1. Introduction
The Boussinesq equations have been used as a model in many geophysical applications. They have been widely studied in both deterministic and stochastic settings. We take random forces into account and formulate the Bénard convection problem as a system of stochastic partial differential equations (SPDEs). The need to take stochastic effects into account for modeling complex systems has now become widely recognized. Stochastic partial differential equations (SPDEs) arise naturally as mathematical models for nonlinear macroscopic dynamics under random influences. The Navier–Stokes equations are coupled with a transport equation for the temperature and with diffusion. The system is subjected to a multiplicative random perturbation, which will be defined later. Here, u describes the fluid velocity field, whereas describes the temperature of the buoyancy-driven fluid, and is the fluid’s pressure.
We study the multiplicative stochastic Boussinesq equations
where
. The processes
and
have initial conditions
and
in
D, respectively. The parameter
denotes the kinematic viscosity of the fluid, and
denotes its thermal diffusivity. These fields satisfy periodic boundary conditions
,
on
, where
,
denotes the canonical basis of
, and
is the pressure.
In dimension 2 without any stochastic perturbation, this system has been extensively studied with a complete picture about its well-posedness and long-time behavior. In the deterministic setting, more investigations have been extended to the cases where and/or , with some partial results.
If the
(resp.,
) norms of
and
are square integrable, it is known that the random system (
1)–(
2) is well-posed, and that there exists a unique solution
in
; see, e.g., [
1,
2].
Numerical schemes and algorithms have been introduced to best approximate the solution to non-linear PDEs. The time approximation is either an implicit Euler or a time-splitting scheme coupled with a Galerkin approximation or finite elements to approximate the space variable. The literature on numerical analysis for SPDEs is now very extensive. In many papers, the models are either linear, have global Lipschitz properties, or, more generally, have some monotonicity property. In this case, the convergence was proven to be in mean square. When nonlinearities are involved that are not of Lipschitz or monotone type, then a rate of convergence in mean square is more difficult to obtain. Indeed, because of the stochastic perturbation, one may not use the Gronwall lemma after taking the expectation of the error bound, since it involves a nonlinear term that is often quadratic; such a nonlinearity requires some localization.
In a random setting, the discretization of the Navier–Stokes equations on the torus has been intensively investigated. Various space–time numerical schemes have been studied for the stochastic Navier–Stokes equations with a multiplicative or an additive noise, where, in the right hand side of (
1) (with no
), we have either
or
. We refer to [
3,
4,
5,
6,
7], where the convergence in probability is stated with various rates of convergence in a multiplicative setting for a time implicit Euler scheme, and [
8] for a time splitting scheme. As stated previously, the main tool used to obtain the convergence in probability is the localization of the nonlinear term over a space of large probability. We studied the strong (that is,
) rate of convergence of the time-implicit Euler scheme (resp., space–time-implicit Euler scheme coupled with finite element space discretization) in our previous papers [
9] (resp., [
10]) for an
-valued initial condition. The method is based on the fact that the solution (and the scheme) have finite moments (bounded uniformly on the mesh). For a general multiplicative noise, the rate is logarithmic. When the diffusion coefficient is bounded (which is a slight extension of an additive noise), the supremum of the
-norm of the solution has exponential moments; we used this property in [
9,
10] to obtain an explicit polynomial strong rate of convergence. However, this rate depends on the viscosity and the strength of the noise, and is strictly less than 1/2 for the time parameter (resp., less than 1 for the spatial one). For a given viscosity, the time rates on convergence increase to 1/2 when the strength of the noise converges to 0. For an additive noise, if the strength of the noise is not too large, the strong (
) rate of convergence in time is the optimal one, and is almost
(see [
11]). Once more, this is based on exponential moments of the supremum of the
-norm of the solution (and of its scheme for the space discretization); this enabled us to have strong polynomial time rates.
In the current paper, we study the time approximation of the Boussinesq Equations (
1) and (
2) in a multiplicative setting. To the best of our knowledge, it is the first result where a time-numerical scheme is implemented for a more general hydrodynamical model with a multiplicative noise. We use a fully implicit time Euler scheme and once more assume that the initial conditions
and
belong to
in order to prove a rate of convergence in
uniformly in time. We prove the existence of finite moments of the
-norms of the velocity and the temperature uniformly in time. Since we are on the torus, this is quite easy for the velocity. However, for the temperature, due to the presence of the velocity in the bilinear term, the argument is more involved and has to be carried out in two steps. It requires higher moments on the
-norm of the initial condition. The time regularity of the solutions
is the same as that of
u in the Navier–Stokes equations. We then study rates of convergence in probability and in
. The rate of convergence in probability is optimal (almost
); we have to impose higher moments on the initial conditions than what is needed for the velocity described by stochastic Navier–Stokes equations. Once more, we first obtain an
convergence on a set where we bound the
norm of the gradients of both the velocity and the temperature. We deduce an optimal rate of convergence in probability that is strictly less than 1/2. When the
-norm of the initial condition has all moments (for example, it is a Gaussian
-valued random variable), the rate of convergence in
is any negative exponent of the logarithm of the number of time steps. These results extend those established for the Navier–Stokes equations subject to a multiplicative stochastic perturbation.
The paper is organized as follows. In
Section 2, we describe the model and the assumptions on the noise and the diffusion coefficients, and describe the fully implicit time Euler scheme. In
Section 3, we state the global well-posedeness of the solution to (
1)–(
2), moment estimates of the gradient of
u and
uniformly in time and the existence of the scheme. We then formulate the main results of the paper about the rates of the convergence in probability and in
of the scheme to the solution. In
Section 4, we prove moment estimates in
of
u and
uniformly on the time interval
if we start with more regular (
) initial conditions. This is essential in order to be able to deduce a rate of convergence from the localized result.
Section 5 states the time regularity results of the solution
both in
and
; this a crucial ingredient of the final results. In
Section 6, we prove that the time Euler scheme is well-defined and prove its moment estimates in
and
.
Section 7 deals with the localized convergence of the scheme in
. This preliminary step is necessary due to the bilinear term, which requires some control of the
norm of
u and
. In
Section 8, we prove the rate of convergence in probability and in
. Finally,
Section 9 summarizes the interest of the model and describes some further necessary/possible extensions of this work.
As usual, except if specified otherwise, C denotes a positive constant that may change throughout the paper, and denotes a positive constant depending on some parameter a.
3. Main Results
In this section, we state the main results about the well-posedness of the solutions , the scheme and the rate of the convergence of the scheme to .
3.1. Global Well-Posedness and Moment Estimates of
The first results state the existence and uniqueness of a weak pathwise solution (that is a strong probabilistic solution in the weak deterministic sense) of (
9) and (
10). It is proven in [
1] (see also [
2]).
Theorem 1. Let and for or . Let the coefficients G and satisfy the conditions(C-u)(i)and(C-)(i), respectively. Then, Equations (9) and (10) have a unique pathwise solution, i.e., u (resp., θ) is an adapted -valued (resp., -valued) process that belongs a.s. to (resp., to );
a.s. we have , and for every and every and .
The following result proves that, if
, the solution
u to (
9) and (
10) is more regular.
Proposition 1. Let and for or some , and let G satisfy condition(C-u)and satisfy condition(C-). Then, the solution u to (9) and (10) belongs a.s. to . Moreover, for some constant C, The next result proves similar bounds for moments of the gradient of the temperature uniformly in time.
Proposition 2. Let and for some and or . Suppose that the coefficients G and satisfy the conditions(C-u)and(C-). There exists a constant C such that 3.2. Global Well-Posedness of the Time Euler Scheme
The following proposition states the existence and uniqueness of the sequences and .
Proposition 3. Let condition(G-u)(i)and(C-)(i)be satisfied, and a.s. The time fully implicit scheme (17) and (18) has a unique solution , . 3.3. Rates of Convergence in Probability and in
The following theorem states that the implicit time Euler scheme converges to the pair in probability with the “optimal” rate “almost 1/2”. It is the main result of the paper. For , set and ; then, .
Theorem 2. Suppose that the conditions(C-u)and(C-)hold. Let and for some , be the solution to (9) and (10) and be the solution to (17) and (18). Then, for every , we have We finally state that the strong (i.e., in ) rate of convergence of the implicit time Euler scheme is some negative exponent of . Note that, if the initial conditions and are deterministic, or if their and -norms have moments of all orders (for example, if and are Gaussian random variables), the strong rate of convergence is any negative exponent of . More precisely, we have the following result.
Theorem 3. Suppose that the conditions(C-u)and(C-)(i)hold. Let and for and some . Then, for some constant C such that for large enough N.
7. Strong Convergence of the Localized Implicit Time Euler Scheme
Due to the bilinear terms
and
, we first prove an
convergence of the
-norm of the error, uniformly on the time grid, restricted to the set
defined below for some
:
and let
. Recall that, for
, set
and
; then,
. Using (
9), (
10), (
17) and (
18), we deduce, for
,
and
, that
and
In this section, we will suppose that N is large enough to have . The following result is a crucial step towards the rate of convergence of the implicit time Euler scheme.
Proposition 7. Suppose that the conditions(C-u)and(C-)hold. Let and for some , be the solution to (9) and (10) and be the solution to (17) and (18). Fix and let be defined by (88). Then, for , there exists a positive constant C, independent of N, such that, for large enough N,wherefor some , and is the constant in the right hand side of the Gagliardo–Nirenberg inequality (6). Proof. Write (
89) with
and (
90) with
; using the equality
, we obtain for
where, by the antisymmetry property (
3), we have that
and
We next prove upper estimates of the terms for and for , and of the expected value of , and .
The Hölder and Young inequalities and the Gagliardo–Nirenberg inequality (
6) imply, for
, that
and, for
, that
Hölder’s inequality and the Sobolev embedding
imply, for
, that
whereas, for
,
Similar arguments prove, for
, that
The Cauchy–Schwarz and Young inequalities imply, for
, that
Using once more the Cauchy–Schwarz and Young inequalities, we deduce
Note that the sequence of subsets is decreasing. Therefore, since , given , we obtain
Hence, for
and
, using Young’s inequality, we deduce, for every
, that
where
The Cauchy–Schwarz and Young inequalities imply that
Using the upper estimates (
103)–(
106), taking expected values and using the Cauchy–Schwarz and Young inequalities, as well as the inequalities (
19), (
20), (
37), (
46), (
59), (
60) and (
72), we deduce that, for
and every
,
for some constant
C independent of
L and
N. Furthermore, the Lipschitz conditions (
12) and (
15), the inclusion
for
and the upper estimates (
46) and (
47) imply that
Finally, the Davis inequality, the inclusion
for
, the local property of stochastic integrals, the Lipschitz condition (
12), the Cauchy–Schwarz and Young inequalities and the upper estimate (
46) imply, for
, that
A similar argument, using the Lipschitz condition (
15) and (
47), yields, for
,
Collecting the upper estimates (
94)–(
111), we obtain, for
,
,
and
,
Therefore, given , choosing and such that , neglecting the sum in the left hand side and using the discrete Gronwall lemma, we deduce, for , that
where
for
and
(and choosing
and
such that
and
). Let
and
. Then, for some
, we have that
Plugging the upper estimate (
113) in (
112), we conclude the proof of (
91). □
9. Conclusions
This paper provides the first result on the rate of the convergence of a time discretization of the Navier–Stokes equations coupled with a transport equation for the temperature, driven by a random perturbation; this is the so-called Boussinesq/Bénard model. The perturbation may depend on both the velocity and temperature of the fluid. The rates of the convergence in probability and in
are similar to those obtained for the stochastic Navier–Stokes equations. The Boussinesq equations model a variety of phenomena in environmental, geophysical and climate systems (see, e.g., [
18,
19]). Even if the outline of the proof is similar to that used for the Navier–Stokes equations, the interplay between the velocity and the temperature is more delicate to deal with in many places. This interplay, which appears in Bénard systems, is crucial for describing more general hydrodynamical models. The presence of the velocity in the bilinear term describing the dynamics of the temperature makes it more difficult to prove bounds of moments for the
-norm of the temperature uniformly in time and requires higher moments of the initial condition. Such bounds are crucial to deduce rates of convergence (in probability and in
) from the localized one.
This localized version of the convergence is the usual first step in a non-linear (non-Lipschitz and non-monotonous) setting. Numerical simulations, which are the ultimate aim of this study since there is no other way to “produce” trajectories of the solution, would require a space discretization, such as finite elements. This is not dealt with in this paper and will be carried out in a forthcoming work. This new study is likely to provide results similar to those obtained for the 2D Navier–Stokes equations.
In addition, note that another natural continuation of this work would be to consider a more general stochastic 2D magnetic Bénard model (as discussed in [
1]) that describes the time evolution of the velocity, temperature and magnetic field of an incompressible fluid.
It would also be interesting to study the variance of the -norm of the error term, in both additive and multiplicative settings, for the Navier-0Stokes equations and more general Bénard systems. This would give some information about the accuracy of the approximation. Proving a.s. the convergence of the scheme for Bénard models is also a challenging question.