# Introduction to Elasticity/Tensors

## Contents

- 1 Tensors in Solid Mechanics
- 1.1 Notation
- 1.2 Motivation
- 1.3 Tensor algebra
- 1.4 Spectral theorem
- 1.5 Polar decomposition theorem
- 1.6 Principal invariants of a tensor
- 1.7 Cayley-Hamilton theorem
- 1.8 Index notation
- 1.8.1 Kronecker delta
- 1.8.2 Einstein summation convention
- 1.8.3 Components of a vector
- 1.8.4 Components of a tensor
- 1.8.5 Operation of a tensor on a vector
- 1.8.6 Dyadic product
- 1.8.7 Matrix notation
- 1.8.8 Tensor inner product
- 1.8.9 Trace of a tensor
- 1.8.10 Magnitude of a tensor
- 1.8.11 Tensor product of a tensor with a vector
- 1.8.12 Permutation symbol

- 1.9 Identities in tensor algebra
- 1.10 Tensor calculus
- 1.11 Tensor Identities
- 1.12 Integral theorems
- 1.13 Directional derivatives
- 1.14 Derivative of the determinant of a tensor
- 1.15 Derivatives of the invariants of a tensor
- 1.16 Derivative of the identity tensor
- 1.17 Derivative of a tensor with respect to itself
- 1.18 Derivative of the inverse of a tensor
- 1.19 Remarks

# Tensors in Solid Mechanics[edit]

A sound understanding of tensors and tensor operation is essential if you want to read and understand modern papers on solid mechanics and finite element modeling of complex material behavior. This brief introduction gives you an overview of tensors and tensor notation. For more details you can read *A Brief on Tensor Analysis* by J. G. Simmonds, the appendix on vector and tensor notation from *Dynamics of Polymeric Liquids - Volume 1* by R. B. Bird, R. C. Armstrong, and O. Hassager, and the monograph by R. M. Brannon. An introduction to tensors in continuum mechanics can be found in *An Introduction to Continuum Mechanics* by M. E. Gurtin. Most of the material in this page is based on these sources.

## Notation[edit]

The following notation is usually used in the literature:

## Motivation[edit]

A *force* has a magnitude and a direction, can be added to another force, be multiplied by a scalar and so on. These properties make the force a *vector*.

Similarly, the *displacement* is a *vector* because it can be added to other displacements and satisfies the other properties of a vector.

However, a force cannot be added to a displacement to yield a physically meaningful quantity. So the physical spaces that these two quantities lie on must be different.

Recall that a constant force moving through a displacement does units of work. How do we compute this product when the spaces of and are different? If you try to compute the product on a graph, you will have to convert both quantities to a single basis and then compute the scalar product.

An alternative way of thinking about the operation is to think of as a *linear* operator that acts on to produce a scalar quantity (work). In the notation of sets we can write

A *first order tensor* is a linear operator that sends vectors to scalars.

Next, assume that the force acts at a point . The moment of the force about the origin is given by which is a *vector*. The vector product can be thought of as an *linear* operation too. In this case *the effect of the operator is to convert a vector into another vector*.

A *second order tensor* is a linear operator that sends vectors to vectors.

According to Simmonds, "the name *tensor* comes from elasticity theory where in a loaded elastic body the stress tensor acting on a unit vector normal to a plane through a point delivers the tension (i.e., the force per unit area) acting across the plane at that point."

Examples of second order tensors are the stress tensor, the deformation gradient tensor, the velocity gradient tensor, and so on.

Another type of tensor that we encounter frequently in mechanics is the *fourth order tensor* that takes strains to stresses. In elasticity, this is the stiffness tensor.

A *fourth order tensor* is a linear operator that sends second order tensors to second order tensors.

## Tensor algebra[edit]

A tensor is a linear transformation from a vector space to . Thus, we can write

More often, we use the following notation:

I have used the "dot" notation in this handout. None of the above notations is obviously superior to the others and each is used widely.

### Addition of tensors[edit]

Let and be two tensors. Then the sum is another tensor defined by

### Multiplication of a tensor by a scalar[edit]

Let be a tensor and let be a scalar. Then the product is a tensor defined by

### Zero tensor[edit]

The zero tensor is the tensor which maps every vector into the zero vector.

### Identity tensor[edit]

The identity tensor takes every vector into itself.

The identity tensor is also often written as .

### Product of two tensors[edit]

Let and be two tensors. Then the product is the tensor that is defined by

In general .

### Transpose of a tensor[edit]

The transpose of a tensor is the unique tensor defined by

The following identities follow from the above definition:

### Symmetric and skew tensors[edit]

A tensor is symmetric if

A tensor is skew if

Every tensor can be expressed uniquely as the sum of a symmetric tensor (the symmetric part of ) and a skew tensor (the skew part of ).

### Tensor product of two vectors[edit]

The tensor (or dyadic) product (also written ) of two vectors and is a tensor that assigns to each vector the vector .

Notice that all the above operations on tensors are remarkably similar to matrix operations.

## Spectral theorem[edit]

The spectral theorem for tensors is widely used in mechanics. We will start off by definining eigenvalues and eigenvectors.

### Eigenvalues and eigenvectors[edit]

Let be a second order tensor. Let be a scalar and be a vector such that

Then is called an **eigenvalue** of and is an **eigenvector** .

A second order tensor has three eigenvalues and three eigenvectors, since the space is three-dimensional. Some of the eigenvalues might be repeated. The number of times an eigenvalue is repeated is called multiplicity.

In mechanics, many second order tensors are symmetric and positive definite. Note the following important properties of such tensors:

- If is positive definite, then .
- If is symmetric, the eigenvectors are mutually orthogonal.

For more on eigenvalues and eigenvectors see Applied linear operators and spectral methods.

## Spectral theorem[edit]Let be a symmetric second-order tensor. Then - the normalized eigenvectors form an orthonormal basis.
- if are the corresponding eigenvalues then .
This relation is called the |

## Polar decomposition theorem[edit]

Let be second order tensor with . Then

- there exist positive definite, symmetric tensors , and a rotation (orthogonal) tensor such that .
- also each of these decompositions is
**unique**.

## Principal invariants of a tensor[edit]

Let be a second order tensor. Then the determinant of can be expressed as

The quantities are called the **principal invariants** of . Expressions of the principal invariants are given below.

Principal invariants of |

Note that is an eigenvalue of if and only if

The resulting equations is called the **characteristic equation** and is usually written in expanded form as

## Cayley-Hamilton theorem[edit]

The Cayley-Hamilton theorem is a very useful result in continuum mechanics. It states that

Cayley-Hamilton theorem If is a second order tensor then it satisfies its own characteristic equation |

## Index notation[edit]

All the equations so far have made no mention of the coordinate system. When we use vectors and tensor in computations we have to express them in some coordinate system (basis) and use the components of the object in that basis for our computations.

Commonly used bases are the Cartesian coordinate frame, the cylindrical coordinate frame, and the spherical coordinate frame.

A *Cartesian coordinate frame* consists of an orthonormal basis together with a point called the origin. Since these vectors are mutually perpendicular, we have the following relations:

### Kronecker delta[edit]

To make the above relations more compact, we introduce the *Kronecker delta* symbol

Then, instead of the nine equations in (1) we can write (in *index notation*)

### Einstein summation convention[edit]

Recall that the vector can be written as

In index notation, equation (2) can be written as

This convention is called the *Einstein summation convention*. If indices are repeated, we understand that to mean that there is a sum over the indices.

### Components of a vector[edit]

We can write the Cartesian components of a vector in the basis as

### Components of a tensor[edit]

Similarly, the components of of a tensor are defined by

Using the definition of the tensor product, we can also write

Using the summation convention,

In this case, the bases of the tensor are and the components are .

### Operation of a tensor on a vector[edit]

From the definition of the components of tensor , we can also see that (using the summation convention)

### Dyadic product[edit]

Similarly, the dyadic product can be expressed as

### Matrix notation[edit]

We can also write a tensor in matrix notation as

Note that the Kronecker delta represents the components of the identity tensor in a Cartesian basis. Therefore, we can write

### Tensor inner product[edit]

The *inner product* of two tensors and is an operation that generates a scalar. We define (summation implied)

The inner product can also be expressed using the trace :

Proof using the definition of the trace below :

### Trace of a tensor[edit]

The trace of a tensor is the scalar given by

(** Needs a proper definition **)

### Magnitude of a tensor[edit]

The *magnitude* of a tensor is defined by

### Tensor product of a tensor with a vector[edit]

Another tensor operation that is often seen is the *tensor product of a tensor with a vector*. Let be a tensor and let be a vector. Then the tensor cross product gives a tensor defined by

### Permutation symbol[edit]

The permutation symbol is defined as

## Identities in tensor algebra[edit]

Let , and be three second order tensors. Then

**Proof:**

It is easiest to show these relations by using index notation with respect to an orthonormal basis. Then we can write

Similarly,

## Tensor calculus[edit]

Recall that the vector differential operator (with respect to a Cartesian basis) is defined as

In this section we summarize some operations of on vectors and tensors.

### The gradient of a vector field[edit]

The dyadic product (or ) is called the *gradient* of the vector field . Therefore, the quantity is a *tensor* given by

In the alternative dyadic notation,

'*Warning:* Some authors define the component of as .

### The divergence of a tensor field[edit]

Let be a tensor field. Then the *divergence* of the tensor field is a *vector* given by

To fix the definition of divergence of a general tensor field (possibly of higher order than 2), we use the relation

where is an arbitrary constant vector.

### The Laplacian of a vector field[edit]

The *Laplacian* of a *vector* field is given by

## Tensor Identities[edit]

Some important identities involving tensors are:

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

## Integral theorems[edit]

The following integral theorems are useful in continuum mechanics and finite elements.

### The Gauss divergence theorem[edit]

If is a region in space enclosed by a surface and is a tensor field, then

where is the unit outward normal to the surface.

### The Stokes curl theorem[edit]

If is a surface bounded by a closed curve , then

where is a tensor field, is the unit normal vector to in the direction of a right-handed screw motion along , and is a unit tangential vector in the direction of integration along .

### The Leibniz formula[edit]

Let be a closed moving region of space enclosed by a surface . Let the velocity of any surface element be . Then if is a tensor function of position and time,

where is the outward unit normal to the surface .

## Directional derivatives[edit]

We often have to find the derivatives of vectors with respect to vectors and of tensors with respect to vectors and tensors. The *directional* directive *provides a systematic way of finding these derivatives.*

The definitions of directional derivatives for various situations are given below. It is assumed that the functions are sufficiently smooth that derivatives can be taken.

### Derivatives of scalar valued functions of vectors[edit]

Let be a real valued function of the vector . Then the derivative of with respect to (or at ) in the direction is the **vector** defined as

for all vectors .

*Properties:*

1) If then

2) If then

3) If then

### Derivatives of vector valued functions of vectors[edit]

Let be a vector valued function of the vector . Then the derivative of with respect to (or at ) in the direction is the **second order tensor** defined as

for all vectors .

*Properties:*

1) If then

2) If then

3) If then

### Derivatives of scalar valued functions of tensors[edit]

Let be a real valued function of the second order tensor . Then the derivative of with respect to (or at ) in the direction is the **second order tensor** defined as

for all second order tensors .

*Properties:*

1) If then

2) If then

3) If then

### Derivatives of tensor valued functions of tensors[edit]

Let be a second order tensor valued function of the second order tensor . Then the derivative of with respect to (or at ) in the direction is the **fourth order tensor** defined as

for all second order tensors .

*Properties:*

1) If then

2) If then

3) If then

3) If then

## Derivative of the determinant of a tensor[edit]

Derivative of the determinant of a tensor The derivative of the determinant of a second order tensor is given by In an orthonormal basis the components of can be written as a matrix . In that case, the right hand side corresponds the cofactors of the matrix. |

**Proof:**

Let be a second order tensor and let . Then, from the definition of the derivative of a scalar valued function of a tensor, we have

Recall that we can expand the determinant of a tensor in the form of a characteristic equation in terms of the invariants using (note the sign of )

Using this expansion we can write

Recall that the invariant is given by

Hence,

Invoking the arbitrariness of we then have

## Derivatives of the invariants of a tensor[edit]

Derivatives of the principal invariants of a tensor The principal invariants of a second order tensor are The derivatives of these three invariants with respect to are |

**Proof:**

From the derivative of the determinant we know that

For the derivatives of the other two invariants, let us go back to the characteristic equation

Using the same approach as for the determinant of a tensor, we can show that

Now the left hand side can be expanded as

Hence

or,

Expanding the right hand side and separating terms on the left hand side gives

or,

If we define and , we can write the above as

Collecting terms containing various powers of , we get

Then, invoking the arbitrariness of , we have

This implies that

## Derivative of the identity tensor[edit]

Let be the second order identity tensor. Then the derivative of this tensor with respect to a second order tensor is given by

This is because is independent of .

## Derivative of a tensor with respect to itself[edit]

Let be a second order tensor. Then

Therefore,

Here is the fourth order identity tensor. In index notation with respect to an orthonormal basis

This result implies that

where

Therefore, if the tensor is symmetric, then the derivative is also symmetric and we get

where the symmetric fourth order identity tensor is

## Derivative of the inverse of a tensor[edit]

Derivative of the inverse of a tensor Let and be two second order tensors, then In index notation with respect to an orthonormal basis We also have In index notation If the tensor is symmetric then |

**Proof:**

Recall that

Since , we can write

Using the product rule for second order tensors

we get

or,

Therefore,

## Remarks[edit]

The boldface notation that I've used is called the *Gibbs* notation. The index notation that I have used is also called *Cartesian tensor* notation.