Finite Element Procedures – Matrix, Vectors, Tensors

Special Matrix

symmetric matrix

identity matrix / unite matrix

symmetric banded matrix

the following matrix is a symmetric banded matrix of order 5 and the half-bandwidth is 2.

\[{\rm{A}} = \left[ {\begin{array}{*{20}{c}}
\end{array}} \right]\]

diagonal matrix: nonzero elements only on the diagonal of the matrix

upper half of the matrix

inverse matrix, the inverse of a matrix

partitioning of matrix

the trace and determinant of a matrix: only defined if the matrix is square

tr(A) = sum of elements on the diagonal

det A = determinant

det (BC…F) = (det B)(det C)…(det F)

orthogonal matrix:\[{{\rm{P}}^T} = {{\rm{P}}^{ – 1}}\]


change of basis: transformation that corresponds to a change of basis\[y = {\rm{A}}x = {{\rm{P}}^{ – 1}}{\rm{AP}}\]

rotation matrix (orthogonal): In general case, this rotation is carried out in the n-dimensional space.

\[{\rm{P}} = \left[ {\begin{array}{*{20}{c}}
{\cos \theta }&{ – \sin \theta }\\
{\sin \theta }&{\cos \theta }
\end{array}} \right]\]

reflection matrix (orthogonal)

\[{\rm{P}} = {\rm{I}} – \alpha {\rm{v}}{{\rm{v}}^T};{\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \alpha = \frac{2}{{{{\rm{v}}^T}{\rm{v}}}}\]

where v can be arbitrary. This matrix is called a reflection matrix, because the vector Pw is the reflection of vector w in the plane to which v is orthogonal.


Cartesian tensors: tensors represented in rectangular Cartesian coordinate frames.

Scalar: an entity is called a scalar if it has only a single component in the coordinates and this component does not change under a coordinate transformation.

A vector or tensor of first order: if it has three components \(\xi _i\) in the unprimed frame and three components \(\xi _i^{,}\) in the primed frame, and if these components are related by characteristic law (using the summation convention).

\[\xi _i^{,} = {p_{ik}}{\xi _k}\]

the scalar product of the vectors u and v

\[{\rm{u}} \times {\rm{v}} = \left| {\rm{u}} \right|\left| {\rm{v}} \right|\cos \theta = {u_i}{v_i}\]

the dot product of the vectors u and v

\[{\rm{w}} = \det \left[ {\begin{array}{*{20}{c}}
\end{array}} \right]\]

the scalar and dot product of vectors are frequently employed in finite element analysis to evaluate angles between two given directions and to establish the direction perpendicular to a given plane.

A second order tensor: it has nine components \(t_{ij}\), i = 1, 2, 3 and j = 1, 2, 3 in the unprimed frameand nine components \(t_{ij}^{,}\) in the primed frame and if these components are related by the characteristic law

\[t_{ij}^{,} = {p_{ij}}{p_{jk}}{t_{kl}}\]

2 comments On Finite Element Procedures – Matrix, Vectors, Tensors

  • Having read tһis I believeⅾ it was really enlightening.

    I appreciate yoս finding thhe time aand energy to put this article
    together. I once avain find myѕelf personally spending a ⅼot of timee
    both reading andd leaving comments. But so what, it
    wɑs stilⅼ ѡorthwhile!

  • Thanks for your appreciation. I will try to make my articles more clear later.

Leave a reply:

Your email address will not be published.

Site Footer

Recording Life, Sharing Knowledge, Be Happy~