# Elasticity/Vectors

## Vectors in Mechanics[edit | edit source]

Vector notation is ubiquitous in the modern literature on solid mechanics, fluid mechanics, biomechanics, nonlinear finite elements and a host of other subjects in mechanics. A student has to be familiar with the notation in order to be able to read the literature. In this section we introduce the notation that is used, common operations in vector algebra, and some ideas from vector calculus.

### Vectors[edit | edit source]

A vector is an object that has certain properties. What are these properties? We usually say that these properties are:

- a vector has a magnitude (or length)
- a vector has a direction.

To make the definition of the vector object more precise we may also say that vectors are objects that satisfy the properties of a vector space.

The standard notation for a vector is lower case bold type (for example ).

In Figure 1(a) you can see a vector in red. This vector can be represented in component form with respect to the *basis* () as

where and are *orthonormal unit vectors*. Recall that *unit vectors* are vectors of length 1. These vectors are also called *basis* vectors.

You could also represent the same vector in terms of another set of basis vectors () as shown in Figure 1(b). In that case, the components of the vector are and we can write

Note that the basis vectors and do not necessarily have to be unit vectors. All we need is that they be *linearly independent*, that is, it should not be possible for us to represent one solely in terms of the others.

In three dimensions, using an *orthonormal basis*, we can write the vector as

where is perpendicular to both and . This is the usual basis in which we express arbitrary vectors.

### Vector algebra[edit | edit source]

Some vector operations are shown in Figure 2.

#### Addition and subtraction[edit | edit source]

If and are vectors, then the sum is also a vector (see Figure 2(a)).

The two vectors can also be subtracted from one another to give another vector .

#### Multiplication by a scalar[edit | edit source]

Multiplication of a vector by a scalar has the effect of stretching or shrinking the vector (see Figure 2(b)).

You can form a unit vector that is parallel to by dividing by the length of the vector . Thus,

#### Scalar product of two vectors[edit | edit source]

The *scalar* product or *inner* product or *dot* product of two vectors is defined as

where is the angle between the two vectors (see Figure 2(b)).

If and are perpendicular to each other, and . Therefore, .

The dot product therefore has the geometric interpretation as the length of the projection of onto the unit vector when the two vectors are placed so that they start from the same point.

The scalar product leads to a scalar quantity and can also be written in component form (with respect to a given basis) as

If the vector is dimensional, the dot product is written as

Using the Einstein summation convention, we can also write the scalar product as

Also notice that the following also hold for the scalar product

- (commutative law).
- (distributive law).

#### Vector product of two vectors[edit | edit source]

The *vector* product (or *cross* product) of two vectors and is another vector defined as

where is the angle between and , and is a unit vector perpendicular to the plane containing and in the right-handed sense (see Figure 3 for a geometric interpretation)

In terms of the orthonormal basis , the cross product can be written in the form of a determinant

In index notation, the cross product can be written as

where is the Levi-Civita symbol (also called the permutation symbol, alternating tensor).

### Identities from vector algebra[edit | edit source]

Some useful vector identities are given below.

- .
- .
- .
- .
- .
- .
- .

### Vector calculus[edit | edit source]

So far we have dealt with constant vectors. It also helps if the vectors are allowed to vary in space. Then we can define derivatives and integrals and deal with vector fields. Some basic ideas of vector calculus are discussed below.

#### Derivative of a vector valued function[edit | edit source]

Let be a vector function that can be represented as

where is a scalar.

Then the derivative of with respect to is

If and are two vector functions, then from the chain rule we get

#### Scalar and vector fields[edit | edit source]

Let be the position vector of any point in space. Suppose that
there is a *scalar function* () that assigns a value to each point in space. Then

represents a *scalar field*. An example of a scalar field is the *temperature*. See Figure4(a).

If there is a *vector function* () that assigns a vector to each point in space, then

represents a *vector field*. An example is the *displacement* field. See Figure 4(b).

#### Gradient of a scalar field[edit | edit source]

Let be a scalar function. Assume that the partial derivatives of the function are continuous in some region of space. If the point has coordinates () with respect to the basis (), the *gradient* of is defined as

In index notation,

The gradient is obviously a *vector* and has a direction. We can think of the gradient at a point being the vector perpendicular to the level contour at that point.

It is often useful to think of the symbol as an operator of the form

#### Divergence of a vector field[edit | edit source]

If we form a scalar product of a vector field with the operator, we get a *scalar* quantity called the
*divergence* of the vector field. Thus,

In index notation,

If , then is called a *divergence-free* field.

The physical significance of the divergence of a vector field is the rate at which some *density* exits a given region of space. In the absence of the creation or destruction of matter, the density within a region of space can change only by having it flow into or out of the region.

#### Curl of a vector field[edit | edit source]

The *curl* of a vector field is a *vector* defined as

The physical significance of the curl of a vector field is the amount of rotation or angular momentum of the contents of a region of space.

#### Laplacian of a scalar or vector field[edit | edit source]

The *Laplacian* of a scalar field is a *scalar* defined as

The Laplacian of a vector field is a vector defined as

#### Green-Gauss divergence theorem[edit | edit source]

Let be a continuous and differentiable vector field on a body with boundary . The *divergence theorem* states that

where is the outward unit normal to the surface (see Figure 5).

In index notation,

### Identities in vector calculus[edit | edit source]

Some frequently used identities from vector calculus are listed below.

- .
- .
- .
- .
- .