Need help understanding eigenvalues and eigenvectors

In summary: This is why the determinant of the above matrix must be equal to zero in order to solve for the principal stresses and their corresponding orientations.
  • #1
Cisneros778
48
0

Homework Statement


Find the principal stresses and the orientation for the principal axis of stress for the following cases of plane stress.

σx = 4,000 psi
σy = 0 psi
τxy = 8,000 psi


Homework Equations


See picture.

The Attempt at a Solution


https://mail.google.com/mail/u/0/?ui=2&ik=bc68d58ae7&view=att&th=139dbef260c42514&attid=0.1&disp=inline&realattid=f_h79p3pz70&safe=1&zw

I solved this problem using Mohr's Circle. However, the solution to the problem is different and I would like to understand it.
I do not know what the steps mean. Why does the determinate of that function must equal zero?
And what is the n1 and n2 about?
Finally, the 2x2 matrices adds two shear stresses where I was only given one τxy where does this other value come from?
 
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  • #2
Any help please?
 
  • #3
I'm not sure if anyone else is having trouble here, but your picture doesn't seem to be loading?
 
  • #4
Cisneros778 said:

Homework Statement


Find the principal stresses and the orientation for the principal axis of stress for the following cases of plane stress.

σx = 4,000 psi
σy = 0 psi
τxy = 8,000 psi


Homework Equations


See picture.

The Attempt at a Solution


https://mail.google.com/mail/u/0/?ui=2&ik=bc68d58ae7&view=att&th=139dbef260c42514&attid=0.1&disp=inline&realattid=f_h79p3pz70&safe=1&zw

I solved this problem using Mohr's Circle. However, the solution to the problem is different and I would like to understand it.
I do not know what the steps mean. Why does the determinate of that function must equal zero?
And what is the n1 and n2 about?
Finally, the 2x2 matrices adds two shear stresses where I was only given one τxy where does this other value come from?

In terms of components and unit vectors, the stress tensor can be written as a sum of terms (similar to a vector) as follows:

σ = σxx ixix + τxy ixiy+ τyx iyix+ σyy iyiy

Since the stress tensor is symmetric, τyx = τxy

Therefore, the stress tensor is given by:

σ = σxx ixix + τxy ixiy+ τxy iyix+ σyy iyiy

This is where the other τxy you were asking about comes from.


If n is a unit vector oriented in some arbitrary horizontal direction within your material, then, in terms of its components in the x and y directions, n can be written as the following sum of terms:

n = nx ix + ny iy

According to the so-called Cauchy stress relationship, the traction (force per unit area) acting on a plane perpendicular to the unit normal n is obtained by dotting the stress tensor σ with the unit normal n:

[itex]\Sigma[/itex]=(σxx nx + τxy ny) ix + (τxy nx + σyy ny) iy

If n corresponds to one of the principal direction of stress, then the traction on the plane normal to n is perpendicular to the plane, and parallel to n:


[itex]\Sigma[/itex]=(σxx nx + τxy ny) ix + (τxy nx + σyy ny) iy = λ (nx ix + ny iy)

where λ is the principal stress.

From the above equation, we get:

σxx nx + τxy ny = λ nx

τxy nx + σyy ny) = λ ny

This defines your eigenvalue problem. The components of the normal define the eigenvector, and the principal stress defines the eigenvalue.
 
  • #5


Hi there,

Eigenvalues and eigenvectors are important concepts in linear algebra and are commonly used in engineering and physics to solve problems involving matrices and vectors. In the context of stress analysis, eigenvalues and eigenvectors can help us determine the principal stresses and their orientations, which are critical in understanding the behavior of structures under different loading conditions.

In the problem given, we are asked to find the principal stresses and their orientations for a plane stress state with known values of σx, σy, and τxy. To solve this, we can use the formula for principal stresses and principal axes:

σ1,2 = (σx + σy)/2 ± √[(σx - σy)/2]^2 + τxy^2

θ1,2 = 1/2 tan^-1(2τxy/(σx - σy))

In order to understand this solution, it is important to first understand the concept of eigenvalues and eigenvectors. An eigenvalue is a scalar value that represents the amount of stretch or compression in a particular direction, while an eigenvector is a vector that represents the direction of this stretch or compression. In the context of stress analysis, the eigenvalues represent the principal stresses, and the eigenvectors represent the principal axes of stress.

In this problem, we are given a 2x2 stress matrix, which can be represented as a linear transformation that maps a vector in the original stress state to a vector in the principal stress state. This transformation can be represented by a characteristic polynomial, which has the form:

det(A - λI) = 0

where A is the stress matrix, λ is the eigenvalue, and I is the identity matrix. Solving this equation for λ will give us the eigenvalues, which in turn can be used to find the principal stresses.

The n1 and n2 values in the solution refer to the eigenvectors corresponding to the two eigenvalues, which represent the principal axes of stress. These can be found by solving the system of equations:

(A - λI)x = 0

where x is the eigenvector. The two eigenvectors correspond to the two principal axes, and their orientations can be determined using the formula given above.

Finally, the 2x2 matrices in the solution represent the transformation matrix from the original stress state to the principal stress state. This transformation matrix is necessary because we are given only one shear stress (τxy
 

Related to Need help understanding eigenvalues and eigenvectors

1. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are mathematical concepts used in linear algebra to describe the behavior of a linear transformation on a vector space. Eigenvalues represent the scaling factor of the eigenvector when the linear transformation is applied to it. In simpler terms, eigenvalues and eigenvectors help us understand how a matrix or linear transformation changes the direction and magnitude of a vector.

2. Why are eigenvalues and eigenvectors important?

Eigenvalues and eigenvectors are important in many areas of mathematics and science, including physics, engineering, and computer graphics. They are used to solve systems of linear equations, diagonalize matrices, and analyze the behavior of dynamic systems. They are also fundamental in understanding quantum mechanics and the properties of physical systems.

3. How are eigenvalues and eigenvectors calculated?

To calculate eigenvalues and eigenvectors, we first find the characteristic polynomial of the matrix. Then, we solve this polynomial to find the eigenvalues. Next, we plug these eigenvalues into the original matrix to solve for the corresponding eigenvectors. There are also various computational methods and algorithms that can be used to calculate eigenvalues and eigenvectors.

4. Can eigenvalues and eigenvectors be complex numbers?

Yes, eigenvalues and eigenvectors can be complex numbers. In fact, for matrices with complex entries, it is common for the eigenvalues and eigenvectors to be complex. This is because the characteristic polynomial of such matrices often has complex roots. However, in many applications, we are interested in real eigenvalues and eigenvectors, which can still exist for matrices with complex entries.

5. What is the relationship between eigenvalues and eigenvectors?

The relationship between eigenvalues and eigenvectors is that each eigenvector corresponds to a specific eigenvalue. In other words, for a given eigenvalue, there exists at least one eigenvector that, when multiplied by the eigenvalue, results in the original vector being scaled. Additionally, the set of all eigenvectors for a given eigenvalue forms a subspace of the vector space. This relationship is important in understanding the behavior of linear transformations and diagonalizing matrices.

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