Open access peer-reviewed chapter

A Novel MDD-Based BEM Model for Transient 3T Nonlinear Thermal Stresses in FGA Smart Structures

Written By

Mohamed Abdelsabour Fahmy

Submitted: April 13th, 2020 Reviewed: May 14th, 2020 Published: November 28th, 2020

DOI: 10.5772/intechopen.92829

From the Edited Volume

Advanced Functional Materials

Edited by Nevin Tasaltin, Paul Sunday Nnamchi and Safaa Saud

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Abstract

The main objective of this chapter is to introduce a novel memory-dependent derivative (MDD) model based on the boundary element method (BEM) for solving transient three-temperature (3T) nonlinear thermal stress problems in functionally graded anisotropic (FGA) smart structures. The governing equations of the considered study are nonlinear and very difficult if not impossible to solve analytically. Therefore, we develop a new boundary element scheme for solving such equations. The numerical results are presented highlighting the effects of the MDD on the temperatures and nonlinear thermal stress distributions and also the effect of anisotropy on the nonlinear thermal stress distributions in FGA smart structures. The numerical results also verify the validity and accuracy of the proposed methodology. The computing performance of the proposed model has been performed using communication-avoiding Arnoldi procedure. We can conclude that the results of this chapter contribute to increase our understanding on the FGA smart structures. Consequently, the results also contribute to the further development of technological and industrial applications of FGA smart structures of various characteristics.

Keywords

  • boundary element method
  • memory-dependent derivative
  • three-temperature
  • nonlinear thermal stresses
  • FGA smart structures

1. Introduction

Smart materials, which are also called intelligent materials, are engineered materials that have the ability to respond to the changes that occur around them in a controlled fashion by external stimuli, such as stress, heat, light, ultraviolet, moisture, chemical compounds, mechanical strength, and electric and magnetic fields. We can simply define smart materials as materials which adapt themselves as per required condition. The history of the discovery of these materials dates back to the 1880s when Jacques and Pierre Curie noticed a phenomenon that pressure generates electrification around a number of minerals such as quartz and tourmaline, and this phenomenon is called piezoelectric effect, so the piezoelectric materials are the oldest type of smart materials, which are utilized extensively in the fabrication of various devices such as transducers, sensors, actuators, surface acoustic wave devices, frequency control, etc. There are a lot of smart material types like piezoelectric materials, thermochromic pigments, shape memory alloys, magnetostrictive, shape memory polymers, hydrogels, electroactive polymers and bi-component fibers, etc.

Anisotropic smart structures (ASSs) are getting great attention of researchers due to their applications in textile, aerospace, mass transit, marine, automotive, computers and other electronic industries, consumer goods applications, mechanical and civil engineering, infertility treatment, micropumps, medical equipment applications, ultrasonic micromotors, microvalves and photovoltaics, rotating machinery applications, and much more [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12].

The classical thermoelasticity (CTE) theory of Duhamel [13] and Neumann [14] has two shortcomings based on parabolic heat conduction equation of this theory: the first does not involve any elastic terms, while the second has infinite propagation speeds of thermoelastic waves. In order to overcome the first shortcoming, Biot [15] proposed the classical coupled thermoelasticity (CCTE). But CTE and CCTE have the second shortcoming. So, several generalized thermoelasticity theories have been developed to overcome the second shortcoming of CTE. Among of these theories are Lord and Shulman (LS) [16], Green and Lindsay (GL) [17], and Green and Naghdi [18, 19] theories of thermoelasticity with and without energy dissipation, dual-phase-lag thermoelasticity (DPLTE) [20, 21] and three-phase-lag thermoelasticity (TPLTE) [22]. Although thermoelastic phenomena in the majority of practical applications are adequately modeled with the classical Fourier heat conduction equation, there are an important number of problems that require consideration of nonlinear heat conduction equation. It is appropriate in these cases to apply the nonlinear generalized theory of thermoelasticity; great attention has been paid to investigate the nonlinear generalized thermoelastic problems by using numerical methods [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34]. Fahmy [35, 36, 37, 38, 39] introduced the mathematical foundations of three-temperature (3T) field to thermoelasticity.

The fractional calculus is the mathematical branch that used to study the theory and applications of derivatives and integrals of arbitrary non-integer order. This branch has emerged in recent years as an effective tool for modeling and simulation of various engineering and industrial applications [40, 41]. Due to the nonlocal nature of fractional order operators, they are useful for describing the memory and hereditary properties of various materials and processes. Also, the fractional calculus has drawn wide attention from the researchers of various countries in recent years due to its applications in solid mechanics, fluid dynamics, viscoelasticity, heat conduction modeling and identification, biology, food engineering, econophysics, biophysics, biochemistry, electrochemistry, electrical engineering, finance and control theory, robotics and control theory, signal and image processing, electronics, electric circuits, wave propagation, nanotechnology, etc. [42, 43, 44].

Several mathematics researchers 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 presented the first formula for the fractional order derivative appeared in 1819, where he introduced the nth derivative of the function y=xmas follows:

dndxn=Γm+1Γmn+1xmnE1

Liouville supposed that dvdxveax=aveaxforv>0to get the following fractional order derivative:

dvxadxv=1vΓa+vΓaxavE2

By using Cauchy’s integral formula for complex valued analytical functions, Laurent defined the integration of arbitrary order v>0as

cDxvfx=Dcxmρfx=dmdxm1Γρcxxtρ1ftdt,0<ρ1E3

where cDxvdenotes differentiation of order vof the function falong the x-axis.

Cauchy presented the following fractional order derivative:

f+α=fτtτα1ΓaE4

In 1967, the Italian mathematician Caputo presented his fractional derivative of order α>0as

Dαft=1Γmα0tfmτtτα+1m,m1<α<m,α<0E5

Diethelm [45] has suggested the Caputo derivative to be in the following form:

Daζfτ=aτKζτξfmξE6

where f(m) is the mth order derivative and mis an integer such that m1<ζm

Kζτξ=τξmζ1ΓmζE7

Wang and Li [46] have introduced a memory-dependent derivative (MDD)

Dωζfτ=1ωτωτKζτξfmξE8

where the first-order ζ=1of MDD for a differentiable function fτcan be expressed as

Dωfτ=1ωτωτKτξfξE9

Based on several practical applications, the memory effect needs weight 0Kτξ<1for ξτωτ, so the MDD magnitude Dωfτis usually smaller than fτ, where the time delay ω>0and the kernel function (0Kτξ1for ξτξτ) can be chosen arbitrarily on the delayed interval τωτ, the practical kernel functions are 1,1τξand 1τξωp, p=14,1,2, etc. These functions are monotonically increasing with K=0for the past time τξand K=1for the present time τ. The main feature of MDD is that the real-time functional value depends also on the past time [τξτ[. So, Dωdepends on the past time (nonlocal operator), while the integration does not depend on the past time (local operator).

As a special case Kτξ1, we have

Dωfτ=1ωτωτfξ=fτfτωωfτE10

The above equation shows that the common derivative dis the limit of Dωas ω0. That is,

Dωfτfτ=limω0fτ+ωfτωE11

Now, the boundary element method (BEM) [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80] is widely adopted for solving several engineering problems due to its easy implementation. In the BEM, only the boundary of the domain needs to be discretized, so it has a major advantage over other methods requiring full domain discretization [81, 82, 83, 84, 85, 86, 87] such as finite difference method (FDM), finite element method (FEM), and finite volume method (FVM) in engineering applications. This advantage of BEM over domain methods has significant importance for modeling of nonlinear generalized thermoelastic problems which can be implemented using BEM with little cost and less input data. Previously scientists have proven that FEM covers more engineering applications than BEM which is more efficient for infinite domain problems. But currently BEM scientists have changed their thinking and vision on BEM, where the BEM researchers developed the BEM technique for solving inhomogeneous and nonlinear problems involving infinite and semi-infinite domains by using a lot of software like FastBEM and ExaFMM.

The main objective of this chapter is to introduce a novel memory-dependent derivative model for solving transient three-temperature nonlinear thermal stress problems in functionally graded anisotropic (FGA) smart structures. The governing equations of the considered model are nonlinear and very difficult if not impossible to solve analytically. Therefore, we develop a new efficient boundary element technique for solving such equations. Numerical results show the effects of MDD on the three-temperature distributions and the influence of MDD and anisotropy on the nonlinear thermal stresses of FGA smart structures. Also, numerical results demonstrate the validity and accuracy of the proposed model.

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 smart material problems, memory-dependent derivative history, and their applications. Section 2 describes the physical modeling of memory-dependent derivative problems of three-temperature nonlinear thermal stresses in FGA structures. Section 3 outlines the BEM implementation for obtaining the temperature field of the considered problem. Section 4 outlines the BEM implementation for obtaining the dispacement field of the considered problem. Section 5 introduces computing performance of the proposed model. Section 6 presents the new numerical results that describe the effects of memory-dependent derivative and anisotropy on the problem’s field variations. Lastly, Section 7 outlines the significant findings of this chapter.

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2. Formulation of the problem

With reference to a Cartesian system x1x2x3with a configuration R bounded by a closed surface S as shown in Figure 1.

Figure 1.

Computational domain of the considered smart structure.

The governing equations for the transient three-temperature nonlinear thermal stresses problems of FGA smart structures with memory-dependent derivatives can be written as [35].

σij,j+ρFi=πüiE12
Di,i=0E13

where

σij=x+1mCijkleδijβabTαTα0+τ1ṪαE14
Di=x+1meijkεjk+fikEkεij=12ui,j+uj,i,E15

where σij, Fi, εij, εijk, ui, and ρare the force stress tensor, mass force vector, strain tensor, alternate tensor, displacement vector, and density, respectively, CijklCijkl=Cklij=Cjiklis the constant elastic moduli, e is the dilatation, βijβij=βjiare the stress-temperature coefficients, Diis the electric displacement, eijkis the piezoelectric tensor, fikis the permittivity tensor, and Ekis the electric field vector.

The two-dimensional three-temperature (2D-3T) radiative heat conduction equations can be expressed as

ceTerττ1ρKeTerτ=WeiTeTiWepTeTpE16
ciTirττ1ρKiTirτ=WeiTeTiE17
cpTp3Tprττ1ρKpTprτ=WepTeTpE18

where e,i,pdenote electron, ion, and phonon, respectively; cecicp, KeKiKp, and TeTiTpare specific heat capacities, conductive coefficients, and temperature functions, respectively; Weiis the electron-ion coefficient; and Wepis the electron-phonon coefficient.

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3. BEM solution of temperature field

This section concerns using a boundary element method to solve the temperature model.

The above 2D-3T radiative heat conduction Eqs. (16)-(18) can be expressed in the context of nonlinear thermal stresses of FGA smart structures as in [36].

δ1jKα+δ2jKαTαrτW´rτ=cαρδ1δ1jDωαTαrτE19

which can be written in the following form:

LabTαrτ=fabE20

where

Lab=δ1jKα+δ2jKαE21
fab=W´rτ+W´rτE22

where

W´rτ=ρWeiTeTi+ρWerTeTp+W´,α=e,δ1=1ρWeiTeTi+W´,α=i,δ1=1ρWerTeTp+W´,α=p,δ1=Tp3E23
W´(r,τ)=F(r,τ)δ2jKαωατωατK(τξ)ξ(2Tα(r,τ))dξ+ρCαδ1δ1jωατωατK(τξ)ξ(Tα(r,τ))dξ+ρCα(τ0+δ1jτ2+δ2j)ωατωατK(τξ)2ξ2(Tα(r,τ))dξE24
Frτ=βabTα0Åδ1ju´a,b+τ0+δ2ju´a,bE25

and

Wei=ρAeiTe2/3,Wer=ρAerTe1/2,Kα=AαTα5/2,α=e,i,Kp=ApTp3+BE26

where δij,ij=12, ωα0α=eip, and Kτξare the Kronecker delta, delay times, and kernel function, respectively.

The total energy can be expressed as

P=Pe+Pi+PpPe=ceTe,Pi=ciTi,Pp=14cpTp4E27

Initial and boundary conditions can be expressed as

Tαxy0=Tα0xy=g1xτE28
KαTαnΓ1=0,α=e,i,TpΓ1=g2xτE29
KαTαnΓ2=0,α=e,i,pE30

By using the fundamental solutions Tαthat satisfies the following differential equation:

LabTα=fabE31

Now, by implementing the technique of Fahmy [35], we can write (19) as

CTα=DKαOτSTαqTαqdS+DKαOτRbTαdR+RTαiTατ=0dRE32

which can be written in the absence of heat sources as follows:

CTα=STαqTαqdSRKαDTατTαdRE33

In order to transform the domain integral in (33) to the boundary, we approximate the temperature time derivative as

Tατi=1NfjrjajτE34

where fjrare known functions and ajτare unknown coefficients.

We assume that Tαjis a solution of

2Tαj=fjE35

Then, Eq. (33) leads to the following boundary integral equation

CTα=STαqTαqdS+i=1NajτD1CTαjSTαjqq̂jTαdSE36

where

q̂j=KαTαjnE37

and

ajτ=i=1Nfji1TαriττE38

where fji1are the coefficients of F1which are defined as [58].

Fji=fjriE39

By discretizing Eq. (36) and using Eq. (38), we get [35].

CṪα+HTα=GQE40

where Q is the heat flux vector and H and G are matrices.

The diffusion matrix can be defined as

C=HTαGQF1D1E41

where

Tij=TjxiE42
Qij=q̂jxiE43

To solve numerically Eq. (41), the functions Tαand q were interpolated as

Tα=1θTαm+θTαm+1E44
q=1θqm+θqm+1E45

where 00=ττmτm+1τm1determines the practical time τof the current time step.

By time differentiation of Eq. (44), we obtain

Ṫα=dTα=Tαm+1Tαmτm+1τm=Tαm+1TαmΔτmE46

By substitution from (44)(46) into (40), we get

CΔτm+θHTαm+1θGQm+1=CΔτm1θHTαm+1θGQmE47

By considering the initial and boundary conditions, we can write the following system of equations

aX=bE48

We apply an explicit staggered algorithm to solve the system (48) and obtain the temperature in terms of the displacement field.

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4. BEM solution of displacement field

By using the weighted residual method, we can write (12) and (13) in the following form:

Rσij,j+UiuidR=0E49
RD,iΦidR=0E50

where

Ui=ρFiρu¨i,E51

where uiand Φiare weighting functions and uiand Φiare approximate solutions.

Now, we assume the following boundary conditions:

ui=u¯ionS1E52
λi=σijnj=λ¯ionS2E53
Φ=Φ¯onS5E54
Q=∂Φn=Q¯onS6E55

By integration by parts for the first term of Eqs. (49) and (50), we have

Rσijui,jdR+RUiuidR=S2λiuidSE56
RDΦi,idR=S6QiΦidSE57

Based on Huang and Liang [88], the boundary integral equation can be expressed as

Rσij,juidR+RUiuidRRDΦi,idR=S2(λiλ¯i)uidS+S1(u¯iui)λidS+S6(QiQ¯i)ΦidS+S5(Φ¯iΦi)QidSE58

By integrating by parts for the left-hand side of (58), we get

RσijεijdR+RUiuidRRDΦi,idR=S2λ¯iuidSS1λiuidS]+S1(u¯iui)λidSS6Q¯iΦidSS5QiΦidS+S5(Φ¯iΦi)QidSE59

Based on Eringen [89], the elastic stress can be expressed as

σij=Aijklεkl,E60

where

Aijkl=AklijE61

Hence, Eq. (59) can be rewritten as

RσijεijdR+RUiuidRRDΦi,idR=S2λ¯iuidSS1λiuidS+S1(u¯iui)λidSS6Q¯iΦidSS5QiΦidS+S5(Φ¯iΦi)QidSE62

By integration by parts again, we obtain

Rσij,iuidR=SuiλidSSΦiQidS+SλiuidS+SQiΦidSE63

The weighting functions of Ui=Δnand Vi=0along e1 can be obtained as follows:

σ1j,j+Δne1=0E64

According to Dragos [90], the fundamental solution can be written as

ui=u1ie1,Φi=Φ1ie1,λi=λ1ie1,Qi=Q1ie1E65

The weighting functions of Ui=0and Vi=Δnalong e1 can be written as follows:

σij,j=0E66

Based on Dragos [90], the fundamental solution can be obtained analytically as

ui=u1ie1,Φi=Φ1ie1,λi=λ1ie1,Qi=Q1ie1E67

By using the weighting functions of (65) and (67) into (63), we have

C1inuin=Sλ1iuidSSQ1iΦidS+Su1iλidS+SΦ1iQidSE68
C1inωin=Sλ1iuidSSQ1iΦidS+Su1iλidS+SΦ1iQidSE69

Thus, we can write

Cnqn=SpqdS+SqpdS+SdΦds+Sf∂ΦndSE70

where

Cn=[C11C12C21C22],q=[u11u120u21u220u31u320],p=[λ11λ120λ21λ220λ31λ320],q=[u1u2ω3],p=[λ1λμ3],d=[d1d20],f=[f1f20]E71

In order to solve (70) numerically, we suppose the following definitions:

q=ψqj,p=ψpj,Φ=ψ0Φj,∂Φn=ψ0∂ΦnjE72

Substituting from (72) into (70) and discretizing the boundary, we obtain

Cnqn=j=1NeΓjpψdΓqj+j=1NeΓjqψdΓpj+j=1NeΓjdψ0dΓΦj+j=1NeΓjfψ0dΓ∂ΦnjE73

Equation after integration can be written as

Ciqi=j=1NeĤijqj+j=1NeĜijpj+j=1NeD̂ijΦi+j=1NeF̂ij∂ΦnjE74

By using the following representation:

Hij=ĤijifijĤij+Ciifi=jE75

Thus, we can write (74) as follows:

j=1NeHijqj=j=1NeĜijpj+j=1NeD̂ijΦj+j=1NeF̂ij∂ΦnjE76

The global matrix equation for all inodes can be expressed as

HQ=GP+DΘ+FSE77

where Qis the displacement vector, Pis the traction vector, Θis the electric potential vector, and Sis the electric potential gradient vector.

Substituting the boundary conditions into (77), we obtain the following system of equations:

AX=BE78

We apply an explicit staggered algorithm to solve the system (78) and obtain the temperature and displacement fields as follows:

  1. From Eq. (48) we obtain the temperature field in terms of the displacement field.

  2. We predict the displacement field and solve the resulted equation for the temperature field.

  3. We correct the displacement field using the computed temperature field for Eq. (78).

An explicit staggered algorithm based on communication-avoiding Arnoldi as described in Hoemmen [91] is very suitable for efficient implementation in Matlab (R2019a) with the aim of specifically improving its performance for the solution of the resulting linear algebraic systems.

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5. Computational performance of the problem

According to Fahmy [35], the computer performance with simulation can be computed based on account and communication process, elements underlying the hardware and functional computation. The main objective of our proposed technique during simulation process is to use the preconditioners which are efficient to improve the overall CPU utilization of the cluster, accelerate the iterative method, and reduce the input/output and the interprocessor communication costs. Also, Fahmy [35] compared the communication-avoiding Krylov methods that are based on the s-step Krylov methods such as communication-avoiding generalized minimal residual (CA-GMRES) of Saad and Schultz [92], communication-avoiding Arnoldi (CA-Arnoldi) of the Arnoldi [93] and communication-avoiding Lanczos (CA-Lanczos) of Lanczos [94], with their corresponding standard Krylov methods. CA-Arnoldi which is also called Arnoldi (s, t) algorithm is different from standard Arnoldi (s) st=1, where s is the number of inner iteration steps and t is the number of outer iteration steps. According to [35], the CA-Arnoldi has numerical stability, convergence, and performance due to the implementation of algorithm shown in Figure 2, which is based on the QR factorization update and block classical Gram-Schmidt (block CGS) approach or block modified Gram-Schmidt (block MGS) approach where

Figure 2.

CA-Arnoldi iteration algorithm.

Vk=vsk+1vsk+2vsk+sE79

and

Qk=Q0Q1Qk1E80

The generalized minimal residual (GMRES) method of Saad and Schultz [92] is a Krylov subspace method for solving nonsymmetric linear systems. The CA-GMRES algorithm is based on Arnoldi (s, t) and equivalent to standard GMRES in exact arithmetic. Also, the GMRES or CA-GMRES are convergent at the same rate for problems, but Hoemmen [91] proved that CA-GMRES algorithm shown in Figure 3 converges for the s-step basis lengths and restart lengths used for obtaining maximum performance. Lanczos method can be considered as a special case of Arnoldi method for symmetric and real case of A or Hermitian and complex case of A. Symmetric Lanczos which is also called Lanczos is different from nonsymmetric Lanczos. We implemented a communication-avoiding version of symmetric Lanczos (CA-Lanczos) for solving symmetric positive definite (SPD) eigenvalue problems. Also, we implement CA-Lanczos iteration algorithm shown in Figure 4, which is also called Lanczos (s, t), where s is the s-step basis length and t is the outer iterations number before restart. This algorithm is based on using rank revealing-tall skinny QR-block Gram-Schmidt (RR-TSQR-BGS) orthogonalization method which connects between TSQR and block Gram-Schmidt, where we have been using the right-shifted basis matrix at outer iteration kas follows:

Figure 3.

CA-GMRES iteration algorithm.

Figure 4.

CA-Lanczos iteration algorithm.

Vk=Vsk+2vsk+sE81

and

Vk=Vkvsk+s+1E82

For more details about the considered preconditioners and algorithms, we refer the interested readers to [91].

The main objective of this section is to implement an accurate and robust preconditioning technique for solving the dense nonsymmetric algebraic system of linear equations arising from the BEM. So, a communication-avoiding Arnoldi of the Arnoldi [93] has been implemented for solving the resulting linear systems in order to reduce the iteration number and CPU time. The BEM discretization is employed in 1280 quadrilateral elements, with 3964 degrees of freedom (DOF). A comparative performance of preconditioned Krylov subspace solvers (CA-Arnoldi, CA-GMRES, and CA-Lanczos) has been shown in Table 1, where the number of DOF is 3964 and “–” was defined as the divergence process. From the results of Table 1. The CA-Arnoldi, CA-GMRES, and CA-Lanczos are more cost-effective than the other Krylov subspace methods Arnoldi, GMRES, and Lanczos, respectively. Also, CA-Arnoldi, CA-GMRES, and CA-Lanczos have been compared with each other in Table 2. It can be seen from this table that the performance of CA-Arnoldi is superior than the other iterative methods.

MethodsPreconditioning techniquesIterationsResidualTime of each iterative step (s)Time of solution
Direct methodsNO9 min 50 s
ArnoldiNO1747.21E–073.8511 min 25 s
JOBI265.22E–073.862 min 38 s
BJOB221.34E–063.862 min 23 s
ILU3471.66E–063.844 min 2 s
ILU5481.38E–063.894 min 6 s
DILU481.53E–065.454 min 18 s
CA–ArnoldiNO3606.96E–071.9511 min 53 s
JOBI204.42E–071.961 min 30 s
BJOB202.30E–081.961 min 30 s
ILU3407.87E–071.962 min 11 s
ILU5601.28E–081.962 min 48 s
DILU601.59E–073.074 min 1 s
GMRESNO2802.36E–081.906 min 20 s
JOBI405.01E–131.912 min 10 s
BJOB402.05E–111.912 min 10 s
ILU3404.70E–081.912 min 10 s
ILU5403.13E–082.602 min 10 s
DILU406.19E–083.072 min 48 s
CA–GMRESNO1206.89E–073.787 min 57 s
JOBI121.00E–053.761 min 41 s
BJOB122.22E–063.761 min 42 s
ILU3263.63E–063.752 min 34 s
ILU5224.05E–063.752 min 20 s
DILU255.19E–065.933 min 18 s
LanczosNO1357.24E–073.808 min 41 s
JOBI224.87E–073.752 min 33 s
BJOB189.27E–075.183 min 2 s
ILU3422.41E–073.813 min 48 s
ILU5366.41E–073.783 min 18 s
DILU382.04E–075.003 min 32 s
CA–LanczosNO1291.30E–043.759 min 22 s
JOBI168.64E–073.762 min 3s
BJOB141.69E–073.772 min 0 s
ILU3249.29E–073.872 min 31 s
ILU5311.91E–073.903 min 1 s
DILU278.11E–075.953 min 31 s

Table 1.

Performances of preconditioned Krylov subspace iterative methods for DOF 3964.

SolversDOF
9651505338039646005
CA–ArnoldiResidual6.81E–125.38E–124.13E–114.17E–117.57E–11
CPU time (s)4.9610.7899.24134.26293.29
Iterations2525252525
CA–GMRESResidual2.98E–121.90E–121.28E–111.36E–111.22E–11
CPU time (s)5.0611.49126.38164.09445.51
Iterations5050505050
CA– LanczosResidual7.20E–113.35E–112.72E–113.97E–118.33E–11
CPU time (s)5.0511.47139.07180.49514.72
Iterations2226283032

Table 2.

The CPU time and the number of iterations for some communication–avoiding Krylov subspace solvers.

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6. Numerical results and discussion

In order to illustrate the numerical results of this study, we consider a monoclinic graphite-epoxy as an anisotropic smart material which has the following constants [35].

The elasticity tensor is expressed as

Cpjkl=430.1130.418.200201.3130.4116.721.00070.118.221.073.6002.400019.88.000008.029.10201.370.12.400147.3GPaE83

The mechanical temperature coefficient is

βpj=1.012.0002.001.480007.52106N/km2E84

The thermal conductivity tensor is

kpj=5.20007.600038.3W/kmE85

Mass density ρ=7820kg/m2and heat capacity c=461J/kgk.

The technique that has been proposed in the current chapter can be applicable to a wide range of three-temperature nonlinear thermal stress problems of FGA structures. The main aim of this chapter is to assess the impact of MDD and anisotropy on the three-temperature nonlinear thermal stress distributions.

The proposed technique that has been implemented in the current study can be applicable to a wide variety of FGA smart structure problems involving three temperatures. All the physical parameters satisfy the initial and boundary conditions. The efficiency of our BEM modeling technique has been improved using an explicit staggered algorithm based on communication-avoiding Arnoldi procedure to decrease the computation time.

Figure 5 shows the variations of the three temperatures Te,TiandTpwith the time τin the presence of MDD. Figure 6 shows the variations of the three temperatures Te,TiandTpwith the time τin the presence of MDD. It can be seen from Figures 5 and 6 that the MDD has a significant effect on the temperature distributions.

Figure 5.

Variation of the three-temperature (with memory) with timeτ.

Figure 6.

Variation of the three-temperature (without memory) with timeτ.

In order to study the anisotropy and MDD effects on the nonlinear thermal stresses, we assume the following four cases: A, B, C, and D, where case A denotes the nonlinear thermal stress distribution in the isotropic material without MDD effect, case B denotes the nonlinear thermal stress distribution in isotropic material with MDD effect, case C denotes the nonlinear thermal stress distribution in anisotropic material without MDD effect, and case D denotes nonlinear thermal stress distribution in anisotropic material with MDD effect.

Figures 79 show the variation of the nonlinear thermal stresses σ11,σ12andσ22with the time τ. It is clear from these figures that both anisotropy and MDD have a significant influence on the nonlinear thermal stress distributions.

Figure 7.

Variation of the nonlinear thermal stressσ11 with timeτ.

Figure 8.

Variation of the nonlinear thermal stressσ12 with timeτ.

Figure 9.

Variation of the nonlinear thermal stressσ22 with timeτ.

Since there are no available results for the considered problem in the literature. Therefore, we only considered the one-dimensional special case for the variations of the nonlinear thermal stress σ11 with the time τas shown in Figure 10. The validity and accuracy of our proposed technique was confirmed by comparing graphically our BEM results with those obtained using the FDM of Pazera and Jędrysiak [95] and FEM of Xiong and Tian [96] results based on replacing one-temperature heat conduction with the total three-temperature TT=Te+Ti+Trheat conduction. It can be noticed that the BEM results are found to agree very well with the FDM and FEM results.

Figure 10.

Variation of the nonlinear thermal stressσ11 with timeτ.

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7. Conclusion

The main aim of this chapter is to introduce a new MDD model based on BEM for obtaining the transient three-temperature nonlinear thermal stresses in FGA smart structures. The governing equations of this model are very hard to solve analytically because of nonlinearity and anisotropy. To overcome this, we propose a new boundary element formulation for solving such equations. Since the CA kernels of the s-step Krylov methods are faster than the kernels of standard Krylov methods. Therefore, we used an explicit staggered algorithm based on CA-Arnoldi procedure to solve the resulted linear equations. The computational performance of the proposed technique has been performed using communication-avoiding Arnoldi procedure. The numerical results are presented highlighting the effects of MDD on the three-temperature distributions and the influence of MDD and anisotropy on the nonlinear thermal stresses of FGA smart structures. The numerical results also demonstrate the validity and accuracy of the proposed technique. It can be concluded from numerical results of our current general problem that all generalized and nonlinear generalized thermoelasticity theories can be combined with the three-temperature radiative heat conduction to describe the deformation of FGA smart structures in the context of memory-dependent derivatives. From the research that has been performed, it is possible to conclude that the proposed BEM technique is effective and stable for transient three-temperature thermal stress problems in FGA smart structures.

The numerical results for our complex and general problem can provide data references for computer scientists and engineers, geotechnical and geothermal engineers, designers of new materials, and researchers in material science as well as for those working on the development of anisotropic smart structures. In the application of three-temperature theories in advanced manufacturing technologies, with the development of soft machines and robotics in biomedical engineering and advanced manufacturing, transient thermal stresses will be encountered more often where three-temperature radiative heat conduction will turn out to be the best choice for thermomechanical analysis in the design and analysis of advanced smart materials and structures.

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Written By

Mohamed Abdelsabour Fahmy

Submitted: April 13th, 2020 Reviewed: May 14th, 2020 Published: November 28th, 2020