Open access peer-reviewed chapter

# A New BEM for Modeling and Optimization of 3T Fractional Nonlinear Generalized Magneto-Thermoelastic Multi-Material ISMFGA Structures Subjected to Moving Heat Source

By Mohamed Abdelsabour Fahmy

Submitted: April 11th 2020Reviewed: May 18th 2020Published: July 23rd 2020

DOI: 10.5772/intechopen.92852

## Abstract

The main purpose of this chapter, which represents one of the chapters of a fractal analysis book, is to propose a new boundary element method (BEM) formulation based on time fractional order theory of thermoelasticity for modeling and optimization of three temperature (3T) multi-material initially stressed multilayered functionally graded anisotropic (ISMFGA) structures subjected to moving heat source. Fractional order derivative considered in the current chapter has been found to be an accurate mathematical tool for solving the difficulty of our physical and numerical modeling. Furthermore, this chapter shed light on the practical application aspects of boundary element method analysis and topology optimization of fractional order thermoelastic ISMFGA structures. Numerical examples based on the multi-material topology optimization algorithm and bi-evolutionary structural optimization method (BESO) are presented to study the effects of fractional order parameter on the optimal design of thermoelastic ISMFGA structures. The numerical results are depicted graphically to show the effects of fractional order parameter on the sensitivities of total temperature, displacement components and thermal stress components. The numerical results also show the effects of fractional order parameter on the final topology of the ISMFGA structures and demonstrate the validity and accuracy of our proposed technique.

### Keywords

• boundary element method
• modeling and optimization
• time fractional order
• three-temperature
• nonlinear generalized thermoelasticity
• initially stressed multilayered functionally graded anisotropic structures
• moving heat source

## 1. Introduction

The fractional calculus has recently been widely used to study the theory and applications of derivatives and integrals of arbitrary non-integer order. This branch of mathematical analysis has emerged in recent years as an effective and powerful tool for the mathematical modeling of various engineering, industrial, and materials science applications [1, 2, 3]. The fractional-order operators are useful in describing the memory and hereditary properties of various materials and processes, due to their nonlocal nature. It clearly reflects from the related literature produced by leading fractional calculus journals that the primary focus of the investigation had shifted from classical integer-order models to fractional order models [4, 5]. Fractional calculus has important applications in hereditary solid mechanics, fluid dynamics, viscoelasticity, heat conduction modeling and identification, biology, food engineering, econophysics, biophysics, biochemistry, robotics and control theory, signal and image processing, electronics, electric circuits, wave propagation, nanotechnology, etc. [6, 7, 8].

Numerous mathematicians have contributed to the history of fractional calculus, where Euler mentioned interpolating between integral orders of a derivative in 1730. Then, Laplace defined a fractional derivative by means of an integral in 1812.

Lacroix introduced the first fractional order derivative which appeared in a calculus in 1819, where he expressed the nthderivative of the function y=xmas follows:

dndxn=Γm+1Γmn+1xmnE1

Liouville assumed that dvdxveax=aveaxforv>0to obtain the following fractional order derivative:

Laurent has been using the Cauchy’s integral formula for complex valued analytical functions to define the integration of arbitrary order v>0as follows:

cDxvfx=cDxmρfx=dmdxm1Γρcxxtρ1ftdt,0<ρ1E3

where cDxvdenotes differentiation of order vof the function falong the xaxis.

Cauchy introduced the following fractional order derivative:

f+α=fτtτα1ΓαE4

Caputo introduced his fractional derivative of order α<0to be defined as follows:

Dαft=1Γmα0tfmτtτα+1m,m1<α<m,α>0E5

Recently, research on nonlinear generalized magneto-thermoelastic problems has received wide attention due to its practical applications in various fields such as geomechanics, geophysics, petroleum and mineral prospecting, earthquake engineering, astronautics, oceanology, aeronautics, materials science, fiber-optic communication, fluid mechanics, automobile industries, aircraft, space vehicles, plasma physics, nuclear reactors, and other industrial applications. Due to computational difficulties in solving nonlinear generalized magneto-thermoelastic problems in general analytically, many numerical techniques have been developed and implemented for solving such problems [9, 10, 11, 12, 13, 14, 15, 16, 17]. The boundary element method (BEM) [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31] has been recognized as an attractive alternative numerical method to domain methods [32, 33, 34, 35, 36] like finite difference method (FDM), finite element method (FEM), and finite volume method (FVM) in engineering applications. The superior feature of BEM over domain methods is that only the boundary of the domain needs to be discretized, which often leads to fewer elements and easier to use. This advantage of BEM over other domain methods has significant importance for modeling and optimization of thermoelastic problems which can be implemented using BEM with little cost and less input data. Nowadays, the BEM has emerged as an accurate and efficient computational technique for solving complicated inhomogeneous and non-linear problems in physical and engineering applications [37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69].

In the present chapter, we introduce a practical engineering application of fractal analysis in the field of thermoelasticity, where the thermal field is described by time fractional three-temperature radiative heat conduction equations. Fractional order derivative considered in the current chapter has high ability to remove the difficulty of our numerical modeling. A new boundary element method for modeling and optimization of 3T fractional order nonlinear generalized thermoelastic multi-material initially stressed multilayered functionally graded anisotropic (ISMFGA) structures subjected to moving heat source is investigated. Numerical results show that the fractional order parameter has a significant effect on the sensitivities of displacements, total three-temperature, and thermal stresses. Numerical examples show that the fractional order parameter has a significant effect on the final topology of ISMFGA structures. Numerical results of the proposed model confirm the validity and accuracy of the proposed technique, and numerical examples results demonstrate the validity of the BESO multi-material topology optimization method.

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 fractional order problems and their applications. Section 2 describes the physical modeling of fractional order problems in three-temperature nonlinear generalized magneto-thermoelastic ISMFGA structures. Section 3 outlines the BEM implementation for modeling of 3T fractional nonlinear generalized magneto-thermoelastic problems of multi-material ISMFGA structures subjected to moving heat source. Section 4 introduces an illustration of the mechanisms of solving design sensitivities and optimization problem of the current chapter. Section 5 presents the new numerical results that describe the effects of fractional order parameter on the problem’s field variations and on the final topology of multi-material ISMFGA structures.

## 2. Formulation of the problem

Consider a multilayered structure with nfunctionally graded layers in the xyplaneof a Cartesian coordinate. The xaxisis the common normal to all layers as shown in Figure 1. The thickness of the layer is denoted by h. The considered multilayered structure has been placed in a primary magnetic field H0acting in the direction of the yaxis.

According to the three-temperature theory, the governing equations of nonlinear generalized magneto-thermoelasticity in an initially stressed multilayered functionally graded anisotropic (ISMFGA) structure for the ith layer can be written in the following form:

σab,b+τab,bΓab=ρix+1mu¨aiE6
σab=x+1mCabfgiuf,giβabiTαiTα0i+τ1Ṫα1E7
τab=μix+1mh˜aHb+h˜bHaδbah˜fHfE8
Γab=Pix+1muaixbubixaE9

According to Fahmy [10], the time fractional order two-dimensional three-temperature (2D-3 T) radiative heat conduction equations in nondimensionless form can be expressed as follows:

DτaTαirτ=ξKαiTαirτ+ξW¯rτ,ξ=1csαiρiδ1E10

where

W¯rτ=ρiWeITeiTIiρiWerTeiTpi+W¯¯,α=e,δ1=1ρiWeITeiTIi+W¯¯,α=I,δ1=1ρiWerTeiTpi+W¯¯,α=p,δ1=Tp3E11

in which

W¯¯rτ=KαiṪα,abi+βabiTa0iτ0u¨a,bi+ρiciτ0T¨αiQxτE12
WeI=ρiAeITei23,Wer=ρiAerTei12,Kα=AαTαi52,α=e,I,Kp=ApTpi3+BE13

The total energy of unit mass can be described by

P=Pe+PI+pp,Pe=ceTei,PI=cITIi,Pp=14cpTp4iE14

where σab, τab, and ukiare mechanical stress tensor, Maxwell’s electromagnetic stress tensor, and displacement vector in the ith layer, respectively, cα(α = c, I, p) are constant Tα0i, Tαi, Cabfgi, and βabiare, respectively, reference temperature, temperature, constant elastic moduli, and stress-temperature coefficients in the ith layer: μi, h˜, Pi, ρi, and ciare, respectively, magnetic permeability, perturbed magnetic field, initial stress, density, isochore specific heat coefficients in the ith layer; τis the time; τ0and τ1are the relaxation times; i=1,2,,nrepresents the parameters in multilayered structure; and mis a functionally graded parameter. Also, we considered in the current study that the medium is subjected to a moving heat source of constant strength moving along xaxiswith a constant velocity v. This moving heat source is assumed to have the following form:

Qxτ=Q0δxE15

where, Q0is the heat source strength and δis the delta function.

## 3. BEM numerical implementation

By using Eqs. (7)(9), we can write (6) as

Lgbufi=ρiu¨aiDaTαiPiubixauaixb=fgbE16

where inertia term, temperature gradient, and initial stress terms are treated as the body forces.

In this section, we are interested in using a boundary element method for modeling the two-dimensional three-temperature radiation heat conduction equations coupled with electron, ion, and phonon temperatures.

According to finite difference scheme of Caputo at times f+1Δtand fΔτ, we obtain [1].

DτaTαif+1+DτaTαifj=0kWa,jTαif+1jrTαifjrE17

where

Wa,0=ΔτaΓ2a,Wa,j=Wa,0j+11aj11aE18

Based on Eq. (17), the fractional order heat Eq. (10) can be replaced by the following system:

Wa,0Tαif+1rKαxTα,IIif+1rKα,IxTα,Iif+1r=Wa,0TαifrKαxTα,IIifrKα,IxTα,Jifrj=1fWa,jTαif+1jrTαifjr+W¯mif+1xτ+W¯mifxτE19

where j=1,2,.,F,f=0,1,2,,F.

Now, according to Fahmy [10], and applying the fundamental solution which satisfies (19), the boundary integral equations corresponding to (10) without heat sources can be expressed as

Tαiξ=STαiqiTαiqidCRfabTαidRE20

Thus, the governing equations can be written in operator form as follows:

Lgbufi=fgb,E21
LabTαi=fabE22

where the operators Lgb, fgb, Lab, and fabare as follows:

LgbDabfxb+Daf+ΛDa1f,Lab=DτaE23
fgb=ρiu¨aiDaTαiPiubixauaixbE24
fab=KαDTαiτE25

where

Dabf=Cabfgε,ε=xg,Daf=μH02xa+δa1Λxf,Da=βabixb+δb1Λ+τ1xb+Λτ,Λ=mx+1.

The differential Eq. (21) can be solved using the weighted residual method (WRM) to obtain the following integral equation:

RLgbufifgbudaidR=0E26

Now, the fundamental solution udfiand traction vectors tdaiand taican be written as follows:

tdai=Cabfgudf,ginbE28
tai=t¯aix+1m=Cabfguf,giβabiTαi+τ1TαinbE29

Using integration by parts and sifting property of the Dirac distribution for (26), then using Eqs. (27) and (29), we can write the following elastic integral representation formula:

udiξ=Cudaitaitdaiuai+udaiβabiTαinbdCRfgbudaidRE30

The fundamental solution Tican be defined as

LabTαi=δxξE31

By using WRM and integration by parts, we can write (23) as follows:

RLabTαiTαiLabTαiTαidR=CqiTαiqiTαidCE32

where

qi=KαiTα,binaE33
qi=KαiTα,binaE34

By the use of sifting property, we obtain from (32) the thermal integral representation formula

Tαiξ=CqiTαiqiTαidCCfabTαidRE35

By combining (30) and (35), we obtain

udiξTαiξ=Ctdaiudaiβabnb0qiuaiTαi+udai00TαitaiqidCRudai00TαifgbfabdRE36

The nonlinear generalized magneto-thermoelastic vectors can be written in contracted notation form as

UAi=uaia=A=1,2,3TαiA=4E37
TαAi=taia=A=1,2,3qiA=4E38
UDAi=udaid=D=1,2,3;a=A=1,2,30d=D=1,2,3;A=40D=4;a=A=1,2,3TαiD=4;A=4E39
T˜αDAi=tdaid=D=1,2,3;a=A=1,2,3u˜did=D=1,2,3;A=40D=4;a=A=1,2,3qiD=4;A=4E40
u˜di=udaiβafnfE41

By using the above vectors, we can express (36) as

The source vector SAcan be divided as

SA=SA0+SAT+SAu+SAṪ+SAT¨+SAu¨E43

where

SA0=0A=1,2,3Q0δxA=4E44
SAT=ωAFUFiwithωAF=DaA=1,2,3;F=4ξKαiotherwiseE45
SAu=ψUFiwithψ=PixbxaA=1,2,3;F=1,2,3,0A=4;F=4E46
SAṪ=ΓAFU̇FiwithΓAF=βabiτ1xb+ΛτA=4;F=4KαiotherwiseE47
SAT¨=δAFU¨FiwithδAF=0A=4;F=4ρiciτ0otherwiseE48
SAu¨=U¨Fiwith=ρiA=1,2,3;F=1,2,3,βabiTα0iτ0A=4;F=4E49

The representation formula (36) can also be written in matrix form as follows:

SA=0Q0δx+DaTαiξKαiTαirτ+Piub,aiua,bi0+βabiτ1xb+ΛṪαiKαiṪαi+ρiciτ00T¨αi+ρiu¨aiβabiTα0iτ0u¨f,giE50

In order to convert the domain integral in (42) into the boundary, we approximate the source vector SAby a series of known functions fAEqand unknown coefficients αEqas

SAq=1EfAEqαEqE51

Thus, the representation formula (42) can be written as follows:

UDξ=CUDAiTαAiT˜αDAiUAidCq=1NRUDAifAEqdRαEqE52

By implementing the WRM to the following equations

Lgbufeiq=faeqE53
LabTαiq=fpjqE54

Then the elastic and thermal representation formulae are given as follows (Fahmy [46]):

udeiqξ=CudaitaeiqtdaiuaeiqdCRudaifaeqdRE55
Tαiqξ=CqiTαiqqiqTαidCRfqTαidRE56

The representation formulae (55) and (56) can be combined into the following single equation:

UDEiqξ=CUDAiTαAEiqTαDAiUAEiqdCRUDAifAEiqdRE57

By substituting from Eq. (57) into Eq. (52), we obtain the following BEM coupled thermoelasticity formula:

UDiξ=CUDAiTαAiTαDAiUAidC+q=1EUDEiqξ+CTαDAiUAEiqUDAiTαAEiqdCαEqE58

In order to compute the displacement sensitivity, Eq. (58) is differentiated with respect to ξlas follows:

UDiξξl=CUDA,liTαAiTαDA,liUAidC+q=1EUDEiqξξlCTαDA,liUαAEiqUDA,liTαAEiqdCαEqE59

According to the procedure of Fahmy [44], we can write (58) in the following form:

ζˇUηTα=ζUˇηˇα¯E60

The generalized displacements and velocities are approximated in terms of known tensor functions fFDqand unknown coefficients γDqand γ˜Dq:

UFiq=1NfFDqxγDqE61

where

fFDq=ffdqf=F=1,2,3;d=D=1,2,3fqF=4;D=40otherwiseE62

Now, the gradients of the generalized displacements and velocities can also be approximated in terms of the tensor function derivatives as

UF,giq=1NfFD,gqxγKqE63

By substituting (63) into Eq. (45), we get

SAT=q=1NSAFfFD,gqγDqE64

By applying the point collocation procedure of Gaul et al. [43] to Eqs. (51) and (61), we obtain

Sˇ=Jα¯,Ui=Jγ,E65

Similarly, applying the same point collocation procedure to Eqs. (64), (46), (47), (48), and (49) yields

SˇTαi=BTγE66
SAu=ψUiE67
SˇTαι̇=Γ¯AFU̇iE68
SˇTαι¨=δ¯AFU¨iE69
Sˇu¨=¯U¨iE70

where ψ¯, Γ¯AF, δ¯AF, and ¯are assembled using the submatrices ψ, ΓAF, δAF, and , respectively.

Solving the system (65) for α¯and γyields

α¯=J1Sˇ,γ=J1UiE71

Now, the coefficient α¯can be written in terms of the unknown displacements Ui, velocities U̇i, and accelerations U¨ias

α¯=J1Sˇ0+BTJ1+ψ¯Ui+Γ¯AFU̇i+¯+δ¯AFU¨iE72

An implicit-implicit staggered algorithm has been implemented for use with the BEM to solve the governing equations which can now be written in a suitable form after substitution of Eq. (72) into Eq. (60) as

MU¨i+ΓU̇i+KUi=QiE73
XT¨αi+AṪαi+BTαi=ZU¨i+RE74

where V=ηˇζUˇJ1,M=V¯+δ¯AF,Γ=VΓ¯AF,K=ζˇ+VBTJ1+ψ¯, Qi=ηTˇ+VSˇ0,X=ρiciτ0,A=Kαi,B=ξKαi,Z=βabiTα0iτ0,R=Q0δx.

where U¨i,U̇i,Ui,Tiand Qiare, respectively, acceleration, velocity, displacement, temperature, and external force vectors, and V,M, Γ, K, A, and Bare, respectively, volume, mass, damping, stiffness, capacity, and conductivity matrices.

In many applications, the coupling term ZU¨n+1ithat appear in the heat conduction equation is negligible. Therefore, it is easier to predict the temperature than the displacement.

Hence Eqs. (73) and (74) lead to the following coupled system of differential-algebraic equations (DAEs):

MU¨n+1i+ΓU̇n+1i+KUn+1i=Qn+1ipE75
XT¨αn+1i+AṪαn+1i+BTαn+1i=ZU¨n+1i+RE76

where Qn+1ip=ηTαn+1ip+VSˇ0and Tαn+1ipis the predicted temperature.

By integrating Eq. (73) and using Eq. (75), we get

U̇n+1i=U̇ni+Δτ2U¨n+1i+U¨ni=U̇ni+Δτ2U¨ni+M1Qn+1ipΓU̇n+1iKUn+1iE77
Un+1i=Uni+Δτ2U̇n+1i+U̇ni=Uni+ΔτU̇ni+Δτ24U¨ni+M1Qn+1ipΓU̇n+1iKUn+1iE78

From Eq. (77) we have

U̇n+1i=γ¯1U̇ni+Δτ2U¨ni+M1Qn+1ipKUn+1iE79

where γ¯=I+Δτ2M1Γ.

Substituting Eq. (79) into Eq. (78), we derive

Un+1i=Uni+ΔτU̇ni+Δτ24U¨ni+M1Qn+1ipΓγ¯1U̇ni+Δτ2U¨ni+M1Qn+1ipKUn+1iKUn+1iE80

Substituting U̇n+1ifrom Eq. (79) into Eq. (75), we obtain

U¨n+1i=M1Qn+1ipΓγ¯1U̇ni+Δτ2U¨ni+M1Qn+1ipKUn+1iKUn+1iE81

By integrating the heat Eq. (74) and using Eq. (76), we obtain

Ṫαn+1i=Ṫni+Δτ2T¨αn+1i+T¨αni=Ṫαni+Δτ2X1ZU¨n+1i+RAṪαn+1iBTαn+1i+T¨αniE82
Tαn+1i=Tαni+Δτ2Ṫαn+1i+Ṫαni=Tαni+ΔτṪαni+Δτ24T¨αni+X1ZU¨n+1i+RAṪαn+1iBTαn+1iE83

From Eq. (82) we get

Ṫαn+1i=γ1Ṫαni+Δτ2X1ZU¨n+1i+RBTαn+1i+T¨αniE84

where γ=I+12AΔτX1.

Substituting Eq. (84) into Eq. (83), we obtain

Tαn+1i=Tαni+ΔτṪαni+Δτ24T¨αni+X1ZU¨n+1i+RAγ1Ṫαni+Δτ2X1ZU¨n+1i+RBTαn+1i+T¨αniBTαn+1iE85

Substituting Ṫn+1ifrom Eq. (84) into Eq. (76), we get

T¨αn+1i=X1ZU¨n+1i+RAγ1Ṫαni+Δτ2X1ZU¨n+1i+RBTαn+1i+T¨αn+1iBTαn+1iE86

Now, a displacement predicted staggered procedure for the solution of (80) and (85) is as follows:

The first step is to predict the propagation of the displacement wave field: Un+1ip=Uni. The second step is to substitute for U̇n+1iand U¨n+1ifrom Eqs. (77) and (75), respectively, in Eq. (85) and solve the resulted equation for the three-temperature fields. The third step is to correct the displacement using the computed three-temperature fields for the Eq. (80). The fourth step is to compute U̇n+1i, U¨n+1i, Ṫαn+1i, and T¨αn+1ifrom Eqs. (79), (81), (82), and (86), respectively.

The continuity conditions for temperature, heat flux, displacement, and traction that have been considered in the current chapter can be expressed as

Tαixzτx=hi=Tαi+1xzτx=hiE87
qixzτx=hi=qi+1xzτx=hiE88
ufixzτx=hi=ufi+1xzτx=hiE89
t¯aixzτx=hi=t¯ai+1xzτx=hiE90

where nis the total number of layers, t¯aare the tractions which is defined by t¯a=σabnb, and i=1,2,,n1.

The initial and boundary conditions of the present study are

ufixz0=u̇fixz0=0forxzRCE91
ufixzτ=ΨfxzτforxzC3E92
t¯aixzτ=ΦfxzτforxzC4,τ>0E93
Tαixz0=Tαixz0=0forxzRCE94
Tαixzτ=f¯xzτforxzC1,τ>0E95
qixzτ=h¯xzτforxzC2,τ>0E96

where Ψf, Φf, f, and h¯are prescribed functions, C=C1C2=C3C4, and C1C2=C3C4=0.

## 4. Design sensitivity and optimization

According to Fahmy [58, 60], the design sensitivities of displacements components and total 3T can be performed by implicit differentiation of (75) and (76), respectively, which describe the structural response with respect to the design variables, and then we can compute thermal stresses sensitivities.

The bi-directional evolutionary structural optimization (BESO) is the evolutionary topology optimization method that allows modification of the structure by either adding or removing material to or from the structure design. This addition or removal depends on the sensitivity analysis. Sensitivity analysis is the estimation of the response of the structure to the modification of design variables and is dependent on the calculation of derivatives [70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80].

The homogenized vector of thermal expansion coefficients αHcan be written in terms of the homogenized elasticity matrix DHand the homogenized vector of stress-temperature coefficients βHas follows:

αH=DH1βHE97

For the material design, the derivative of the homogenized vector of thermal expansion coefficients can be written as

αHXklm=DH1βHXklmDHXklmαHE98

where DHXklmand βHXklmfor any lth material phase can be calculated using the adjoint variable method [73] as

DHXklm=1ΩYIBmUmTDmXklmIBmUmdyE99

and

βHXklm=1YYIBmUmTDmXklmαmBmφmdy+1ΩYIBmUmTαmXklmdyE100

where Ωis the volume of the base cell.

## 5. Numerical examples, results, and discussion

In order to show the numerical results of this study, we consider a monoclinic graphite-epoxy as an anisotropic thermoelastic material which has the following physical constants [57].

The elasticity tensor is expressed as

Cpjkl=430.1130.418.200201.3130.4116.721.00070.118.221.073.6002.400019.88.000008.029.10201.370.12.400147.3GPaE101

The mechanical temperature coefficient is

βpj=1.012.0002.001.480007.52106NKm2E102

The thermal conductivity tensor is

kpj=5.20007.600038.3W/KmE103

Mass density ρ=7820kg/m3and heat capacity c = 461 J/kg K.

The proposed technique that has been utilized in the present chapter can be applicable to a wide range of three-temperature nonlinear generalized thermoelastic problems of ISMFGA structures. The main aim of this chapter was to assess the impact of fractional order parameter on the sensitivities of total three-temperature, displacement components, and thermal stress components.

Figure 2 shows the variation of the total temperature sensitivity along the xaxis. It was shown from this figure that the fraction order parameter has great effects on the total three-temperature sensitivity.

Figures 3 and 4 show the variation of the displacement components u1and u2along the xaxisfor different values of fractional order parameter. It was noticed from these figures that the fractional order parameter has great effects on the displacement sensitivities.

Figures 57 show the variation of the thermal stress components σ11, σ12, and σ22, respectively, along the xaxisfor different values of fractional order parameter. It was noted from these figures that the fractional order parameter has great influences on the thermal stress sensitivities.

Since there are no available results for the three-temperature thermoelastic problems, except for Fahmy’s research [10, 11, 12, 13, 14]. For comparison purposes with the special cases of other methods treated by other authors, we only considered one-dimensional numerical results of the considered problem. In the special case under consideration, the displacement u1and thermal stress σ11results are plotted in Figures 8 and 9. The validity and accuracy of our proposed BEM technique were demonstrated by comparing our BEM results with the FEM results of Xiong and Tian [81], it can be noticed that the BEM results are found to agree very well with the FEM results.

Example 1. Short cantilever beam.

The mean compliance has been minimized, to obtain the maximum stiffness, when the structure is subjected to moving heat source. In this example, we consider a short cantilever beam shown in Figure 10, where the BESO final topology of considered short cantilever beam shown in Figure 11a for α=0.5and shown in Figure 11b for α=1.0. It is noticed from this figure that the fractional order parameter has a significant effect on the final topology of the multi-material ISMFGA structure.

Example 2. MBB beam.

It is known that extraordinary thermo-mechanical properties can be accomplished by combining more than two materials phases with conventional materials [75]. For this reason, it is essential that the topology optimization strategy permits more than two materials phases within the design domain. In this example, we consider a MBB beam shown in Figure 12, where the BESO final topology of MBB beam has been shown in Figure 13a for α=0.5and shown in Figure 13b for α=1.0to show the effect of fractional order parameter on the final topology of the multi-material ISMFGA structure.

Example 3. Roller-supported beam.

In this example, we consider a roller-supported beam shown in Figure 14, where the BESO final topology of a roller-supported beam shown in Figure 15a for α=0.5and shown in Figure 15b for α=1.0.

Example 4. Cantilever beam (validation example).

In order to demonstrate the validity of our implemented BESO topology optimization technique, we consider isotropic case of a cantilever beam shown in Figure 16 as a special case of our anisotropic study to interpolate the elasticity matrix and the stress-temperature coefficients using the design variables XM, then we compare our BESO final topology shown in Figure 17a with the material interpolation scheme of the solid isotropic material with penalization (SIMP) shown in Figure 17b.

The BESO topology optimization problem implemented in Examples 1 and 4, to find the distribution of the Mmaterial phases, with the volume constraint can be stated as

1. Find XM

2. That minimize CM=12PMTuM=12fM,ter+fM,mecTuM

3. Subject to VM,i=1NViMXiM=0

KMuM=PM
XiM=xminV1

where XMis the design variable; CMis the mean compliance; Pis the total load on the structure, which is the sum of mechanical and thermal loads; uMis the displacement vector; VM,is the volume of the solid material; Nis the total number of elements; KMis the global stiffness matrix; xminis a small value (e.g., 0.0001), which it guarantee that none of the elements will be removed completely from design domain; fM,mecis the mechanical load vector; and fM,teris the thermal load vector. Also, the BESO parameters considered in Examples 1–4 can be seen in Tables 14, respectively.

Variable nameVariable descriptionVariable value
VfMFinal volume fraction0.5
ERMEvolutionary ratio1%
rminMFilter ratio3 mm
τConvergence tolerance0.1%
NConvergence parameter5

### Table 1.

BESO parameters for minimization of a short cantilever beam.

Variable nameVariable descriptionVariable value
Vf1MFinal volume fraction of the material 1 for both interpolations0.10
Vf2MFinal volume fraction of the material 2 for both interpolations0.20
ERMEvolutionary ratio for interpolation 12%
ERMEvolutionary ratio for interpolation 23%
ARmaxMVolume addition ratio for interpolation 13%
ARmaxMVolume addition ratio for interpolation 22%
rminMFilter ratio for interpolation 14 mm
rminMFilter ratio for interpolation 23 mm
τConvergence tolerance for both interpolations0.01%
NConvergence parameter for both interpolations5

### Table 2.

Multi-material BESO parameters for minimization of a MBB beam.

Variable nameVariable descriptionVariable value
Vf1MFinal volume fraction of the material 1 for both interpolations0.25
Vf1MFinal volume fraction of the material 2 for both interpolations0.25
ERMEvolutionary ratio for interpolation 13%
ERMEvolutionary ratio for interpolation 23%
ARmaxMVolume addition ratio for interpolation 11%
ARmaxMVolume addition ratio for interpolation 21%
rminMFilter ratio for interpolation 14 mm
rminMFilter ratio for interpolation 24 mm
τConvergence tolerance for both interpolations0.5 %
NConvergence parameter for both interpolations5

### Table 3.

Multi-material BESO parameters for minimization of a roller-supported beam.

Variable nameVariable descriptionVariable value
VfMFinal volume fraction0.4
ERMEvolutionary ratio1.2%
rminMFilter ratio0.19 mm
τConvergence tolerance0.1%
NConvergence parameter5

### Table 4.

BESO parameters for minimization of a cantilever beam.

The BESO topology optimization problem implemented in Examples 2 and 3, to find the distribution of the two materials in the design domain, which minimize the compliance of the structure, subject to a volume constraint in both phases can be stated as.

1. Find XM

2. That minimize CM=12PMTuM=12fM,ter+fM,mecTuM

3. Subject to VjM,i=1NViMXijMi=1j1ViM,=0;j=1,2

KMuM=PM
XiM=xminV1;j=1,2

where VjM,is the volume of jth material phase and iand jdenote the element ith which is made of jth material.

## 6. Conclusion

The main purpose of this chapter is to describe a new boundary element formulation for modeling and optimization of 3T time fractional order nonlinear generalized thermoelastic multi-material ISMFGA structures subjected to moving heat source, where we used the three-temperature nonlinear radiative heat conduction equations combined with electron, ion, and phonon temperatures.

Numerical results show the influence of fractional order parameter on the sensitivities of the study’s fields. The validity of the present method is examined and demonstrated by comparing the obtained outcomes with those known in the literature. Because there are no available data to confirm the validity and accuracy of our proposed technique, we replace the three-temperature radiative heat conduction with one-temperature heat conduction as a special case from our current general study of three-temperature nonlinear generalized thermoelasticity. In the considered special case of 3T time fractional order nonlinear generalized thermoelastic multi-material ISMFGA structures, the BEM results have been compared graphically with the FEM results; it can be noticed that the BEM results are in excellent agreement with the FEM results. These results thus demonstrate the validity and accuracy of our proposed technique. Numerical examples are solved using the multi-material topology optimization algorithm based on the bi-evolutionary structural optimization method (BESO). Numerical results of these examples show that the fractional order parameter affects the final result of optimization. The implemented optimization algorithm has proven to be an appropriate computational tool for material design.

Nowadays, the knowledge of 3T fractional order optimization of multi-material ISMFGA structures, can be utilized by mechanical engineers for designing heat exchangers, semiconductor nano materials, thermoelastic actuators, shape memory actuators, bimetallic valves and boiler tubes. As well as for chemists to observe the chemical processes such as bond breaking and bond forming.

chapter PDF
Citations in RIS format
Citations in bibtex format

## More

© 2020 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

### Cite this chapter Copy to clipboard

Mohamed Abdelsabour Fahmy (July 23rd 2020). A New BEM for Modeling and Optimization of 3T Fractional Nonlinear Generalized Magneto-Thermoelastic Multi-Material ISMFGA Structures Subjected to Moving Heat Source, Fractal Analysis - Selected Examples, Robert Koprowski, IntechOpen, DOI: 10.5772/intechopen.92852. Available from:

### Related Content

Next chapter

#### Using Algebraic Fractals in Steganography

By Oleg Sheluhin and Dzhennet Magomedova

#### Medical and Biological Image Analysis

Edited by Robert Koprowski

First chapter

#### Image Segmentation

By Kumaravel Subramaniam Tamilselvan and Govindasamy Murugesan

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.