Intuitionistic fuzzy hypersoft set is an extension of the intuitionistic fuzzy soft set used to express insufficient evaluation, uncertainty, and anxiety in decision-making. It is a new technique to realize computational intelligence and decision-making under uncertain conditions. The intuitionistic fuzzy hypersoft set can deal with uncertain and fuzzy information more effectively. The concepts and properties of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft sets are proposed in the following research. A prioritization technique for order preference by similarity to ideal solution (TOPSIS) based on correlation coefficients and weighted correlation coefficients is introduced under the intuitionistic fuzzy hypersoft sets. We also introduced aggregation operators, such as intuitionistic fuzzy hypersoft weighted average and intuitionistic fuzzy hypersoft weighted geometric operators. Based on the established TOPSIS method and aggregation operators, the decision-making algorithm is proposed under an intuitionistic fuzzy hypersoft environment to resolve uncertain and confusing information. A case study on decision-making difficulties proves the application of the proposed algorithm. Finally, a comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates this method's effectiveness.
Citation: Rana Muhammad Zulqarnain, Xiao Long Xin, Muhammad Saeed. Extension of TOPSIS method under intuitionistic fuzzy hypersoft environment based on correlation coefficient and aggregation operators to solve decision making problem[J]. AIMS Mathematics, 2021, 6(3): 2732-2755. doi: 10.3934/math.2021167
[1] | Lorena Bociu, Giovanna Guidoboni, Riccardo Sacco, Maurizio Verri . On the role of compressibility in poroviscoelastic models. Mathematical Biosciences and Engineering, 2019, 16(5): 6167-6208. doi: 10.3934/mbe.2019308 |
[2] | Aftab Ahmed, Javed I. Siddique . The effect of magnetic field on flow induced-deformation in absorbing porous tissues. Mathematical Biosciences and Engineering, 2019, 16(2): 603-618. doi: 10.3934/mbe.2019029 |
[3] | Alexander N. Ostrikov, Abdymanap A. Ospanov, Vitaly N. Vasilenko, Nurzhan Zh. Muslimov, Aigul K. Timurbekova, Gulnara B. Jumabekova . Melt flow of biopolymer through the cavities of an extruder die: Mathematical modelling. Mathematical Biosciences and Engineering, 2019, 16(4): 2875-2905. doi: 10.3934/mbe.2019142 |
[4] | Panagiotes A. Voltairas, Antonios Charalambopoulos, Dimitrios I. Fotiadis, Lambros K. Michalis . A quasi-lumped model for the peripheral distortion of the arterial pulse. Mathematical Biosciences and Engineering, 2012, 9(1): 175-198. doi: 10.3934/mbe.2012.9.175 |
[5] | Xu Bie, Yuanyuan Tang, Ming Zhao, Yingxi Liu, Shen Yu, Dong Sun, Jing Liu, Ying Wang, Jianing Zhang, Xiuzhen Sun . Pilot study of pressure-flow properties in a numerical model of the middle ear. Mathematical Biosciences and Engineering, 2020, 17(3): 2418-2431. doi: 10.3934/mbe.2020131 |
[6] | Abdulaziz Alsenafi, M. Ferdows . Effects of thermal slip and chemical reaction on free convective nanofluid from a horizontal plate embedded in a porous media. Mathematical Biosciences and Engineering, 2021, 18(4): 4817-4833. doi: 10.3934/mbe.2021245 |
[7] | Xiawei Yang, Wenya Li, Yaxin Xu, Xiurong Dong, Kaiwei Hu, Yangfan Zou . Performance of two different constitutive models and microstructural evolution of GH4169 superalloy. Mathematical Biosciences and Engineering, 2019, 16(2): 1034-1055. doi: 10.3934/mbe.2019049 |
[8] | Martina Bukač, Sunčica Čanić . Longitudinal displacement in viscoelastic arteries:A novel fluid-structure interaction computational model, and experimental validation. Mathematical Biosciences and Engineering, 2013, 10(2): 295-318. doi: 10.3934/mbe.2013.10.295 |
[9] | Rebecca Vandiver . Effect of residual stress on peak cap stress in arteries. Mathematical Biosciences and Engineering, 2014, 11(5): 1199-1214. doi: 10.3934/mbe.2014.11.1199 |
[10] | Y. -N. Young, Michael J. Shelley, David B. Stein . The many behaviors of deformable active droplets. Mathematical Biosciences and Engineering, 2021, 18(3): 2849-2881. doi: 10.3934/mbe.2021145 |
Intuitionistic fuzzy hypersoft set is an extension of the intuitionistic fuzzy soft set used to express insufficient evaluation, uncertainty, and anxiety in decision-making. It is a new technique to realize computational intelligence and decision-making under uncertain conditions. The intuitionistic fuzzy hypersoft set can deal with uncertain and fuzzy information more effectively. The concepts and properties of the correlation coefficient and the weighted correlation coefficient of the intuitionistic fuzzy hypersoft sets are proposed in the following research. A prioritization technique for order preference by similarity to ideal solution (TOPSIS) based on correlation coefficients and weighted correlation coefficients is introduced under the intuitionistic fuzzy hypersoft sets. We also introduced aggregation operators, such as intuitionistic fuzzy hypersoft weighted average and intuitionistic fuzzy hypersoft weighted geometric operators. Based on the established TOPSIS method and aggregation operators, the decision-making algorithm is proposed under an intuitionistic fuzzy hypersoft environment to resolve uncertain and confusing information. A case study on decision-making difficulties proves the application of the proposed algorithm. Finally, a comparative analysis with the advantages, effectiveness, flexibility, and numerous existing studies demonstrates this method's effectiveness.
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,
symbol | value | units |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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.
[1] | L. A. Zadeh, Fuzzy Sets, Information and Control, 8 (1965), 338–353. |
[2] | K. Atanassov, Intuitionistic Fuzzy Sets, Fuzzy Set. Syst., 20 (1986), 87–96. |
[3] | H. Garg, G. Kaur, Cubic intuitionistic fuzzy sets and its fundamental properties, J. Mult-valued Log. S., 33 (2019), 507–537. |
[4] |
K. Atanassov, G. Gargov, Interval valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 31 (1989), 343–349. doi: 10.1016/0165-0114(89)90205-4
![]() |
[5] |
H. Garg, K. Kumar, Linguistic interval-valued Atanassov intuitionistic fuzzy sets and their applications to group decision-making problems, IEEE T. Fuzzy Syst., 27 (2019), 2302–2311. doi: 10.1109/TFUZZ.2019.2897961
![]() |
[6] |
J. C. R. Alcantud, A. Z. Khameneh, A. Kilicman, Aggregation of infinite chains of intuitionistic fuzzy sets and their application to choices with temporal intuitionistic fuzzy information, Information Sciences, 514 (2020), 106–117. doi: 10.1016/j.ins.2019.12.008
![]() |
[7] | D. Molodtsov, Soft set theory-First results, Comput. Math. Appl., 37 (1999), 19–31. |
[8] | P. K. Maji, R. Biswas, A. R. Roy, Fuzzy soft sets, Journal of Fuzzy Mathematics, 9 (2001), 589–602. |
[9] | G. Ali, M. Akram, A. N. A. Koam, J. C. R. Alcantud, Parameter Reductions of Bipolar Fuzzy Soft Sets with Their Decision-Making Algorithms, Symmetry, 11 (2019), 1–25. |
[10] | P. Maji, R. Biswas, A. Roy, Intuitionistic fuzzy soft sets, Journal of Fuzzy Mathematics, 9 (2001), 677–692. |
[11] |
X. Yang, T. Y. Lin, J. Yang, Y. Li, D. Yu, Combination of interval-valued fuzzy set and soft set, Comput. Math. Appl., 58 (2009), 521–527. doi: 10.1016/j.camwa.2009.04.019
![]() |
[12] |
M. Akram, G. Ali, J. C. R. Alcantud, New decision-making hybrid model: intuitionistic fuzzy N-soft rough sets, Soft Comput., 23 (2019), 9853–9868. doi: 10.1007/s00500-019-03903-w
![]() |
[13] |
T. Gerstenkorn, J. Mafiko, Correlation of intuitionistic fuzzy sets, Fuzzy Set. Syst., 44 (1991), 39–43. doi: 10.1016/0165-0114(91)90031-K
![]() |
[14] | C. Yu, Correlation of fuzzy numbers, Fuzzy Set. Syst., 55 (1993), 303–307. |
[15] | D. A. Chiang, N. P. Lin, Correlation of fuzzy sets, Fuzzy Set. Syst., 102 (1999), 221–226. |
[16] | H. Garg, An improved cosine similarity measure for intuitionistic fuzzy sets and their applications to decision-making process, Hacet. J. Math. Stat., 47 (2018), 1578–1594. |
[17] |
H. Garg, K. Kumar, An advanced study on the similarity measures of intuitionistic fuzzy sets based on the set pair analysis theory and their application in decision making, Soft Comput., 22 (2018), 4959–4970. doi: 10.1007/s00500-018-3202-1
![]() |
[18] | H. Garg, D. Rani, A robust correlation coefficient measure of complex intuitionistic fuzzy sets and their applications in decision-making, Appl. Intell., 49 (2018), 496–512. |
[19] |
W. L. Hung, J. W. Wu, Correlation of intuitionistic fuzzy sets by centroid method, Information Sciences., 144 (2002), 219–225. doi: 10.1016/S0020-0255(02)00181-0
![]() |
[20] |
H. Bustince, P. Burillo, Correlation of interval-valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 74 (1995), 237–244. doi: 10.1016/0165-0114(94)00343-6
![]() |
[21] |
D. H. Hong, A note on correlation of interval-valued intuitionistic fuzzy sets, Fuzzy Set. Syst., 95 (1998), 113–117. doi: 10.1016/S0165-0114(96)00311-9
![]() |
[22] |
H. B. Mitchell, A correlation coefficient for intuitionistic fuzzy sets, Int. J. Intell. Syst., 19 (2004), 483–490. doi: 10.1002/int.20004
![]() |
[23] |
H. Garg, R. Arora, TOPSIS method based on correlation coefficient for solving decision-making problems with intuitionistic fuzzy soft set information, AIMS Mathematics, 5 (2020), 2944–2966. doi: 10.3934/math.2020190
![]() |
[24] |
H. L. Huang, Y. Guo, An Improved Correlation Coefficient of Intuitionistic Fuzzy Sets, J. Intell. Syst., 28 (2019), 231–243. doi: 10.1515/jisys-2017-0094
![]() |
[25] |
S. Singh, S. Sharma, S. Lalotra, Generalized Correlation Coefficients of Intuitionistic Fuzzy Sets with Application to MAGDM and Clustering Analysis, Int. J. Fuzzy Syst., 22 (2020), 1582–1595. doi: 10.1007/s40815-020-00866-1
![]() |
[26] | C. L. Hwang, K. Yoon, Multiple Attribute Decision Making Methods and Applications, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag Berlin Heidelberg, 1981. |
[27] | M. Zulqarnain, F. Dayan, M. Saeed, TOPSIS Analysis for The Prediction of Diabetes Based on General Characteristics of Humans, International Journal of Pharmaceutical Sciences and Research, 9 (2018), 2932–2939. |
[28] | A. Sarkar, A TOPSIS method to evaluate the technologies, International Journal of Quality & Reliability Management, 31 (2013), 2–13. |
[29] | R. M. Zulqarnain, S. Abdal, B. Ali, L. Ali, F. Dayan, M. I. Ahamad, et al. Selection of Medical Clinic for Disease Diagnosis by Using TOPSIS Method, International Journal of Pharmaceutical Sciences Review and Research, 61 (2020), 22–27. |
[30] |
C. T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Set. Syst., 114 (2000), 1–9. doi: 10.1016/S0165-0114(97)00377-1
![]() |
[31] |
L. Dymova, P. Sevastjanov, A. Tikhonenko, An approach to generalization of fuzzy TOPSIS method, Information Sciences, 238 (2013), 149–162. doi: 10.1016/j.ins.2013.02.049
![]() |
[32] |
M. Zulqarnain, F. Dayan, Choose Best Criteria for Decision Making Via Fuzzy Topsis Method, Mathematics and Computer Science, 2 (2017), 113–119. doi: 10.11648/j.mcs.20170206.14
![]() |
[33] | A. Y. Yayla, A. Özbek, A. Yildiz, Fuzzy TOPSIS method in supplier selection and application in the garment industry, Fibres Text. East.Eur., 20 (2012), 20–23. |
[34] | M. Zulqarnain, F. Dayan, Selection Of Best Alternative For An Automotive Company By Intuitionistic Fuzzy TOPSIS Method, International journal of scientific & technology research, 6 (2017), 126–132. |
[35] | M. Akram, A. Adeel, J. C. R. Alcantud, Multi-Criteria Group Decision-Making Using an m-Polar Hesitant Fuzzy TOPSIS Approach, Symmetry, 11 (2019), 1–23. |
[36] |
H. Garg, R. Arora, Generalized and group-based generalized intuitionistic fuzzy soft sets with applications in decision-making, Appl. Intell., 48 (2018), 343–356. doi: 10.1007/s10489-017-0981-5
![]() |
[37] |
K. Zhang, J. Zhan, X. Wang, TOPSIS-WAA method based on a covering-based fuzzy rough set: an application to rating problem, Information Sciences, 539 (2020), 397–421. doi: 10.1016/j.ins.2020.06.009
![]() |
[38] |
H. Jiang, J. Zhan, B. Sun, J. C. R. Alcantud, An MADM approach to covering-based variable precision fuzzy rough sets: an application to medical diagnosis, Int. J. Mach. Learn. Cyb., 11 (2020), 2181–2207. doi: 10.1007/s13042-020-01109-3
![]() |
[39] |
T. Mahmood, Z. Ali, Entropy measure and TOPSIS method based on correlation coefficient using complex q-rung orthopair fuzzy information and its application to multi-attribute decision making, Soft Comput., 11 (2020), 1–27. doi: 10.5121/ijsc.2020.11401
![]() |
[40] |
J. Zhan, H. Jiang, Y. Yao, Covering-based variable precision fuzzy rough sets with PROMETHEE-EDAS methods, Information Sciences, 538 (2020), 314–336. doi: 10.1016/j.ins.2020.06.006
![]() |
[41] |
J. Ye, J. Zhan, Z. Xu, A novel decision-making approach based on three-way decisions in fuzzy information systems, Information Sciences, 541 (2020), 362–390. doi: 10.1016/j.ins.2020.06.050
![]() |
[42] | J. Zhan, H. Jiang, Y. Yao, Three-way multi-attribute decision-making based on outranking relations, IEEE T. Fuzzy Syst., 2020, DOI: 10.1109/TFUZZ.2020.3007423. |
[43] |
L. Zhang, J. Zhan, Y. Yao, Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: an application to biomedical problems, Information Sciences, 517 (2020), 315–339. doi: 10.1016/j.ins.2020.01.003
![]() |
[44] |
J. Zhan, B. Sun, Covering-based intuitionistic fuzzy rough sets and applications in multi-attribute decision-making, Artif. Intell. Re., 53 (2020), 671–701. doi: 10.1007/s10462-018-9674-7
![]() |
[45] | F. Smarandache, Extension of Soft Set to Hypersoft Set, and then to Plithogenic Hypersoft Set, Neutrosophic Sets and Systems, 22 (2018), 168–170. |
[46] | S. Rana, M. Qayyum, M. Saeed, F. Smarandache, Plithogenic Fuzzy Whole Hypersoft Set: Construction of Operators and their Application in Frequency Matrix Multi Attribute Decision Making Technique, Neutrosophic Sets and Systems, 28 (2019), 34–50. |
[47] | R. M. Zulqarnain, X. L. Xin, M. Saqlain, F. Smarandache, Generalized Aggregate Operators on Neutrosophic Hypersoft Set, Neutrosophic Sets and Systems, 36 (2020), 271–281. |
[48] | S. Alkhazaleh, Plithogenic Soft Set, Neutrosophic Sets and Systems, 33 (2020), 256–274. |
[49] |
M. Abdel-Basset, W. Ding, R. Mohamed, N. Metawa, An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries, Risk Manag., 22 (2020), 192–218. doi: 10.1057/s41283-020-00061-4
![]() |
[50] | M. Abdel-Basset, R. Mohamed, A. E. N. H. Zaied, F. Smarandache, A Hybrid Plithogenic Decision-Making Approach with Quality Function Deployment for Selecting Supply Chain Sustainability Metrics, Symmetry, 11 (2019), 1–21. |
[51] | R. Arora, H. Garg, Robust aggregation operators for multi-criteria decision-making with intuitionistic fuzzy soft set environment, Sci. Iran., 25 (2018), 931–942. |
[52] |
Z. Xu, J. Chen, J. Wu, Clustering algorithm for intuitionistic fuzzy sets, Information Sciences, 178 (2008), 3775–3790. doi: 10.1016/j.ins.2008.06.008
![]() |
[53] | H. M. Zhang, Z. S. Xu, Q. Chen, On clustering approach to intuitionistic fuzzy sets, Control and Decision, 22 (2007), 882–888. |
1. | 2019, 9780128125182, 805, 10.1016/B978-0-12-812518-2.00047-0 | |
2. | Giovanna Guidoboni, Riccardo Sacco, Marcela Szopos, Lorenzo Sala, Alice Chandra Verticchio Vercellin, Brent Siesky, Alon Harris, Neurodegenerative Disorders of the Eye and of the Brain: A Perspective on Their Fluid-Dynamical Connections and the Potential of Mechanism-Driven Modeling, 2020, 14, 1662-453X, 10.3389/fnins.2020.566428 | |
3. | Julia Arciero, Lucia Carichino, Simone Cassani, Giovanna Guidoboni, 2019, Chapter 5, 978-3-030-25885-6, 101, 10.1007/978-3-030-25886-3_5 | |
4. | Riccardo Sacco, Giovanna Guidoboni, Aurelio Giancarlo Mauri, 2019, 9780128125182, 653, 10.1016/B978-0-12-812518-2.00041-X | |
5. | Abdul Mohizin, Donghee Lee, Jung Kyung Kim, Impact of the mechanical properties of penetrated media on the injection characteristics of needle-free jet injection, 2021, 126, 08941777, 110396, 10.1016/j.expthermflusci.2021.110396 | |
6. | Y.-N. Young, Yoichiro Mori, Michael J. Miksis, Slightly deformable Darcy drop in linear flows, 2019, 4, 2469-990X, 10.1103/PhysRevFluids.4.063601 | |
7. | Alon Harris, Giovanna Guidoboni, Brent Siesky, Sunu Mathew, Alice C. Verticchio Vercellin, Lucas Rowe, Julia Arciero, Ocular blood flow as a clinical observation: Value, limitations and data analysis, 2020, 78, 13509462, 100841, 10.1016/j.preteyeres.2020.100841 | |
8. | Lorena Bociu, Sarah Strikwerda, Optimal control in poroelasticity, 2022, 101, 0003-6811, 1774, 10.1080/00036811.2021.2008372 | |
9. | Irfan Aditya Dharma, Daisuke Kawashima, Marlin Ramadhan Baidillah, Panji Nursetia Darma, Masahiro Takei, In-vivo viscoelastic properties estimation in subcutaneous adipose tissue by integration of poroviscoelastic-mass transport model (pve-MTM) into wearable electrical impedance tomography (w-EIT), 2021, 7, 2057-1976, 045019, 10.1088/2057-1976/abfaea | |
10. | Lorena Bociu, Sarah Strikwerda, 2022, Chapter 5, 978-3-031-04495-3, 103, 10.1007/978-3-031-04496-0_5 | |
11. | Lorena Bociu, Justin T. Webster, Nonlinear quasi-static poroelasticity, 2021, 296, 00220396, 242, 10.1016/j.jde.2021.05.060 | |
12. | Lorena Bociu, Giovanna Guidoboni, Riccardo Sacco, Daniele Prada, Numerical simulation and analysis of multiscale interface coupling between a poroelastic medium and a lumped hydraulic circuit: Comparison between functional iteration and operator splitting methods, 2022, 466, 00219991, 111379, 10.1016/j.jcp.2022.111379 | |
13. | Shenbao Chen, Jingchen Zhu, Jian Xue, Xiaolong Wang, Peng Jing, Lüwen Zhou, Yuhong Cui, Tianhao Wang, Xiaobo Gong, Shouqin Lü, Mian Long, Numerical simulation of flow characteristics in a permeable liver sinusoid with leukocytes, 2022, 121, 00063495, 4666, 10.1016/j.bpj.2022.10.022 | |
14. | Lorena Bociu, Sunčica Canic, Boris Muha, Justin T. Webster, Multilayered Poroelasticity Interacting with Stokes Flow, 2021, 53, 0036-1410, 6243, 10.1137/20M1382520 | |
15. | Lorena Bociu, Boris Muha, Justin T. Webster, Mathematical Effects of Linear Visco-elasticity in Quasi-static Biot Models, 2023, 0022247X, 127462, 10.1016/j.jmaa.2023.127462 | |
16. | Alireza Hosseinkhan, Ralph E. Showalter, Semilinear Degenerate Biot–Signorini System, 2023, 55, 0036-1410, 5643, 10.1137/22M1505335 | |
17. | Lorena Bociu, Matthew Broussard, Giovanna Guidoboni, Sarah Strikwerda, Analysis of a Multiscale Interface Problem Based on the Coupling of Partial and Ordinary Differential Equations to Model Tissue Perfusion, 2025, 23, 1540-3459, 1, 10.1137/23M1628073 | |
18. | L. Bociu, M. Broussard, G. Guidoboni, D. Prada, S. Strikwerda, Comparing Interface Conditions for a 3D–0D Multiscale Interface Coupling With Applications in Tissue Perfusion, 2025, 41, 2040-7939, 10.1002/cnm.70017 |
symbol | value | units |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|