
at
hand,
we
may
apply
a
generalized
Weier-
strass
theorem
(see
[41,
Sect.
38.3])
to
conclude
that
there
exists
a
pair
(
y
∗
,
u
∗
)
∈
S
weak
(
U
)
satisfying
(26).
This
completes
the
proof
of
(a).
Further,
we
prove
(b).
Suppose,
for
contradiction,
that
there
exists
a
subsequence
{
m
}
∞
=1
such
that
lim
→∞
V
J
(
U
m
)
<
V
J
(
U
0
)
.
(47)
Let
{
(
y
∗
m
,
u
∗
m
)
}
∞
=1
be
a
sequence
such
that
(
y
∗
m
,
u
∗
m
)
∈
S
opt
weak
(
U
m
)
for
each
.
By
Definition
8,
we
have
−
n
i
=1
Ω
y
∗
m
i
y
∗
m
·
∂
v
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
∗
m
)
|
)
D
(
y
∗
m
)
:
D
(
v
)
d
x
−
α
n
i
=1
Ω
y
∗
m
i
D
(
y
∗
m
)
:
∂
D
(
v
)
∂x
i
d
x
+
κ
Ω
y
∗
m
·
v
d
x
+
β
Ω
|
y
∗
m
|
y
∗
m
·
v
d
x
=
Ω
C
u
∗
m
·
v
d
x,
∀
v
∈
H
3
0
,σ
(Ω)
,
∈
Z
+
,
(48)
and
Ω
η
(
|
M
D
(
y
∗
m
)
|
)
|
D
(
y
∗
m
)
|
2
d
x
+
κ
Ω
|
y
∗
m
|
2
d
x
+
β
Ω
|
y
∗
m
|
3
d
x
≤
Ω
C
u
∗
m
·
y
∗
m
d
x,
∀
∈
Z
+
.
(49)
Furthermore,
by
the
definition
of
the
value
function
V
J
,
V
J
(
U
m
)
=
J
(
y
∗
m
,
u
∗
m
)
,
∀
∈
Z
+
.
(50)
We
claim
that
the
number
sets
{
y
∗
m
H
1
0
,σ
(Ω)
}
∞
=1
and
{
u
∗
m
L
2
(Ω)
}
∞
=1
are
bounded.
Indeed,
by
the
triangle
inequality,
sup
u
∈U
m
u
L
2
(Ω)
≤
sup
u
∈U
0
u
L
2
(Ω)
+
ε
m
,
(51)
where
ε
m
:=
d
L
2
(Ω)
(
U
m
,
U
0
)
.
From
(28),
the
sequence
{
ε
m
}
∞
=1
converges
and
is
therefore
bounded.
Optimal
distributed
control
of
second-grade
fluid
flows
226
Let
ε
∗
:=
sup
∈
Z
+
ε
m
.
Then
(51)
gives
sup
∈
Z
+
u
∗
m
L
2
(Ω)
≤
sup
u
∈U
0
u
L
2
(Ω)
+
ε
∗
.
(52)
Applying
(46)
with
y
=
y
∗
m
and
U
=
U
m
,
we
get
y
∗
m
H
1
0
,σ
(Ω)
≤
1
η
min
C
L
(
L
2
(Ω))
Id
L
(
H
1
0
,σ
(Ω)
,
L
2
(Ω))
sup
u
∈U
m
u
L
2
(Ω)
.
Together
with
(51),
this
yields
sup
∈
Z
+
y
∗
m
H
1
0
,σ
(Ω)
≤
1
η
min
C
L
(
L
2
(Ω))
Id
L
(
H
1
0
,σ
(Ω)
,
L
2
(Ω))
×
sup
u
∈U
0
u
L
2
(Ω)
+
ε
∗
.
(53)
Since
U
0
is
bounded
in
L
2
(Ω),
(52)
and
(53)
imply
the
boundedness
of
the
sequence
{
(
y
∗
m
,
u
∗
m
)
}
∞
=1
in
H
1
0
,σ
(Ω)
×
L
2
(Ω).
Hence,
passing
to
a
subsequence
if
necessary,
we
may
assume
that
y
∗
m
w
−
→
y
∗
in
H
1
0
,σ
(Ω)
as
→
∞
,
(54)
u
∗
m
w
−
→
u
∗
in
L
2
(Ω)
as
→
∞
,
(55)
for
some
(
y
∗
,
u
∗
)
∈
H
1
0
,σ
(Ω)
×
L
2
(Ω).
Arguing
as
in
Step
2,
we
pass
to
the
limit
→
∞
in
(48)
and
(49),
obtaining
−
n
i
=1
Ω
y
∗
i
y
∗
·
∂
v
∂x
i
d
x
+
Ω
η
(
|
M
D
(
y
∗
)
|
)
D
(
y
∗
)
:
D
(
v
)
d
x
−
α
n
i
=1
Ω
y
∗
i
D
(
y
∗
)
:
∂
D
(
v
)
∂x
i
d
x
+
κ
Ω
y
∗
·
v
d
x
+
β
Ω
|
y
∗
|
y
∗
·
v
d
x
=
Ω
C
u
∗
·
v
d
x
and
Ω
η
(
|
M
D
(
y
∗
)
|
)
|
D
(
y
∗
)
|
2
d
x
+
κ
Ω
|
y
∗
|
2
d
x
+
β
Ω
|
y
∗
|
3
d
x
≤
Ω
C
u
∗
·
y
∗
d
x.
Now,
for
each
∈
Z
+
,
there
exists
u
∈
U
0
such
that
u
∗
m
−
u
L
2
(Ω)
≤
ε
m
.
E.S.
Baranovskii
227
Since
lim
→∞
ε
m
=
0,
we
have
lim
→∞
u
∗
m
−
u
L
2
(Ω)
=
0
.
Combining
this
with
(55),
we
get
u
w
−
→
u
∗
in
L
2
(Ω)
as
→
∞
.
(56)
Since
U
0
is
sequentially
weakly
closed
in
L
2
(Ω)
and
{
u
}
∞
=1
⊂
U
0
,
the
limit
u
∗
arising
from
(56)
belongs
to
U
0
.
Thus,
we
have
shown
that
(
y
∗
,
u
∗
)
∈
S
weak
(
U
0
)
.
Using
(50),
(54),
(55),
and
condition
(H.6),
we
obtain
V
J
(
U
0
)
=
inf
{
J
(
y,
u
)
:
(
y,
u
)
∈
S
weak
(
U
0
)
}
≤
J
(
y
∗
,
u
∗
)
≤
lim
inf
→∞
J
(
y
∗
m
,
u
∗
m
)
=
lim
→∞
V
J
(
U
m
)
,
which
contradicts
(47).
This
contradiction
completes
the
proof
of
(b).
Assertion
(c)
follows
immediately
from
Proposition
7
and
(27).
Indeed,
if
a
classical
solution
exists,
the
energy
balance
holds
as
the
equality
J
d
=
0,
so
the
infimum
of
J
d
over
all
weak
solutions
cannot
exceed
zero.
Since
J
d
≥
0
for
all
weak
solutions,
the
infimum
is
exactly
zero.
Thus,
Theorem
11
is
completely
proved.
6
Conclusions
In
this
paper,
we
have
developed
a
rigorous
framework
for
optimal
dis-
tributed
control
of
steady-state
flows
of
a
second-grade
fluid
with
variable
viscosity
through
a
porous
medium.
The
main
result
of
the
work
is
the
proof
of
the
existence
of
an
optimal
weak
solution
for
the
strongly
nonlinear
sys-
tem
describing
such
flows.
This
result
is
obtained
by
applying
an
abstract
solvability
principle
for
degenerate
nonlinear
operator
equations
involving
weakly
continuous
and
completely
continuous
mappings
in
Sobolev
spaces.
The
key
novelty
of
the
present
study
is
the
combination
of
three
essential
features
that
have
not
been
considered
together
in
the
existing
literature:
Optimal
distributed
control
of
second-grade
fluid
flows
228
•
the
second-grade
rheological
model,
which
accounts
for
viscoelastic
effects;
•
the
presence
of
a
porous
medium,
modeled
by
the
linear
Darcy
term
κ
y
and
the
nonlinear
Forchheimer
term
β
|
y
|
y
;
•
the
nonlocal
dependence
of
the
viscosity
on
the
shear
rate,
which
allows
the
material
response
at
a
point
to
be
influenced
by
the
deformation
in
a
neighbourhood
of
that
point.
These
features
lead
to
a
strongly
nonlinear
system
whose
mathematical
anal-
ysis
requires
careful
compactness
arguments
and
special
energy
estimates.
In
addition
to
the
existence
result,
we
have
introduced
and
analysed
the
value
function
associated
with
the
admissible
control
set
and
established
its
lower
semicontinuity
with
respect
to
the
one-sided
Hausdorff
distance.
This
implies
that
optimal
control
is
stable
in
the
sense
that
small
perturbations
of
the
admissible
control
set
cannot
lead
to
a
significant
reduction
of
the
optimal
value
of
the
cost
functional.
Furthermore,
a
necessary
condition
for
the
existence
of
classical
solutions
has
been
derived
in
terms
of
the
value
function
and
the
energy
discrepancy
functional.
Several
directions
for
future
research
naturally
arise
from
the
results
of
this
work.
Of
particular
interest
is
the
extension
to
optimal
boundary
control
problems,
where
the
control
action
is
applied
through
boundary
conditions
rather
than
through
body
forces.
Such
a
formulation
is
more
realistic
for
many
applications
but
requires
a
different
functional
framework
and
new
estimates
for
solutions.
Another
important
direction
is
the
analysis
of
non-
stationary
problems,
which
involve
time
derivatives
and
necessitate
the
use
of
Bochner
spaces
and
compactness
results
for
time-dependent
functions.
Finally,
the
development
of
efficient
numerical
schemes
for
solving
optimal
control
problems
remains
an
open
challenge,
especially
given
the
nonlocal
nature
of
the
viscosity
operator.
Thus,
the
results
obtained
in
this
paper
provide
a
foundation
for
further
developments
in
the
theory
of
optimal
control
for
complex
rheological
models
in
porous
media
and
open
up
perspectives
for
applications
in
the
petroleum
industry,
biomechanics,
and
chemical
engineering.
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