On The Cauchy Problem for A 1D Euler-Alignment System in Besov Spaces ()
1. Introduction
The Cucker-Smale Model. The flocking behaviors are widespread in biological systems, such as the swimming of fish, the movement of wildebeest groups and the migration of birds called as self-organization biological behaviors, which have attracted much attention in [1] [2] [3] [4] [5] . Understanding the population properties of interacting systems, and how individual components function, are important questions. In biology, studying the collective behaviors of animals can better understand the structure of ecosystems and provide guidance for ecosystem management and conservation. In medicine, such as cancer cells, there is a collective arrangement of patterns in the human body, and studying this collective pattern of destructive cells can more effectively affect them and promote the understanding of disease. The flocking behavior refers to the motion of a cluster of finite particles in which the velocity of each particle is consistent with the weighted average of the velocities of its neighbors. The discrete Vicsek model in time and two-dimensional spaces is usually used to describe the flocking behaviors in [6] : the velocity angle
of j-th particle satisfies
(1)
Here,
(
), a random variable
is uniformly distributed in
, and a parameter
is used to measure the intensity of the noise. Furthermore, a generalization of the Vicsek model was proposed by Cucker and Smale in [7] :
(2)
where
refers to the position and velocity of the agents i, and N is the total number of two groups. The nonnegative and decreased communication weight function
measures the strength of the interaction between two particles, since the distance of the particles increases, the interaction usually becomes smaller. The Cucker-Smale model can be used to analyze the flocking behaviors based on the decay properties of the kernel
, that is to say, if
decays weaker than
as
, the velocities
of the agents converge to a limit velocity
, and the relative positions
also converge to a limit position
as
. This is what we would call the flocking behavior: all particles move with nearly identical velocities.
A Kinetic Cucker-Smale Model. When the number of particles is large, to make it easier to simulate the movement of each particle, kinetic models are often used to describe the behaviors of global flocking by the density function
with
,
. Furthermore, about the kinetic limit of the Cucker-Smale model (2), Ha and Tadmor established a nonlinear and non-local kinetic equation in [8] :
(3)
there
is the velocity alignment force field given by
(4)
Here, (3)-(4) is a nonlinear and non-local kinetic version of the Cucker-Smale model. When
decays weaker than
as
, the solutions of Equations (3)-(4) show that the global flocking behaviors in [9] , where the size
of the support in x is uniformly bounded:
and the size
of the support in v remains decreasing:
A 1D Euler-alignment Model. In order to deduce the standard form of the hydrodynamic limit for nonlinear kinetic equations, one can consider the mono-kinetic ansatz of the form in (3)-(4)
(5)
In this case, the model becomes a local alignment model which is different from the global alignment model, the particles travel locally at a single speed in which the velocities of the particles are different in space. Under the one-dimensional pressureless condition, the flocking behaviors are known as a 1D pressureless Euler-alignment system with a non-local alignment term. Submitting (5) into (3)-(4), we arrive at the pressureless one-dimensional Euler-alignment system which consists of the mass conservation equation
(6)
and the momentum conservation equation
(7)
where the right side of (7) is the non-local alignment term, owing to the presence of the density
, the density is higher, the alignment effect between the agents becomes stronger. The 1D pressureless Euler-alignment system with a non-local alignment term simulates the population movement from irregular movement to regular movement with constant relative distance and relative velocity in one-dimensional space.
Furthermore, by submitting (6) into (7), one can get the following 1D Euler-alignment system:
(8)
Many researchers showed that the characteristic of communication weight
plays an important role in the regularity of the solutions for system (8): when communication weight is symmetric and uniformly bounded, Carrillo showed that system (8) exists a critical threshold of initial data in [10] : if the initial data lies above the subcritical region in the sense that
for all
, there has a global classical solution for 1D Euler-alignment system (8) if the initial data lies above the supercritical region in the sense that
for all
, the solutions can blow up in a finite time for the 1D Euler-alignment system (8).
In [11] , let
, Tan rewrote model (8) as the following equivalent equations:
(9)
where communication weight is integrable, so communication weight can be either regular or weakly singular (see [12] ). If communication weight is weakly singular, that is to say, it has an integrable singularity at the origin, Tan showed that system (8) exists a critical threshold of initial data: if the initial data lies
above the subcritical region in the sense that
for all
, then there exists a globally regular solution for 1D Euler-alignment system (8), if the initial data lies above the supercritical region in the sense that
for all
, then the solutions blow up in a finite time for the 1D Euler-alignment system (8) in [11] .
If communication weight
, Tan established the local well-posedness in Sobelov spaces
with
on the whole real line or the periodic domain in [11] . One natural question is: whether or not the system (8) is local well-posedness in
for
. The Lagrange coordinate transformation does not change the dynamic nature of the system and can make the equation easier to solve. Indeed, if there exists a small enough time
, based on that the characteristic
is a homeomorphism in a small time interval
, we will obtain the uniqueness of the solutions. Note that
↪
, by compactness theory and coordinate transformation, we want to explore the local well-posedness of the Cauchy problem for the 1D Euler-alignment system
(8) in Besov spaces
with
(in the rest of this paper
unless otherwise noted).
2. Main Results
Theorem 2.1. Suppose that
and the initial data
. Then there exists a time
such that Equation (9) has a unique solution
in
and
. Moreover, the solutions depend continuously on the initial data.
Recall that
↪
(
) is locally compact (see
Proposition 1.3.5 in [13] ), using the same argument of the proof in Theorem 2.1, we can easily get the following local well-posedness of the solutions for Equation (8) in the Besov spaces
:
Assume that
,
,
and the initial data
. Then there exists a time
such that the Cauchy problem (8) has a unique solution
, and the map
is continuous from a neighborhood of
in
into
when
whereas
when
.
Now, another natural question is raised: whether or not the data-to-solution map of the system (8) continues in
for
and
. In the following theorem, we deduce that this data-to-solution map is in ill-posedness in
.
Theorem 2.2. Suppose that
and
, then the system
(9) is ill-posedness in Besov spaces
. More precisely, there exists
and a positive constant
such that the Cauchy problem
for system (9) has a unique solution
for some
, while
Theorem 1.3 in [11] shows that the solutions admit a finite time blow up for the 1D Euler-alignment system (8), in the sense of
for all
, if and only if
. Theorem 2.1 in [11] shows that the solutions of
the system (8) stay smooth up to time T, in the sense of
, if and only if
. Next, we show that the solutions of the system (8) stay smooth only depending on the slope of u but not involving the components of G and
in the following theorem.
Theorem 2.3. Suppose that
is the solution of the Cauchy problem (9) with the initial data
. Let T is the
maximal existence time of the solutions
to Equation (9). Then the solutions blow up in finite time if and only if
The paper is organized as follows. In Section 2, we introduce several important results on the Littlewood-Paley decomposition, the nonhomogeneous Besov spaces and their useful properties. In Section 3, we establish the local well-posedness result in Besov spaces of the solutions for Equation (9). Moreover, we prove the blow-up criteria of the solutions to the problem (9) in Section 4. Finally, the ill-posedness result of the solutions for Equation (9) is presented in Section 5. Note that we denote a general constant
only depending on s and
, since all function spaces in the following sections are over
, for simplicity, we drop
in the notation of function spaces if there is no ambiguity.
3. Preliminaries
In this section, for the convenience of readers, we introduce some facts on the Littlewood-Paley theory, which is frequently used in the following arguments. Then we introduce some properties of nonhomogeneous Besov spaces which will play a key role in proving the local well-posedness and other properties of the system (9). One may refer to [13] [14] for more details.
Proposition 3.1. (See Proposition 2.10 in [13] ) Let
and
. There exists two radial functions
and
such that
Moreover, let
and
. Then for all
, the dyadic operators
and
can be defined as follows
where
in
, and the right-hand side is called the nonhomogeneous Littlewood-Paley decomposition of
.
Definition 3.2. (See Definition 2.68 in [13] ) Let
,
,
. The nonhomogenous Besov space
is defined by
where
If
,
.
Proposition 3.3. (See Corollary 2.86 in [13] ) For any positive real number s and any
in
, the space
is an algebra and a constant C exists such that
If
or
,
, then we have
Proposition 3.4. Suppose that
,
. We have
1) (See Proposition 1.3.5 in [14] ) Topological properties:
is a Banach space which is continuously embedded in
.
2) (See Proposition 1.3.5 in [14] ) Density:
is dense in
.
3) (See Proposition 1.3.5 in [14] ) Embedding:
↪
, if
and
.
↪
is locally compact, if
.
4) (See Proposition 1.4.3 in [14] ) Algebraic properties: for all
,
is an algebra. Moreover,
is an algebra
↪
(or
and
).
5) (See Proposition 1.3.5 in [14] ) Complex interpolation:
for all
and
.
6) (See Proposition 1.3.5 in [14] ) Fatou lemma: If
is bounded in
and
in
, then
and a subsequence
exists such that
Lemma 3.5. (See Lemma 4.1 in [15] ) The transport equation is one of the fundamental partial differential equations and appears in many mathematical problems. By virtue of the uniqueness of the transport equation, one obtains the
estimates of the source term
. Let
with
and
. Define
, for
, denote by
the solutions of
Assume for some
,
. If
converges in
in
, then the sequence
converges in
.
Lemma 3.6. (See Lemma 2.8 in [16] ) Suppose that
and
. Assume
,
and
If
solves
1) Then there exists a constant C such that the following statements
where
2) If
, then for all
, (1) holds with
.
3) If
, then
. If
, then
for all
.
Lemma 3.7. (See Corollary 2.86 in [13] ) Assume that
,
, for
,
, (
if
) and
, the following estimates holds
where the constant C is independent of f and g.
Lemma 3.8. (See Theorem 2.100 in [13] ) Let
,
and
. Let v be a vector field over
. Define
. There exists a constant C such that
where
. Furthermore, if
then
4. Local Well-Posedness
From the relationship
, we can claim that
(1)
Firstly, Young inequality results in
. On the other hand, according to the definition of Besov spaces, we can get
Applying
to
and taking
-norm to the above relationship yields
(2)
By the definition of Besov spaces and Minkowski’s inequality, for
, we can obtain
The case
can be easily treated as above, this completes the proof of the claim (3.1).
Moreover, by Bernstein-Type Lemmas (see Lemma 2.1 in [13] ), one can get
Proof. In order to prove Theorem 2.1, we proceed as the following steps.
Step 1: Existence
Firstly, we aim to construct approximate solutions to smooth solutions of some linear equations. Let
, for
, we can define the induction sequence
by solving the following linear transport equations
(3)
By induction, we firstly assume that
for all
. Owing to
, it implies that
is an algebra. Combining Lemma 3.6 and (3), we deduce that there exists a global solution
and
. Making use of Lemma 3.6, we can obtain the following inequality
(4)
because
is an algebra and
↪
, according to (1), we can obtain
then submittting the above inequalities into (4), we can conclude
(5)
By a similar argument as above to the component G, we have
(6)
and
(7)
we can see that
then submitting the inequality into (7), we can get
(8)
Let
, combining inequalities (5), (6), and (8), we can obtain
(9)
where
. For fixed
such that
and suppose that
In fact, we assume that the inequality for all
is valid, then for
, we can have
applying the above inequalities into (9), we can get
The above derivation implies that
is uniformly bounded in
. Based on this, we can get that
is uniformly bounded in
. Therefore,
is uniformly bounded in
.
In order to obtain a solution z of Equation (9), we make use of the compactness theory for the approximating sequence
. We take a sequence
of smooth functions with values in
, supported in
and equals to 1 on
. It is easy to find that the map
is compact from
to
by the virtue of Theorem 2.94 in [13] . Taking advantage of
Ascoli’s theorem, there exists some function
such that the sequence
converges to
for any
. At the same time, according to the Cantor diagonal process, there exists a subsequence of
such that
converges to
in
for any
. Owing to
, we can get
. Hence, there exists some function z such that the sequence
tends to
in
for any
. Then on the basis of uniform boundeness of
and the Fatou property, we can obtain that z is bounded in
. Taking advantage of the Fatou property yields that
tends to
in
for any
small enough.
Furthermore, set any
and combining with the duality for
, as
, we can obtain
The main problem is the third term, for the sake of convenience, we treat only the term of
. Therefore, we can have
(10)
Firstly, we can estimate the first term of the inequality (10)
(11)
For the second term of the inequality (10), taking the same approach. We have Proven that
in
, and
is bounded in
, then we can get that (11) tends to 0 uniformly on
as
. So (11) tends to 0 when
. Applying the similar argument, the second term of the inequality (10) also tends to 0 as
, then
. Hence, we deduce that z is the solution of the Equation (9), and belongs to
.
Step 2: Uniqueness
In this step, taking advantage of the Lagrangian coordinate, we will prove the uniqueness of the smooth solution z. Introducing a new variable
, and we define
as
Next, Define the new variables
and
, then we can obtain
and
. At the same time, the function
is a solution of
(12)
where
Moreover,
is a solution of
(13)
Taking the derivative of the Equation (12) with respect to variable
, we can have
Similarly, we can infer that
and
and
Since the fact that
is
and the embedding
↪
, we can deduce that
is uniformly bounded in
easily. In addition, we can prove that
is uniformly bounded in
. Moreover, based on the above discussion, for
sufficiently small
, we have
. By the continuous method and
the boundedness of
in
, let t is small enough, we can get
Through the above inequalities, for any
, we have
,
,
and
satisfying (12) and (13).
Set
is two solutions in
of Equation (9) with the same initial data, for
, the function
is a solution of
Obviously, we also have
, and
for sufficiently small
.
Next, we will establish the estimate
, the key step is to estimate
. For
and
, we can get
By Young inequality and Hölder inequality, then we shall get an estimate of
-the norm
Similarly, we can have
then we shall get an estimate about
-norm
Similarly, applying this routine process to prove, we can also get
Moreover,
Using the Growall inequality yields
where owing to
, it follows that
Using similar estimate, we can obtain
Based on the embedding
↪
, we can deduce
Therefore, if
and
, the uniqueness of the solutions is deduced. By a similar argument as above to the component G, we can conclude that z is the unique solution of the Equation (9).
Step 3: The continuous dependence
Let
,
is the solution with the initial data
,
, and the initial data
↪
in
. Combining Step 1 and Step 2, we can obtain that
,
are uniformly bounded in
, and
(14)
This means that
tends to
in
. Under the theorem of interpolation, for any
, we can see that
in
. If
, we will have
in
. Combining (14) and the above relationship, we just need to prove that
in
. Set
,
, we split
and
with
,
satisfying the following equations
and
Taking advantage of the fact that
,
are uniformly bounded in
, we can have
and
Here, owing to
and
, we can get
. In addition, applying Lemma 3.7, let
and
, we can get
Then for all
, using the above inequalities, we can have
from which it follows
Since we prove that
and
in
,
in
, and according to Lemma 3.5, we can deduce that
in
,
in
. Furthermore, we obtain that
in
and
in
. Then according to Lemma 3.6 and
, we can get that
in
, and
in
.
Finally, we can deduce
which indicates that
in
. By a similar argument as above to the component G, we can have
in
. Combining Step 1 to Step 3, we complete the proof of Theorem 2.1. □
5. Ill-Posedness
In this section, we shall explore that the solutions of the Equation (9) are
ill-posedness in Besov spaces
with the index
and
. To localize the frequency region, we have to introduce smooth radial cut-off functions. Set
,
is an non-negative, even and real-valued function on
satisfying
Define the initial data
(15)
there
and
. Then we can have
So we can get
At the same time, we can obtain
On the basis of definition of Besov spaces, we can make an estimate
where C is some positive constant.
Lemma 5.1. For the above constructed initial data
, if some n large enough, we can obtain
(16)
Proof. According to the definition of (15), we can get
Because
is a real-valued continuous function on
, there exists
such that for any
(17)
by applying (17), we can obtain
taking a large enough
such that
, then we obtain (16). □
Lemma 5.2. For the above constructed initial data
, there exists
for
, we can obtain
(18)
Proof. Owing to
, the system (9) has a unique solution
, and
(19)
Applying the differential mean value theorem, Lemma 3.7 and (19)
, we can obtain
and
By a similar argument as above to the component G,
. Therefore, we complete the proof of Lemma 5.2. □
Lemma 5.3. According to the assumption of Theorem 2.2 for the above-constructed initial data
,
, there
, we can obtain
(20)
there
,
, and
.
Proof. Define
Applying the differential mean value theorem,
, we can obtain
(21)
and
(22)
Using proposition 3.4, Lemma 3.7, and (19), we can deduce
(23)
Taking advantage of proposition 3.4, Lemma 3.7, Lemma 5.2, and (19), we can obtain
(24)
and
(25)
Applying proposition 3.4, Lemma 3.7, and (19), we can get
(26)
and
(27)
Taking (23)-(24) into (21) and (25)-(27) into (22), we can deduce
similarly,
Thus, we complete the proof of Lemma 5.3. Next, we prove Theorem 8. □
Proof. On the basis of the definition of the Besov norm, we can obtain
(28)
Making the following analysis
and
Applying Lemma 3.8, we can obtain
Then using above inequalities into (28), we can get
Here, we take that
is a enough large number. If taking that
and
satisfy
, then we can have
When n is a fixed number, let
, choosing
yields
similarly, we can deduce
This completes the proof of Theorem 2.2. □
6. Blow-Up Criteria
In this section, we will present blow-up criteria for Equation (9). We first need to establish support from the following Lemma.
Lemma 6.1. Suppose that
is the solution of the Cauchy problem (9) with the initial data
. Then the component
satisfies the following expression
Proof. Considering the following initial value problem
Set
is the maximal existence time. Owing to
and
, this implies
According to the second formula for (9), we can get
thus,
is independent on t, we can obtain
Consequently, we prove the Lemma 6.1. □
Theorem 6.2. Set the initial value
and
.
Suppose that the maximal existence time of the Cauchy problem (9) solutions is
. If
satisfies that
then the solution
of the Cauchy problem (9) does not blow up in
.
Proof. According to Lemma 6.1, we can establish the following estimate
this is because the embedding
↪
. If
satisfies that
,
, then we can have
(29)
similarly,
(30)
According to the result of local well-posedness, applying
to (9) yields
Multiplying both sides of the above equation by
,
, and
and integrating over
respectively, we can obtain
and
multiplying both sides of the above inequality by
and taking
-norm respectively, we can get
and
On the basis of Lemma 3.8, we can deduce
and
by the estimate (1) and Lemma 3.4, we can obtain
and
Similarly, by the same deduction, we can deduce
from the above inequalities, we can get
Applying Gronwall’s inequality, we can obtain
so we complete the proof. □
Then we shall establish the proof of Theorem 2.3.
Proof. Set
is the solution of the Cauchy problem (9) with the initial data
, and the maximal existence time of
the Cauchy problem (9) solutions is
. We suppose that the solution of Equation (9) blows up in finite time T, and there exists a constant
such that
(31)
Applying to (29) and (30), then we can get
(32)
By (31) and (32), we can obtain a contradiction with Theorem 6.2. On the basis of Theorem 6.2 and Sobolev embedding theorem, if
then the solution will blow up in a finite time.
This completes the proof of the Theorem 2.3. □