Open access peer-reviewed chapter - ONLINE FIRST

Boundary Element Modeling and Simulation Algorithm for Fractional Bio-Thermomechanical Problems of Anisotropic Soft Tissues

By Mohamed Abdelsabour Fahmy

Submitted: October 9th 2020Reviewed: January 27th 2021Published: April 5th 2021

DOI: 10.5772/intechopen.96268

Downloaded: 14


The main purpose of this chapter is to propose a novel boundary element modeling and simulation algorithm for solving fractional bio-thermomechanical problems in anisotropic soft tissues. The governing equations are studied on the basis of the thermal wave model of bio-heat transfer (TWMBT) and Biot’s theory. These governing equations are solved using the boundary element method (BEM), which is a flexible and effective approach since it deals with more complex shapes of soft tissues and does not need the internal domain to be discretized, also, it has low RAM and CPU usage. The transpose-free quasi-minimal residual (TFQMR) solver are implemented with a dual-threshold incomplete LU factorization technique (ILUT) preconditioner to solve the linear systems arising from BEM. Numerical findings are depicted graphically to illustrate the influence of fractional order parameter on the problem variables and confirm the validity, efficiency and accuracy of the proposed BEM technique.


  • boundary element method
  • modeling and simulation algorithm
  • bio-heat transfer
  • fractional bio-thermomechanical problems
  • anisotropic soft tissues

1. Introduction

Human body is a complex thermal system, Arsene d’Arsonval and Claude Bernard have shown that the temperature difference between arterial blood and venous blood is due to oxygenation of blood [1]. A large number of research papers in bio-heat transfer over the past few decades have focused on an understanding of the impact of blood flow on the temperature distribution within living tissues. Pennes [2] was the first attempt to explain the temperature distribution in human tissue with blood flow effect. The improvement of numerical models for determination of temperature distribution in living tissues has been a topic of interest for numerous researchers. Askarizadeh and Ahmadikia [3] introduced analytical solutions for the transient Fourier and non-Fourier bio-heat transfer equations. Li et al. [4] studied the bio-thermomechanical interactions within the human skin in the context of generalized thermoelasticity.

Analytical solutions for the current problem [5, 6] are very difficult to obtain, so numerical methods have become the main way for solving these problems [7, 8, 9, 10]. The boundary element method (BEM) [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] is one of the numerical methods used to solve the current general problem [22, 23, 24, 25, 26, 27, 28, 29, 30, 31]. Generally, Laplace-domain fundamental solutions are easier to obtain than time-domain fundamental solutions for engineering and scientific problems [32, 33]. consequently, the BEM is very helpful for problems that did not have time-domain fundamental solutions, because it requires the Laplace-domain fundamental solutions of the problem’s governing equations. So, BEM expands the range of engineering problems that can be solved with the classical time-domain BEM.

The main aim of this chapter is to propose a new boundary element fractional model for describing the bio-thermomechanical properties of anisotropic soft tissues. The dual reciprocity boundary element method has been used to solve the TWMBT for obtaining the temperature distribution, and then the BEM has been used to obtain the displacement and stress at each time step. The linear systems from BEM were solved by the TFQMR solver with the ILUT preconditioner which reduces the number of iterations and the total CPU time.

A brief summary of the chapter is as follows: Section 1 introduces the background and provides the readers with the necessary information to books and articles for a better understanding of bio-thermomechanical problems in anisotropic soft tissues Section 2 describes the BEM modeling of the bio-thermomechanical interactions and introduces the partial differential equations that govern its related problems. Section 3 outlines the dual reciprocity boundary element method (DRBEM) for temperature field. Section 4 discusses the convolution quadrature boundary element method (CQBEM) for poro-elastic field. Section 5 presents the new numerical results that describe the bio-thermomechanical problems in anisotropic soft tissues.


2. Formulation of the problem

Consider an anisotropic soft tissue in the Cartesian coordinate system Ox1x2x3as shown in Figure 1. It occupies the region Ω=x1x2x3:0<x1<α¯0<x2<β¯0<x3<γ¯with boundary Γthat is subdivided into two non-intersective parts ΓDand ΓN.

Figure 1.

Geometry of the current problem.

The governing equations which model the fractional bio-thermomechanical problems in anisotropic soft tissues can be written as follows [34, 35].


where the fluid was modeled by the following Darcy’s law [36].


The fractional order equation which describes the TWMBT can be expressed as [37].


where σ, , Cajlg, ρ=ρs1ϕ+ϕρf, ρs, ρf, u, uf, FFand qqare total stress tensor, linear strain tensor, constant elastic moduli, bulk density, solid density, fluid density, solid displacement, fluid displacement, bulk body forces and specific flux of the fluid, respectively, Bare stress-temperature coefficients, trdenotes the trace, A=ϕ1+Q/Ris Biot’s coefficient, Qand Rare the solid–fluid coupling parameters, pis the fluid pressure in the vasculature, ζis the fluid volume variation measured in unit reference volume, ϕ=VfVis the porosity, Vfis the fluid volume, V=Vf+Vsis the bulk volume, Vsis the solid volume, τis the time, Kis the permeability, ρa=Cϕρfwhere C=0.66at low frequency [38], Kis the soft tissue thermal conductivity, Wbis the blood perfusion rate, Cbis the blood specific heat, Tbis the arterial blood temperature, Tis the soft tissue temperature, τ¯is the thermal relaxation time ρis the soft tissue density, Cis the soft tissue specific heat, Qmetis the metabolic heat generation and Qextis the external heat generation.

According to Bonnet [39], our problem can be expressed as a matrix system as [40].




3. Boundary element implementation for bioheat transfer field

Through this chapter, we supposed that Qmetand Tbare constants and θrτ=TrτTr0. Thus, Eq. (7) can be written as


According to finite difference scheme of Caputo [22] and using the fundamental solution of difference equation resulting from fractional bio-heat Eq. (11) [41], we can write the following dual reciprocity boundary integral equation


in which


where nis the outward unit normal vector to boundary Γ, ris the distance between source point iand considered point j, Nis the number of boundary nodes and Lis the number of internal nodes.



The discretization process for Eq. (12) leads to


After interpolation and integration processes over boundary elements, Eq. (15) can be expressed as


The matrix form of Eq. (16) can be written using (14) as


which also can be written




The boundary and initial conditions


The time discretization has been performed as follows


Substituting from Eqs. (22)(25) into (20), we obtain


Thus, with the temperature θdetermined, the remaining task is to solve the problem (8).

4. Boundary element implementation for the poro-elastic fields

The representation formula of (8) that describes the unknown field ûgcan be written as




For anisotropic case, the Laplace domain fundamental solution Ûrand the corresponding traction T̂vcan be expressed as [40].


where the solid displacement fundamental solution Ûsrmay be expressed as




which can be expressed as [36].


The fundamental solution of solid displacement Ûsrcan be dismantled into singular Ûssrand regular Ûrsrparts as


in which


The remaining parts of Ûras in (30) can be described as [36].


On the basis of limiting process x˜ΩxΓon (28) we get


According to limiting process x˜ΩxΓon (28) we obtain [42].






By using (39)-(42), we can write


By applying the inverse Laplace transform, we obtain


where is the time convolution.

According to [40], the fundamental solution is


On the basis of Stokes theorem, we obtain


which can be expressed as


On the basis of [40], we get


in which My=yyTTyyT.

By applying (48) to a formula a=vuwe obtain [43].


Making use of (34) and (45), we can express T̂sas


On the basis of [40], we obtain


which may be expressed using (34) as


By applying (29) (53), we obtain


Based on [42], we have


In the in right-side of (55), we can write second term as follows


in which


According to [40], we can write


By augmenting Ûssto Ûs, we obtain


According to [41], we get




On the basis of Lubich [44, 45], the integration weights ωnare calculated using Cauchy’s integral formula as


Polar coordinate transformation z=Reis used with the trapezoidal rule to approximate the integral (62) as


Substitution of Eq. (63) into Eq. (61), we get




According to the procedure [43], the convolution operator (44) can be expressed as


which may be written as


Let the boundary Γis discretized into Neboundary elements τeas follows


Now, we assume that we have




where k=1,2,3,4are the poro-elastic degrees of freedom, φiαkare icontinuous polynomial shape functions and ψiβkare jpiecewise discontinuous polynomial shape functions.

Thus, from (67), we can write the following N+1algebraic systems of equations


5. Numerical results and discussion

In the current study, a Krylov subspace iterative method is used for solving the resulting linear systems. In order to reduce the number of iterations, a dual threshold incomplete LU factorization technique (ILUT) which is one of the well-known preconditioning techniques is implemented as a robust preconditioner for TFQMR (Transpose-free quasi minimal residual) [46] to accelerate the convergence of the solver TFQMR.

To illustrate the numerical calculations computed by the proposed technique, the physical parameters for transversely isotropic soft tissue are given as follows [47]:

The elasticity tensor


in which




and therefore


where Eand E0are the respectively, vand v0are Poisson’s ratio in the isotropy plane and in the fiber direction respectively, and μ0is the shear moduli in any direction within a plane perpendicular to isotropy plane.

Since for strongly anisotropic soft tissue both bulk moduli are positive, we used the following physical parameters for anisotropic soft tissue [48].


and therefore


and other constants considered in the calculations are as follows.

ρs=1600kg/m3, ρF=1113kg/m3, p=25MPap=25MPa, ϕ=0.15and Q/R=0.65.(80)

The domain boundary of the current problem has been discretized into 21 boundary elements and 42 internal points as depicted in Figure 2. The computation was done using Matlab R2018a on a MacBook Pro with 2.9GHz quad-core Intel Core i7 processor and 16GB RAM.

Figure 2.

Boundary element model of the current problem.

Figure 3 shows the variation of the temperature Talong xaxisfor different values of fractional order parameter. It can be seen from this figure that the fractional order parameter has a significant influence on the temperature.

Figure 3.

Variation of the temperatureTalongxaxis.

Figure 4 illustrates the variation of the displacement u1along xaxisfor different values of fractional order parameter. It can be seen from this figure that the fractional order parameter has a significant influence on the displacement u1.

Figure 4.

Variation of the displacementu1alongxaxis.

Figure 5 shows the variation of the displacement u2along xaxisfor different values of fractional order parameter. It can be seen from this figure that the fractional order parameter has a significant influence on the displacement u2.

Figure 5.

Variation of the displacementu2alongxaxis.

Figure 6 shows the variation of the fluid pressure palong xaxisfor different values of fractional order parameter. It can be seen from this figure that the fractional order parameter has a significant influence on the fluid pressure p.

Figure 6.

Variation of the fluid pressurepalongxaxis.

Figure 7 shows the variation of the bio-thermal stress σ11along xaxisfor different values of fractional order parameter. It can be seen from this figure that the fractional order parameter has an important influence on the bio-thermal stress σ11.

Figure 7.

Variation of the bio-thermal stressσ11alongxaxis.

Since there are no findings available for the problem under consideration. Therefore, some literatures may be regarded as special cases from our general problem. In the special case under consideration, the results of the bio-thermal stress caused by coupling between the temperature and displacement fields are plotted in Figure 8 to illustrate the variation of the bio-thermal stress σ11along xaxisfor BEM, FDM and FEM, where the boundary of the special case problem has been discretized into 21, 42 and 84 boundary elements (bes). The validity, accuracy and efficiency of our proposed technique have been confirmed by a graphical comparison of the three different boundary elements (21, 42 and 84) with those obtained using the FDM results of Shen and Zhang [49] and FEM results of Torvi and Dale [50] for the special case under consideration, the increase of BEM boundary elements leads to improve the accuracy and efficiency of the BEM, also, it can be noted that the BEM findings are in excellent agreement with the FDM and FEM results, we refer the interested reader to recent work [51, 52, 53, 54, 55] for understanding the BEM methodology.

Figure 8.

Variation of the bio-thermal stressσ11alongxaxisfor BEM, FDM and FEM.


6. Conclusion

  1. A novel boundary element model based on the TWMBT and Biot’s theory was established for describing the bio-thermomechanical interactions in anisotropic soft tissues.

  2. The bio-heat transfer equation has been solved using the dual reciprocity boundary element method (DRBEM) to obtain the temperature distribution.

  3. The mechanical equation has been solved using the convolution quadrature boundary element method (CQBEM) to obtain the displacement and fluid pressure for different temperature distributions at each time step.

  4. Due to the advantages of DRBEM and CQBEM such as dealing with more complex shapes of soft tissues and not needing the discretization of the internal domain, also, they have low RAM and CPU usage. Therefore, they are a versatile and powerful methods for modeling of fractional bio-thermomechanical problems in anisotropic soft tissues.

  5. The linear systems resulting from BEM have been solved by TFQMR solver with the ILUT preconditioner which reduces the number of iterations and the total CPU time.

  6. Numerical findings are presented graphically to show the effect of fractional order parameter on the problem variables temperature, displacements and fluid pressure.

  7. Numerical findings confirm the validity, efficiency and accuracy of the proposed BEM technique.

  8. The proposed technique can be applied to a wide variety of fractional bio-thermomechanical problems in anisotropic soft tissues.

  9. For open boundary problems of soft tissues, such as the considered problem, the BEM users need only to deal with real geometry boundaries. But for these problems, FDM and FEM use artificial boundaries, which are far away from the real soft tissues. Also, these artificial boundaries are also becoming a big challenge for FDM users and FEM users. So, BEM becomes the best method for the considered problem.

  10. The presence of fractional order parameter in the current study plays a significant role in all the physical quantities during modeling and simulation in medicine and healthcare.

  11. From the research that has been performed, it is possible to conclude that the proposed BEM is an easier, effective, predictable, and stable technique in the treatment of the bio-thermomechanical soft tissue models.

  12. It can be concluded from this chapter that Biot’s equations for the dynamic response of poroelastic media can be combined with the bio-heat transfer models to describe the fractional bio-thermomechanical interactions of anisotropic soft tissues.

  13. Current numerical results for our complex and general problem may provide interesting information for researchers and scientists in bioengineering, heat transfer, mechanics, neurophysiology, biology and clinicians.



Biot’s coefficient


linear elastostatics operator


considered boundary


Dirichlet boundary


Neumann boundary


specific heat of soft tissue


shape factor


specific heat of the blood


specific heat of the blood


bulk body forces


Dirichlet datum


Neumann datum


dynamic permeability


thermal conductivity of soft tissue


iterative parameter


pore pressure


heating power


solid–fluid coupling parameters


metabolic heat source


external heat source


scattering coefficient


soft tissue temperature


arterial blood temperature


traction derivative


solid displacement


fluid displacement


bulk volume


fluid volume


solid volume


blood perfusion rate


stress-temperature coefficients

linear strain tensor


fluid volume variation


bulk density


mass density of soft tissue


blood density


total stress tensor




phase lag for heat flux


phase lag for temperature gradient


continuous polynomial shape functions




discontinuous polynomial shape functions


considered region

Download for free

chapter PDF

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Mohamed Abdelsabour Fahmy (April 5th 2021). Boundary Element Modeling and Simulation Algorithm for Fractional Bio-Thermomechanical Problems of Anisotropic Soft Tissues [Online First], IntechOpen, DOI: 10.5772/intechopen.96268. Available from:

chapter statistics

14total chapter downloads

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

More About Us