1 INTRODUCTION
1.1 Motivation
The mathematical theory of fluid–structure interactions has seen vast progress in the last two decades. This has largely been motivated by the variety of applications ranging from hydroelasticity and aeroelasticity to biomechanics and hemodynamics. Many analytical results in the literature are concerned with the existence of solutions as well as the qualitative properties of the underlying systems of nonlinear partial differential equations (PDEs). See Section 1.3 for an overview. Most of these results are focused on incompressible Newtonian fluids. Clearly, only simple fluids such as water can be realistically described in such a way. Complex fluids, on the other hand, require more complicated models. Nevertheless, it is also common to work with Newton's rheological law for the viscous stress tensor even in the context of complex fluids. A particular instance is hemodynamics where one studies the flow of blood in vessels, which deform elastically as a response. Blood has a very complex behavior and the incompressible Navier–Stokes equations fail to capture all of it. In fact, there only exists a few results on the mathematical analysis of non-Newtonian fluids (where Newton's rheological law is replaced by a nonlinear stress–strain relation) interacting with elastic structures, see [32, 37]. A different Ansatz to model the behavior of complex fluids is to consider polymeric fluid models. Here, an additional stress tensor is obtained which describes the prolongation vector of polymer chains arising from a micro- or mesoscopic model, see the next subsection. The mathematical theory for such models (in fixed domains) is in a mature state (we give an overview of the literature below). Although they arise naturally in many applications, mathematical results concerning the interaction of a polymeric fluid with a flexible structure are virtually missing in the literature.
Motivated by this absence, we introduced in our previous work [12], a model for the interaction of a polymeric fluid with a flexible Koiter shell whose energy is a nonlinear function of the first and second fundamental forms of the moving boundary. The full system is a solute–solvent–structure three-scale model, where the solute (dilute polymer molecules) is described on a mesoscopic scale by a Fokker–Planck equation (Kolmogorov forward equation) for the probability density function of the bead–spring polymer chain configuration, the solvent is described on the macroscopic scale by the incompressible Navier–Stokes equation, and also on the macroscopic scale, the structure is a fully nonlinear fourth-order hyperbolic Koiter model that describes the shell movement. We proved the existence of a weak solution for the aforementioned system with an existence time that is only restricted by a possible self-intersection of the structure. That is the only result that we are aware of for such a complex multi-scale polymeric fluid structure interaction problem. A fully macroscopic variant is also a subject of current research [48-50]. We continue in this direction and construct a strong solution to the earlier (linearized) multi-scale polymeric fluid–structure problem which exists locally in time.
1.2 The model
We consider a solute–solvent–structure mutually coupled problem describing the interaction between a polymeric fluid and a flexible structure. Here, the polymeric fluid consists of a mixture of a solvent, say, water, and a solute made up of a pair of monomers linked by a finitely extensible nonlinear elastic (FENE) spring described by the FENE dumbbell model of Warner-type [58]. More precisely, our system is described by the three-dimensional incompressible Navier–Stokes–Fokker–Planck system of equations defined on coupled with a two-dimensional viscous beam equation defined on . Here, is the time interval, is the configuration of the moving spatial domain at a time (which arises by deforming the reference domain in the normal direction with amplitude , see Section 2 for the set-up), and the domain for the elongation vector of the monomer molecules is taken as the ball centered at the origin with radius . Finally, represents ; the boundary of the reference domain . For technical simplification, we identify with the two-dimensional torus. The normal vectors on and are denoted by and , respectively.
We wish to find the structure displacement
, the fluid's velocity field
, the fluid's pressure
and the probability density function
such that for
, where
is given in (
1.8), the equations
()
()
()
() are satisfied almost everywhere in
. Here, the tensor field
is given by
() and
are positive parameters all of which we henceforth set to 1 for simplicity. There are two external forcing terms given by
and
. The elastic stress tensor
in the momentum equation (
1.2) is given by
() where the spring potential
is given by
() The spring potential is also related to the associated Maxwellian
via the relation
()
The initial conditions for the polymer fluid are
()
()
() with given functions
,
and
. With respect to boundary conditions, we supplement the viscous beam equation (
1.1) with periodic boundary conditions and we impose
() at the polymer fluid-structure interface with the normal vector
on
. Finally, for the solute, we have
()
()
When the probability density function is identically zero, the system (1.3) and (1.4) reduces to a normal fluid–structure problem for an unsteady three-dimensional viscous incompressible fluid interacting with an elastic structure. Let us point out that the reference spatial domain in our set-up is an arbitrary smooth subset of (such as cylinders or spheres), rather than a flat one. That is, we cover viscoelastic shells rather than simple elastic plates.
1.3 Bibliographical overview
We may broadly classify analytic works on fluid–structure interaction problems into the construction of weak and strong solutions. In this paper, we are only interested in strong solutions but let us refer to [2, 3, 10, 17, 22, 28, 38, 51] for some important works on the construction of weak solutions.
When it comes to strong solutions, the short time existence and uniqueness of solutions in Sobolev spaces iss studied in [18, 19] for a viscous incompressible fluid interacting with a nonlinear thin elastic shell. The shell equation, for the former [18], is modeled by the nonlinear Saint–Venant–Kirchhoff constitutive law whereas that of the latter [19] is modeled by the nonlinear Koiter shell model. In [23], however, the authors prove the existence of a unique local strong solution, without restriction on the size of the data, when the elastic structure is now governed by quasilinear elastodynamics.
In [33], the elastic structure is modeled by a damped wave equation with additional boundary stabilization terms. For sufficiently small initial data, subject to said boundary stabilization terms, global-in-time existence of strong solutions and exponential decay of the solutions are shown. The free boundary fluid–structure interaction problem consisting of a Navier–Stokes equation and a wave equation defined in two different but adjacent domains is studied in [36]. A local strong solution is constructed under suitable compatibility conditions for the data. In [40], however, a damped wave equation describes the displacement of a part of the boundary of the fluid domain. The local existence of a unique strong solution for any initial data or the global existence of a unique strong solution for small initial data are shown. This replicate an earlier result by the same author [39], where the damped wave equation was replaced by the damped beam equation.
When the elastic response of the fluid's domain is modeled by a damped Kirchhoff plate model, the authors in [24] construct a unique strong solution for small data in the -framework and in general dimension. A similar -theory for strong solutions can be found in [1], where the authors study the coupling of both Newtonian and non-Newtonian fluids with a moving rigid body.
Another local-in-time strong existence result is [44], where the viscous Newtonian fluid is now interacting with an elastic structure modeled by a nonlinear damped shell equation. Finally, a local strong solution is constructed for the motion of a linearly elastic Lamé solid moving in a viscous fluid in [52].
For a fixed geometry and an identically zero solution of the structure equation, the system (1.3) and (1.4) reduces to an incompressible Navier–Stokes–Fokker–Planck system for a polymeric fluid with center-of-mass diffusion. Weak solutions for such a system have been studied in, for example, [4-9, 27, 31, 43].
For strong solutions, however, a unique local-in-time strong solution was first shown to exist in [53], which unfortunately excludes the physically relevant FENE dumbbell models. The local theory was then revisited in [34] for the stochastic FENE model for the simple Couette flow and in [26] where the authors analyzed the incompressible Navier–Stokes equations coupled with a system of SDEs describing the configuration of the spring. The corresponding deterministic system was then studied in [41, 59]. The existence of Lyapunov functionals and smooth solutions was shown to exist in [20]. Finally, a global-in-time strong solution for the 2D system is shown in [46](see also [21, 42]).
The only result on the fully coupled system (1.3) and (1.4) is our previous paper [12], in which we prove the existence of a weak solution (allowing even the fully nonlinear original Koiter model for the structure displacement).
1.4 Main result and novelties
Our main result is the existence of a unique local-in-time strong solution to (
1.1)–(
1.4). The precise statement can be found in Theorem
2.4. The proof consists of constructing a fixed point of the following solution map in a suitable topology (see Section
5 for details): given a probability density function
, we solve the solvent–structure problem (
1.1)–(
1.3) leading to a solution
. Eventually, we solve the Fokker–Planck equation (
1.4) in a given moving domain
with a given velocity field
yielding a solution
. Then, we consider the map
in (a subset of) a function space
. Such a strategy is also applied in [
46] and other papers and it turns out that the velocity field needs to belong at least to
with respect to the spatial variable to close the argument. In [
46], the Navier–Stokes–Fokker–Planck system is considered without center-of-mass diffusion (in a fixed domain). At first glance, it will seem easier to do the same in the case
(leaving the difficulty of a moving boundary beside for the moment). This is certainly true if the Navier–Stokes–Fokker–Planck equations are studied on the whole space or with respect to periodic boundary conditions. However, in the case of bounded domains, where (
1.4) must be complemented with Neumann boundary conditions as in (
1.13) and (
1.14), it is not clear if one can obtain higher-order spatial derivatives for the probability density function even for smooth velocity fields. As a consequence, a result similar to [
46] for the problem with center-of-mass diffusion and a nontrivial boundary does not seem to exist in the literature.
As a by-product of our theory, we close this gap via the following idea: we first differentiate (1.4) once in space (formerly testing by ). As just explained, this is not sufficient to close the fixed point argument but does not create problems with the boundary conditions either. Eventually, we differentiate in time and obtain the same estimate for the time derivative of the probability density function. Details can be found in Section 4, where for a given velocity field and moving geometry, we construct a strong solution to the Fokker–Planck equation. Here, due to the linear structure of the equation, we rely on an approximation procedure similar to [13, 46]. The analysis here is, however, more complicated due to the flexible nature of the given geometry.
Let us now comment on the fluid-structure system (1.1)–(1.3). Its solvability, for a given , is an intermediate step for the fixed point problem just described but it is also of independent interest. It is worth pointing out that, different from most of the previous results on strong solutions such as [19, 30, 39, 44, 54], we consider a general non-flat geometry. The first results in this direction were only provided very recently in [11], where the existence of a unique global-in-time strong solution was proved in the 2D case. The existence of a local strong solution to the 3D fluid-structure problem has been recently shown in [14]. However, this strong solution is not regular enough to couple the solvent–structure system with the Fokker–Planck equation. For this reason we devote Section 3 to obtaining higher space-time regularity for the strong solution constructed in [14] by way of a fixed-point argument. This is of independent interest, and it is the first result of the higher regularity of the strong solution to the incompressible fluid–structure problem in the case of shells. Although one might expect that taking higher-order derivatives will be easy, the problem of compatibility conditions of the data occurs (typical for parabolic equations in bounded domains, see the classical works [56, 57]). Such a condition is needed to control the initial pressure (see the proof of Proposition 3.6), a problem that is absent in [14].
In two dimensions, if the co-rotational model is considered (i.e., is replaced by in the drag term in (1.4)), we prove the existence of a unique global strong solution, cf. Theorem 6.8. It is a consequence of a novel estimate for the Fokker–Planck equation derived in Section 6.1 combined with the recent results from [11] on the fluid–structure problem. Again, the result for the Navier–Stokes–Fokker–Planck system seems new even for fixed domains (the counterpart without center-of-mass diffusion in the Fokker–Planck equation is given in [46]).
2 PRELIMINARIES AND MAIN RESULTS
Without loss of generality, henceforth, we set all the parameters (, …, ) in (1.1)–(1.14) to 1. For two non-negative quantities and , we write if there is a such that . If and both hold, we use the notation . The symbol may be used in four different contexts. For a scalar function , denotes the absolute value of . For a vector , denotes the Euclidean norm of . For a square matrix , shall denote the Frobenius norm . Finally, if is a (sub)set, then is the -dimensional Lebesgue measure of .
The spatial domain
is assumed to be an open bounded subset of
, with a smooth boundary and an outer unit normal
. We assume that
can be parameterized by an injective mapping
for some sufficiently large
. We suppose for all points
that the pair of vectors
,
, are linearly independent. For a point
in the neighborhood of
we can define the functions
and
by
Moreover, we define the projection
. We define
to be the largest number such that
and
are well-defined on
, where
() Due to the smoothness of
for
small enough we have
for all
. This implies that
. For a given function
we parameterize the deformed boundary by
()
With some abuse of notation, we define the deformed spacetime cylinder as
The corresponding function spaces for variable domains are defined as follows.
Definition 2.1. (Function spaces)Let be the Maxwellian (1.8). For , we denote by
the Maxwellian-weighted
and
spaces over
with respective norms
For
,
, and
with
, we define for
,
Higher-order Sobolev spaces can be defined accordingly. For
with
, we define the fractional Sobolev space
as the class of
-functions
for which the norm
is finite. Accordingly, we can also introduce fractional differentiability in time for the spaces on moving domains. When we combine the function spaces defined on
and on spacetime, we obtain spaces of the form
and more generally
For various purposes, it is useful to relate the time-dependent domain and the fixed domain. This can be done by means of the Hanzawa transform. Its construction can be found in [
38, pp. 210, 211]. Note that variable domains in [
38] are defined via functions
rather than functions
(clearly, one can link them by setting
). For any
at time point
, we let
be the Hanzawa transform defined by
() and with inverse
. Here,
is such that
in a neighborhood of
and
in a neighborhood of 1. It is shown in [
11] that if for some
, we assume that
holds, then for any
,
,
and for any
, we have that
()
() holds uniformly in time with the hidden constants depending only on the reference geometry, on
and
. The estimate (
2.4) holds without the 1 on the right-hand side when in addition,
.
Our interest is to construct a strong and regular solution to the system (1.1)–(1.4) (i.e., a solution that satisfies (1.1)–(1.4) pointwise almost everywhere in spacetime with additional regularity properties which will soon be made precise) emanating from the initial conditions (1.9)–(1.11). To make the notion of a strong solution precise, we first present the following notion of a a weak solution.
Definition 2.2. (Weak solution)Let be a dataset such that
() We call
a
weak solution to the system (
1.1)–(
1.4) with data
provided that the following holds:
- (a) the shell displacement satisfies and
- (b) the velocity is such that on and
- (c) the probability density function satisfies
- (d) for all with , and , we have
with and a.e. in , a.e. in as well as a.e. in .
The existence of a weak solution (
1.1)–(
1.4) in the sense of Definition
2.2 is shown in [
12].
1 For this solution to be regular, we impose below, additional regularity assumptions on the initial conditions and the forcing terms in (
1.1)–(
1.4). More precisely, we suppose that the dataset
satisfies
() with the compatibility condition
() on
, where
is the solution to
Note that (
2.8) is in line with the compatibility condition for the fluid–structure interaction problems studied in [
19].
As far as the Fokker–Planck equation is concerned, we require that the function
defined by
() in
is such that
() With this regularized dataset, we can now make precise, what we mean by a strong solution of (
1.1)–(
1.4).
Definition 2.3. (Strong solution)Let be a dataset satisfying (2.7)–(2.10). We call a strong solution of (1.1)–(1.4) with data provided that:
- (a) is a weak solution of (1.1)–(1.4) in the sense of Definition 2.2;
- (b) satisfies
- (c) satisfies
a.e. in .
Our main result now reads as follows.
Theorem 2.4.Let be a dataset satisfying (2.7)–(2.10). There is a time such that a unique strong solution of (1.1)–(1.4), in the sense of Definition 2.3, exists.
Remark 2.5.We note that the Fokker–Planck equation is conservative and its solution is an actual probability density function (meaning that ) within the flexible geometry under consideration. More precisely, we note that if we integrate (1.4) over and use (1.12)–(1.14) together with Reynold's transport theorem, we obtain
and thus it is conservative at all times. Furthermore, the solution
of (
1.4) advected by the velocity field
remains nonnegative if it were initially nonnegative. Indeed, if we test (
1.4) with the nonpositive part
of
, integrate over
and use (
1.12)–(
1.14) together with Reynold's transport theorem, we obtain
If we now apply Grönwall's lemma, then for a nonnegative initial data
, it follows that
Therefore,
a.e. in
and thus,
. See [
25] for the corresponding argument for the fixed geometry.
Remark 2.6.The reason for the choice of the topology in Definition 2.3 comes from the coupling of the Navier–Stokes equations and the Fokker–Planck equation with center-of-mass diffusion in a bounded domain: the velocity field must be Lipschitz in space, we cannot allow more than two spatial derivatives for the probability density function (this is related to (1.13) and (1.14)). Moreover, we need additional temporal regularity to close the fixed point argument for the fully coupled system in Section 5. As we have explained in Section 1.4 these difficulties are not related to the moving boundary and our result is even new for fixed boundaries (referring to in (1.1) and (1.9)).
Remark 2.7.It remains open if a result similar to Theorem 2.4 holds if the Fokker–Planck equation without center-of-mass diffusion, that is in (1.4), is considered. This is related to the nontrivial boundary conditions for the fluid as well as the moving domain. For the fixed point argument for the fully coupled system in Section 5, we need to prove a stability estimate for two different solutions of (1.4) being posed in two different moving domains. This requires to transform them to the reference domain, which eventually creates several boundary terms. They can only be controlled with the help of the additional regularity coming from the center-of-mass diffusion.
3 SOLVING THE SOLVENT–STRUCTURE PROBLEM
In this section, we assume that a solution
for the equation of the solute described by the Fokker–Planck equation is known and that
and its associated elastic stress tensor
has sufficient regularity. For given body forces
and
, our goal now is to construct a local-in-time strong solution of the solvent–structure coupled system given by
()
()
() where
is given by
In the weak formulation, one considers a pair of test-functions
with
,
and
, obtaining
Note that this formulation is pressure-free. The pressure can be recovered by solving a.e. in time
and setting
. If
is Lipschitz uniformly in time (which follows from Definition
3.1 (a)) the solution operator to the Robin problem above has the usual properties, that is, the solution belongs to
if the right-hand side belongs to
and the boundary datum is in
.
We must complement
by a function
depending on only time which is uniquely determined by the structure equation. Setting
and testing the structure equation with 1 we obtain
() This equation can be solved for
provided the integral on the left-hand side is strictly positive (which certainly holds if the
-norm of
is not too large, cf. (
2.2)).
Definition 3.1. (Strong solution)Let be a dataset such that
() We call
a
strong solution of (
3.1)–(
3.3) with data
provided that the following holds:
- (a) the structure-function is such that and
- (b) the velocity is such that on and
- (c) the pressure is such that
- (d) Equations (3.1)–(3.3) are satisfied a.e. in spacetime with and a.e. in as well as a.e. in .
The existence of a unique local-in-time strong solution to (3.1)–(3.3) in the sense of Definition 3.1 is shown in [14].
In particular, the following result holds true:
Theorem 3.2.Suppose that the dataset satisfies (3.5). There is a time such that there exists a unique strong solution to (3.1)–(3.3) in the sense of Definition 3.1.
The regularity obtained is, however, not sufficient for the coupling with the Fokker–Planck equation. Hence, we are going to prove a corresponding result in higher-order Sobolev spaces. Our main theorem is the following:
Theorem 3.3.Suppose that the dataset satisfies (2.7) and (2.8) (which is stronger than (3.5)) and
There is a time such that (3.1)–(3.3) admits a unique strong solution , in the sense of Definition 3.1, that further satisfies
Remark 3.4.The choice of the rather unusual topology for the solution in Theorem 3.3 is due to the coupling with the Fokker–Planck equation which is our main aim (see Section 1.4 and Remark 2.6 for the explanation). For instance, one can also construct solutions provided it only holds
() rather than (
2.7). In this case, the solution belongs to the following regularity class:
Similarly, if we have
() then the solution satisfies
In order to prove Theorem
3.3, we follow the following strategy which has been successfully implemented before, for instance in [
11, 14, 29, 30, 39].
- We transform the solvent–structure system to its reference domain.
- We then linearize the resulting system on the reference domain and obtain estimates for the linearized system.
- We construct a contraction map for the linearized problem (by choosing the end time small enough) which gives the local solution to the system on its original/actual domain.
3.1 Transformation to the reference domain
For a solution
of (
3.1)–(
3.3), we define
and
as well as define
where
. The following result holds true and can be found in [
11, Lemma 4.2].
Theorem 3.5.Suppose that the dataset satisfies (3.5).
Then, is strong solution to (3.1)–(3.3), in the sense of Definition 3.1, if and only if is a strong solution of
()
()
() in
with
on
.
3.2 The linearized problem
In this section, we let
be as before in Theorem
3.5. In addition, we take
that satisfies
and consider the following linear system:
()
()
() in
with
on
and with
and
a.e. in
as well as
a.e. in
. As shown in [
14, Proposition 3.3], we obtain
() For a dataset that is more regular in time and space, our goal now is to obtain higher-in-time (and then in space) regularity for the strong solution above. This requires assuming the compatibility condition
2
() on
for the data. Here, the initial pressure
is the unique solution to the elliptic problem
() in
with the Neumann boundary condition
() on
. Our main result in this subsection is the following.
Proposition 3.6.Suppose that the dataset satisfies (3.5) and in addition
() with the compatibility condition (
3.15). Then, a strong solution
of (
3.11)–(
3.13) satisfies
() where
()
Proof.The proof of Proposition 3.6 will be obtained in three steps. First, we differentiate in time, each of the equations in (3.11)–(3.13) as well as the interface condition . Since the system (3.11)–(3.13) is linear, the resulting system after differentiating in time will be of the same form except for the extra time derivative applied to the individual terms in the system. Also, the initial conditions are no longer given but now solve PDEs as well. Consequently, in the first instant, our new system will also satisfy the inequality (3.14) (for the time derivatives of each term). However, since the initial conditions solve PDEs, we will have to estimate them as well. The estimate for these initial conditions is our second step. Finally, our third step will consist of obtaining estimates for the remaining terms on the left-hand side of (3.19) (which happens to be the highest spatial regularity for the velocity field and the pressure) in terms of as defined in (3.20).
Let us now give further details. We argue formerly, a rigorous proof can be obtained by working with a Galerkin approximation (see also [14, Section 3] and [54, Section 4]). This is also where the compatibility condition (3.15) comes into play to obtain sufficient temporal regularity. First, in order to simplify notation, let us set
() We now obtain the system
()
()
() with
on
and with the initial conditions
()
() Here, the initial data
satisfy
()
()
() The initial pressure
is prescribed by the data via (
3.16)–(
3.17). Because the system (
3.22)–(
3.24) is of the same form as (
3.11)–(
3.13), we can directly infer from (
3.14) that
() Now, since the initial data solve Equations (
3.27)–(
3.29), we have to further estimate the right-hand side of (
3.30) above to get the right-hand side of (
3.19).
Since and (3.21) holds, clearly,
() It remains to estimate
() From (
3.28) and (
3.29),
() The last boundary terms can be estimated using the trace theorem to obtain
() If we now combine (3.33) and (3.34), we obtain
() To get suitable bounds for the pressure , we study (3.16). In particular, to get -bound for , we consider
() where, by ellipticity of
,
() and
() with
and
() Therefore,
() with a constant depending on the
-norm of
. Note that we used again the trace theorem to estimate the boundary terms. Choosing
small enough and using Sobolev embedding yields
() where
() In order to control the
-norm of
, we must also control its
-norm. Let us write
, where
. We obtain from (
3.15) (multiplying it by
)
using ellipticity of
as well as
. By the trace theorem and interpolation, we finally get
() On the other hand, setting
we obtain from (
3.16) the elliptic problem
() subject to the boundary condition
() where
() By classical elliptic estimates, it follows that
() which implies that
() where we have used the trace theorem. If we now combine (
3.41), (
3.43) (with
sufficiently small), and (
3.48), and transform back to the reference domain (using the regularity of
), we get that
() Combining (
3.35), (
3.31), and (
3.49), gives
() where
is as defined in (
3.42). We obtain
() We now proceed to obtain the maximal-in-space regularity estimate for the velocity and pressure pair, that is, the
-in-time estimate for the terms
and
. To obtain this higher spatial regularity, we apply the maximal regularity theorem to the momentum equation rather than differentiate the equations in our fluid system with respect to the spatial variable like it was done for the time regularity. First of all, we transform (
3.13) and (
3.12) by applying
to them. By setting
,
and
, we obtain
in
with
on
. By the maximal regularity theory, it follows that
If we now transform back to
and use that
we obtain
() Now take
() which is just (
3.22). By using (
3.30) (and (
3.50)) as well as
() which follows from the trace theorem, we obtain from (
3.51) and (
3.53)
() where
is given by
() with
given by (
3.42). Substituting this into (
3.52) and using (
3.51) again to estimate the term involving
yield
() Using regularity theory for Equation (
3.11) (recall that we consider periodic boundary conditions) and setting
we have
with
defined in (
3.20). The proof is now complete.
Interpolating between (3.14) and Proposition 3.6 with interpolation parameter3 , we obtain the following corollary.
Corollary 3.7.Suppose that the dataset satisfies (3.5) and in addition
() with the compatibility condition (
3.15). Then, a strong solution
of (
3.11)–(
3.13) satisfies
() where
()
Taking into account estimate (3.51), we also obtain the following.
Corollary 3.8.Suppose that the dataset satisfies (3.5) and in addition
() with the compatibility condition (
3.15). Then, a strong solution
of (
3.11)–(
3.13) satisfies
() where
3.3 Fixed-point argument
In this section, we assume that the triplet
are given and we wish to solve
()
()
() with
on
. Here,
is to be determined later. Let us define the space
equipped with the norm
Note that for
, we only keep track of the highest-order terms. However, on account of the embeddings
() the norm
actually controls
Now, let
be defined as
for some
large enough, where the data are chosen to satisfy (
3.15). We want to show that the solution map
defined by
maps the ball
into itself and that it is a contraction. By so doing we obtain the existence of a unique fixed point. See, for example, [
35, Lemma 2.3].
We will show these two properties of
in two different spaces where one space is contained in the other. The fact that
maps the ball into itself will be shown in the space
defined above. For the contraction property, we consider the auxiliary space
defined by
and equipped with its corresponding canonical norm
. By keeping (
3.66) in mind, one observes that
. Furthermore, with
in hand, we refer to [
14] where we show that for any
,
, we can find
such that
Thus,
is a contraction.
To show the mapping
, we need to show that for any
, we have that
() Indeed, from (
3.59) and (
3.62), we can deduce that the solution to (
3.63)–(
3.65) satisfies
() where for
large enough, the dataset estimate
() is such that
() Here,
is the constant in the inequality (
3.68). Let us now estimate
in (
3.68). First of all, we write
Now, note that it follows from (
2.4)–(
2.5) and the continuous embedding
() that
() On the other hand, due to the continuous embedding
and (
3.66) it follows that
() Similar to (
3.73), we can use the embeddings
to obtain
It follows from (
3.72)–(3.74) that
() To estimate
, note that
Using again the embedding (
3.71) it follows from (
2.4)–(
2.5) that
() and similarly,
() holds due to the embedding
It follows that
() Next, note that
where
are lower-order terms satisfying
() Due to the continuous embeddings (
3.71), it follows from the definition
and (
2.4)–(
2.5) that
() By using the embeddings
we obtain
() Also, by using the embeddings
we obtain
() Next, we have that
() Similarly, we have
() It follows from (
3.79)–(
3.83) that
() Our next goal is to estimate
. First of all, note that
holds uniformly. Due to the continuous embeddings
it follows from (
2.4) and (
2.5) that
() Similarly, we obtain
() using (
3.70) to control
in the last step.
By using (
3.85)–(
3.86), it follows that
() Let us now obtain an estimate for
. First, note that
where
are lower-order terms satisfying
() Furthermore, the other terms can be treated as was done for
leading to
() The estimate for
is similar to that of
by noticing that
where
are lower-order terms also satisfying (
3.88).
Consequently, we obtain
() Similarly, we also obtain
() By collecting the estimates (
3.74), (
3.77), (
3.84), (
3.87), (
3.89), (
3.90) (
3.91) and combining it with (
3.68)–(
3.70), we have shown that for
-dependent constants
Choosing
in
so that
yields our desired result (
3.67).
4 SOLVING THE EQUATION FOR THE SOLUTE
In this section, for a known moving domain
and a known solenoidal velocity field
, we aim to construct a strong solution of the Fokker–Planck equation
() in
, where the Maxwellian
is given by
with
. Equation (
4.1) is complemented with the conditions
()
()
() Let us start with a precise definition of what we mean by a strong solution.
Definition 4.1.Assume that the triplet satisfies
()
()
() We call
a
strong solution of (
4.1) with data
if
- (a) satisfies
- (b) for all , we have
()
We now formulate our result on the existence of a unique strong solution of (4.1).
Theorem 4.2.Let satisfy (4.5)–(4.7) and suppose further that , where is given by
() Then, there is a unique strong solution
of (
4.1)–(
4.4), in the sense of Definition
4.1, such that
() holds for any
with a constant depending on the
-norm of
but otherwise being independent of the data.
We will obtain a solution of (
4.1) by way of a limit to the following approximation:
() Here, we solve the equation under the boundary conditions
and
and consider the same initial condition
. Also,
is a cut-off function that is identically equal to 1 on a large part of the ball
and converges as
to 1. In the following lemmas, we will derive several estimates for (
4.11) which are uniform with respect to
. They transfer directly to (
4.1) as the latter is linear. As far as (
4.11) is concerned, we proceed formerly. A rigorous proof can be achieved by working with a Galerkin approximation as was done in [
12, Section 4]. The next result is the following.
Lemma 4.3.Let satisfy (4.5)–(4.7) and let be the corresponding solution to (4.11). Then, we have
() uniformly in
.
Proof.If we test (4.11) with and integrate the resulting equation over the ball , we obtain by using the boundary condition and the property of the cut-off function that
() If we now integrate (
4.13) over spacetime, apply Reynolds transport theorem and Grönwall's lemma (keeping (
4.7) in mind), we obtain (
4.12).
Remark 4.4.As it is common for parabolic equations, the proof of Lemma 4.3 can be repeated for powers of obtaining (ignoring the dissipative terms)
() uniformly in
. Checking that the
-dependent constant does not explode, we obtain the maximum principle
4
() a minimum principle can be proved similarly, but it is not needed for our purposes.
Next, we show the following lemma.
Lemma 4.5.Let satisfy (4.5)–(4.7) and let be the corresponding solution to (4.11). Then, we have
() for any
uniformly in
.
Proof.Now, we test (4.11) with . First of all, note that by (4.3), the Reynolds transport theorem and (4.7),
() where, by interpolation, the trace theorem and Young's inequality,
() Next,
() For the dissipative term, we obtain
() Next, we use (
4.3) and (
4.4) and Sobolev embeddings and we obtain
() where
. By combining (
4.18)–(
4.21) and applying Sobolev embeddings to the
-terms, we obtain (
4.16) uniformly in
.
Our next lemma is the following.
Lemma 4.6.Let satisfy (4.5)–(4.7) and let be the corresponding solution to (4.11). Then, we have
() for any
uniformly in
.
Proof.Test (4.11) with . This yields
() By Reynold's transport theorem,
() where, by the trace theorem, (
4.6) and Lemma
4.5 for
By Gauss theorem and (
4.3), we obtain
. Also, by Lemma
4.5, for any
,
Using integration by parts and applying Reynolds transport theorem,
() where, by (
4.5)–(
4.7) as well as Lemmas
4.3 and
4.5,
() Also,
holds for any
, where the second term will be handled using Grönwall's lemma. Also, by Lemma
4.5,
By the trace theorem, (
4.5)–(
4.7), Lemmas
4.3 and
4.5,
Collecting all estimates, we obtain the desired estimate (
4.22).
Lemma 4.7.Let satisfy (4.5)–(4.7) and let be the corresponding solution to (4.11). Suppose further that satisfies (4.9). Then, we have
() for all
uniformly in
.
Proof.Now set and consider the following equation:
() in
subject to
()
()
() and where
satisfies (
4.9).
We now test (4.28) with . Since the left-hand side of (4.28) is of the same form as (4.11), we obtain similar to (4.13)
() where the second term on the right-hand side is due to (
4.30). For the boundary term, we use the trace theorem and Lemma
4.5 to obtain
Next, we use (
4.5) and Lemma
4.5 to obtain
() Finally, we use Lemma
4.3 to also obtain
Subsequently, similar to (
4.12), we use Reynold's transport theorem and the embedding
for any
and obtain
() Applying Grönwall's lemma yields the claim.
5 THE FULLY COUPLED SYSTEM
In the section, we use yet again, a fixed point argument to establish the existence of a unique local strong solution to the fully mutually coupled solute–solvent–structure system. As already shown in Section
3, such a fixed point argument requires showing the closedness of an anticipated solution in a ball and a contraction argument. These two properties will be shown in two different spaces, where one space is a subspace of the other. More precisely, we consider
equipped with their canonical norms
and
, respectively. Here, and in what follows, we have abused notation by reusing
, where
is such that
with
being the local time on which the solution to the purely solvent–structure system was constructed in Section
3. Accordingly, we also abuse notation and set
.
For the purpose of a contraction argument, which is to be performed in the larger space
, it is convenient to transform the moving domain to the fixed reference domain. For this reason, we also introduce the space
equipped with its canonical norm
.
Now, for
, let
be a unique solution of (
3.3)–(
3.2) with dataset
as shown in Section
3. On the other hand, for
let
be the solution of (
4.1) with dataset
as shown in Section
4. Now, define the mapping
, where
and let
Let show that
maps
into
, that is, for any
, we have that
Indeed if we let
then by the a priori estimate (
4.10),
()
We now aim to derive an estimate for the terms in the exponential. By interpolation we obtain for some
and, similarly, for some
,
Finally, we have for some
By Theorem
3.3, we can control
() in terms of
() where
()
However, by using [
46, (3.4)] (for
), we obtain
()
For small enough and for very large, we obtain from (5.1)–(5.5) that . Thus, maps into .
Next, we show that is a contraction in the larger space . For this reason, on one hand, we let and be two solutions of (4.1) with data and , respectively. On the other hand, we let and be two solutions of (3.3) and (3.2) with dataset and , respectively. For the former, by setting , and , we wish to obtain a bound for in the norm in terms of a suitable norm of and . This bound is achieved by transforming Equation (4.1) from the moving domain to the fixed reference domain and obtaining the equivalent bound for in the norm in terms of a suitable norm of and . Here, and with and , . Before obtaining this latter bound, let us see how a single strong solution of (4.1) with a data transforms to a fixed domain. The difference equation will then be deduced from the equation of the single equation.
Let
,
and
. Since
is a strong solution of (
4.1) with a data
, it follows from (
4.8) that
and thus,
Replace
with
to obtain
Set
,
and
. Then, we obtain
where
Take
as test function. Note that the estimate from Theorem
4.2 does not yields boundedness of
,
in spacetime. However, the maximum principle from Remark
4.4 does the job and will be used repeatedly in the following. To estimate
, we need the following estimates for the critical terms
() Subsequently, by using (
2.5), we obtain
() for any
. Next,
as well as
() Similar to (
5.6), we have that
() We also have that
() as well as
() Next, we obtain
() By combining the above with the ellipticity of
and applying Grönwall's lemma, we have shown that
() with a constant
.
Now, let us consider the two solutions
and
of (
3.3) and (
3.2) with datasets
respectively, and let
and
, for
, be the transformations onto the fixed reference domain. Since
,
, by setting
,
,
and
, we obtain
() where
() and
() Also,
() It, therefore, follows from (
5.14)–(
5.17) that for
,
() However, by [
14, Remark 5.2],
() where
If we now combine (
5.13) with (
5.18) and (
5.19) and the fact that
() we obtain
() choosing first
and then
accordingly. The existence of the desired fixed point now follows.
6 THE 2D CO-ROTATIONAL MODEL
6.1 Solving the equation for the solute
In this section, for a known moving domain
and a known solenoidal velocity field
with skew-symmetric gradient
, we aim to construct a strong solution of the Fokker–Planck equation
() in
, where the Maxwellian
is given by
with
. Equation (
6.1) is complemented with the conditions
()
()
() Let us start with a precise definition of what we mean by a strong solution.
Definition 6.1.Assume that the triplet satisfies
()
()
() We call
a
strong solution of (
6.1) with data
if
- (a) satisfies
- (b) for all , we have
()
We now formulate our result on the existence of a unique strong solution of (6.1).
Theorem 6.2.Let satisfy (6.5)–(6.7)
Then, there is a unique strong solution of (6.1)–(6.4), in the sense of Definition 6.1, such that
() holds with a constant depending on
,
and
.
We will obtain a solution of (
6.1) by way of a limit to the following approximation:
() Here, we solve the equation under the boundary conditions
and consider the same initial condition
.
Remark 6.3.Unlike the case where is replaced by considered in Section 4, we do not require a cutoff here since holds for the co-rotational case.
In the following couple of lemmas, we will derive estimates for (6.10) which are uniform with respect to . They transfer directly to (6.1) as the latter is linear.
The first of two results leading to the proof of Theorem 6.2 is the following.
Lemma 6.4.Let satisfy (6.5)–(6.7) and let be the corresponding solution to (6.10). Then, we have
() uniformly in
.
Proof.Before we begin, we first note that since
- ,
- ,
- ,
for any
, we have that
() We only require
at this point. The case
will be used in Remark
6.5.
Now, if we test (6.10) with and integrate the resulting equation over the ball , we obtain by using the boundary conditions that
() If we now integrate (
6.13) over spacetime and apply Reynolds transport theorem (using also (
6.7)), we obtain (
6.11).
Remark 6.5.The proof of Lemma 6.4 can be repeated for powers of obtaining (ignoring the dissipative terms and using (6.12))
() uniformly in
. Checking that the
-dependent constant does not explode, we obtain the maximum principle
() a minimum principle can be proved similarly, but it is not needed for our purposes.
Next, we show the following lemma.
Lemma 6.6.Let satisfy (6.5)–(6.7) and let be the corresponding solution to (6.10). Then, we have
() uniformly in
, where the hidden constant also depends on
,
and
.
Proof.Now, we test (6.10) with . First of all, note that by (6.3), the Reynolds transport theorem and (6.7),
() where, by interpolation, the trace theorem and Young's inequality,
() Next, by Ladyszenskaya's inequality,
() For the dissipative term, we obtain
() Finally, we have by (
6.3)
The last term vanishes again because of the skew-symmetry of
, while the first one is bounded by (employing the maximum principle, cf. (
6.15))
() where
. Finally, we note that
() as a consequence of (
6.3). By combining (
6.18)–(
6.22) we obtain (
6.16) uniformly in
.
As far as the temporal regularity is concerned we have the following result.
Lemma 6.7.Let satisfy (6.5)–(6.7) and let be the corresponding solution to (6.10). Suppose further that , where is given by
() Then, we have
() uniformly in
, where the hidden constant also depends on
,
,
and
.
Proof.Now, set and consider the following equation:
() in
subject to
()
()
() and where
satisfies (
4.9). We now test (
6.25) with
. Since the left-hand side of (
6.25) is of the same form as (
6.10), we obtain similar to (
6.13)
() where the second term on the right-hand side is due to (
6.27). For the boundary term, we use the trace theorem and Lemma
6.6 to obtain
Next, we use (
6.5) and the maximum principle (
6.15) to infer
() Finally, we use Lemma
6.4 to also obtain
Subsequently, we use Reynold's transport and Gronwall's lemma yielding the claim.
6.2 The fully coupled system
We consider now the set of equations
()
()
()
() where
subject to initial conditions
and boundary conditions
()
()
() A weak solution to (
6.31)–(
6.37) can be defined as in Definition
2.2 (simply replacing
by
in the last integral of (d)). Its existence follows again from [
12]; indeed replacing
by
does not alter the arguments there. We speak about a strong solution, if a weak solution satisfies
and
solves the momentum equation a.a. in
. We have the following result:
Theorem 6.8.Let be a dataset satisfying
() There is a unique strong solution
of (
6.31)–(
6.37). The interval of existence is of the form
, where
only in case
approaches a self-intersection when
or it degenerates
5 (namely, if
or
for some
).
Proof.Take a weak solution to (6.31)–(6.37) which exists according to [12]. By [11] with right-hand side there is a strong solution to the fluid–structure system (which belongs to the correct function space). By weak–strong uniqueness (see [14, Remark 5.2]) it must coincide with the weak solution. By Lemma 6.6, we also get spatial regularity of such that the constructed solution lives in the claimed function spaces and the proof is complete.
Remark 6.9. (Temporal regularity)Having (as in the proof of Theorem 6.8 above) the weak solution from [11] at hand, we apply Lemma 6.6 and, eventually, Lemma 6.7. By Lemma 6.7 we get
() If a flat reference geometry is considered (the case of elastic plates), by [
54, Theorem 4.4]
6 the right-hand side is controlled by the initial data and
hence the estimate can be closed by Gronwall's lemma. Again by [
54, Theorem 4.4] one gets temporal regularity for the fluid. In conclusion, for elastic shells one obtains
The same result certainly applies when considering the problem in a fixed fluid-domain (as the estimate from [
54, Theorem 4.4] is well-known then). However, it remains open whether the result from [
54] holds for elastic shells to conclude in the same way.
ACKNOWLEDGMENTS
The authors would like to thank S. Schwarzacher for valuable suggestions concerning the compatibility conditions fluid–structure interaction.
Open access funding enabled and organized by Projekt DEAL.