Short-term load forecasting is of great significance to the operation of power systems. Various uncertain factors, such as meteorological social data, have already been combined with historical power data to create more accurate load forecasting models. In traditional systems, data from various industries and regions are centralized for knowledge extraction. However, concerns regarding data security and privacy often prevent industries from sharing their data, limiting both the quantity and diversity of data available for forecasting models. These challenges drive the adoption of federated learning (FL) to address issues related to data silos and privacy. In this paper, a novel framework for short-term load forecasting was proposed using historical data from industries such as power, meteorology, and finance. Long short-term memory (LSTM) networks were utilized for forecasting, and federated learning (FL) was implemented to protect data privacy. FL allows clients in multiple regions to collaboratively train a shared model without exposing their data. To further enhance security, the homomorphic encryption (HE) using Paillier algorithm was introduced during the federated process. Experimental results demonstrate that the federated model, which extracts knowledge from different regions, outperforms locally trained models. Furthermore, longer HE keys have little effect on predictive performance but significantly slow down encryption and decryption, thereby increasing training time.
Citation: Mengdi Wang, Rui Xin, Mingrui Xia, Zhifeng Zuo, Yinyin Ge, Pengfei Zhang, Hongxing Ye. A federated LSTM network for load forecasting using multi-source data with homomorphic encryption[J]. AIMS Energy, 2025, 13(2): 265-289. doi: 10.3934/energy.2025011
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Short-term load forecasting is of great significance to the operation of power systems. Various uncertain factors, such as meteorological social data, have already been combined with historical power data to create more accurate load forecasting models. In traditional systems, data from various industries and regions are centralized for knowledge extraction. However, concerns regarding data security and privacy often prevent industries from sharing their data, limiting both the quantity and diversity of data available for forecasting models. These challenges drive the adoption of federated learning (FL) to address issues related to data silos and privacy. In this paper, a novel framework for short-term load forecasting was proposed using historical data from industries such as power, meteorology, and finance. Long short-term memory (LSTM) networks were utilized for forecasting, and federated learning (FL) was implemented to protect data privacy. FL allows clients in multiple regions to collaboratively train a shared model without exposing their data. To further enhance security, the homomorphic encryption (HE) using Paillier algorithm was introduced during the federated process. Experimental results demonstrate that the federated model, which extracts knowledge from different regions, outperforms locally trained models. Furthermore, longer HE keys have little effect on predictive performance but significantly slow down encryption and decryption, thereby increasing training time.
Fluid flow through deformable porous media is relevant for many applications in biology, medicine and bioengineering. Some important examples include blood flow through tissues in the human body [9, 11] and fluid flow inside cartilages, bones and engineered tissue scaffolds [10, 13, 21, 31, 37]. The mechanics of biological tissues typically exhibits both elastic and viscoelastic behaviors resulting from the combined action of various components, including elastin, collagen and extracellular matrix [22, 23, 26, 30]. Thus, from the mathematical viewpoint, the study of fluid flows through deformable porous biological structures requires the coupling of poro-elasticity with structural viscoelasticity, leading to poro-visco-elastic models.
The theoretical study of fluid flow through deformable porous media has attracted a lot of attention since the beginning of the last century, initially motivated by applications in geophysics and petroleum engineering. The development of the field started with the work of Terzaghi in 1925 [38], which focused on finding an analytic solution for a one-dimensional (1D) model. However, it was Biot's work in 1941 [5] that set up the framework and ignited the mathematical development for fluid flow through poro-elastic media. To date, several books and articles have been devoted to the mathematical analysis and numerical investigation of poro-elastic models, such as [8, 12, 14, 15, 16, 25, 27, 28, 29, 34, 35, 36, 37, 40], with applications ranging from engineering and geophysics to medicine and biology. Recently, our team has developed a theoretical and numerical framework to study both poro-elastic and poro-visco-elastic models, as motivated by biological applications [6]. The study showed that structural viscoelasticity plays a crucial role in determining the regularity requirements for volumetric and boundary forcing terms, as well as for the corresponding solutions. Moreover, in [2] it has been shown that the solution of the fluid-solid mixture (elastic displacement, fluid pressure, and Darcy velocity) is more sensitive to the boundary traction in the elastic case than in the visco-elastic scenario. These theoretical findings are also supported by experimental and clinical evidences showing that changes in tissue viscoelasticity are associated with various pathological conditions, including atherosclerosis [24], osteoporosis [1], renal disease [20] and glaucoma [17].
Interestingly, the study in [6] provided numerical clues that sudden changes in body forces and/or stress boundary conditions may lead to uncontrolled fluid-dynamical responses within the medium in the absence of structural viscoelasticity. This finding led us to formulate a novel hypothesis concerning the causes of damage in biological tissues, namely that abrupt time variations in stress conditions combined with lack of structural viscoelasticity could lead to microstructural damage due to local fluid-dynamical alterations, as illustrated in Fig. 1.
The importance of the coupling between structural mechanics and fluid dynamics in the damage of deformable porous media has been investigated by several authors [39, 23, 33]. In the present study, we focus on a particular aspect of this coupling and we aim at characterizing and quantifying the influence of structural viscoelasticity on the biomechanical and fluid-dynamical responses to sudden changes in stress conditions.
Biomechanical applications are characterized by the fact that tissues
have a mass density that is similar to that of water.
For this reason, we consider in these pages
the case of deformable porous media constituted by incompressible solid and fluid components.
To mathematically define this concept, we introduce the following
relation between fluid pressure
ζ=c0p+α∇⋅u, | (1a) |
where
ζ=∇⋅u. | (1b) |
Notice that unlike in standard elasticity theory, incompressibility of each component of a deformable
porous medium does not mean that both solid displacement and fluid velocity are divergence-free, rather,
that the volumetric deformation of the solid constituent corresponds to the
variation of fluid volume per unit volume of porous material, with the convention that
Building upon our theoretical and numerical results presented in [6], we devise a 1D problem for which we exhibit an explicit solution where the discharge velocity goes to infinity if the stress boundary condition is not sufficiently smooth in time and the solid component is not viscoelastic. Interestingly, this blow-up in the velocity occurs even in the simple case where the permeability of the medium is assumed to be constant, in comparison to the general case of nonlinear permeability depending on dilation [6, 8] or pressure [36]. In addition, we perform a dimensional analysis that allows us to identify the parameters influencing the solution blow-up, thereby opening the path to sensitivity analysis on the system, and providing practical directions on how to control the biomechanical and fluid-dynamical response of the fluid-solid mixture, prevent microstructural damage and, in perspective, aid the experimental design of bioengineered tissues [18].
The article is organized as follows. Poro-visco-elastic models are described in Section 2, along with a summary of the related theoretical results. Section 3 focuses on a special 1D case, for which an explicit solution is derived and its well-posedness is studied in the presence or absence of viscoelasticity. The dimensional analysis of the 1D problem is carried out in Section 4. Solution properties are explored in detail for the particular case of boundary traction with a discontinuity in time, see Section 5, and with a trapezoidal time profile, see Section 6. The application of this analysis to the case of confined compression of biological tissues is discussed in Section 7. Conclusions and perspectives are summarized in Section 8.
Following the same notation as in [6], let
∇⋅σ+F=0inΩ×(0,T) | (2a) |
∂ζ∂t+∇⋅v=SinΩ×(0,T) | (2b) |
respectively, where
σ=σe+σv−pI | (2c) |
σe=λe(∇⋅u) I+2μeϵ(u) | (2d) |
σv=λv(∇⋅∂u∂t) I+2μvϵ(∂u∂t) | (2e) |
ζ=∇⋅u | (2f) |
v=−K∇p | (2g) |
where
Most of the theoretical studies focused on the poro-elastic case without accounting for structural viscoelasticity. In the case of constant permeability, the coupling between the elastic and fluid subproblems is linear and the well-posedness and regularity of solutions have been studied by several authors [25, 35, 40]. In the case of non-constant permeability, the coupling between the two subproblems becomes nonlinear and only few theoretical results have been obtained. In [36], Showalter utilized monotone operator theory techniques in order to provide well-posedness of solutions in the case where the permeability is a nonlinear function of pressure.
To the best of our knowledge, Cao et al in [8] were the first to consider the permeability as a nonlinear function of dilation and provide existence of weak solutions for this nonlinear poro-elastic case. However, the analysis in [8] is performed upon assuming homogeneous boundary conditions for both pressure and elastic displacement, which is often not the case from the viewpoint of applications.
Our recent paper in [6] extends the works mentioned above by considering
poro-elastic and poro-visco-elastic models with dilation-dependent permeability, non-zero volumetric sources of mass and momentum and non-homogeneous, mixed Dirichlet-Neumann boundary conditions. More precisely, in [6] we assumed that the boundary of
σ⋅n=g,v⋅n=0onΓN×(0,T) | (3) |
u=0,p=0onΓDp×(0,T) | (4) |
u=0,v⋅n=ψonΓDv×(0,T). | (5) |
Our analysis showed that the data time regularity requirements and the smoothness of solutions significantly differ depending on whether the model is poro-elastic or poro-visco-elastic. In particular, in the visco-elastic case, if the source of linear momentum
Let the space domain
∂σ∂x=0in(0,L)×(0,T) | (6a) |
∂2u∂t∂x+∂v∂x=0in(0,L)×(0,T). | (6b) |
The associated constitutive equations are given by
σ0=μ∂u∂x+η∂2u∂t∂xin(0,L)×(0,T) | (7a) |
σ=σ0−pin(0,L)×(0,T) | (7b) |
v=−K∂p∂xin(0,L)×(0,T) | (7c) |
where we have set
K=K(x,∂u∂x). |
We complete the system with the following boundary and initial conditions:
u(0,t)=v(0,t)=0for0<t<T | (8a) |
p(L,t)=0 ; σ(L,t)=−P(t)for0<t<T | (8b) |
u(x,0)=0for0<x<L | (8c) |
where
If
System (6a)-(8c) can be rewritten solely in
terms of the displacement. Indeed, integration of (6b
) with respect to
∂u∂t(x,t)+v(x,t)=A(t) |
where
∂u∂t−K∂p∂x=0 in (0,L)×(0,T) . |
On the other hand, using (7b) and (6a), we derive that
∂p∂x=∂σ0∂x . |
Therefore the system (6a)-(8c) reduces to the following
initial boundary value problem in terms of the sole elastic displacement
∂u∂t−Kμ∂2u∂x2−Kη∂3u∂t∂x2=0in (0,L)×(0,T) | (9a) |
μ∂u∂x(L,t)+η∂2u∂t∂x(L,t)=−P(t)for0<t<T | (9b) |
u(0,t)=0for0<t<T | (9c) |
u(x,0)=0for0<x<L . | (9d) |
Subsequently, we can recover the solid part of the stress tensor, discharge velocity,
pressure and total stress tensor on
σ0=μ∂u∂x+η∂2u∂t∂x | (10a) |
v=−∂u∂t=−K∂σ0∂x | (10b) |
p=σ0+P(t) | (10c) |
σ=−P(t) . | (10d) |
Remark 1. For later reference, we write below the explicit form of the purely elastic problem which corresponds to setting
∂u∂t−Kμ∂2u∂x2=0in (0,L)×(0,T) | (11a) |
μ∂u∂x(L,t)=−P(t)for0<t<T | (11b) |
u(0,t)=0for0<t<T | (11c) |
u(x,0)=0for0<x<L . | (11d) |
Remark 2. An important quantity associated with the fluid-solid mixture is the fluid
power density
P(t)=∫L01K|v(x,t)|2dx. | (12) |
From its definition, it follows that
Let us further assume that the permeability is constant, i.e.
K=K(x,∂u∂x)≡K0=constant>0. |
In this case, system (9) is a linear initial boundary value problem, whose solution can be obtained by Fourier series expansion as described below.
Case 1. (
w(x,t)=u(x,t)+U(t)μx |
having introduced the auxiliary function
U(t)=μη∫t0exp(−μη(t−s))P(s)ds=μηexp(−μηt)∗P(t) |
where the star symbol denotes convolution. Thus,
∂w∂t−K0μ∂2w∂x2−K0η∂3w∂t∂x2=xμU′(t)in (0,L)×(0,T)μ∂w∂x(L,t)+η∂2w∂t∂x(L,t)=0for0<t<Tw(0,t)=0for0<t<Tw(x,0)=0for0<x<L | (13) |
where the prime symbol denotes differentiation for functions of a single variable. The associated eigenvalue problem is
Find y=y(x), 0<x< L, such that |
y′′+λy=0,y(0)=y′(L)=0 . |
The eigenvalues and the corresponding eigenfunctions are given by
λn=(2n+1)2π24L2andyn(x)=sin(2n+1)πx2Lforn=0,1,… | (14) |
We seek a solution of the form
w(x,t)=∞∑n=0cn(t)yn(x) |
where the coefficients
x=2L∞∑n=0(−1)nλnyn(x),0≤x≤L |
the uniqueness of the Fourier expansion leads to the family of ordinary differential equations
(1+K0ηλn)c′n(t)+K0μλncn(t)=2(−1)nμLλnU′(t),cn(0)=0 |
whose solution is given by
cn(t)=2(−1)nμLλn(1+K0ηλn)exp(−K0μλn1+K0ηλnt)∗U′(t) . |
Therefore, we get
u(x,t)=−U(t)μx+2μL∞∑n=0(−1)nyn(x)λn(1+K0ηλn)exp(−K0μλn1+K0ηλnt)∗U′(t). |
In conclusion, after performing integration by parts in the convolution term and using the
identity
u(x,t)=−2K0L∞∑n=0(−1)nyn(x)1+K0ηλnexp(−K0μλn1+K0ηλnt)∗P(t) . | (15) |
Case 2. (
u(x,t)=−2K0L∞∑n=0(−1)nyn(x)exp(−K0μλnt)∗P(t) | (16) |
is the formal solution of the purely elastic problem.
In particular, in the case
In this section we prove that the
formal solutions (15) and (16) indeed solve the visco-elastic problem (9) and the purely elastic problem (11), respectively, in well-defined functional spaces. Let us begin by introducing the
functional framework. We consider the real Hilbert
space
(f,g)H=∫L0f(x)g(x) dx∀f,g∈H, |
and endowed with the induced norm
‖f‖H=√(f,f)H. |
The orthonormal sequence of eigenfunctions
v(x)=∞∑n=0cnyn(x), | (17) |
with coefficients
∞∑n=0|cn|2<∞. |
If this is the case, the series expansion of
V={v∈H:v′∈H,v(0)=0} |
be the real Hilbert space equipped with the scalar product
(v,w)V=(v′,w′)H∀v,w∈V, |
and endowed with the induced norm (due to Poincaré's inequality)
‖v‖V=‖v′‖H. |
Sobolev's Embedding Theorem ensures that
∞∑n=0λn|cn|2<∞. |
More generally, the eigenfunction expansion (17) enables us to define a
one-parameter family
∞∑n=0λsn|cn|2<∞ . |
In particular, we have that
Case 1. (
Definition 3.1. A function
(ⅰ)
(ⅱ) for every
(u′(t),v)H+K0(μu(t)+ηu′(t),v)V=−K0P(t)v(L); | (18) |
(ⅲ)
where we used the notation
Remark 3. The initial condition (9d) is satisfied by
Theorem 3.2. Suppose
Proof. For sake of exposition, we rewrite
u(x,t)=∞∑n=0un(t)yn(x) | (19) |
where
un(t)=−2K0L(−1)n1+K0ηλnexp(−K0μλn1+K0ηλnt)∗P(t) | (20) |
so that
∂u∂t(x,t)=∞∑n=0u′n(t)yn(x) | (21) |
where
u′n(t)=−2K0L(−1)n1+K0ηλnP(t)−K0μλn1+K0ηλnun(t). | (22) |
[Regularity]. Firstly, we show that
|e−at∗P(t)|≤1√2a‖P‖L2(0,T). |
Upon applying this estimate to (20) and (22) we see that there are
positive constants, generically denoted by
|un(t)|≤Cλn‖P‖L2(0,T) | (23) |
and
|u′n(t)|≤Cλn(|P(t)|+‖P‖L2(0,T)). | (24) |
Thus,
[Existence]. In order to show that
(u′(t),yn)H=L2u′n(t) |
(u(t),yn)V=(∂u∂x(⋅,t),dyndx(⋅))H=L2λnun(t) |
(u′(t),yn)V=(∂2u∂t∂x(⋅,t),dyndx(⋅))H=L2λnu′n(t) . |
Adding the above three terms, we obtain that
(u′(t),yn)H+K0(μu(t)+ηu′(t),yn)V=L2[(1+K0ηλn)u′n(t)+K0μλnun(t)] . |
Thus, from (22) and the fact that
[Uniqueness] From the linearity of the equation, it suffices to
show that
(u′(t),v)H+K0(μu(t)+ηu′(t),v)V=0,v∈V . |
If we choose
(u′(t),u(t))H+K0μ‖u(t)‖2V+K0η(u′(t),u(t))V=0 |
implying that
12ddt(‖u(t)‖2H+K0η‖u(t)‖2V)=−K0μ‖u(t)‖2V≤0 . |
Integrating with respect to time and using the initial condition
‖u(t)‖2H+K0η‖u(t)‖2V≤0 |
Hence
Remark 4. From (23), the fact that
Case 2. (
un(t)=−2K0L(−1)nexp(−K0μλnt)∗P(t). |
Then, estimates (23) and (24) become, respectively,
|un(t)|≤C√λn‖P‖L2(0,T) |
and
|u′n(t)|≤C√λn(|P(t)|+‖P‖L2(0,T)). |
As a consequence, it can only be asserted that
|un(t)|≤Cλn‖P‖L∞(0,T) |
and
|u′n(t)|≤C‖P‖L∞(0,T). |
Hence, in this case,
Definition 3.3. A function
(ⅰ)
(ⅱ) for every
⟨u′(t),v⟩+K0μ(u(t),v)V=−K0P(t)v(L) | (25) |
where the brackets
(ⅲ)
Theorem 3.4. Suppose
The goal of this section is to rewrite problem (9) in dimensionless form so that we can identify combinations of geometrical and physical parameters that most influence the solution properties. Dimensional analysis relies on the choice of a set of characteristic values that can be used to scale all the problem variables. Let us use the hat symbol to indicate dimensionless (or scaled) variables and the square brackets to indicate the characteristic value of that quantity. Then, for the problem at hand we would write:
ˆx=x[x],ˆt=t[t],ˆη=η[η],ˆλn=λn[λn],ˆP=P[P],ˆu=u[u],ˆv=v[v],ˆP=P[P] . | (26) |
It is important to emphasize that there is no trivial choice for the characteristic values and, in general, this choice is not unique. In this particular case, though, we will leverage our knowledge of the forcing terms and the explicit formulas we obtained for the solution to guide us in the choice of some of these values. Since the problem is driven by the boundary condition on the traction with the given function
[P]=Pref | (27) |
where
[λn]=1L2and[x]=L | (28) |
and, consequently, the expression for
[η]=1K0[λn]=L2K0,[t]=1K0μ[λn]=L2K0μ,[u]=K0L[P][t]=PrefLμ , | (29) |
whereas the expressions for
[v]=[u][t]=K0LPref | (30) |
and
[P]=LK0[v]2=K0LP2ref | (31) |
respectively. Using the above scalings, we obtain the following dimensionless problem:
∂ˆu∂ˆt−∂2ˆu∂ˆx2−ˆη∂3ˆu∂ˆt∂ˆx2=0in (0,1)×(0,ˆT) | (32a) |
∂ˆu∂ˆx(1,ˆt)+ˆη∂2ˆu∂ˆt∂ˆx(1,ˆt)=−ˆP(ˆt)for0<ˆt<ˆT | (32b) |
ˆu(0,ˆt)=0for0<ˆt<ˆT | (32c) |
ˆu(ˆx,0)=0for0<ˆx<1 | (32d) |
where
ˆu(ˆx,ˆt)=−2∞∑n=0(−1)nyn(ˆx)1+ˆηˆλnexp(−ˆλn1+ˆηˆλnˆt)∗ˆP(ˆt) | (33) |
ˆv(ˆx,ˆt )=2∞∑n=0(−1)nyn(ˆx)1+ˆηˆλn{ˆP(ˆt )−ˆλn1+ˆηˆλnexp(−ˆλn1+ˆηˆλnˆt)∗ˆP(ˆt)} | (34) |
ˆP(ˆt)=2∞∑n=01(1+ˆηˆλn)2{ˆP(ˆt )−ˆλn1+ˆηˆλnexp(−ˆλn1+ˆηˆλnˆt)∗ˆP(ˆt)}2 . | (35) |
Remark 5. As already mentioned above, the choice for the characteristic values is not unique. In this regard, it is worth noticing that our choice for
Let the boundary traction
ˆP(ˆt )=H(ˆt )={0ifˆt<01ifˆt≥0 . | (36) |
In this case, the dimensionless solid displacement (33), discharge velocity (34) and
power density (35) (hereon denoted with the subscript
ˆuˆη(ˆx,ˆt )=−2∞∑n=0(−1)nˆλn{1−exp(−ˆλnˆt1+ˆηˆλn)}yn(ˆx)=−ˆx+2∞∑n=0(−1)nˆλnexp(−ˆλnˆt1+ˆηˆλn)yn(ˆx) | (37a) |
ˆvˆη(ˆx,ˆt)=2∞∑n=0(−1)n1+ˆηˆλnexp(−ˆλnˆt1+ˆηˆλn)yn(ˆx) | (37b) |
ˆPˆη(ˆt)=2∞∑n=01(1+ˆηˆλn)2exp(−2ˆλnˆt1+ˆηˆλn) . | (37c) |
The solution in the purely elastic case can be obtained by setting
Remark 6. If the unit step is shifted at
ˆu(ˆx,ˆt)=ˆuˆη(ˆx,ˆt−α)H(ˆt−α)={0if0≤ˆt<αˆuˆη(ˆx,ˆt−α)ifˆt≥α. |
The space-time behavior of
ˆP0(0)=2∞∑n=01=+∞. |
Proceeding analogously in the case of the discharge velocity, the Fourier expansion in the purely elastic case is given by
ˆv0(ˆx,0)=2∞∑n=0(−1)nyn(ˆx) |
which clearly lacks pointwise convergence for any
From the physical viewpoint, this means that,
at the switch on time of the driving term, here set at
ˆvˆη(ˆx,0)=2∞∑n=0(−1)n1+ˆηˆλnyn(ˆx) . |
Here, the
In order to further investigate this blow-up and its dependence on the structural viscoelasticity, we observe that the maximum value of
ˆvmax(ˆη)=max0≤ˆx≤1ˆt≥0|ˆvˆη(ˆx,ˆt)|=ˆvˆη(1,0)=2∞∑n=011+ˆηˆλn . |
The above series may be summed ([19], formula no. 1.4212) and the final result is
ˆvmax(ˆη)=1√ˆηtanh(1√ˆη) . | (38) |
Similarly, the dimensionless power density (37c) is decreasing in time and its
maximum is attained at
ˆPmax(ˆη)=maxˆt≥0ˆPˆη(ˆt)=ˆPˆη(0)=2∞∑n=01(1+ˆηˆλn)2=12ˆη(tanh21√ˆη+√ˆηtanh1√ˆη−1) . |
The behaviors of
Clearly, the dimensionless parameter
vmax(ˆη)=PrefK0Lˆvmax(ˆη) | (39) |
Pmax(ˆη)=P2refK0LˆPmax(ˆη). | (40) |
We can see that the magnitude
Let us now consider the case of a driving term given by a trapezoidal pulse, where the signal switch on and switch off are characterized by linear ramps. Thus,
let
ˆP(ˆt)={0ifˆt<0ˆtˆεif0≤ˆt<ˆε1ifˆε≤ˆt<ˆε+ˆτˆτ−ˆtˆε+2ifˆε+ˆτ≤ˆt<2ˆε+ˆτ0ifˆt≥2ˆε+ˆτ | (41) |
where
Let us now compute the dimensionless
discharge velocity resulting from the application of the trapezoidal pulse at the boundary, henceforth denoted by
ˆP(ˆt)=1ˆε{ˆtH(ˆt)−(ˆt−ˆε)H(ˆt−ˆε) | (42) |
−(ˆt−ˆτ−ˆε)H(ˆt−ˆτ−ˆε)+(ˆt−ˆτ−2ˆε)H(ˆt−ˆτ−2ˆε)} |
where the function
ˆu(ˆx,ˆt)=∫ˆt0ˆuˆη(ˆx,s)ds |
solves problem (32) with
ˆVˆη(ˆx,ˆt)=1ˆε{−ˆuˆη(ˆx,ˆt)H(ˆt)+ˆuˆη(ˆx,ˆt−ˆε)H(ˆt−ˆε)+ˆuˆη(ˆx,ˆt−ˆτ−ˆε)H(ˆt−ˆτ−ˆε)−ˆuˆη(ˆx,ˆt−ˆτ−2ˆε)H(ˆt−ˆτ−2ˆε)} . | (43) |
An illustration of the typical form of
The maximum possible discharge velocity occurs at
ˆVmax(ˆη,ˆε)=max0≤ˆx≤1ˆt≥0|ˆVˆη(ˆx,ˆt)|=ˆVˆη(1,ˆε)=2ˆε∞∑n=01ˆλn{1−exp(−ˆλnˆε1+ˆηˆλn)}. | (44) |
The behavior of
ˆVmax(ˆη,ˆε)≤ˆVmax(ˆη,0) |
and
ˆVmax(ˆη,0)=ˆvmax(ˆη) |
since the trapezoidal pulse reduces to a rectangular pulse as
ˆVmax(ˆη,ˆε)≤ˆVmax(0,ˆε)=2ˆε∞∑n=01−exp(−ˆλnˆε)ˆλn, |
hence no blow-up takes place even in the purely elastic case when the pulse is trapezoidal.
In this section, we utilize the mathematical analysis developed above to study some interesting features of confined compression tests, which are often utilized in biomechanics to characterize the properties of biological tissues.
A schematic of the confined compression experimental setting is depicted in Fig. 13, where a compressive load
is applied at the chamber top surface while
the bottom surface is maintained fixed.
Due to confinement,
deformation occurs only in the
The 1D model described in Section 3 allows us to generalize the mathematical analysis carried out in [37] by quantifying the effect of structural viscoelasticity on the tissue response to sudden changes in external pressure during confined compression experiments.
In this perspective,
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Characteristic velocities for the confined compression experiments reported in [37] are of the order of 0.35
ˆVmax(ˆη,ˆε)<ˆVth=LPrefK0 Vth=16.3 , | (45) |
where
To better visualize the various regions of interest in the
Interestingly, expression (29) defines
η=μτe | (46a) |
where
τe=L√ρμ | (46b) |
is the characteristic elastic time constant of the porous material under compression and
ˆη=τe[t]=K0L√ρμ. | (46c) |
Replacing the parameter values of Table 1 into (46c)
we obtain
The exact solutions obtained for the 1D models considered in this article allowed us to clearly identify a blow-up in the solution of certain poroelastic problems.
Compared to the analysis carried out in [6], in which
singularities in the solution were demonstrated by numerical evidences when the hypotheses of the existence
theorem were not satisfied, the 1D study conducted here allowed us, via analytical solution,
to prove that singularities may arise even in the simple case of constant permeability.
The analysis allowed us to identify the main factors that give rise to the blow-up, namely the absence of structural viscoelasticity and the time-discontinuity of the boundary source of traction. It is very important to emphasize that even a small viscoelastic contribution, namely
These findings actually provide an evidence of a blow-up that was hypothesized by the theoretical work presented in [6]. Interestingly, the a priori estimates derived in [6] were not sufficient to bound the power density if the data were not regular enough. The 1D examples considered in this article show that indeed it is not possible to bound the power density if the structure is purely elastic and the boundary forcing term is discontinuous in time. Moreover, we have shown that this blow-up occurs even in the simple case of constant permeability. We expect that similar results can be obtained for the 3D fluid-solid mixture described in (2a)-(2g) with boundary conditions (3)-(5). In particular, if one considers the coupling driven by a source of linear momentum and a source of boundary traction that are not time differentiable, then one can only obtain local in time existence of weak solutions for the system (in comparison to [6]), and prove blow-up of solutions in finite time (in the norm of the fluid energy). We plan to provide this analysis in a subsequent paper.
In real situations, we will never see the fluid velocity spiking to infinity as predicted by the mathematical blow-up, since something will break first! For example, if the poroelastic model represents a biological tissue perfused by blood flowing in capillaries, as the maximum velocity becomes too high, capillaries will break letting blood out. Thus, from a practical perspective, it is crucial to identify parameters that can control the maximum value of the fluid velocity within a deformable porous medium in order to avoid microstructure damage. Our analysis showed that the maximum fluid velocity can be limited by:
(ⅰ) decreasing
It is worth noting that the extreme sensitivity of biological tissues to the active role of viscoelasticity predicted by our theoretical analysis seems to agree with the conclusions of [32] where experimental data based on magnetic resonance elastography show that viscoelasticity of the brain is a result of structural alteration occurring in the course of physiological aging, this suggesting that cerebral viscoelasticity may provide a sensitive marker for a variety of neurological diseases such as normal pressure hydrocephalus, Alzheimer's disease, or Multiple Sclerosis.
Further extensions of the current work may include: (1) the study of porous deformable media with compressible components. This case mathematically corresponds to considering (1a) instead of (1b), and is of interest in the wider context of applications of the poro-visco-elastic model to problems in geomechanics (cf. the original contribution by Biot in [5] and the more recent works [27, 28, 29] and [3, 4], devoted to computational analysis and theoretical investigations, respectively); (2) the investigation of time discontinuities in the volumetric sources of linear momentum, which were identified by the analysis in [6] as additional possible causes of blow-up. This case is actually extremely relevant in situations where the gravitational acceleration varies abruptly, such as during space flight take off and landing.
Finally, it is worth emphasizing that the 1D analysis presented in this article has made available a series of testable conditions leading to blow-up, and consequent microstructural damage, that could indeed be verified in laboratory experiments. We see the design and implementation of such experiments as the most interesting development of the present work.
Dr. Bociu has been partially supported by NSF CAREER DMS-1555062. Dr. Guidoboni has been partially supported by the award NSF DMS-1224195, the Chair Gutenberg funds of the Cercle Gutenberg (France) and the LabEx IRMIA (University of Strasbourg, France). Dr. Sacco has been partially supported by Micron Semiconductor Italia S.r.l., SOW nr. 4505462139.
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