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# Eigenvalues, Eigenvectors of Tensors Civil Engineering (CE) Notes | EduRev

## Civil Engineering (CE) : Eigenvalues, Eigenvectors of Tensors Civil Engineering (CE) Notes | EduRev

The document Eigenvalues, Eigenvectors of Tensors Civil Engineering (CE) Notes | EduRev is a part of the Civil Engineering (CE) Course Advanced Solid Mechanics - Notes, Videos, MCQs & PPTs.
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Eigenvalues, eigenvectors of tensors
The scalar Î»i characterize eigenvalues (or principal values) of A if there exist corresponding nonzero normalized eigenvectors  (or principal directions or principal axes) of A, so that

To identify the eigenvectors of a tensor, we use subsequently a hat on the vector quantity concerned, for example,  .
Thus, a set of homogeneous algebraic equations for the unknown eigenvalues Î»i , i = 1,2,3, and the unknown eigenvectors  , i = 1,2,3, is
(A âˆ’ Î»i1) = o, (i = 1,2,3; no summation).                                            (2.137)

For the above system to have a solution  â‰  o the determinant of the system must vanish. Thus,

det(A âˆ’ Î»i1) = 0                                                                                        (2.138)

where

This requires that we solve a cubic equation in Î», usually written as

called the characteristic polynomial (or equation) for A, the solutions of which are the eigenvalues Î»i , i = 1,2,3. Here, Ii , i = 1,2,3, are the so-called principal scalar invariants of A and are given by

If A is invertible then we can compute I2 using the expression I2 = tr(Aâˆ’1 ) det(A). A repeated application of tensor A to equation (2.136) yields  i = 1,2,3, for any positive integer Î±. (If A is invertible then Î± can be any integer; not necessarily positive.) Using this relation and (2.140) multiplied by , we obtain the well-known Cayley-Hamilton equation:

It states that every second order tensor A satisfies its own characteristic equation. As a consequence of Cayley-Hamilton equation, we can express AÎ± in terms of A2 , A, and principal invariants for positive integer, Î± > 2. (If A is invertible, the above holds for any integer value of Î± positive or negative provided Î± â‰  {0, 1, 2}.)

For a symmetric tensor S the characteristic equation (2.140) always has three real solutions and the set of eigenvectors form a orthonormal basis {nË†i} (the proof of this statement is omitted). Hence, for a positive definite symmetric tensor A, all eigenvalues Î»i are (real and) positive since, using (2.136), we have Î»i = ï¿½  > 0, i = 1,2,3.

Any symmetric tensor S may be represented by its eigenvalues Î», i = 1,2,3, and the corresponding eigenvectors of S forming an orthonormal basis {}. Thus, S can be expressed as

(2.143)

called the spectral representation (or spectral decomposition) of S. Thus, when orthonormal eigenvectors are used as the Cartesian basis to represent S then
(2.144)

The above holds when all the three eigenvalues are distinct. On the other hand, if there exists a pair of equal roots, i.e., Î»1 = Î»2 â‰   Î»3, with an unique eigenvector  associated with Î»3, we deduce that

where   and  denote projection tensors introduced in (2.109) and (2.110) respectively. Finally, if all the three eigenvalues are equal, i.e., Î»1 = Î»= Î»3 = Î», then

S = Î»1,                                                                              (2.146)

where every direction is a principal direction and every set of mutually orthogonal basis denotes principal axes.
It is important to recognize that eigenvalues characterize the physical nature of the tensor and that they do not depend on the coordinates chosen.
Square root theorem Let C be symmetric and positive definite tensor. Then there is a unique positive definite, symmetric tensor U such that

U2 = C.                            (2.147)

We write  for U. If spectral representation of C is:

(2.148)

then the spectral representation for U is:

(2.149)

where we have assumed that the eigenvalues of C, Î»are distinct. On the other hand if Î»1 = Î»2 = Î»3 = Î» then

(2.150)

If Î»1 = Î»2 = Î» â‰  Î»3, with an unique eigenvector  associated with Î»3 then

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## Advanced Solid Mechanics - Notes, Videos, MCQs & PPTs

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