From KratosWiki
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| :<math>N_1=1 - \alpha - \beta \qquad N_2=\alpha \qquad N_3=\beta \,</math> | | :<math>N_1=1 - \alpha - \beta \qquad N_2=\alpha \qquad N_3=\beta \,</math> |
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− | === Derivatives of the shape functions ===
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− | For isoparametric elements (those using the same shape functions to interpolate the geometry and the unknowns), we have:
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− | :<math>x = \sum_{i=1}^n N_i(L_1,L_2,L_3) x_i \qquad y = \sum_{i=1}^n N_i(L_1,L_2,L_3) y_i </math>
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− | To obtain the derivatives of the shape functions:
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− | ::<math>\frac{\partial N_i}{\partial \alpha} = \frac{\partial N_i}{\partial x} \frac{\partial x}{\partial \alpha} + \frac{\partial N_i}{\partial y} \frac{\partial y}{\partial \alpha}</math>
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− | ::<math>\frac{\partial N_i}{\partial \beta} = \frac{\partial N_i}{\partial x} \frac{\partial x}{\partial \beta} + \frac{\partial N_i}{\partial y} \frac{\partial y}{\partial \beta}</math>
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− | ::<math>
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial \alpha} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial \beta}
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− | \end{Bmatrix}
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− | =
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− | \underbrace{
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− | \begin{bmatrix}
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− | \displaystyle \frac{\partial x}{\partial \alpha} & \displaystyle \frac{\partial y}{\partial \alpha} \\ \quad \\
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− | \displaystyle \frac{\partial x}{\partial \beta} & \displaystyle \frac{\partial y}{\partial \beta}
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− | \end{bmatrix}
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− | }_{\mathbf{J^{(e)}}}
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial x} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial y}
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− | \end{Bmatrix}
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− | =
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− | \mathbf{J^{(e)}}
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial x} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial y}
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− | \end{Bmatrix}
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− | </math>
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− | with <math>\mathbf{J^{(e)}} \,</math> the Jacobian matrix with a determinant <math>\mathbf{|J^{(e)}|} \,</math>
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− | ::<math>
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial x} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial y}
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− | \end{Bmatrix}
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− | =
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− | \begin{bmatrix}
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− | \mathbf{J^{(e)}}
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− | \end{bmatrix}^{-1}
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial \alpha} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial \beta}
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− | \end{Bmatrix}
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− | =
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− | \displaystyle
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− | \frac{1}{ \mathbf{|J^{(e)}|}}
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− | \begin{bmatrix}
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− | \displaystyle \frac{\partial y}{\partial \beta} & \displaystyle -\frac{\partial y}{\partial \alpha} \\ \quad \\
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− | \displaystyle -\frac{\partial x}{\partial \beta} & \displaystyle \frac{\partial x}{\partial \alpha}
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− | \end{bmatrix}
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− | \begin{Bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial \alpha} \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial \beta}
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− | \end{Bmatrix}
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− | </math>
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− | From here is easy to obtain:
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− | ::<math>\partial x \partial y = \mathbf{|J^{(e)}|} \partial \alpha \partial \beta \,</math>
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− | ::<math>\frac{\partial x}{\partial \alpha} = \sum_{i=1}^n \frac{\partial N_i}{\partial \alpha} x_i \qquad
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− | \frac{\partial x}{\partial \beta} = \sum_{i=1}^n \frac{\partial N_i}{\partial \beta} x_i</math>
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− | ::<math>\frac{\partial y}{\partial \alpha} = \sum_{i=1}^n \frac{\partial N_i}{\partial \alpha} y_i \qquad
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− | \frac{\partial y}{\partial \beta} = \sum_{i=1}^n \frac{\partial N_i}{\partial \beta} y_i</math>
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− | ::<math>\mathbf{J^{(e)}} = \sum_{i=1}^n
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− | \begin{bmatrix}
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− | \displaystyle \frac{\partial N_i}{\partial \alpha} x_i & \displaystyle \frac{\partial N_i}{\partial \alpha} y_i \\ \quad \\
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− | \displaystyle \frac{\partial N_i}{\partial \beta} x_i & \displaystyle \frac{\partial N_i}{\partial \beta} y_i
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− | \end{bmatrix}
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− | </math>
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Revision as of 19:29, 4 November 2009
Shape functions are selected to fit as exact as possible the Finite Element Solution. If this solution is a combination of polynomial functions of nth order, these functions should include a complete polynomial of equal order.
That is, a complete polynomial of nth order can be written as:
with:
the number of terms.
More specifically:
polynomial order n |
number of terms p |
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Constant: |
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Linear: |
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Quadratic: |
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A quick way to easily obtain the terms of a complete polynomial is by using the Pascal's triangle:
order n |
new polynomial terms |
number of terms p
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Linear |
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Quadratic |
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Cubic |
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Quartic |
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Shape Functions for Triangular Elements
The Three Node Linear Triangle
- The solution
for each triangular element can be approached by their corresponding
to be expressed using the shape functions:
- If the shape functions are lineal polynomials (three-node triangular element, n=3), and remembering:
- this expression can be written as:
with
the element area and
- And the system of equations is:
- The element area is computed as the half of the determinant of the coordinates matrix:
- Finally, the different parameters can be expressed in terms of the nodal local coordinates as:
- with
Areal Coordinates
In order to generalise the procedure to obtain the shape functions, the areal coordinates is a very useful transformation.
In a triangle, areal or barycentric coordinates are defined as each of the partial subareas obtained by dividing the triangle in three sections.
That is, if we use a inner point P of the triangle of area A as the common vertex of the three subareas A1, A2 and A3, then:
Note that:
- A1 + A2 + A3 = A
- L1 + L2 + L3 = 1
- If P is the Centroid or Center of Mass of the triangle, then L1 = L2 = L3 = 1/3
For the Finite Element Method is also interesting to note that:
with xp and yp the coordinates of P or any other point inside the triangle (x,y). This is equivalent to the following system of equations:
that is exactly the shape functions for a triangular element of three nodes.
Natural Coordinates
It is usual to define the triangle in terms of a normalised geometry (natural coordinates) as is showed in the figure:
Therefore:
References
- Pascal's triangle
- Barycentric Coordinates (Areal Coordinates)
- Centroid
- Jacobian