**General Relativity For
Tellytubbys**

**The Tensor**

**Sir Kevin Aylward B.Sc.,
Warden of the Kings Ale**

**Back
to the Contents section**

__Vector
Refresher__

A vector is a tensor of rank one, what a tensor is will shortly become clearer, so bare with us for a bit please, but in short, a tensor may be thought of as a product of vectors with "transformation law" restrictions. There are many other descriptions, but we'll leave that for now.

A vector can be described as numbers or functions pointing in certain directions, i.e.

the a's are the numbers or
functions and are called the "components" of the "basis
vectors" which are the **e**'s in
the above. Note the position of the indexes for the components and basis
vectors. This form of a vector is called the contravariant form, why?, beats
me.

The other form, below, is called the covariant form, for the same reason as above.

These two forms of the same vector are called reciprocal to each other, and once again, always pay attention to what indexes are upstairs and downstairs, it will greatly simplify things if this is recognized at the outset as of deep significance.

__First
Important Notes:__

1) There is no limit, mathematically to the number of terms, in G.R. there are 4.

2) The basis vectors are *not* constant or unit vectors in general.

__Summation
Convention__

Instead of writing

We could write

But this is shortened to

That is, the sum sign is
dropped, but it is understood that whenever an index appears twice in any
product, then a summation is inherently implied. *Unless otherwise specified, all such products imply an equation with
sums of values*

__The
Metric__

An element of arc length, by drawing a little diagram my little Tellytubbys, in the direction of the basis can be expressed as.

, Not summed here.

,
Note that the implied summation *is*
used here

or

Where the definition is now made for the metric tensor components, i.e.

Thus we have our first real
2^{nd} order tensor, the metric tensor. Not to be confused with the
inches tensor, and as a side note, its symmetric to boot.

__Conversion
between Covariant and Contravariant Components__

Recalling from the above,
somewhere, the definition of reciprocal vectors

Which, is conveniently expressed as

, and noting what is clearly an obvious definition for the new delta symbol introduced here.

So, given that a vector can have components expressed in contravariant form, the components in covariant form can now be obtained:

And obviously

So one can raise and lower indexes, by multiplying by the appropriate metric tensor.

And just to make sure we know what the above means, it means a system of equations thus

, in expanded form, means

__Tensor
Sums__

Tensor expressions usually result in sums of products of terms, such as

because *all* the summing indexes take on *all*
values you can swap between indexes that are repeated in a single product. e.g.
the above can be written also as:

without changing anything. Write it out to check for yourself.

Notes:

The second term indexes of the equation above do not need to be changed, although if desired feel free to do so.

You cannot swap indexes that are not being summed, unless you swap them everywhere.

A "units" check must make like match like, i.e. repeated indexes in a product reduces the order of the tensor by two. Both sides of the equation must match.

__Tensor
Transformation Law__

The job here is to find out how to calculate components in one coordinate system when one knows the components in another coordinate system.

Consider an example 3^{ }variable
position vector

Since the x's are independent by construction, the covariant basis vectors can be seen to be given by

,
these vectors are *tangent* to the
coordinate lines.

Now consider the same vector represented in two different coordinate systems by

then,

and where

But we also have

hence,

Is the transformation law from one coordinate system to another coordinate system.

And with a bit of pissing around you can find out for yourself, for example

Is the transformation law
for a 2^{nd} order tensor

And no surprise here, covariant tensor (vector) transforms as

Hence, it is now clear why the upstairs and downstairs indexes are where the are.

Rounding off this section,
consider the vectors *normal* to the
surfaces defined by

These are given by,

Why are they not queer vectors then? Well consider

Then

and considering

So, its down with a pint of Guinness to let this all sink in

© Kevin Aylward 2000 - 2015

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Website last modified 31^{st} May
2015

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