### Chapter 1 - University of Rhode Island

```Common Variable Types in Elasticity
Elasticity theory is a mathematical model of material deformation. Using principles of
continuum mechanics, it is formulated in terms of many different types of field
variables specified at spatial points in the body under study. Some examples include:
Scalars - Single magnitude
mass density , temperature T, modulus of elasticity E, . . .
Vectors – Three components in three dimensions
displacement vector u  ue1  ve 2  we 3 , e1, e2, e3 are unit basis vectors
Matrices – Nine components in three dimensions
stress matrix
  x  xy  xz 


[]   yx  y  yz 
  zx  zy  z 


Other – Variables with more than nine components
Elasticity
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
Index/Tensor Notation
With the wide variety of variables, elasticity formulation makes use of a tensor
formalism using index notation. This enables efficient representation of all
variables and governing equations using a single standardized method.
 a1 
 a11 a12 a13 
Index notation is a shorthand scheme whereby
a whole set of numbers or components can be a i  a 2  , a ij  a 21 a 22 a 23 
represented by a single symbol with subscripts
 a 3 
a 31 a 32 a 33 
In general a symbol aij…k with N distinct indices represents 3N distinct numbers
Addition, subtraction, multiplication and equality of index symbols are defined in
the normal fashion; e.g.
 a1  b1 
 a11  b11
ai  bi  a 2  b2  , aij  bij  a 21  b21



 a3  b3 
a 31  b31
 a1 
 a11
ai  a 2  , aij  a 21



a 3 
a 31
Elasticity
a12
a 22
a 32
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
a13 
a 23 

a 33 
a12  b12
a 22  b22
a32  b32
a13  b13 
a 23  b23 

a33  b33 
 a1b1
ai b j  a 2 b1

 a 3b1
a1b2
a 2 b2
a 3b2
a1b3 
a 2 b3 

a 3b3 
Notation Rules and Definitions
Summation Convention - if a subscript appears twice in the same term,
summation over that subscript from one to three is implied; for example
3
aii   aii  a11  a22  a33
i 1
3
aij b j   aij b j  ai1b1  ai 2b2  ai 3b3
j 1
A symbol aij…m…n…k is said to be symmetric with respect to index pair mn if
aij...m...n...k  aij...n...m...k
A symbol aij…m…n…k is said to be antisymmetric with respect to index pair mn if
aij...m...n...k  aij...n...m...k
1
2
1
2
Useful Identity aij  (aij  a ji )  (aij  a ji )  a( ij)  a[ij]
a( ij) 
Elasticity
1
( aij  a ji ) . . . symmetric
2
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
a[ ij ] 
1
( aij  a ji ) . . . antisymmetric
2
Example 1-1: Index Notation Examples
The matrix aij and vector bi are specified by
1 2 0 
2


aij  0 4 3 , bi  4
2 1 2
0
Determine the following quantities: aii , aij aij , aij a jk , aij b j , aij bi b j , bi bi , bi b j , a(ij) , a[ij]
Indicate whether they are a scalar, vector or matrix.
Following the standard definitions given in section 1.2,
aii  a11  a 22  a33  7 (scalar)
aij aij  a11 a11  a12 a12  a13 a13  a 21 a 21  a 22 a 22  a 23 a 23  a31 a31  a32 a32  a33 a33
 1  4  0  0  16  9  4  1  4  39 (scalar)
1 10 6 
aij a jk  ai1 a1k  ai 2 a 2 k  ai 3 a3k  6 19 18 (matrix)
6 10 7 
10
aij b j  ai1b1  ai 2 b2  ai 3b3  16 (vector)
 8 
a (ij )
aij bi b j  a11b1b1  a12 b1b2  a13b1b3  a 21b2 b1    84 (scalar)
bi bi  b1b1  b2 b2  b3b3  4  16  0  20 (scalar)
 4 8 0
bi b j  8 16 0 (matrix)
0 0 0
Elasticity
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M.H. Sadd , University of Rhode Island
a[ij ]
1
1
1
 aij  a ji   0
2
2
2
1
1
1
 aij  a ji   0
2
2
2
2 0
1
1

4 3   2
2
0
1 2
2 0
1
1

4 3   2
2
0
1 2
0 2 1 1 1 
4 1   1 4 2
3 2 1 2 2
0 2  0 1
4 1    1 0
3 2  1  1
(matrix)
 1
1  (matrix)
0 
Special Index Symbols
Kronecker Delta
1 0 0
1 , if i  j ( no sum) 
ij  
 0 1 0


0 , if i  j
0 0 1
ij   ji
ii  3 , i i  1
Properties: ij a j  ai , ij ai  a j
ij a jk  aik ,  jk aik  aij
ij aij  aii , ij ij  3
Alternating or Permutation Symbol  ijk
 1 , if ijk is an even permutation of 1,2,3

   1 , if ijk is an odd permutation of 1,2,3
 0 , otherwise

123 = 231 = 312 = 1, 321 = 132 = 213 = -1, 112 = 131 = 222 = . . . = 0
a11 a12 a13
Useful in evaluating determinants
det[aij ]  | aij |  a 21 a 22 a 23   ijk a1i a 2 j a3k   ijk ai1a j 2 a k 3
and vector cross-products
a31
Elasticity
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M.H. Sadd , University of Rhode Island
a32
a33
Coordinate Transformations
x3
x 3
v
e3
e3
x 2
e2
e2
e1
x2
e1
x1
To express elasticity variables in different
coordinate systems requires development of
transformation rules for scalar, vector, matrix
and higher order variables – a concept
connected with basic definitions of tensor
variables. The two Cartesian frames (x1,x2,x3)
and ( x 1, x 2 , x 3 ) differ only by orientation
x 1
Using Rotation Matrix Qij  cos(xi, x j )
e1  Q11e1  Q12 e2  Q13 e3
e2  Q21e1  Q22 e2  Q23 e3
e3  Q31e1  Q32 e2  Q33 e3
v  v1e1  v2 e2  v3e3  vi ei
 v1e1  v2 e2  v3 e3  viei
Elasticity
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M.H. Sadd , University of Rhode Island
ei  Qije j
ei  Q ji e j
transformation laws
for Cartesian vector
vi  Q ji vj
components
vi  Qij v j
Cartesian Tensors
General Transformation Laws
Scalars, vectors, matrices, and higher order quantities can be represented by an
index notational scheme, and thus all quantities may then be referred to as
tensors of different orders. The transformation properties of a vector can be
used to establish the general transformation properties of these tensors.
Restricting the transformations to those only between Cartesian coordinate
systems, the general set of transformation relations for various orders are:
a   a , zero order (scalar)
ai  Qip a p , first order (vect or)
aij  QipQ jq a pq , secondorder (mat rix)
  QipQ jqQkr a pqr , t hirdorder
aijk
  QipQ jqQkrQls a pqrs , fourt horder
aijkl

 ...m  QipQ jqQkr    Qmt a pqr ...t general order
aijk
Elasticity
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
Example 1-2 Transformation Examples
The components of a first and second order tensor in a particular coordinate frame are given by
1 
1 0 3


ai  4 , aij  0 2 2
 


2
3 2 4
x3
x 3
Determine the components of each tensor in a new coordinate system
Choose a counterclockwise rotation when viewing down the negative
x3-axis, see Figure 1-2.
x 2
The original and primed coordinate systems are shown in Figure 1-2.
The solution starts by determining the rotation matrix for this case
 cos60 cos30 cos90  1 / 2

Qij  cos150 cos60 cos90   3 / 2


 cos90 cos90 cos0   0
3 / 2 0

1 / 2 0
0
1
60
x2
o
x 1
x1
The transformation for the vector quantity follows from equation (1.5.1)2
 1/ 2
3 / 2 0 1 1 / 2  2 3 




ai  Qij a j   3 / 2 1 / 2 0 4   2  3 / 2 
 
 0
 2 

0
1
2




and the second order tensor (matrix) transforms according to (1.5.1)3
aij  Qip Q jq a pq
Elasticity
 1/ 2

  3 / 2
 0

3 / 2 0 1 0 3  1 / 2


1 / 2 0 0 2 2  3 / 2
0
1 3 2 4  0
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
T
 7/4
3 / 2 0
3/4
3/ 2  3 



1 / 2 0   3 / 4
5/ 4
1  3 3 / 2
3 / 2  3 1  3 3 / 2

0
1
4


Principal Values and Directions for
Symmetric Second Order Tensors
The direction determined by unit vector n is said to be a principal direction or
eigenvector of the symmetric second order tensor aij if there exists a parameter
 (principal value or eigenvalue) such that
aij n j  ni
(aij   ij )n j  0
Relation is a homogeneous system of three linear algebraic equations in the
unknowns n1, n2, n3. The system possesses nontrivial solution if and only if
determinant of coefficient matrix vanishes
det[aij   ij ]  3  I a 2  IIa   IIIa  0
scalars Ia, IIa and IIIa are called the fundamental invariants of the tensor aij
I a  aii  a11  a 22  a33
II a 
a
1
( aii a jj  aij aij )  11
a 21
2
IIIa  det[aij ]
Elasticity
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M.H. Sadd , University of Rhode Island
a12
a 22

a 22
a 23
a32
a33

a11
a13
a31
a33
Principal Axes of Second Order Tensors
It is always possible to identify a right-handed Cartesian coordinate system such
that each axes lie along principal directions of any given symmetric second order
tensor. Such axes are called the principal axes of the tensor, and the basis
vectors are the principal directions {n(1), n(2) , n(3)}
x3
x3
 a11
aij  a21
 a31
a12
a22
a32
a13 
a23 
a33 
 1
aij   0

 0
n(3)
0
2
0
0
0

 3 
n(2)
x2
n(1)
x1
x1
Original Given Axes
Elasticity
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M.H. Sadd , University of Rhode Island
Principal Axes
x2
Example 1-3 Principal Value Problem
Determine the invariants, and principal values and directions of
2 0 0 
a ij  0 3 4 


0 4  3
First determine the principal invariants
2 0
3 4
2 0
I a  aii  2  3  3  2 , II a 


 6  25  6  25
0 3
4 3
0 3
2 0 0
IIIa  0 3 4  2(9  16)  50
0 4 3
The characteristic equation then becomes
det[aij   ij ]  3  22  25  50  0  (  2)(2  25)  0
 1  5 ,  2  2 ,  3  5
Thus for this case all principal values are distinct
For the 1 = 5 root, equation (1.6.1) gives the system
 3n1(1)  0
 2n2(1)  4n3(1)  0
4n2(1)  8n3(1)  0
1
( 2e 2  e 3 )
which gives a normalized solution n(1)  
5
1
In similar fashion the other two principal directions are found to be n( 2)  e1 n( 3)  
( e 2  2e 3 )
5
It is easily verified that these directions are mutually orthogonal.
Note for this case, the transformation matrix Qij defined by (1.4.1) becomes
0 2 / 5 1 / 5 


Qij  1
0
0 
0 1 / 5  2 / 5 


Elasticity
5 0 0 
aij  0 2 0 


0 0  5
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
Vector, Matrix and Tensor Algebra
Scalar or Dot Product
Vector or Cross Product
a  b  a1b1  a2b2  a3b3  ai bi
e1
e2
e3
a  b  a1
b1
a2
b2
a 3   ijk a j bk ei
b3
Common Matrix Products
Aa  [ A]{a}  Aij a j  a j Aij
aT A  {a}T [ A]  ai Aij  Aij ai
AB  [ A][B]  Aij B jk
ABT  Aij Bkj
AT B  A ji B jk
tr( AB)  Aij B ji
tr( ABT )  tr( AT B)  Aij Bij
Elasticity
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
Second Order
Transformation Law
aij  Qip Q jq a pq 
a   QaQ T
Calculus of Cartesian Tensors
Field concept for tensor components a  a ( x1 , x2 , x3 )  a ( xi )  a ( x )
ai  ai ( x1 , x2 , x3 )  ai ( xi )  ai ( x )
aij  aij ( x1 , x2 , x3 )  aij ( xi )  aij ( x )

Comma notation for partial differentiation a,i 



a , ai , j 
ai , aij ,k 
aij , 
xi
x j
xk
If differentiation index is distinct, order of the tensor will be increased by one;
e.g. derivative operation on a vector produces a second order tensor or matrix
ai , j
Elasticity
 a1
 x
 1
a
 2
 x1
 a
 3
 x1
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
a1
x 2
a 2
x 2
a 3
x 2
a1 
x3 
a 2 
x3 
a 3 

x3 
Vector Differential Operations
Directional Derivative of Scalar Field
df f dx f dy f dz



 n  f
ds x ds y ds z ds
n  unit normal vector in direction of s 
  vector differential operator  e1
dx
dy
dz
e1  e 2  e 3
ds
ds
ds



 e2
 e3
x
y
z
f  grad f  gradient of scalar function f  e1
f
f
f
 e2
 e3
x
y
z
Common Differential Operations
Gradient of a Scalar   ,i ei
u  u i , j e i e j
Laplacian of a Scalar  2       ,ii
Divergence of a Vector   u  ui ,i
Curl of a Vector
Laplacian of a Vector
Elasticity
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M.H. Sadd , University of Rhode Island
  u   ijk uk , j ei
 2 u  ui ,kk ei
Example 1-4: Scalar/Vector Field Example
Scalar and vector field functions are given by   x 2  y 2 u  2xe1  3 yze 2  xye 3
Calculate the following expressions, , 2,  ∙ u, u,   u.
Using the basic relations:
Contours =constant and vector distributions of 
vector field  is orthogonal to -contours (ture in general )
  2 xe1  2 ye 2
2  2  2  0 - (satisfies Laplace equation)
 ∙ u  2  3z  0  2  3z
2 0 0 
u  ui , j   0 3 z 3 y 


 y x 0 
e1
e2
e3
  u   / x  / y  / z  ( x  3 y )e1  ye 2
2x
3 yz
xy
10
8
6
4
2
y
0
-2
x
-4
-6
-8
-10
Elasticity
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
-10
-5
0
5
10
Vector/Tensor Integral Calculus
Divergence Theorem
Stokes Theorem

S
u  n dS     u dV
 u  dr  
C

V
S
(  u)  n dS

C
S
aij...k nk dS   aij...k ,k dV
V
aij...k dxt    rst aij...k ,s nr dS
S
Green’s Theorem in the Plane
 g f 
S  x  y dxdy  C ( fdx  gdy)
Zero-Value Theorem
Elasticity

V
g
S x dxdy  C gnx ds ,
f ij...k dV  0  f ij...k  0  V
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
f
S y dxdy  C fn y ds
Orthogonal Curvilinear Coordinate Systems
x3
x3
eˆz
z
eˆ R
eˆ
e1


eˆr
e3
x1
e3
x2
e2
r
Cylindrical Coordinate System (r,,z)
x1  r cos  , x2  r sin  , x3  z
r  x12  x22 ,   tan 1
x2
, z  x3
x1
e1
R
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
eˆ
e2

x2
x1
Spherical Coordinate System (R,,)
x1  R cos  sin  , x2  R sin  sin  , x3  R cos 
R  x12  x22  x32
  cos 1
x3
x12  x22  x32
  tan 1
Elasticity
eˆ
x2
,
x1
General Curvilinear Coordinate Systems
Common Differential Forms
x3
3
eˆ2
eˆ1
x2
e2
 
1
h1h2 h3
 
i
m
1
2
3
m
m
1
2
3
(ds)2  (h1d1 )2  (h2d2 )2  (h3d3 )2
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island

  h1 h2 h3

u i  
i 
 hi

1
h1h2 h3
i
i
  u  
   ( x , x , x ) , x  x ( ,  ,  )
Elasticity
1 f
1 f
1 f
1 f
 eˆ 2
 eˆ3
  eˆ i
1
2
3
h1 
h2 
h3 
hi  i
i
2 
x1
m
f  eˆ1
u 
1
e1
1 
1 
1 
1 
 eˆ 2
 eˆ3
  eˆ i
1
2
3
h1 
h2 
h3 
hi  i
i
2
eˆ3
e3
  eˆ1
u  
i
j
  h1h2 h3  


i 
2
i 
(
h
)


 i

 ijk

(uk  hk )eˆi
h j hk  j
j
k
eˆ i
hi
eˆ j
 u  j 

eˆ j  u  j  i
i

 



eˆ j  
 eˆ   
eˆ  u
  k   j  eˆ j  u  j 


 2 u    i

i  
k
k 
h
h






i
j
k
i
k 

 

Example 1-5: Polar Coordinates
From relations (1.9.5) or simply using the geometry shown in Figure
eˆ r  cose1  sin e 2
eˆ   sin e1  cose 2
x2
eˆ
eˆ
eˆ r
eˆ
 eˆ ,   eˆ r , r    0


r
r
eˆ
eˆr
The basic vector differential operations then follow to be

1 
 eˆ 
r
r 

1 
  eˆ r
 eˆ 
r
r 
1 
1 u 
u 
(ru r ) 
r r
r 
1     1  2 
2
 
r  
r r  r  r 2  2
  eˆ r
1 u r 
1 
u  
(ru  ) 
eˆ z
r  
 r r
u
u
1  u
1  u


u  r eˆ r eˆ r   eˆ r eˆ    r  u  eˆ  eˆ r     u r eˆ  eˆ 
r
r
r  
r  


u 
u 
2 u
2 u


 2 u    2 u r  2   2r eˆ r    2 u   2 r  2 eˆ 
r  r 
r  r 


where u  ur eˆ r  ueˆ θ , eˆ z  eˆ r  eˆ θ
Elasticity
Theory, Applications and Numerics
M.H. Sadd , University of Rhode Island
e2
r

e1
x1
(ds ) 2  (dr ) 2  (rd) 2  h1  1 , h2  r
```