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Lecture 1IntroductionThe rules of the gamePrint version Lecture Theory of Elasticity and Plasticity of
Dr. D. Dinev, Department of Structural Mechanics, UACEG
1.1
Contents
1 Introduction 11.1 Elasticity and plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Overview of the course . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Course organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Mathematical preliminaries 62.1 Scalars, vectors and tensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Index notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.3 Kronecker delta and alternating symbol . . . . . . . . . . . . . . . . . . . . . . 82.4 Coordinate transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.5 Cartesian tensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.6 Principal values and directions . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.7 Vector and tensor algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.8 Tensor calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.2
1 Introduction
1.1 Elasticity and plasticity
Introduction
Elasticity and plasticity
• What is the Theory of elasticity (TE)?
– Branch of physics which deals with calculation of the deformation of solid bodies inequilibrium of applied forces
– Theory of elasticity treats explicitly a linear or nonlinear response of structure toloading
• What do we mean by a solid body?
– A solid body can sustain shear
– Body is and remains continuous during the deformation- neglecting its atomic struc-ture, the body consists of continuous material points (we can infinitely ”zoom-in”and still see numerous material points)
• What does the modern TE deal with?
– Lab experiments- strain measurements, photoelasticity, fatigue, material description
– Theory- continuum mechanics, micromechanics, constitutive modeling
– Computation- finite elements, boundary elements, molecular mechanics1.3
1
Introduction
Elasticity and plasticity
• Which problems does the TE study?
– All problems considering 2- or 3-dimensional formulation1.4
Introduction
Elasticity and plasticity
• Shell structures1.5
Introduction
Elasticity and plasticity
• Plate structures1.6
Introduction
2
Elasticity and plasticity• Disc structures (walls)
1.7
Introduction
Mechanics of Materials (MoM)• Makes plausible but unsubstantial assumptions• Most of the assumptions have a physical nature• Deals mostly with ordinary differential equations• Solve the complicated problems by coefficients from tables (i.e. stress concentration fac-
tors)
Elasticity and plasticity• More precise treatment• Makes mathematical assumptions to help solve the equations• Deals mostly with partial differential equations• Allows us to assess the quality of the MoM-assumptions• Uses more advanced mathematical tools- tensors, PDE, numerical solutions
1.8
1.2 Overview of the course
Introduction
Overview of the course• Topics in this class
– Stress and relation with the internal forces
– Deformation and strain
– Equilibrium and compatibility
– Material behavior
– Elasticity problem formulation
– Energy principles
– 2-D formulation
– Finite element method
– Plate analysis
– Shell theory
– Plasticity
Note• A lot of mathematics• Few videos and pictures
1.9
3
1.3 Course organization
Introduction
Course organization
• Lecture notes- posted on a web-site: http://www.uacg.bg/UACEG_site/acadstaff/viewProfile.php?lang=en&perID=151
• Instructor
– Dr. D. Dinev- Room 514, E-mail: [email protected]
• Teaching assistant
– Dr. I. Kerelezova- Room 436
• Office hours
– Instructor: Mon: 10-11; Tue: 13-14
– TA: . . . . . . . . . . . .
Note
• For other time→ by appointment1.10
Introduction
Course organization
• Textbooks
– Elasticity theory, applications, and numerics, Martin H. Sadd, 2nd edition, Elsevier2009
– Energy principles and variational methods in applied mechanics, J. N. Reddy, JohnWiley & Sons 2002
– Fundamental finite element analysis and applications, M. Asghar Bhatti, John Wiley& Sons 2005
– Theories and applications of plate analysis, Rudolph Szilard, John Wiley & Sons2004
– Thin plates and shells, E. Ventsel and T. Krauthammer, Marcel Dekker 20011.11
Introduction
Course organization
• Other references
– Elasticity in engineering mechanics, A. Boresi, K. Chong and J. Lee, John Wiley &Sons, 2011
– Elasticity, J. R. Barber, 2nd edition, Kluwer academic publishers, 2004
– Engineering elasticity, R. T. Fenner, Ellis Horwood Ltd, 1986
– Advanced strength and applied elasticity, A. Ugural and S. Fenster, Prentice hall,2003
– Introduction to finite element method, C.A. Felippa, lecture notes, University of Col-orado at Boulder
– Lecture handouts from different universities around the world1.12
4
Introduction
123456740 50 60 70 80 90 100
GradePoints
Course organization
• Grading1.13
Introduction
Course organization
• Grading is based on
– Homework- 20%
– Two mid-term exams- 35%
– Final exam- 45%
• Participation
– Class will be taught with a mixture of lecture and student participation
– Class participation and attendance are expected of all students
– In-class discussions will be more valuable to you if you read the relevant sectionsof the textbook before the class time
1.14
Introduction
Course organization
• Homeworks
– Homework is due at the beginning of the Thursday lectures
– The assigned problems for the HW’s will be announced via web-site
• Late homework policy
– Late homework will not be accepted and graded
• Team work
– You are encouraged to discuss HW and class material with the instructor, the TA’sand your classmates
– However, the submitted individual HW solutions and exams must involve only youreffort
– Otherwise you’ll have terrible performance on the exam since you did not learn tothink for yourself
1.15
5
2 Mathematical preliminaries
2.1 Scalars, vectors and tensors
Mathematical preliminaries
Scalars, vectors and tensor definitions• Scalar quantities- represent a single magnitude at each pint in space
– Mass density- ρ
– Temperature- T
• Vector quantities- represent variables which are expressible in terms of components in a2-D or 3-D coordinate system
– Displacement- u = ue1 + ve2 +we3
• Matrix quantities- represent variables which require more than three components to quan-tify
– Stress matrix
σ =
σxx σxy σxzσyx σyy σyzσzx σzy σzz
where e1, e2 and e3 are unit basis vectors in the coordinate system
1.16
2.2 Index notation
Mathematical preliminaries
Index notation• Index notation is a shorthand scheme where a set of numbers is represented by a single
symbol with subscripts
ai =
a1a2a3
, ai j =
a11 a12 a13a21 a22 a23a31 a32 a33
– a1 j → first row– ai1 → first column
• Addition and subtraction
ai±bi =
a1±b1a2±b2a3±b3
ai j±bi j =
a11±b11 a12±b12 a13±b13a21±b21 a22±b22 a23±b23a31±b31 a32±b32 a33±b33
1.17
Mathematical preliminaries
Index notation• Scalar multiplication
λai =
λa1λa2λa3
, λai j =
λa11 λa12 λa13λa21 λa22 λa23λa31 λa32 λa33
• Outer multiplication (product)
aib j =
a1b1 a1b2 a1b3a2b1 a2b2 a2b3a3b1 a3b2 a3b3
1.18
6
Mathematical preliminaries
Index notation
• Commutative, associative and distributive laws
ai +bi = bi +ai
ai jbk = bkai j
ai +(bi + ci) = (ai +bi)+ ci
ai(b jkc`) = (aib jk)c`
ai j(bk + ck) = ai jbk +ai jck
1.19
Mathematical preliminaries
Index notation
• Summation convention (Einstein’s convention)- if a subscript appears twice in the sameterm, then summation over that subscript from one to three is implied
aii =3
∑i=1
aii = a11 +a22 +a33
ai jb j =3
∑j=1
ai jb j = ai1b1 +ai2b2 +ai3b3
– j- dummy index– subscript which is repeated into the notation (one side of theequation)
– i- free index– subscript which is not repeated into the notation1.20
Mathematical preliminaries
Index notation- example
• The matrix ai j and vector bi are
ai j =
1 2 00 4 32 1 2
, bi =
240
• Determine the following quantities
– aii = a11 +a22 + . . . = . . . (scalar)- no free index
– ai jai j = a11a11 + a12a12 + a13a13 + . . . = 1× 1 + 2× 2 + . . . = . . . (scalar)- no freeindex
ai jb j = ai1b1 +ai2b2 +ai3b3
=
a11b1 +a12b1 +a13b3. . .. . .
=
. . .. . .. . .
(vector)- one free index
1.21
Mathematical preliminaries
Index notation- example
• Determine the following quantities
ai ja jk = ai1a1k +ai2a2k +ai3a3k
=
i = 1 a11a1k +a12a2k +a13a3ki = 2 a21a1k + . . .i = 3 a31a1k + . . .
7
The first expression gives the components of the 1-st row
a11a1k +a12a2k +a13a3k = k = 1 a11a11 +a12a21 +a13a31 = . . .k = 2 a11a12 +a12a22 +a13a32 = . . .k = 3 a11a13 +a12a23 +a13a33 = . . .
(matrix)- two free indexes• ai jbib j = a11b1b1 +a12b1b2 +a13b1b3 + . . . = . . . (scalar)- no free index
1.22
Mathematical preliminaries
Index notation- example• Determine the following quantities
– bibi = b1b1 +b2b2 + . . . = . . . (scalar)- no free index
bib j =
b1b jb2b jb3b j
=
b1b1 b1b2 b1b3. . .. . .
= . . .
(matrix)- two free indexes1.23
Mathematical preliminaries
Index notation- example• Determine the following quantities
– Unsymmetric matrix decomposition
ai j =12(ai j +a ji)︸ ︷︷ ︸symmetric
+12(ai j−a ji)︸ ︷︷ ︸
antisymmetric
– Symmetric part
12(ai j +a ji) = . . .
– Antisymmetric part
12(ai j−a ji) = . . .
1.24
2.3 Kronecker delta and alternating symbol
Mathematical preliminaries
Kronecker delta and alternating symbol• Kronecker delta is defined as
δi j ={
1 if i = j0 if i 6= j =
1 0 00 1 00 0 1
• Properties of δi j
δi j = δ ji
δii = 3
δi ja j =
δ11a1 +δ12a2 +δ13a3 = a1. . .. . .
= ai
δi ja jk = aik
δi jai j = aii
δi jδi j = 31.25
8
Mathematical preliminaries
Kronecker delta and alternating symbol• Alternating (permutation) symbol is defined as
εi jk =
+1 if i jk is an even permutation of 1,2,3−1 if i jk is an odd permutation of 1,2,3
0 otherwise=
• Therefore
ε123 = ε231 = ε312 = 1ε321 = ε132 = ε213 =−1ε112 = ε131 = ε222 = . . . = 0
• Matrix determinant
det(ai j) = |ai j|=
∣∣∣∣∣∣a11 a12 a13a21 a22 a23a31 a32 a33
∣∣∣∣∣∣= εi jka1ia2 ja3k = εi jkai1a j2ak3
1.26
2.4 Coordinate transformations
Mathematical preliminaries
Coordinate transformations• Consider two Cartesian coordinate systems with different orientation and basis vectors
1.27
Mathematical preliminaries
Coordinate transformations• The basis vectors for the old (unprimed) and the new (primed) coordinate systems are
ei =
e1e2e3
, e′i =
e′1e′2e′3
• Let Ni j denotes the cosine of the angle between x′i-axis and x j-axis
Ni j = e′i · e j = cos(x′i,x j)
• The primed base vectors can be expressed in terms of those in the unprimed by relations
e′1 = N11e1 +N12e2 +N13e3
e′2 = N21e1 +N22e2 +N23e3
e′3 = N31e1 +N32e2 +N33e3
1.28
9
Mathematical preliminaries
Coordinate transformations
• In matrix form
e′i = Ni je j
ei = N jie′j
• An arbitrary vector can be written as
v = v1e1 + v2e2 + v3e3 = viei
= v′1e′1 + v′2e′2 + v′3e′3 = v′ie′i
1.29
Mathematical preliminaries
Coordinate transformations
• Or
v = viN jie′j
• Because v = v′je′j thus
v′j = N jivi
• Similarly
vi = N jiv′j
• These relations constitute the transformation law for the Cartesian components of a vectorunder a change of orthogonal Cartesian coordinate system
1.30
2.5 Cartesian tensors
Mathematical preliminaries
Cartesian tensors
• General index notation scheme
a′ = a, zero order (scalar)a′i = Nipap, first order (vector)a′i j = NipN jqapq, second order (matrix)
a′i jk = NipN jqNkrapqr, third order
. . .
• A tensor is a generalization of the above mentioned quantities
Example
• The notation v′i = Ni jv j is a relationship between two vectors which are transformed toeach other by a tensor (coordinate transformation). The multiplication of a vector by atensor results another vector (linear mapping).
1.31
10
Mathematical preliminaries
Cartesian tensors
• All second order tensors can be presented in matrix form
Ni j =
N11 N12 N13N21 N22 N23N31 N32 N33
• Since Ni j can be presented as a matrix, all matrix operation for 3×3-matrix are valid• The difference between a matrix and a tensor
– We can multiply the three components of a vector vi by any 3×3-matrix
– The resulting three numbers (v′1,v′2v′3) may or may not represent the vector compo-
nents
– If they are the vector components, then the matrix represents the components of atensor Ni j
– If not, then the matrix is just an ordinary old matrix1.32
Mathematical preliminaries
Cartesian tensors
• The second order tensor can be created by a dyadic (tensor or outer) product of the twovectors v′ and v
N = v′⊗v =
v′1v1 v′1v2 v′1v3v′2v1 v′2v2 v′2v3v′3v1 v′3v2 v′3v3
1.33
Mathematical preliminaries
Transformation example
• The components of a first and a second order tensor in a particular coordinate frame aregiven by
bi =
142
, ai j =
1 0 30 2 23 2 4
• Determine the components of each tensor in a new coordinates found through a rotation of
60◦ about the x3-axis1.34
Mathematical preliminaries
Transformation example
11
• The rotation matrix is
Ni j = cos(x′i,x j)
=
cos300◦ cos30◦ cos90◦cos210◦ cos300◦ cos90◦cos90◦ cos90◦ cos0◦
=
12
√3
2 0−√
32
12 0
0 0 1
1.35
Mathematical preliminaries
Transformation example
• The transformation of the vector bi is
b′i = Ni jb j =
12
√3
2 0−√
32
12 0
0 0 1
1
42
= . . .
• The second order tensor transformation is
a′i j = NipN jpapq =
12
√3
2 0−√
32
12 0
0 0 1
1 0 30 2 23 2 4
12
√3
2 0−√
32
12 0
0 0 1
T
= . . .
1.36
2.6 Principal values and directions
Mathematical preliminaries
Principal values and directions for symmetric tensor
• The tensor transformation shows that there is a coordinate system in which the componentsof the tensor take on maximum or minimum values• If we choose a particular coordinate system that has been rotated so that the x′3-axis lies
along the vector, then vector will have components
v =
00|v|
1.37
12
Mathematical preliminaries
Principal values and directions for symmetric tensor• It is of interest to inquire whether there are certain vectors n that have only their lengths
and not their orientation changed when operated upon by a given tensor A• That is, to seek vectors that are transformed into multiples of themselves• If such vectors exist they must satisfy the equation
A ·n = λn, Ai jn j = λni
• Such vectors n are called eigenvectors of A• The parameter λ is called eigenvalue and characterizes the change in length of the eigen-
vector n• The above equation can be written as
(A−λ I) ·n = 0, (Ai j−λδi j)n j = 0
1.38
Mathematical preliminaries
Principal values and directions for symmetric tensor• Because this is a homogeneous set of equations for n, a nontrivial solution will not exist
unless the determinant of the matrix (. . .) vanishes
det(A−λ I) = 0, det(Ai j−λδi j) = 0
• Expanding the determinant produces a characteristic equation in terms of λ
−λ3 + IAλ
2− IIAλ + IIIA = 0
1.39
Mathematical preliminaries
Principal values and directions for symmetric tensor• The IA, IIA and IIIA are called the fundamental invariants of the tensor
IA = tr(A) = Aii = A11 +A22 +A33
IIA =12(tr(A)2− tr(A2)
)=
12(AiiA j j−Ai jAi j)
=∣∣∣∣ A11 A12
A21 A22
∣∣∣∣+ ∣∣∣∣ A22 A23A32 A33
∣∣∣∣+ ∣∣∣∣ A11 A13A31 A33
∣∣∣∣IIIA = det(A) = det(Ai j)
• The roots of the characteristic equation determine the values for λ and each of these maybe back-substituted into (A−λ I) ·n = 0 to solve for the associated principle directions n.
1.40
Mathematical preliminaries
Example• Determine the invariants and principal values and directions of the following tensor:
A =
3 1 11 0 21 2 0
• The invariants are
IA = . . . , IIA = . . . IIIA = . . .
• The characteristic equation is
−λ3 +3λ
2 +6λ −8 = 0
• The roots are λ1 =−2, λ2 = 1 and λ3 = 41.41
13
Mathematical preliminaries
Example
• For λ1 =−2 we have (A−λ1I) ·n1 = 0 5 1 11 2 21 2 2
n11n21n31
=
000
• The homogeneous set of equations have linear dependent equations and the solution rep-
resents only the ratio between the solution set• Applying n31 = 1 and solving the first end second equations we get
n1 = . . .
• Similarly for λ2 = 1 and λ3 = 41.42
2.7 Vector and tensor algebra
Mathematical preliminaries
Vector and tensor algebra
• Scalar product (dot product, inner product)
a ·b = |a||bcosθ
• Magnitude of a vector
|a|= (a ·a)1/2
• Vector product (cross-product)
a×b = det
e1 e2 e13a1 a2 a3b1 b2 b3
• Vector-matrix products
Aa = Ai ja j = a jAi j
aT A = aiAi j = Ai jai
1.43
Mathematical preliminaries
Vector and tensor algebra
• Matrix-matrix products
AB = Ai jB jk
ABT = Ai jBk j
AT B = A jiB jk
tr(AB) = Ai jB ji
tr(ABT ) = tr(AT B) = Ai jBi j
where ATi j = A ji and tr(A) = Aii = A11 +A22 +A33
1.44
14
2.8 Tensor calculus
Mathematical preliminaries
Tensor calculus
• Common tensors used in field equations
a = a(x1,x2,x3) = a(xi) = a(x)− scalarai = ai(x1,x2,x3) = ai(xi) = ai(x)−vectorai j = ai j(x1,x2,x3) = ai j(xi) = ai j(x)− tensor
• Comma notations for partial differentiation
a,i =∂
∂xia
ai, j =∂
∂x jai
ai j,k =∂
∂xkai j
1.45
Mathematical preliminaries
Tensor calculus- example
• Vector differentiation
ai, j =∂ai
∂x j=
∂a1∂x1
∂a1∂x2
∂a1∂x3
∂a2∂x1
∂a2∂x2
∂a2∂x3
∂a3∂x1
∂a3∂x2
∂a3∂x3
1.46
Mathematical preliminaries
Tensor calculus
• Directional derivative
– Consider a scalar function φ . Find the derivative of the φ with respect of direction s
dφ
ds=
∂φ
∂xdxds
+∂φ
∂ydyds
+∂φ
∂ zdzds
– The unit vector in the direction of s is
n =dxds
e1 +dyds
e2 +dzds
e3
– The directional derivative can be expressed as a scalar product
dφ
ds= n ·∇φ
1.47
15
Mathematical preliminaries
Tensor calculus
• Directional derivative
– ∇φ is called the gradient of the scalar function φ and is defined by
∇φ = e1∂φ
∂x+ e2
∂φ
∂y+ e3
∂φ
∂ z
– The symbolic operator ∇ is called del operator (nabla operator) and is defined as
∇ = e1∂
∂x+ e2
∂
∂y+ e3
∂
∂ z
– The operator ∇2 is called Laplacian operator and is defined as
∇2 =
∂ 2
∂x2 +∂ 2
∂y2 +∂ 2
∂ z2
1.48
Mathematical preliminaries
Tensor calculus• Common differential operations and similarities with multiplications
Name Operation Similarities OrderGradient of a scalar ∇φ ≈ λu vector ↑Gradient of a vector ∇u = ui, jeie j ≈ u⊗v tensor ↑
Divergence of a vector ∇ ·u = ui, j ≈ u ·v dot ↓Curl of a vector ∇×u = εi jkuk, jei ≈ u×v cross→
Laplacian of a vector ∇2u = ∇ ·∇u = ui,kkei
NoteThe ∇-operator is a vector quantity
1.49
Mathematical preliminaries
Tensor calculus- example
• Scalar and vector functions are φ = x2− y2 and u = 2xe1 + 3yze2 + xye3. Calculate thefollowing expressions• Gradient of a scalar
∇φ = . . .
• Laplacian of a scalar
∇2φ = ∇ ·∇φ = . . .
• Divergence of a vector
∇ ·u = . . .
• Gradient of a vector
∇u = . . .
1.50
16
Mathematical preliminaries
Tensor calculus- example
• Curl of a vector
∇×u = det
e1 e2 e3∂
∂x∂
∂y∂
∂ z2x 3yz xy
= . . .
1.51
Mathematical preliminaries
Tensor calculus
• Divergence (Gauss) theorem ∫S
u ·ndS =∫
V∇ ·udV
where n is the outward normal vector to the surface S1.52
Mathematical preliminaries
The End
• Welcome and good luck• Any questions, opinions, discussions?
1.53
17