1. Vectors and Linear Spaces Jan 2013

SSG 805 Mechanics of Continua
INSTRUCTOR: Fakinlede OA oafak@unilag.edu.ng oafak@hotmail.com
ASSISTED BY: Akano T manthez@yahoo.com
Oyelade AO oyelade.akintoye@gmail.com
Department of Systems Engineering, University of Lagos

 Available to beginning graduate students in
Engineering
 Provides a background to several other courses such
as Elasticity, Plasticity, Fluid Mechanics, Heat Transfer,
Fracture Mechanics, Rheology, Dynamics, Acoustics,
etc. These courses are taught in several of our
departments. This present course may be viewed as
an advanced introduction to the modern approach to
these courses
 It is taught so that related graduate courses can build
on this modern approach.
Purpose of the Course
oafak@unilag.edu.ng 12/31/2012
Department of Systems Engineering, University of Lagos 2

 The slides here are quite extensive. They are meant to
assist the serious learner. They are NO SUBSTITUTES
for the course text which must be read and followed
concurrently.
 Preparation by reading ahead is ABSOLUTELY
necessary to follow this course
 Assignments are given at the end of each class and
they are due (No excuses) exactly five days later.
 Late submission carry zero grade.
What you will need

Scope of Instructional Material
Course Schedule:
The read-ahead materials are from Gurtin except the
part marked red. There please read Holzapfel. Home
work assignments will be drawn from the range of
pages in the respective books.
Slide Title Slides Weeks Text Read Pages
Vectors and Linear Spaces 70 2 Gurtin 1-8
Tensor Algebra 110 3 Gurtin 9-37
Tensor Calculus 119 3 Gurtin 39-57
Kinematics: Deformation & Motion 106 3 Gurtin 59-123
Theory of Stress & Heat Flux 43 1 Holz 109-129
Balance Laws 58 2 Gurtin 125-205

The only remedy for late submission is that you fight for
the rest of your grade in the final exam if your excuse is
considered to be genuine. Ordinarily, the following will
hold:
Examination
Evaluation Obtainable
Home work 50
Midterm 25
Final Exam 25
Total 100

 This course was prepared with several textbooks and
papers. They will be listed below. However, the main
course text is: Gurtin ME, Fried E & Anand L, The
Mechanics and Thermodynamics of Continua,
Cambridge University Press, www.cambridge.org
2010
 The course will cover pp1-240 of the book. You can
view the course as a way to assist your reading and
understanding of this book
 The specific pages to be read each week are given
ahead of time. It is a waste of time to come to class
without the preparation of reading ahead.
 This preparation requires going through the slides
and the area in the course text that will be covered.
Course Texts

 The software for the Course is Mathematica 9 by
Wolfram Research. Each student is entitled to a
licensed copy. Find out from the LG Laboratory
 It your duty to learn to use it. Students will find some
examples too laborious to execute by manual
computation. It is a good idea to start learning
Mathematica ahead of your need of it.
 For later courses, commercial FEA Simulations
package such as ANSYS, COMSOL or NASTRAN will be
needed. Student editions of some of these are
available. We have COMSOL in the LG Laboratory
Software

 Gurtin, ME, Fried, E & Anand, L, The Mechanics and
Thermodynamics of Continua, Cambridge University Press,
www.cambridge.org 2010
 Bertram, A, Elasticity and Plasticity of Large
Deformations, Springer-Verlag Berlin Heidelberg, 2008
 Tadmore, E, Miller, R & Elliott, R, Continuum Mechanics
and Thermodynamics From Fundamental Concepts to
Governing Equations, Cambridge University Press,
www.cambridge.org , 2012
 Nagahban, M, The Mechanical and Thermodynamical
Theory of Plasticity, CRC Press, Taylor and Francis Group,
June 2012
 Heinbockel, JH, Introduction to Tensor Calculus and
Continuum Mechanics, Trafford, 2003
Texts

 Bower, AF, Applied Mechanics of Solids, CRC Press, 2010
 Taber, LA, Nonlinear Theory of Elasticity, World Scientific,
2008
 Ogden, RW, Nonlinear Elastic Deformations, Dover
Publications, Inc. NY, 1997
 Humphrey, JD, Cadiovascular Solid Mechanics: Cells,
Tissues and Organs, Springer-Verlag, NY, 2002
 Holzapfel, GA, Nonlinear Solid Mechanics, Wiley NY, 2007
 McConnell, AJ, Applications of Tensor Analysis, Dover
Publications, NY 1951
 Gibbs, JW “A Method of Geometrical Representation of
the Thermodynamic Properties of Substances by Means
of Surfaces,” Transactions of the Connecticut Academy of
Arts and Sciences 2, Dec. 1873, pp. 382-404.
Texts

 Romano, A, Lancellotta, R, & Marasco A, Continuum
Mechanics using Mathematica, Fundamentals,
Applications and Scientific Computing, Modeling and
Simulation in Science and Technology, Birkhauser, Boston
2006
 Reddy, JN, Principles of Continuum Mechanics, Cambridge
University Press, www.cambridge.org 2012
 Brannon, RM, Functional and Structured Tensor Analysis
for Engineers, UNM BOOK DRAFT, 2006, pp 177-184.
 Atluri, SN, Alternative Stress and Conjugate Strain
Measures, and Mixed Variational Formulations Involving
Rigid Rotations, for Computational Analysis of Finitely
Deformed Solids with Application to Plates and Shells,
Computers and Structures, Vol. 18, No 1, 1984, pp 93-116
Texts

 Wang, CC, A New Representation Theorem for Isotropic
Functions: An Answer to Professor G. F. Smith's Criticism
of my Papers on Representations for Isotropic Functions
Part 1. Scalar-Valued Isotropic Functions, Archives of
Rational Mechanics, 1969 pp
 Dill, EH, Continuum Mechanics, Elasticity, Plasticity,
Viscoelasticity, CRC Press, 2007
 Bonet J & Wood, RD, Nonlinear Mechanics for Finite
Element Analysis, Cambridge University Press,
www.cambridge.org 2008
 Wenger, J & Haddow, JB, Introduction to Continuum
Mechanics & Thermodynamics, Cambridge University
Press, www.cambridge.org 2010
Texts

 Li, S & Wang G, Introduction to Micromechanics and
Nanomechanics, World Scientific, 2008
 Wolfram, S The Mathematica Book, 5th Edition
Wolgram Media 2003
 Trott, M, The Mathematica Guidebook, 4 volumes:
Symbolics, Numerics, Graphics &Programming,
Springer 2000
 Sokolnikoff, IS, Tensor Analysis, Theory and
Applications to Geometry and Mechanics of
Continua, John Wiley, 1964
Texts

 Continuum Mechanics can be thought of as the grand
unifying theory of engineering science.
 Many of the courses taught in an engineering
curriculum are closely related and can be obtained as
special cases of the general framework of continuum
mechanics.
 This fact is easily lost on most undergraduate and
even some graduate students.
Unified Theory

Continuum Mechanics

Continuum Mechanics views matter as continuously
distributed in space. The physical quantities we are
interested in can be
• Scalars or reals, such as time, energy, power,
• Vectors, for example, position vectors, velocities, or
forces,
• Tensors: deformation gradient, strain and stress
measures.
Physical Quantities

 Since we can also interpret scalars as zeroth-order tensors,
and vectors as 1st-order tensors, all continuum mechanical
quantities can generally be considered as tensors of
different orders.
 It is therefore clear that a thorough understanding of
Tensors is essential to continuum mechanics. This is NOT
always an easy requirement;
 The notational representation of tensors is often
inconsistent as different authors take the liberty to express
themselves in several ways.
Physical Quantities

 There are two major divisions common in the literature:
Invariant or direct notation and the component notation.
 Each has its own advantages and shortcomings. It is
possible for a reader that is versatile in one to be
handicapped in reading literature written from the other
viewpoint. In fact, it has been alleged that
 “Continuum Mechanics may appear as a fortress surrounded
by the walls of tensor notation” It is our hope that the
course helps every serious learner overcome these
difficulties
Physical Quantities

 The set of real numbers is denoted by R
 Let R 𝑛
be the set of n-tuples so that when 𝑛 = 2,
R 2
we have the set of pairs of real numbers. For
example, such can be used for the 𝑥 and
𝑦 coordinates of points on a Cartesian coordinate
system.
Real Numbers & Tuples

A real vector space V is a set of elements (called
vectors) such that,
1. Addition operation is defined and it is commutative
and associative underV : that is, 𝒖 + 𝐯 ∈V, 𝒖 + 𝐯 =
𝐯 + 𝒖, 𝒖 + 𝐯 + 𝒘 = 𝒖 + 𝐯 + 𝒘, ∀ 𝒖, 𝐯, 𝒘 ∈ V.
Furthermore,V is closed under addition: That is, given
that 𝒖, 𝐯 ∈ V, then 𝒘 = 𝒖 + 𝐯 = 𝐯 + 𝒖, ⇒ 𝒘 ∈ V.
2. V contains a zero element 𝒐 such that 𝒖 + 𝒐 = 𝒖 ∀
𝒖 ∈V. For every 𝒖 ∈ V, ∃ − 𝒖: 𝒖 + −𝒖 = 𝒐.
3. Multiplication by a scalar. For 𝛼, 𝛽 ∈ R and 𝒖, 𝒗 ∈ V,
𝛼𝒖 ∈ V , 1𝒖 = 𝒖, 𝛼 𝛽𝒖 = 𝛼𝛽 𝒖, 𝛼 + 𝛽 𝒖 = 𝛼𝒖 +
𝛽𝒖, 𝛼 𝒖 + 𝐯 = 𝛼𝒖 + 𝛼𝐯.
Vector Space

 We were previously told that a vector is something
that has magnitude and direction. We often represent
such objects as directed lines. Do such objects
conform to our present definition?
 To answer, we only need to see if the three conditions
we previously stipulated are met:
Magnitude & Direction

From our definition of the Euclidean space, it is easy to see
that,
𝐯 = 𝛼1𝒖1 + 𝛼2𝒖2 + ⋯ + 𝛼𝑛𝒖𝑛
such that, 𝛼1, 𝛼2, ⋯ , 𝛼𝑛 ∈ R and 𝒖1, 𝒖2, ⋯ , 𝒖𝑛 ∈ E, is also a
vector. The subset
𝒖1, 𝒖2, ⋯ , 𝒖𝑛 ⊂ E
is said to be linearly independent or free if for any choice of
the subset 𝛼1, 𝛼2, ⋯ , 𝛼𝑛 ⊂ R other than 0,0, ⋯ , 0 , 𝐯 ≠ 0.
If it is possible to find linearly independent vector systems of
order 𝑛 where 𝑛 is a finite integer, but there is no free system
of order 𝑛 + 1, then the dimension ofE is 𝑛. In other words,
the dimension of a space is the highest number of linearly
independent members it can have.
Dimensionality

 Any linearly independent subset ofE is said to form a
basis forE in the sense that any other vector inE can
be expressed as a linear combination of members of
that subset. In particular our familiar Cartesian
vectors 𝒊, 𝒋 and 𝒌 is a famous such subset in three
dimensional Euclidean space.
 From the above definition, it is clear that a basis is not
necessarily unique.
Basis

1. Addition operation for a directed line segment is defined by
the parallelogram law for addition.
2. V contains a zero element 𝒐 in such a case is simply a point
with zero length..
3. Multiplication by a scalar 𝛼. Has the meaning that
0 < 𝛼 ≤ 1 → Line is shrunk along the same direction by 𝛼
𝛼 > 1 → Elongation by 𝛼
Negative value is same as the above with a change of direction.
Directed Line

 Now we have confirmed that our original notion of a
vector is accommodated. It is not all that possess
magnitude and direction that can be members of a
vector space.
 A book has a size and a direction but because we
cannot define addition, multiplication by a scalar as
we have done for the directed line segment, it is not a
vector.
Other Vectors

Complex Numbers. The set C of complex numbers is a real
vector space or equivalently, a vector space over R.
2-D Coordinate Space. Another real vector space is the set of
all pairs of 𝑥𝑖 ∈ R forms a 2-dimensional vector space overR
is the two dimensional coordinate space you have been
graphing on! It satisfies each of the requirements:
𝒙 = {𝑥1, 𝑥2}, 𝑥1, 𝑥2 ∈ R, 𝒚 = {𝑦1, 𝑦2}, 𝑦1, 𝑦2 ∈ R.
 Addition is easily defined as 𝒙 + 𝒚 = {𝑥1 + 𝑦1, 𝑥2 + 𝑦2} clearly
𝒙 + 𝒚 ∈ R 2 since 𝑥1 + 𝑦1, 𝑥2 + 𝑦2 ∈ R. Addition operation creates other
members for the vector space – Hence closure exists for the operation.
 Multiplication by a scalar: 𝛼𝒙 = {𝛼𝑥1, 𝛼𝑥2}, ∀𝛼 ∈ R.
 Zero element: 𝒐 = 0,0 . Additive Inverse: −𝒙 = −𝑥1, −𝑥2 , 𝑥1, 𝑥2 ∈ R
Type equation here.A standard basis for this : 𝒆1 = 1,0 , 𝒆2 = 0,1
Type equation here.Any other member can be expressed in terms of
this basis.
Other Vectors

𝒏 −D Coordinate Space. For any positive number 𝑛, we may
create 𝑛 −tuples such that, 𝒙 = {𝑥1, 𝑥2, … , 𝑥𝑛} where
𝑥1, 𝑥2, … , 𝑥𝑛 ∈ R are members of R 𝑛
– a real vector space
over theR. 𝒚 = {𝑦1, 𝑦2, … , 𝑦𝑛}, 𝑦1, 𝑦2, … , 𝑦𝑛 ∈ R. 𝒙 + 𝒚 =
{𝑥1 + 𝑦1, 𝑥2 + 𝑦2, … , 𝑥𝑛 + 𝑦𝑛},. 𝒙 + 𝒚 ∈ R 𝑛
since 𝑥1 +
𝑦1, 𝑥2 + 𝑦2, … , 𝑥𝑛 + 𝑦𝑛 ∈ R
Addition operation creates other members for the vector
space – Hence closure exists for the operation.
 Multiplication by a scalar: 𝛼𝒙 = {𝛼𝑥1, 𝛼𝑥2, … , 𝛼𝑥𝑛}, ∀𝛼 ∈
𝑅.
 Zero element 𝒐 = 0,0, … , 0 . Additive Inverse −𝒙 =
−𝑥1, −𝑥2, … , −𝑥𝑛 , 𝑥1, 𝑥2, … , 𝑥𝑛 ∈ R
There is also a standard basis which is easily proved to be
linearly independent:
𝒆1 = 1,0, … , 0 , 𝒆2 = 0,1, … , 0 , … , 𝒆𝑛 = {0,0, … , 0}
N-tuples

Let R𝑚×𝑛
denote the set of matrices with entries that are
real numbers (same thing as saying members of the real
spaceR, Then, R𝑚×𝑛
is a real vector space. Vector
addition is just matrix addition and scalar multiplication is
defined in the obvious way (by multiplying each entry by
the same scalar). The zero vector here is just the zero
matrix. The dimension of this space is 𝑚𝑛. For example, in
R3×3
we can choose basis in the form,

1 0 0
0 0 0
0 0 0
,
0 1 0
0 0 0
0 0 0
, …,
0 0 0
0 0 0
0 0 1
Matrices

Forming polynomials with a single variable 𝑥 to order 𝑛
when 𝑛 is a real number creates a vector space. It is left as
an exercise to demonstrate that this satisfies all the three
rules of what a vector space is.
The Polynomials

A mapping from the Euclidean Point spaceE to the
product space R 3 is called a global chart.
𝜙: E → R 𝑛
So that ∀ 𝑥 ∈ 𝑉, ∃ {𝜙1 𝑥 , 𝜙2 𝑥 , 𝜙3(𝑥)} which are the
coordinates identified with each 𝑥.
 A global chart is sometimes also called a coordinate
system.
Global Chart

 An Inner-Product (also called a Euclidean Vector)
SpaceE is a real vector space that defines the scalar
product: for each pair 𝒖, 𝐯 ∈ E, ∃ 𝑙 ∈ R such that,
𝑙 = 𝒖 ⋅ 𝐯 = 𝐯 ⋅ 𝒖 . Further, 𝒖 ⋅ 𝒖 ≥ 0, the zero value
occurring only when 𝒖 = 0. It is called “Euclidean”
because the laws of Euclidean geometry hold in such
a space.
 The inner product also called a dot product, is the
mapping
" ⋅ ": V × V → R
from the product space to the real space.
Euclidean Vector Space

 A mapping from a vector space is also called a
functional; a term that is more appropriate when we
are looking at a function space.
 A linear functional 𝐯 ∗: V → R on the vector spaceV
is called a convector or a dual vector. For a finite
dimensional vector space, the set of all convectors
forms the dual spaceV ∗ ofV . If V is an Inner
Product Space, then there is no distinction between
the vector space and its dual.
Co-vectors

Magnitude The norm, length or magnitude of 𝒖,
denoted 𝒖 is defined as the positive square root of
𝒖 ⋅ 𝒖 = 𝒖 2. When 𝒖 = 1, 𝒖 is said to be a unit
vector. When 𝒖 ⋅ 𝐯 = 0, 𝒖 and 𝐯 are said to be
orthogonal.
Direction Furthermore, for any two vectors 𝒖 and 𝐯, the
angle between them is defined as,
cos−1
𝒖 ⋅ 𝐯
𝒖 𝐯
The scalar distance 𝑑 between two vectors 𝒖 and 𝐯
𝑑 = 𝒖 − 𝐯
Magnitude & Direction Again

 A 3-D Euclidean space is a Normed space because the
inner product induces a norm on every member.
 It is also a metric space because we can find distances
and angles and therefore measure areas and volumes
 Furthermore, in this space, we can define the cross
product, a mapping from the product space
" × ": V × V → V
Which takes two vectors and produces a vector.
3-D Euclidean Space

Without any further ado, our definition of cross product
is exactly the same as what you already know from
elementary texts. We simply repeat a few of these for
emphasis:
1. The magnitude
𝒂 × 𝒃 = 𝒂 𝒃 sin 𝜃 (0 ≤ 𝜃 ≤ 𝜋)
of the cross product 𝒂 × 𝒃 is the area 𝐴(𝒂, 𝒃)
spanned by the vectors 𝒂 and 𝒃. This is the area of
the parallelogram defined by these vectors. This
area is non-zero only when the two vectors are
linearly independent.
2. 𝜃 is the angle between the two vectors.
3. The direction of 𝒂 × 𝒃 is orthogonal to both 𝒂 and 𝒃
Cross Product

Cross Product
The area 𝐴(𝒂, 𝒃) spanned by the vectors 𝒂 and 𝒃. The unit
vector 𝒏 in the direction of the cross product can be
obtained from the quotient,
𝒂×𝒃
𝒂×𝒃
.

The cross product is bilinear and anti-commutative:
Given 𝛼 ∈ R , ∀𝒂, 𝒃, 𝒄 ∈ V ,
𝛼𝒂 + 𝒃 × 𝒄 = 𝛼 𝒂 × 𝒄 + 𝒃 × 𝒄
𝒂 × 𝛼𝒃 + 𝒄 = 𝛼 𝒂 × 𝒃 + 𝒂 × 𝒄
So that there is linearity in both arguments.
Furthermore, ∀𝒂, 𝒃 ∈ V
𝒂 × 𝒃 = −𝒃 × 𝒂
Cross Product

The trilinear mapping,
[ , ,] ∶ V × V × V → R
From the product set V × V × V to real space is defined by:
[ u,v,w] ≡ 𝒖 ⋅ 𝒗 × 𝒘 = 𝒖 × 𝒗 ⋅ 𝒘
Has the following properties:
1. [ a,b,c]=[b,c,a]=[c,a,b]=−[ b,a,c]=−[c,b,a]=−[a,c,b]
HW: Prove this
1. Vanishes when a, b and c are linearly dependent.
2. It is the volume of the parallelepiped defined by a, b and c
Tripple Products

Tripple product
Parallelepiped defined by u, v and w

 We introduce an index notation to facilitate the
expression of relationships in indexed objects.
Whereas the components of a vector may be three
different functions, indexing helps us to have a
compact representation instead of using new
symbols for each function, we simply index and
achieve compactness in notation. As we deal with
higher ranked objects, such notational conveniences
become even more important. We shall often deal
with coordinate transformations.
Summation Convention

 When an index occurs twice on the same side of any
equation, or term within an equation, it is understood
to represent a summation on these repeated indices
the summation being over the integer values
specified by the range. A repeated index is called a
summation index, while an unrepeated index is called
a free index. The summation convention requires that
one must never allow a summation index to appear
more than twice in any given expression.

 Consider transformation equations such as,
𝑦1 = 𝑎11𝑥1 + 𝑎12𝑥2 + 𝑎13𝑥3
𝑦2 = 𝑎21𝑥1 + 𝑎22𝑥2 + 𝑎23𝑥3
𝑦3 = 𝑎31𝑥1 + 𝑎32𝑥2 + 𝑎33𝑥3
 We may write these equations using the summation symbols as:
𝑦1 = 𝑎1𝑗𝑥𝑗
𝑛
𝑗=1
𝑛
𝑗=1
𝑛
𝑗=1

 In each of these, we can invoke the Einstein
summation convention, and write that,
 Finally, we observe that 𝑦1, 𝑦2, and 𝑦3 can be
represented as we have been doing by 𝑦𝑖, 𝑖 = 1,2,3
so that the three equations can be written more
compactly as,
𝑦𝑖 = 𝑎𝑖𝑗𝑥𝑗, 𝑖 = 1,2,3

Please note here that while 𝑗 in each equation is a dummy
index, 𝑖 is not dummy as it occurs once on the left and in each
expression on the right. We therefore cannot arbitrarily alter
it on one side without matching that action on the other side.
To do so will alter the equation. Again, if we are clear on the
range of 𝑖, we may leave it out completely and write,
𝑦𝑖 = 𝑎𝑖𝑗𝑥𝑗
to represent compactly, the transformation equations above.
It should be obvious there are as many equations as there are
free indices.

If 𝑎𝑖𝑗 represents the components of a 3 × 3 matrix 𝐀, we can
show that,
𝑎𝑖𝑗𝑎𝑗𝑘 = 𝑏𝑖𝑘
Where 𝐁 is the product matrix 𝐀𝐀.
To show this, apply summation convention and see that,
for 𝑖 = 1, 𝑘 = 1, 𝑎11𝑎11 + 𝑎12𝑎21 + 𝑎13𝑎31 = 𝑏11
for 𝑖 = 1, 𝑘 = 2, 𝑎11𝑎12 + 𝑎12𝑎22 + 𝑎13𝑎32 = 𝑏12
for 𝑖 = 1, 𝑘 = 3, 𝑎11𝑎13 + 𝑎12𝑎23 + 𝑎13𝑎33 = 𝑏13
for 𝑖 = 2, 𝑘 = 1, 𝑎21𝑎11 + 𝑎22𝑎21 + 𝑎23𝑎31 = 𝑏21
for 𝑖 = 2, 𝑘 = 2, 𝑎21𝑎12 + 𝑎22𝑎22 + 𝑎23𝑎32 = 𝑏22
for 𝑖 = 2, 𝑘 = 3, 𝑎21𝑎13 + 𝑎22𝑎23 + 𝑎23𝑎33 = 𝑏23
for 𝑖 = 3, 𝑘 = 1, 𝑎31𝑎11 + 𝑎32𝑎21 + 𝑎33𝑎31 = 𝑏31
for 𝑖 = 3, 𝑘 = 2, 𝑎31𝑎12 + 𝑎32𝑎22 + 𝑎33𝑎32 = 𝑏32
for 𝑖 = 3, 𝑘 = 3, 𝑎31𝑎13 + 𝑎32𝑎23 + 𝑎33𝑎33 = 𝑏33

The above can easily be verified in matrix notation as,
𝐀𝐀 =
𝑎11 𝑎12 𝑎13
𝑎21 𝑎22 𝑎23
𝑎31 𝑎32 𝑎33
𝑎11 𝑎12 𝑎13
𝑎21 𝑎22 𝑎23
𝑎31 𝑎32 𝑎33
=
𝑏11 𝑏12 𝑏13
𝑏21 𝑏22 𝑏23
𝑏31 𝑏32 𝑏33
= 𝐁
In this same way, we could have also proved that,
𝑎𝑖𝑗𝑎𝑘𝑗 = 𝑏𝑖𝑘
 Where 𝐁 is the product matrix 𝐀𝐀T. Note the
arrangements could sometimes be counter intuitive.

Suppose our basis vectors 𝐠𝑖, 𝑖 = 1,2,3 are not only not
unit in magnitude, but in addition are NOT orthogonal.
The only assumption we are making is that 𝐠𝑖 ∈ V , 𝑖 =
1,2,3 are linearly independent vectors.
With respect to this basis, we can express vectors
𝐯, 𝐰 ∈ V in terms of the basis as,
𝐯 = 𝑣𝑖 𝐠𝑖, 𝐰 = 𝑤𝑖 𝐠𝑖
Where each 𝑣𝑖 is called the contravariant component of
𝐯
Vector Components

Clearly, addition and linearity of the vector space ⇒
𝐯 + 𝐰 = (𝑣𝑖+𝑤𝑖)𝐠𝑖
Multiplication by scalar rule implies that if 𝛼 ∈ R , ∀𝐯 ∈
V ,
𝛼 𝐯 = (𝛼𝑣𝑖)𝐠𝑖
Vector Components

For any basis vectors 𝐠𝑖 ∈ V , 𝑖 = 1,2,3 there is a
reciprocal basis defined by the reciprocity relationship:
𝐠𝑖 ⋅ 𝐠𝑗 = 𝐠𝑗 ⋅ 𝐠𝑖 = 𝛿𝑗
𝑖
Where 𝛿𝑗
𝑖
is the Kronecker delta
𝛿𝑗
𝑖
=
0 if 𝑖 ≠ 𝑗
1 if 𝑖 = 𝑗
Let the fact that the above equations are actually nine
equations each sink. Consider the full meaning:
Reciprocal Basis

Kronecker Delta: 𝛿𝑖𝑗, 𝛿𝑖𝑗
or 𝛿𝑗
𝑖
has the following properties:
𝛿11 = 1, 𝛿12 = 0, 𝛿13 = 0
𝛿21 = 0, 𝛿22 = 1, 𝛿23 = 0
𝛿31 = 0, 𝛿32 = 0, 𝛿33 = 1
As is obvious, these are obtained by allowing the indices to attain
all possible values in the range. The Kronecker delta is
defined by the fact that when the indices explicit values
are equal, it has the value of unity. Otherwise, it is zero.
The above nine equations can be written more compactly
as,
𝛿𝑗
𝑖
=
0 if 𝑖 ≠ 𝑗
1 if 𝑖 = 𝑗
Kronecker Delta

 For any ∀𝐯 ∈ V ,
𝐯 = 𝑣𝑖𝐠𝑖 = 𝑣𝑖𝐠𝑖
Are two related representations in the reciprocal bases.
Taking the inner product of the above equation with the
basis vector 𝐠𝑗, we have
𝐯 ⋅ 𝐠𝑗 = 𝑣𝑖𝐠𝑖 ⋅ 𝐠𝑗 = 𝑣𝑖𝐠𝑖 ⋅ 𝐠𝑗
Which gives us the covariant component,
𝐯 ⋅ 𝐠𝑗 = 𝑣𝑖
𝑔𝑖𝑗 = 𝑣𝑖𝛿𝑗
𝑖
= 𝑣𝑗
Covariant components

In the same easy manner, we may evaluate the
contravariant components of the same vector by taking
the dot product of the same equation with the
contravariant base vector 𝐠𝑗:
𝐯 ⋅ 𝐠𝑗 = 𝑣𝑖𝐠𝑖 ⋅ 𝐠𝑗 = 𝑣𝑖𝐠𝑖 ⋅ 𝐠𝑗
So that,
𝐯 ⋅ 𝐠𝑗 = 𝑣𝑖𝛿𝑖
𝑗
= 𝑣𝑖𝑔𝑖𝑗 = 𝑣𝑗
Contravariant Components

The nine scalar quantities, 𝑔𝑖𝑗 as well as the nine related
quantities 𝑔𝑖𝑗 play important roles in the coordinate
system spanned by these arbitrary reciprocal set of
basis vectors as we shall see.
They are called metric coefficients because they metrize
the space defined by these bases.
Metric coefficients

 The Levi-Civita Symbol: 𝑒𝑖𝑗𝑘
 𝑒111 = 0, 𝑒112 = 0, 𝑒113 = 0, 𝑒121 = 0, 𝑒122 = 0, 𝑒123 =
1, 𝑒131 = 0, 𝑒132 = −1, 𝑒133 = 0
𝑒211 = 0, 𝑒212 = 0, 𝑒213 = −1, 𝑒221 = 0, 𝑒222 = 0, 𝑒223
= 0, 𝑒231 = 1, 𝑒232 = 0, 𝑒233 = 0
𝑒311 = 0, 𝑒312 = 1, 𝑒313 = 0, 𝑒321 = −1, 𝑒322 = 0, 𝑒323
= 1, 𝑒331 = 0, 𝑒332 = 0, 𝑒333 = 0
Levi Civita Symbol

 While the above equations might look arbitrary at first, a
closer look shows there is a simple logic to it all. In fact,
note that whenever the value of an index is repeated, the
symbol has a value of zero. Furthermore, we can see that
once the indices are an even arrangement (permutation)
of 1,2, and 3, the symbols have the value of 1, When we
have an odd arrangement, the value is -1. Again, we desire
to avoid writing twenty seven equations to express this
simple fact. Hence we use the index notation to define the
Levi-Civita symbol as follows:
 𝑒𝑖𝑗𝑘 =
1 if 𝑖, 𝑗 and 𝑘 are an even permutation of 1,2 and 3
−1 if 𝑖, 𝑗 and 𝑘 are an odd permutation of 1,2 and 3
0 In all other cases
Levi Civita Symbol

Given that 𝑔 = det 𝑔𝑖𝑗 of the covariant metric coefficients, It is
not difficult to prove that
𝐠𝑖 ∙ 𝐠𝑗 × 𝐠𝑘 = 𝜖𝑖𝑗𝑘 ≡ 𝑔𝑒𝑖𝑗𝑘
 This relationship immediately implies that,
𝐠𝑖 × 𝐠𝑗 = 𝜖𝑖𝑗𝑘𝐠𝑘.
 The dual of the expression, the equivalent contravariant
equivalent also follows from the fact that,
𝐠1 × 𝐠2 ⋅ 𝐠3 = 1/ 𝑔
Cross Product of Basis Vectors

This leads in a similar way to the expression,
𝐠𝑖
× 𝐠𝑗
⋅ 𝐠𝑘
=
𝑒𝑖𝑗𝑘
𝑔
= 𝜖𝑖𝑗𝑘
It follows immediately from this that,
𝐠𝑖 × 𝐠𝑗 = 𝜖𝑖𝑗𝑘𝐠𝑘
Cross Product of Basis Vectors

 Given that, 𝐠𝟏, 𝐠2 and 𝐠3 are three linearly
independent vectors and satisfy 𝐠𝑖 ⋅ 𝐠𝑗 = 𝛿𝑗
𝑖
, show
that 𝐠1 =
1
𝑉
𝐠2 × 𝐠3, 𝐠2 =
𝟏
𝑽
𝐠3 × 𝐠1, and 𝐠3 =
1
𝑉
𝐠1 ×
𝐠2, where 𝑉 = 𝐠1 ⋅ 𝐠2 × 𝐠3
Exercises

It is clear, for example, that 𝐠1 is perpendicular to 𝐠2 as
well as to 𝐠3 (an obvious fact because 𝐠1 ⋅ 𝐠2 = 0 and
𝐠1 ⋅ 𝐠3 = 0), we can say that the vector 𝐠1 must
necessarily lie on the cross product 𝐠2 × 𝐠3 of 𝐠2 and
𝐠3. It is therefore correct to write,
𝐠1 =
𝟏
𝑽
𝐠2 × 𝐠3
Where 𝑉−1is a constant we will now determine. We can
do this right away by taking the dot product of both
sides of the equation (5) with 𝐠1 we immediately
obtain,
𝐠1 ⋅ 𝐠1 = 𝑉−1 𝐠1 ⋅ 𝐠2 × 𝐠3 = 1
So that, 𝑉 = 𝐠1 ⋅ 𝐠2 × 𝐠3
the volume of the parallelepiped formed by the three
vectors 𝐠1, 𝐠2, and 𝐠3 when their origins are made to
coincide.

Show that 𝑔𝑖𝑗𝑔𝑗𝑘 = 𝛿𝑖
𝑘
First note that 𝐠𝑗 = 𝑔𝑖𝑗𝐠𝑖 = 𝑔𝑗𝑖𝐠𝑖.
Recall the reciprocity relationship: 𝐠𝑖 ⋅ 𝐠𝑘
= 𝛿𝑖
𝑘
. Using the above, we can write
𝐠𝑖 ⋅ 𝐠𝑘
= 𝑔𝑖𝛼𝐠𝛼
⋅ 𝑔𝑘𝛽
𝐠𝛽
= 𝑔𝑖𝛼𝑔𝑘𝛽
𝐠𝛼
⋅ 𝐠𝛽
= 𝑔𝑖𝛼𝑔𝑘𝛽𝛿𝛽
𝛼
= 𝛿𝑖
𝑘
which shows that
𝑔𝑖𝛼𝑔𝑘𝛼
= 𝑔𝑖𝑗𝑔𝑗𝑘
= 𝛿𝑖
𝑘
As required. This shows that the tensor 𝑔𝑖𝑗 and 𝑔𝑖𝑗
are inverses of each other.

Show that the cross product of vectors 𝒂 and 𝒃 in general coordinates is
𝑎𝑖
𝑏𝑗
𝜖𝑖𝑗𝑘𝐠𝑘
or 𝜖𝑖𝑗𝑘
𝑎𝑖𝑏𝑗𝐠𝑘 where 𝑎𝑖
, 𝑏𝑗
are the respective contravariant
components and 𝑎𝑖, 𝑏𝑗 the covariant.
Express vectors 𝒂 and 𝒃 as contravariant components: 𝒂 = 𝑎𝑖
𝐠𝑖, and
𝒃 = 𝑏𝑖
𝐠𝑖. Using the above result, we can write that,
𝒂 × 𝒃 = 𝑎𝑖𝐠𝑖 × 𝑏𝑗𝐠𝑗
= 𝑎𝑖𝑏𝑗𝐠𝑖 × 𝐠𝑗 = 𝑎𝑖𝑏𝑗𝜖𝑖𝑗𝑘𝐠𝑘.
Express vectors 𝒂 and 𝒃 as covariant components: 𝒂 = 𝑎𝑖𝐠𝑖 and 𝒃 = 𝑏𝑖𝐠𝑖.
Again, proceeding as before, we can write,
𝒂 × 𝒃 = 𝑎𝑖𝐠𝑖 × 𝑏𝑗𝐠𝑗
= 𝜖𝑖𝑗𝑘𝑎𝑖𝑏𝑗𝐠𝑘
Express vectors 𝒂 as a contravariant components: 𝒂 = 𝑎𝑖
𝐠𝑖 and 𝒃 as
covariant components: 𝒃 = 𝑏𝑖𝐠𝑖
𝒂 × 𝒃 = 𝑎𝑖
𝐠𝑖 × 𝑏𝑗𝐠𝑗
= 𝑎𝑖𝑏𝑗 𝐠𝑖 × 𝐠𝑗
= 𝒂 ⋅ 𝒃 𝐠𝑖 × 𝐠𝑗

Given that,
𝛿𝑖𝑗𝑘
𝑟𝑠𝑡
≡ 𝑒𝑟𝑠𝑡
𝑒𝑖𝑗𝑘 =
𝛿𝑖
𝑟
𝛿𝑗
𝑟
𝛿𝑘
𝑟
𝛿𝑖
𝑠
𝛿𝑗
𝑠
𝛿𝑘
𝑠
𝛿𝑖
𝑡
𝛿𝑗
𝑡
𝛿𝑘
𝑡
Show that 𝛿𝑖𝑗𝑘
𝑟𝑠𝑘
= 𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟
Expanding the equation, we have:
𝑒𝑖𝑗𝑘𝑒𝑟𝑠𝑘 = 𝛿𝑖𝑗𝑘
𝑟𝑠𝑘
= 𝛿𝑖
𝑘
𝛿𝑗
𝑟
𝛿𝑘
𝑟
𝛿𝑗
𝑠
𝛿𝑘
𝑠 − 𝛿𝑗
𝑘 𝛿𝑖
𝑟
𝛿𝑘
𝑟
𝛿𝑖
𝑠
𝛿𝑘
𝑠 + 3
𝛿𝑖
𝑟
𝛿𝑗
𝑟
𝛿𝑖
𝑠
𝛿𝑗
𝑠
= 𝛿𝑖
𝑘
𝛿𝑗
𝑟
𝛿𝑘
𝑠
− 𝛿𝑗
𝑠
𝛿𝑘
𝑟
− 𝛿𝑗
𝑘
𝛿𝑖
𝑟
𝛿𝑘
𝑠
− 𝛿𝑖
𝑠
𝛿𝑘
𝑟
+ 3 𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟
= 𝛿𝑗
𝑟
𝛿𝑖
𝑠
− 𝛿𝑗
𝑠
𝛿𝑖
𝑟
− 𝛿𝑖
𝑟
𝛿𝑗
𝑠
+ 𝛿𝑖
𝑠
𝛿𝑗
𝑟
+3(𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟
) = −2(𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟
) + 3(𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟
)
= 𝛿𝑖
𝑟
𝛿𝑗
𝑠
− 𝛿𝑖
𝑠
𝛿𝑗
𝑟

Show that 𝛿𝑖𝑗𝑘
𝑟𝑗𝑘
= 2𝛿𝑖
𝑟
Contracting one more index, we have:
𝑒𝑖𝑗𝑘𝑒𝑟𝑗𝑘
= 𝛿𝑖𝑗𝑘
𝑟𝑗𝑘
= 𝛿𝑖
𝑟
𝛿𝑗
𝑗
− 𝛿𝑖
𝑗
𝛿𝑗
𝑟
= 3𝛿𝑖
𝑟
− 𝛿𝑖
𝑟
= 2𝛿𝑖
𝑟
These results are useful in several situations.

Show that 𝐮 ⋅ 𝐮 × 𝐯 = 0
𝐮 × 𝐯 = ϵ𝑖𝑗𝑘𝑢𝑖𝑣𝑗𝐠𝑘
𝐮 ⋅ 𝐮 × 𝐯 = 𝑢𝛼𝐠𝛼 ⋅ ϵ𝑖𝑗𝑘𝑢𝑖𝑣𝑗𝐠𝑘
= 𝑢𝛼 ϵ𝑖𝑗𝑘𝑢𝑖𝑣𝑗 𝛿𝑘
𝛼
= ϵ𝑖𝑗𝑘𝑢𝑖𝑣𝑗𝑢𝑘
= 0
On account of the symmetry and antisymmetry
in 𝑖 and 𝑘.

Show that 𝐮 × 𝐯 = − 𝐯 × 𝐮
𝐮 × 𝐯 = ϵ𝑖𝑗𝑘𝑢𝑖𝑣𝑗𝐠𝑘
= −ϵ𝑗𝑖𝑘
𝑢𝑖𝑣𝑗𝐠𝑘 = −ϵ𝑖𝑗𝑘
𝑣𝑖𝑢𝑗𝐠𝑘
= − 𝐯 × 𝐮

Show that 𝐮 × 𝐯 × 𝐰 = 𝐮 ⋅ 𝐰 𝐯 − 𝐮 ⋅ 𝐯 𝐰.
Let 𝐳 = 𝐯 × 𝐰 = ϵ𝑖𝑗𝑘
𝑣𝑖𝑤𝑗𝐠𝑘
𝐮 × 𝐯 × 𝐰 = 𝐮 × 𝐳 = 𝜖𝛼𝛽𝛾𝑢𝛼
𝑧𝛽
𝐠𝛾
= 𝜖𝛼𝛽𝛾 𝑢𝛼
𝑧𝛽
𝐠𝛾
= 𝜖𝛼𝛽𝛾𝑢𝛼
ϵ𝑖𝑗𝛽
𝑣𝑖𝑤𝑗𝐠𝛾
= ϵ𝑖𝑗𝛽
𝜖𝛾𝛼𝛽𝑢𝛼
= 𝛿𝛾
𝑖
𝛿𝛼
𝑗
− 𝛿𝛼
𝑖
𝛿𝛾
𝑗
𝑢𝛼
= 𝑢𝑗𝑣𝛾𝑤𝑗𝐠𝛾 − 𝑢𝑖𝑣𝑖𝑤𝛾𝐠𝛾
= 𝐮 ⋅ 𝐰 𝐯 − 𝐮 ⋅ 𝐯 𝐰

1. Vectors and Linear Spaces Jan 2013

Recommended

More Related Content

What's hot (19)

Similar to 1. Vectors and Linear Spaces Jan 2013 (20)

1. Vectors and Linear Spaces Jan 2013