Prof geraint f lewis sydney institute for astronomy the university of sydney
This presentation is the property of its rightful owner.
Sponsored Links
1 / 40

A Physicists’ Introduction to Tensors PowerPoint PPT Presentation


  • 113 Views
  • Uploaded on
  • Presentation posted in: General

Prof Geraint F. Lewis Sydney Institute for Astronomy The University of Sydney. A Physicists’ Introduction to Tensors. Tensors. The Goals: Understand what a Tensor is Understand what a Tensor does Tensor Algebra Tensor Contractions The Metric Tensor Coordinates and Invariance

Download Presentation

A Physicists’ Introduction to Tensors

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Prof geraint f lewis sydney institute for astronomy the university of sydney

Prof Geraint F. Lewis

Sydney Institute for Astronomy

The University of Sydney

A Physicists’ Introduction to Tensors


Tensors

Tensors

The Goals:

  • Understand what a Tensor is

  • Understand what a Tensor does

  • Tensor Algebra

  • Tensor Contractions

  • The Metric Tensor

  • Coordinates and Invariance

  • Tensor Calculus

Typical undergraduate reaction to the word “Tensor”.


Scalars vectors

Scalars & Vectors

Magnitude Magnitude and Direction


Scalars vectors1

Scalars & Vectors

Scalar Field: Temperature

has one component per position.

Vector Field: Fluid Velocity

has three components per position.


Vectors components

Vectors & Components

Must make a distinction between a vector and its representation in a particular coordinate systems.

A vector in a particular Cartesian coordinate system will have a particular set of components. In a different Cartesian (i.e. rotated) coordinate system, the vector will be unchanged, but the components will be different.

In fact, a change of coordinate system will, in general, result in a change of vector components.

The vector, however, is coordinate independent.


Vectors notation

Vectors & Notation

We will use a new notation for the representation of a vector in a particular coordinate system:

Here, α is an index and can take on the values of 1, 2 or 3 (when working in 3 dimensions). This extracts the particular component of the vector.

This is often written as


Vectors notation1

Vectors & Notation

With this, we can write a physical law in a coordinate independent fashion;

Or in terms of the components of the vectors;

Note that this expression is actually three equations (in 3D);

or etc…


On to tensors

On to Tensors

A tensor of rank 0 (no index) is a scalar.

A tensor of rank 1 (one index) is a vector.

But the converse is not generally true (more later).

We can clearly generalize this and create a tensor of rank 2;

This clearly has 3n=9 components.


Physical example stress

Physical Example: Stress

From Wikipedia


The dot product

The Dot Product

We can write a vector as

where eare the basis vectors and we have made use of the Einstein summation convention; any repeated index must be summed over. With this, the dot product is

where gαβ is the Metric Tensor.


The metric tensor

The Metric Tensor

In Cartesian coordinates, the metric tensor is simply

And we can see that the magnitude (squared) of a vector is

Note that the metric tensor is different in non-Cartesian coordinate systems, and tensors can have indicies `downstairs’ as well as up.


Metric tensors

Metric Tensors

For 3-D spaces, metrics are of the form

Normally the metric is symmetric, so gαβ=gβα.

Off-diagonal terms in the metric implies that the basis vectors are not orthogonal;


General tensors

General Tensors

A tensor of rank n in 3 dimensions has 3n components. These components can be referenced in terms of nindicies. But how do you know if the indicies go up-stairs or down-stairs?

Suppose we have a rank 3 tensor, V, then we can actually describe it in a number of coordinate representations;

Each is a valid coordinate representation. This important thing is to follow a few simple index rules.


Tensor algebra

Tensor Algebra

Generally, tensors can be multiplied by a scalar to produce another tensor of the same rank

Note that when considering the component representation, the indicies on both sides of the equation must be the same. Two incorrect tensor equations are;


Tensor algebra1

Tensor Algebra

We can also add tensors together, but again, they must be of the same rank. As before, in the component representation, the indicies must match.

We can multiply tensors together to make new tensors

Note that in the last example there is one free index, α, and so this represents three equations.


Tensor contraction indicies

Tensor Contraction & Indicies

We can construct tensors out of themselves through contraction. This reduces the number of indicies and so the rank of the tensor.

So tensor algebra is straight-forward, but we still need to learn how to raise and lower indicies. The first step is the definition of the `inverse-metric’, defined such that


Raising lowering indicies

Raising & Lowering Indicies

The metric tensor is a machine for raising and lowering indicies.

This implies that the magnitude (squared) of a vector is

But what are vector components with the index down-stairs? We need to consider coordinates and duals.


Physical example

Physical Example

We usually see the magnetic permeability as a scalar relating magnetic flux (B) and magnetization (H) through

This simply scales the vector. For more exotic materials, the permeability;

The result is that the vector is not only scaled, but also rotated (compare to cross product).


Vectors duals

Vectors & Duals

Let’s work on a 2-D plane. We can place a regular Cartesian coordinate system over the plane, and it’s easy to see that;

With this, it should be clear that for any vector in the plane, then the components of the vector are;

What does this mean? Remember, these are components of a vector, and so;


Vectors duals1

Vectors & Duals

We can write the vector in terms of its up-stairs (contravariant) components or down-stairs (covariant) components, so;

The first e‘swe are familiar with, this are simply our basis vectors. The second e’s are the basis of our dual vectors.

But the metric and its inverse are the same, so the vector and dual bases are the same, and the components of the vector and dual vector are the same.

This is true in Cartesian coordinates, but what about in general.


Coordinate transformations

Coordinate Transformations

Let’s start by considering a little displacement vector in Cartesian coordinates;

What is the length of this vector? Clearly,

However, the length of a vector does not depend on the underlying coordinate system, i.e. it is an invariant.

What if we change to polar coordinates?


Coordinate transformations1

Coordinate Transformations

The transformation between polar and Cartesian coordinates is

and it is simple to show that the length of a displacement vector is given by;

This implies that the metric (and inverse) are given by


Coordinate transformations2

Coordinate Transformations

This immediately implies that the basis vectors in the θ-direction change.

  • For vector representation (Vα), the θ-basis increases with r.

  • For dual representation (Vα), the θ-basis decreases with r.

    Hence, the components of the vector and dual vector, which is just the projection of the vector onto the bases, cannot be the same (except at r=1).

    But this illustrates the only difference in a tensor index being upstairs or downstairs is projection on to vector or dual basis.


Coordinate transformations3

Coordinate Transformations

What was the point of all of that?

Well, as we noted at the start, vectors (and tensors) are coordinate independent things, and so our laws of physics must also be coordinate independent (they are invariant).

You should be surprised is a physical prediction made in Cartesian coordinates was different to the prediction made in polar coordinates.

Writing physical laws in tensorial form will be coordinate independent because of the way they transform.


Coordinate transformations4

Coordinate Transformations

Transforming tensors is straight-forward and simply uses components of the Jacobian. For an up-stairs vector, the transformation is;

Note that the indicies match. So between polar and Cartesian, the components of a vector change as

and a similar expression for y.


Coordinate transformations5

Coordinate Transformations

For more indicies, we have more transformation terms;

But what about down-stairs indicies? Easy


Transforming physical eqns

Transforming Physical Eqns

Let’s consider we have a physical law in one coordinate system;

Let’s transform to another coordinate system

Excellent! Tensorial form in one coordinate system is the same as any other (we have a covariant form).


Tensor calculus

Tensor Calculus

Of course, algebra is only part of physical law. What we also need is tensor calculus.

Consider a particle on a 2-D plane at the position (1,1) in Cartesian coordinates. Also suppose it has a velocity;

and no acceleration. What is it’s motion as a function of t?


Tensor calculus1

Tensor Calculus

What if we repeat the problem in polar coordinates? We can easily transform the initial position:

and we can use our tensor rules to transform the velocity

and a similar expression for θ. With this we get


Tensor calculus2

Tensor Calculus

We are told there is no acceleration, so I guess that means the velocity doesn’t change, so we must have equations of motion of the form;

Plotting this on the Cartesian

plane gives;

Clearly, something is wrong!


Covariant derivative

Covariant Derivative

The problem is the derivative. Derivatives of scalars are OK as they are coordinate independent, but we have a problem with the components of vectors (and tensors in general).

If we think about a 2-D polar coordinate system, then moving a vector that maintains its magnitude and direction will, in general, change the vector components.

We need a definition of the derivative that can account for these change in components as the vector (or tensor) moves across the coordinate system; this is the covariant derivative.


Covariant derivative1

Covariant Derivative

The covariant derivative of a vector, vα, is

where the comma is a partial derivative, and the Γ is a Christoffel symbol and is given by

As these depend upon derivatives of the metric, it should be clear that in Cartesian coordinates these are all zero.


Covariant derivative2

Covariant Derivative

Derivatives of higher rank tensors requires an additional Christoffel symbol for each contravariant index;

For covariant (down-stairs) indicies, the covariant derivative is the same, except with a minus sign;

Note, Christoffel symbols are not tensors!


Covariant derivative3

Covariant Derivative

The covariant derivative transforms like a tensor. So, the definition of a tensor which does not “change” when you move from one place to the other is;

If the the covariant derivative is not zero, then the vector changes (either magnitude, orientation or both).

If this was a velocity vector, a non-zero covariant derivative this would mean that there was an acceleration acting.


Covariant kinematics

Covariant Kinematics

Suppose we have a particle undergoing an acceleration, a, with particular initial conditions. What are the equations of motion in a general coordinate system?

At each point along its path, we can define the velocity to be;

The position is not a vector, but the velocity is. So;


Non tensors

Non-Tensors

We can define a scalar field that is not a tensor.

A simple example is “the distance between the point of interest and the origin of the coordinate system”.

Clearly, if I define a new coordinate system with a different origin, the value of the scalar field at a particular point will change.

Hence, not all scalars, “vectors”, or generally collections of numbers, are not tensors. They do not transform correctly.


Where will i need this

Where will I need this?

3-D tensors (often considered in Cartesian form only) appear in EM, mechanics (stress & strain), fluid mechanics, etc.

Moving to special relativity (4-D space-time) we use vectors with 4-components, such that a 4-velocity is;

with the Minkowski metric;

Note, spatial part of metric has Cartesian coordinates.


Where will we need this

Where will we need this?

Combing EM and SR, we get the covariant form of the EM which is based upon the Maxwell tensor.

with Maxwell’s equations being


Where will we need this1

Where will we need this?

Much of life is made easy with Cartesian coordinates, with equal vectors and duals, and zero Christoffel symbols. This leads to sloppy handling of indicies.

Once place you cannot be sloppy is in General Relativity, where we deal with physics on a curved “manifold” with messy coordinates. Respecting your indicies makes it a lot easier.


A physicists introduction to tensors

The End

See you in GR

In Week 2 


  • Login