Acoustic vibration problem for dissipative fluids

By Felipe Lepe, Salim Meddahi, David Mora, and Rodolfo Rodríguez

Abstract

In this paper we analyze a finite element method for solving a quadratic eigenvalue problem derived from the acoustic vibration problem for a heterogeneous dissipative fluid. The problem is shown to be equivalent to the spectral problem for a non-compact operator and a thorough spectral characterization is given. The numerical discretization of the problem is based on Raviart-Thomas finite elements. The method is proved to be free of spurious modes and to converge with optimal order. Finally, we report numerical tests which allow us to assess the performance of the method.

1. Introduction

This paper deals with the numerical approximation of an acoustic dissipative fluid system. This kind of problem has attracted much interest, since it is frequently encountered in engineering applications (see, for instance, Reference 5Reference 20Reference 32). One typical example is to achieve optimal designs that reduce noise and vibrations in fluid-structure systems like cars, aircraft, or dams.

Although dissipation is usually neglected in standard acoustics, modeling this phenomenon is essential to study the effect of noise reduction techniques. Indeed, in most real situations, damping mechanisms that transform mechanical energy into heat do exist. Sometimes these mechanisms are based on surface damping arising from viscoelastic materials placed on the boundary of the propagation domain. In these cases, the dissipative effects are typically included in the model by means of a surface impedance in the boundary conditions (see, for instance, Reference 4Reference 6Reference 7). The present paper addresses damping when it arises in the propagation media itself due to friction and heat conduction. A general approach to this topic can be found in the books by Landau and Lifshitz Reference 22, Morse Reference 31, and Pierce Reference 37, all of which include extensive bibliographic references on the subject.

This paper focuses on computing the (complex) vibration frequencies and modes of an acoustic dissipative fluid system within a rigid cavity. One motivation for considering this problem is that it constitutes a stepping stone towards the more challenging goal of devising numerical approximations for coupled systems involving fluid-structure interaction between viscous fluids and solid structures. The natural model for the fluid system should be based on the Stokes equations for compressible fluids. However, since in real applications the viscosity is typically very small, the resulting problem turns out to be a singular perturbation of the problem for an inviscid fluid. This fact leads to a kind of dilemma, since appropriate finite elements for the Stokes equations introduce spurious modes in the limit case of a vanishing viscosity, whereas the finite elements that avoid such spectral pollution fail when applied to the Stokes equation.

To circumvent this drawback, we resort to an alternative model based on a curl-free displacement formulation (see Reference 8 for the derivation of a similar model in the time domain from basic mechanical laws). Let us remark that in principle the fluid displacement does not need to be curl-free. However, since the viscosity term due to vorticity is typically very small, except perhaps near the walls of the enclosure, it may be neglected in the interior of the enclosure and eventually modeled as a wall impedance on its boundary (see Reference 32 for a similar model).

This curl-free displacement formulation for a viscous fluid leads to a quadratic eigenvalue problem (QEP), as happens in Reference 4. However, the resulting problem involves additional challenges related to the fact that the essential spectrum does not reduce to a single point as in Reference 4. In fact, in this case, we can only prove that the essential spectrum is well-separated from the physically relevant eigenvalues when the viscosity is sufficiently small (as happens in practice). On the other hand, the associated solution operator is not regularizing. Because of this, we need to split it into two terms for the numerical analysis. One of them is dealt with from the techniques in Reference 4, but the spectral approximation analysis for the other is new.

As is shown below (cf. Remark 2.1), the vibration frequencies and modes of a viscous homogeneous irrotational fluid within a rigid cavity can be obtained without actually solving a QEP. In fact, these frequencies can be algebraically computed from those of the analogous inviscid fluid, whose approximation is nowadays a well-known subject (see, for instance, Reference 5). However, this is not the case for a heterogeneous fluid and this is the reason why we choose this as our model problem. In particular, we consider the QEP arising from the acoustic vibration problem for a dissipative fluid system that consists of two homogeneous viscous immiscible fluids contained in a rigid cavity.

QEP has many applications in the study of vibration for solid systems, acoustic fluids, electrical circuits, etc., where the damping effects are involved. A state of the art work on the QEP up to the beginning of this century can be found in Reference 39. However, there are not many works with a rigorous mathematical framework in the context of the numerical approximation of the eigenvalue problem of a partial differential operator involving damping. The first article proving this type of results is Reference 4, where the authors have considered a displacement formulation for a fluid in a rigid cavity with absorbing walls. The theory of non-compact operators of Reference 16 is used to obtain error estimates with minor modifications due to non-conformity.

On the other hand, alternative formulations for the absorbing wall problem have been studied in the engineering literature. For instance, a formulation for the QEP in terms of the fluid pressure has been proposed in Reference 23. This type of formulation leads to a rational eigenvalue problem, for which different algorithms to compute the vibration frequencies have been introduced. Alternatively, two formulations, one based on the fluid pressure and the other on the fluid displacement, have been considered in Reference 14, where an improved Arnoldi algorithm has been proposed to solve the discrete problem. On the other hand, an application of the damping effects in electromechanical-thermoelastic systems is presented in Reference 1. Moreover, an a posteriori estimator for a QEP contextualized in the photonic crystal applications is proposed in Reference 17. Nevertheless, all the previously mentioned studies present different numerical technologies to solve the QEP, but without a rigorous mathematical analysis. Such a rigorous analysis is present instead in the recent paper Reference 27, where an efficient multiscale technique based on localized orthogonal decompositions to solve discrete generic damped vibration problems has been proposed (see also Reference 26).

In the present paper, we consider a displacement-based variational formulation of the transmission eigenproblem resulting from our physical model. This approach leads to a QEP, which is transformed into an equivalent double-size linear eigenvalue problem that fits within the functional framework for non-self-adjoint and non-compact bounded operators. At the continuous level, we follow Reference 21 to obtain an appropriate spectral characterization. Next, we propose an -conforming mixed finite element approximation of the problem and adapt the abstract spectral approximation theory for non-compact operators developed in Reference 15Reference 16 to prove that the spectrum is correctly approximated and to obtain error estimates.

The discrete analysis relies partly on the techniques used in the Raviart-Thomas mixed approximation of the grad-div eigenvalue problem. This spectral problem emerged in the study of coupled fluid-solid systems in Reference 3 (see also Reference 28 for a similar setting in elasticity). The grad-div spectral problem is posed in and it is closely related to the Maxwell eigenvalue problem, which is formulated in terms of the curl-curl operator in (see Reference 10Reference 13Reference 30). Although the two spectral problems have been initially studied in isolation from each other, a common framework now becomes clear thanks to the language of differential forms and the approach based on the finite element exterior calculus provided by Reference 2 (see also Reference 11, Part 4).

With representing either or , we let be the kernel of the div or the curl operator in the corresponding space. If we denote by a conforming mixed finite element approximation of and consider , then the main tool in the proof of the correct spectral approximation relies on the construction of a projector onto such that

Here stands for -orthogonal complement of in and represents the -orthogonal complement of in .

To our knowledge, this tool was first introduced in the -setting in Reference 3 and more recently in Reference 28, Lemma 4.2. The same tool appeared independently in the context of the Maxwell eigenvalue problem; see for example Reference 18, Lemma 4.5 where the projector is named the Hodge mapping. A related abstract approach is considered in Reference 12 where the so-called GAP property is introduced. It is shown in Reference 12 that the GAP property is equivalent to Equation 1.1 when (as in the cases considered here). It is also shown that the GAP property implies the discrete compactness property which was used in Reference 13 as another tool to study edge element approximation of the Maxwell eigenproblem. See also the discussion given in Reference 11, Section 19 regarding this topic in the language of differential forms.

The negative impact that material parameters have on the regularity of the solution of the boundary value problem complicates the analysis in this common framework (see Reference 18, Remark 13). Here, we follow the lines of the methodology presented originally in Reference 3 and use the information about interface singularities of solutions of the Neumann boundary value problem for ), with piecewise constant, to obtain (Equation 1.1) for the eigenproblem considered in this paper.

The paper is organized as follows: in Section 2, we introduce the spectral problem and the corresponding variational formulation, which leads to a quadratic eigenvalue problem. We introduce an auxiliary unknown to transform the quadratic eigenvalue problem into a linear one. Moreover, we introduce the corresponding solution operator for the spectral problem. In Section 3, we provide a thorough spectral characterization of the solution operator, based on the theory developed in Reference 21. We also consider the limit problem (i.e., the case when the viscosity vanishes) and the relation between the solutions of the dissipative and non-dissipative problems. In Section 4, we introduce a finite element discretization using Raviart-Thomas elements for both fluids and imposing the continuity of the corresponding normal components on the interface. We analyze the discrete spectral problem analogously as in the continuous case and introduce the corresponding discrete solution operator. We use the abstract theory from Reference 15 to prove the convergence. We also prove error estimates for our problem by adapting the arguments from Reference 4. Finally, in Section 5, we report some numerical tests which allow us to asses the performance of the proposed method.

Throughout the paper, is a generic Lipschitz bounded domain of (), with outer unit normal vector . We denote by the space of infinitely smooth functions compactly supported in . For , stands indistinctly for the norm of the Hilbertian Sobolev spaces or with the convention . We also define the Hilbert space , whose norm is given by , and its subspace .

Finally, represents a generic constant independent of the discretization parameters, which may take different values at different places.

2. The model problem

We take as our model problem the case of two immiscible fluids within a rigid cavity. Let with be the polygonal (in the 2D case) or polyhedral (in the 3D case) Lipschitz domains occupied by each of the fluids. Let be the corresponding densities, the fluid viscosities, and the acoustic speeds, which we consider all constant, and strictly positive, and non-negative. We denote by the outward unit normal vectors corresponding to each subdomain. We define , , and , . We assume that each domain as well as are simply connected (see Figure 1).

We consider small displacements of a compressible viscous fluid at rest neglecting convective terms. The equation of motion derived from the Navier-Stokes equation reads

where denotes the fluid displacement and the pressure fluctuation in the domain , . The dot represents derivation with respect to time. (See Reference 8 and Reference 37 for a more detailed derivation.) Moreover, since the fluid is compressible, we consider the isentropic relation

Since we are considering irrotational fluids, we assume that . Hence, considering the identity , we conclude that . Then, the equations of our model problem are the following:

Let us remark that a similar argument leads to exactly the same equations in 2D.

Multiplying equations Equation 2.2 and Equation 2.4 by a test function , integrating by parts, and using the boundary conditions and the transmission conditions on , we obtain

where

Using Equation 2.3 and Equation 2.5 we eliminate in Equation 2.10 and write

The vibration modes of this problem are complex solutions of the form with . Looking for this kind of solutions leads to the following quadratic eigenvalue problem.

Problem 1.

Find and such that

Let us remark that in the absence of viscosity (i.e., ) we are left with the free vibration problem of two inviscid fluids in contact (whose numerical approximation has not been analyzed either). The eigenvalues of such a problem are negative real numbers (as will be proved below), so that are purely imaginary, namely, with being the so-called natural vibration frequencies which correspond to periodic in time solutions of the time domain problem. This is the reason why, for , Problem 1 is usually written as follows: Find and such that

In the applications, is typically very small. As we will show below, in such a case there are eigenvalues of Problem 1 that lie close to with being a natural vibration frequency (i.e., a solution of Equation 2.12). Actually, we will prove below that those converge to as goes to zero. On solving Problem 1, the aim is to compute the eigenvalues close to the smallest natural vibration frequencies , which are the most relevant in the applications.

Remark 2.1.

In the case of a homogeneous viscous fluid, , , and are constant in the whole . Then, Problem 1 can be written as

Hence, in such a case, is an eigenpair of Problem 1 if and only if with a solution to problem Equation 2.12. Therefore, for a homogeneous viscous fluid, can be algebraically computed from the solution of Equation 2.12 as follows:

We denote endowed with the weighted inner product

and we denote with the inner product

Notice that the inner products in and induce norms and on each of these spaces equivalent to the classical and norms, respectively. Therefore, when it might be convenient, we will use these classical norms.

Clearly is an eigenvalue of Problem 1 with associated eigenspace

We define

Since is a closed subspace of , clearly Notice that and are also orthogonal in the inner product. Hence,

The following result brings a characterization of the space .

Lemma 2.1.

There holds

Proof.

We will prove this result by checking the double inclusion. Let Then, for all , since , we have

Thus, in . Since is simply connected, this implies that there exists such that . Hence, . Conversely, let and . Let be such that . Then,

Therefore, . The proof is complete.

In what follows we prove additional regularity for the functions in on each subdomain. From now on, will denote a positive number such that the following lemma holds true.

Lemma 2.2.

There exists (with depending on , , and ) such that, for all , and

where is a positive constant independent of .

Proof.

According to Lemma 2.1, there exists such that . Consequently, is the unique solution of the following well-posed Neumann problem:

Hence, in the 3D case, the result follows from Reference 34, Lemma 2.20, while in the 2D case, it follows by applying Reference 34, Lemma 4.3. (See Reference 36 for more details.)

Remark 2.2.

The above lemma establishes the existence of a regularity exponent that will play a role in the error estimates of the numerical method proposed in this paper. In spite of the fact that we refer to Reference 34 in the proof of this lemma, the value of that arises from this reference is far from being optimal, since it is valid for global regularity in and for any arbitrary geometrical setting of the subdomains and . In most of the applications, the subdomains at which is constant are similar to those shown in Figure 1. In such a case, a detailed analysis of how this exponent depends on the geometry of the domain and on the coefficient can be found in Reference 25 for the 2D case and in Reference 24 for 3D problems (see also Reference 9Reference 33). Let us remark that, although the analysis of these references is for problems with Dirichlet boundary conditions, similar results hold true for Neumann boundary conditions as in our case (see Reference 25, Remark III.5.2). In particular, for instance, Lemma 2.2 holds true for in the example reported as Test 1 in Section 5 (see Figure 2).

From the physical point of view, the time domain problem Equation 2.11 is dissipative in the sense that its solution should decay as increases. The latter happens if and only if the so-called decay rate, , is negative. The following result shows that this is the case in our formulation.

Lemma 2.3.

Let be a solution of Problem 1. If , then

Proof.

The proof is similar to that of Lemma 2.1 of Reference 4.

Remark 2.3.

Any eigenpair of Problem 1 satisfies

Since the coefficients are constant in each subdomain, if in , by testing with we obtain that , . On the other hand, if in ( or ), then, for , by testing again with , we obtain that in . Thus, in any case, ,

For the theoretical analysis it is convenient to transform Problem 1 into a linear eigenvalue problem. With this aim we introduce the new variable , as usual in quadratic problems, and the space endowed with the corresponding product norm, which carry us to the following.

Problem 2.

Find and such that

We observe that is an eigenvalue of Problem 2 and its associated eigenspace is . Let be the orthogonal complement of in . Notice that .

We introduce the sesquilinear continuous form defined by

and the sesquilinear continuous forms defined as follows:

In what follows we prove that and are elliptic in and , respectively.

Lemma 2.4.

The sesquilinear form is -elliptic and, consequently, is -elliptic.

Proof.

The proof is a direct consequence of Lemma 2.2.

Let be the bounded linear operator defined by , where is the unique solution of the following problem:

It is easy to check that

and

As a consequence of the above equalities, we have that is an eigenvalue of with associated eigenspace , which is non-trivial since . The following lemma shows that the non-zero eigenvalues of are exactly the reciprocals of the non-zero eigenvalues of Problem 2 with the same corresponding eigenfunctions.

Lemma 2.5.

There holds that is an eigenpair of with if and only if is a solution of Problem 2 with

Proof.

The proof is similar to that of Lemma 2.3 from Reference 4.

3. Spectral characterization

The goal of this section is to characterize the spectrum of the solution operator . Since the inclusion is not compact, it is easy to check from Equation 2.16 that is not compact either. However, we will show that the essential spectrum has to lie in a small region of the complex plane, well-separated from the isolated eigenvalues which, according to Lemma 2.5, correspond to the solutions of Problem 2. With this aim, we will resort to the theory described in Reference 21 to appropriately decompose . Let be the operators given by

It is easy to check that these operators are self-adjoint with respect to . Moreover is non-negative and is positive with respect to (namely, and , ). Moreover, we have the following result.

Lemma 3.1.

The operator is compact.

Proof.

Since is -elliptic (cf. Lemma 2.4), applying Lax-Milgram’s Lemma, we know that problem Equation 3.2 is well-posed and has a unique solution . Moreover, according to Lemma 2.2, we know that there exists such that . On the other hand, notice that Equation 3.2 also holds for , since in such a case for . Hence, since , we have that

Then, by testing this equation with , we have that in , so that . Therefore, since and are positive constants in each subdomain and , we have that , Since the inclusions and are compact, we derive that is compact too.

The operator can be written in terms of the operators and given above as follows:

Moreover, by defining as in Reference 21 the operators

we have that . We note that the eigenvalues of and and their algebraic multiplicities coincide. Moreover the corresponding Jordan chains have the same length. In fact, let be a Jordan chain associated with the eigenvalue of . Then, using the identities above, we observe that

This shows that is a Jordan chain of of the same length. Actually, the whole spectra of and coincide as is shown in the following result, which has been proved in Lemma 3.2 of Reference 4.

Lemma 3.2.

There holds

Moreover, , too.

The operator can be written as the sum of a self-adjoint operator and a compact operator :

Then, applying the classical Weyl’s Theorem (see Reference 38), we have that and the rest of the spectrum consists of isolated eigenvalues with finite algebraic multiplicity. Moreover, .

Our next goal is to show that the essential spectrum of must lie in a small region of the complex plane. Actually, we will localize the whole spectrum of . With this aim, we analyze separately for which values the operator is not necessarily one-to-one and for which values it is not necessarily onto.

If is not one-to-one, then there exists , , such that , namely,

Then, testing with and using that in each subdomain the coefficients and are positive, we deduce that

(we recall that for , because of Lemma 2.2). Hence,

On the other hand, is onto if and only if for any there exists such that , which from Equation 3.1 reads

By writing with , the equation above reads:

We observe that for all the problem above has a solution and hence the operator is onto. On the other hand, if , then has to be real. In such a case, the operator will still be onto when has the same sign in the whole domain . This happens at least in two cases:

(i)

when , in which case ,

(ii)

when , in which case .

Therefore, if is not onto, then , too.

Now we are in a position to write the following spectral characterization of the solution operator .

Theorem 3.1.

The spectrum of consists of

with

and , which is a set of isolated eigenvalues of finite algebraic multiplicity.

Proof.

As a consequence of the classical Weyl’s Theorem (see Reference 38) and Lemma 3.2,

whereas the inclusion follows from the above analysis.

In what follows, we will show that for small enough some of the eigenvalues of are well-separated from its essential spectrum. To this end, given , by testing Equation 3.1 with and using the definition of , we have that

Therefore as goes to zero. Consequently, converges in norm to the operator

as goes to zero. Thus, from the classical spectral approximation theory (see Reference 19), the isolated eigenvalues of converge to those of .

Since the isolated eigenvalues of and coincide (cf. Lemma 3.2), in order to localize those of , we begin by characterizing those of . Let be an isolated eigenvalue of and the corresponding eigenfunction. It is easy to check that

Since is compact, self-adjoint, and positive, its spectrum consists of a sequence of positive eigenvalues that converge to zero and zero itself. Notice that the spectrum of is related with the solution of the eigenvalue problem Equation 2.12. In fact, this problem has as an eigenvalue with corresponding eigenspace . The rest of the eigenvalues are strictly positive and the corresponding eigenfunctions , so that they are also solutions of the following problem: Find and such that

Clearly is an eigenpair of the above problem with if and only if . Thus, by virtue of Equation 3.3, we have that the eigenvalues of are given by and hence they are purely imaginary.

Now we are in a position to establish the following result.

Theorem 3.2.

For each isolated eigenvalue of of algebraic multiplicity , let be such that the disc intersects only in . Then, there exists such that if , there exist eigenvalues of , (repeated according to their respective algebraic multiplicities), lying in the disc . Moreover, as goes to zero.

As claimed above, the eigenvalues of that are relevant in the applications are those which are close to for the smallest positive vibration frequencies of Equation 2.12. According to the above theorem, these eigenvalues are well-separated from the real axis and, hence, from the essential spectrum of (cf. Theorem 3.1).

4. Spectral approximation

In this section, we propose and analyze a finite element method to approximate the solutions of Problem 1. To this end, we introduce appropriate discrete spaces. Let be a family of regular partitions of such that are partitions of , . We introduce the lowest-order Raviart-Thomas finite element space:

The discretization of Problem 1 reads as follows.

Problem 3.

Find and such that

We proceed as we did in the continuous case and introduce a new discrete variable to rewrite the problem above in the following equivalent form.

Problem 4.

Find and such that

We observe that is an eigenvalue of this problem and its associated eigenspace is with the eigenspace of in Problem 3. At the beginning of Section 5, we will show that Problem 4 is well-posed, in the sense that it is equivalent to a generalized matrix eigenvalue problem with a symmetric positive definite right-hand side matrix.

We introduce the well-known Raviart-Thomas interpolation operator, , (see Reference 29), for which the approximation result

and the commuting diagram property

hold, where

is the standard -orthogonal projector. Then, for any we have that

Let be the orthogonal complement of in , and let be endowed with the corresponding product norm. Note that and hence .

The following result provides estimates for the terms in the Helmholtz decomposition of functions in . Let us recall that, here and thereafter, denotes the optimal regularity exponent such that Lemma 2.2 holds true.

Lemma 4.1.

For any , there exist and such that

with , and the following estimates hold:

Proof.

The proof follows by repeating the arguments of the proof of Lemma 4.1 from Reference 4, taking care of the presence of the discontinuous coefficient .

Remark 4.1.

We notice that Lemma 4.1 provides (1.1) with . It is worthwhile to mention that, with the regularity results for at hand (see Lemma 2.2), Lemma 4.1 may also be deduced by considering the case of differential -forms in Lemma 5.10 of Reference 2.

The following result is a direct consequence of Lemma 4.1.

Lemma 4.2.

The sesquilinear form is -elliptic, with ellipticity constant not depending on . Consequently, is -elliptic uniformly in .

Now, we are in a position to introduce the discrete version of the operator . Let be defined by with the solution of

It is easy to check that if and only if

where is the -orthogonal projection onto , and solves

Since , holds (cf. Reference 3, Lemma 4.1). Thus, we will restrict our attention to .

As claimed above, at the beginning of Section 5, Problem 4 will be shown to be equivalent to a well-posed generalized matrix eigenvalue problem. This problem has as an eigenvalue with corresponding eigenspace . The rest of the eigenvalues are related with the spectrum of according to the following lemma.

Lemma 4.3.

There holds that is an eigenpair of with if and only if is a solution of Problem 4 with .

Proof.

The proof essentially follows that of Lemma 2.5, by using the fact that

Our next goal is to show that any isolated eigenvalue of with algebraic multiplicity is approximated by exactly eigenvalues of (repeated according to their respective algebraic multiplicities) and that spurious eigenvalues do not arise. To this end, we will adapt to our problem the theory from Reference 4, which in turn uses arguments introduced in Reference 15Reference 16 to deal with non-compact operators. From now on, let , , be a fixed isolated eigenvalue of finite algebraic multiplicity . Let be the invariant subspace of corresponding to . Our analysis will be based on proving the following two properties:

Let and . From Equation 2.17, we can write with satisfying

and

The following result states some properties of the solutions of the problems above.

Lemma 4.4.

For , let and consider the decomposition as above. Hence, , , , and the following estimates hold true:

Proof.

Since , due to Lemma 2.2 we have that and . Moreover, note that Equation 4.6 also holds for and hence for all . Then, we write

Thus, taking test functions in we have . Since , and are piecewise constant, we have that is piecewise constant as well; namely, .

On the other hand, since , by applying Lemma 2.2 again we have that and . To prove additional regularity for , we use Lemma 4.1 to write with , , and . Moreover, since is constant in each subdomain , also , . Then, from Equation 4.7 we have that

Since the above equation trivially holds for too, it holds for all . Then, by testing it with we have that . Therefore, by restricting to , , we have that . Since and are piecewise constant, we conclude that , and

Hence, we conclude the proof.

We consider a similar decomposition in the discrete case. For , let . We write with and satisfying

and

These are the finite element discretizations of problems Equation 4.6 and Equation 4.7, respectively, and the following error estimates hold true.

Lemma 4.5.

Let . Let be the solutions of problems Equation 4.6 and Equation 4.7, respectively, and those of problems Equation 4.10 and Equation 4.11, respectively. Then, the following estimates hold true:

Proof.

Since , we will resort to the second Strang Lemma, which for problems Equation 4.6 and Equation 4.10 reads as follows:

Because of Lemma 4.4, is well-defined. Since , there exist and such that . Then, since is orthogonal to , we observe that

The first term on the right-hand side above is bounded as follows:

where we have used Equation 4.1, Equation 4.8, and the fact that , which in turn follows from Equation 4.6 by taking . On the other hand, the second term vanishes because of Equation 4.2 since (cf. Lemma 4.4). Hence, , which allows us to control the approximation term in Equation 4.14.

For the consistency term, it is enough to recall that Equation 4.6 holds for all . Then, by using Equation 4.10, it is easy to check that for all . From this, the Strang estimate for reads as follows:

Thus Equation 4.12 holds true.

To prove Equation 4.13, we resort again to the second Strang Lemma:

Since (cf. Lemma 4.4), we have that is well-defined. We proceed as above and write with and to obtain

For the first term on the right-hand side above, Equation 4.1 and Lemma 4.4 yield

For the second term, we have from Equation 4.3 and from Lemma 4.4 again

Hence, , which allows us to bound the approximation term in Equation 4.15.

For the consistency term, given we apply Lemma 4.1 to write with , , and . Then, from Equation 4.7 we have

On the other hand, from Equation 4.11,

Therefore,

and, hence,

which allows us to complete the proof.

Now, we are in a position to establish the following result.

Lemma 4.6.

Property P1 holds true. Moreover,

Proof.

For , let and . From Equation 2.16 and Equation 4.4 we have that . Hence, by writing and as in Lemma 4.5, we have from this lemma

Thus, we conclude the proof.

Our next goal is to prove property P2. With this aim, first we will prove the following additional regularity result.

Lemma 4.7.

Let . Then, , , and

Proof.

We prove the above inequalities for all the generalized eigenfunctions of . Let be a Jordan chain of the operator associated with . Then, , , with . We will use an induction argument on . Assume that and belong to and satisfy Equation 4.17 and Equation 4.18, respectively (which obviously hold for ). First note that, because of the boundedness of , we have

On the other hand, by using Equation 2.16 and Equation 2.17 we have that

and that satisfies

Hence, .

We observe that the equation above also holds for any . Then,

Thus, considering test functions in we obtain

Let us assume that in both and (we discuss the other case at the end of the proof). Hence, since , , and are constant in each , , , and

Now, since , due to Lemma 2.2 we have that . Then, from Equation 2.13 and the previous estimate we have

On the other hand, from Equation 4.20 we obtain

and, from Equation 4.21,

Finally, from Equation 4.20 again,

Hence, from inequalities Equation 4.22Equation 4.25, the inductive assumption, and Equation 4.19, we derive Equation 4.17 and Equation 4.18 provided in both and .

In the case that vanishes in , or , arguing as in Remark 2.3 we obtain that and, once again, an induction argument allows us to conclude that in , . The proof is complete.

Now, we are in a position to establish property P2.

Lemma 4.8.

Property P2 holds true. Moreover, for any , there exist , such that

Proof.

Let . According to Lemma 4.7, we have that and . Let be the Raviart-Thomas interpolant of . Since , we decompose with and . The same arguments from the proof of Lemma 4.5 that lead to Equation 4.13 apply in this case and combined with Lemma 4.7 allow us to prove that . A similar procedure can be used to define and to prove that .

We also have the following auxiliary result when the source terms are in , whose proof follows the arguments in Case 2 of Lemma 4.7 of Reference 4.

Lemma 4.9.

For , let and . Then,

The above lemmas are the ingredients to prove spectral convergence and to obtain error estimates. Our first result is the following theorem which has been proved in Reference 15 as a consequence of property P1 (cf. Lemma 4.6) and which shows that the proposed method is free of spurious modes.

Theorem 4.1.

Let be a compact set such that . Then, there exists such that, for all ,

Let be a closed disk centered at , such that . Let be the eigenvalues of contained in (repeated according to their algebraic multiplicities). Under assumptions P1 and P2, it is proved in Reference 15 that for small enough and that for .

On the other hand the arguments used in Section 5 of Reference 4 can be readily adapted to our problem, to obtain error estimates. We recall the definition of the gap between two closed subspaces and of :

with

Let be the invariant subspace of relative to the eigenvalues converging to . From Lemmas 4.64.9, we derive the following results for which we do not include proofs to avoid repeating step by step those of Reference 4, Section 5.

Theorem 4.2.

There exist constants and such that, for all ,

Theorem 4.3.

There exist constants and such that, for all ,

where is the ascent of the eigenvalue of .

5. Numerical results

We implemented the proposed method in a MATLAB code. We report in this section the results of some numerical tests, in order to assess its performance. To this end, first we introduce a convenient matrix form of the discrete problem which allows us to use standard eigensolvers. As a byproduct, this matrix form also allows us to prove that Problems 3 and 4 are well-posed.

Let be a nodal basis of . We define the matrices , , and as follows:

The matrix form of Problem 3 reads

where we denote by the vector of components of in the nodal basis of .

Analogously, the matrix form of Problem 4 reads

with the vector of components of . However, this problem is not suitable to be solved with standard eigensolvers, since neither the right-hand side nor the left-hand side matrix are Hermitian and positive definite.

Alternatively, for , let . Then, problem Equation 5.1 is equivalent to

Introducing , the problem above is equivalent to

which in turn is equivalent to

Thus, the last problem is equivalent to Problem 3 except for and the matrix in its right-hand side is Hermitian and positive definite. Hence, it is well-posed and can be safely solved by standard eigensolvers.

Test 1.

We applied the proposed method to a 2D rectangular rigid cavity filled with two fluids with different physical parameters as shown in Figure 2. The domain occupied by the fluids are and . For such a simple geometry, it is possible to calculate an analytical solution which will be used to validate our method.

Let be a solution of Problem 1. Testing it with we have . Then, . Hence, . Moreover, , which implies that on . Then, we write problem Equation 2.2Equation 2.9 in terms of as follows:

We proceed by separation of variables. Assuming that , we are left with the following problem:

From Equation 5.2 we have that and are constant. Moreover, from Equation 5.5 and Equation 5.6, it is easy to check that and cannot vanish simultaneously and (actually, it is derived that , but the constant can be chosen equal to one without loss of generality).

From the fact that is constant and Equation 5.3, we have that

On the other hand, from the fact that is also constant and Equation 5.4 we derive

where and are constants and

Since and cannot vanish simultaneously, Equation 5.5 and Equation 5.6 lead to

respectively. Thus, substituting Equation 5.7 into these equations yields the following linear system for the coefficients and :

For this system to have non-trivial solutions, its determinant must vanish, which yields the following non-linear equation in for whose roots are the eigenvalues of Problem 1:

We have computed some roots of the above equation and used these roots as exact eigenvalues to compare those obtained with the method proposed in this paper. For the geometrical parameters, we have taken , , and .

We have used physical parameters of water and air for the density and acoustic speed of the fluids in and , respectively: , , , and . We have used uniform meshes as those shown in Figure 3. The refinement parameter refers to the number of elements per width of the rectangle.

In the presence of dissipation (), the eigenvalues are complex numbers , with the decay rate and the vibration frequency. In the absence of dissipation (), the eigenvalues are purely imaginary (). The same holds for the computed eigenvalues .

In our first test, we neglected the viscosity damping effects by taking . In this case, the eigenvalues are actually purely imaginary. Table 1 shows the four smallest eigenvalues computed with the proposed method on successively refined meshes. Accurate values of the zeros of obtained with the MATLAB command applied to are also reported on the last line of the table as exact eigenvalues.

As predicted by the theory, these eigenvalues are purely imaginary. The high accuracy of the computed eigenvalues can be observed from Table 1 even for the coarsest mesh. We have used a least squares fitting to estimate the convergence rate for each eigenvalue, which are also reported in Table 1. A clear order can be seen in all cases.

Secondly, we have used the same physical parameters as above for both fluids, but considering now non-vanishing viscosities. In order to make the dissipation effects more visible, we have used unrealistically large viscosity values: and . We have repeated the scheme used above. We report in Table 2 the computed and “exact” eigenvalues and the estimated convergence rates, which are in accordance with the theory once again. Notice that now all have negative real parts (decay rate) as predicted by the theory.

It can be seen from Table 2 that even in the coarsest mesh the vibration frequencies are computed with at least three correct significant digits. In turn, the decay rates are computed with at least two correct significant digits, in spite of the fact that they are much smaller than the vibration frequencies. Moreover, a least square fitting of the computed decay rates shows that they also converge quadratically. As a consequence, the decay rates computed with the finest mesh attain three or four significant correct digits.

Finally, Figure 4 shows the real and imaginary parts of the computed pressure as defined in Equation 2.1 for the smallest eigenvalue.

Test 2.

As a second test, we have applied our code to solve a problem with a curved interface. In spite of the fact that such a problem does not lie in our theoretical framework, we will report experimental results which will allow us to assess the performance of the method in this case.

The whole domain is the same as in the previous experiment, but with a curved interface . In particular, we have taken as an arc of a circle with center at the point and endpoints and . We report in Table 3 the computed eigenvalues. In this case, there is no analytical solution available. Therefore, we have obtained more accurate approximations of the exact eigenvalues by means of a least square fitting. These more accurate values are reported on the last line of Table 3 as “Exact”. We also report in this table the estimated order of convergence, which once more is clearly quadratic.

Finally, Figure 5 shows the real and imaginary parts of the computed pressure as defined in Equation 2.1 for the smallest eigenvalue. The curved interface can be clearly appreciated in the imaginary part of the pressure.

Figures

Figure 1.

2D sketch of the polygonal domains for the fluids.

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Figure 2.

Test 1. Two fluids in a rectangular rigid cavity.

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Figure 3.

Test 1. Meshes for (left) and (right).

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Table 1.

Test 1. Computed and exact eigenvalues for dissipative fluids in a rigid cavity.

Order
Exact
Table 2.

Test 1. Computed and exact eigenvalues for dissipative fluids in a rigid cavity.