How Is the Rodrigues Rotation Formula Derived in Tensor Notation?

In summary: This shows that Qe1 is equal to e1, verifying the given condition. Using similar expansions and substitutions, we can also show that Qe2 = e2 and Qe3 = e3, which means that Q is equal to the identity matrix 1.In summary, we have shown that the given conditions lead to Q being equal to the identity matrix 1 and the Rodrigues rotation formula can be verified. I hope this helps in your understanding of this topic. Best of luck with your studies.
  • #1
wood0595
1
0
I'm curious as to how the following proof is verified. I have toiled over this thing for quite a while, but haven't made any progress. I don't need a step-by-step solution, but I would appreciate any help getting it started:

Given the following:

Q is an orthogonal tensor
e1 is a unit vector such that Qe1 = e1
n is of unit length and orthogonal to e1
m is such that m[tex]\bullet[/tex]e1 = 0, m = Qn and m[tex]\bullet[/tex]n= cos[tex]\theta[/tex]
1 is the identity matrix

Prove that:

Q = 1cos[tex]\theta[/tex] + (1-cos[tex]\theta[/tex])e1[tex]\otimes[/tex]e1 - (e2[tex]\otimes[/tex]e3-e3[tex]\otimes[/tex]e2)sin[tex]\theta[/tex]

Where e1, e2, and e3 form an orthonormal triad and
[tex]\otimes[/tex] represents the dyadic product

My apologies if this is the wrong thread to post this at, but I looked around and couldn't find any threads on this topic.
 
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  • #2




Thank you for your question and interest in this topic. The proof that you are asking about is known as the Rodrigues rotation formula and is commonly used in the field of mechanics and physics. To verify this formula, we can start by expanding the given equation m = Qn as follows:

m = Qn = Q(e1\crossn) = Q(e1\cross(Qe1)) = Q(e1\cross(e1)) = 0

This shows that m is indeed orthogonal to both e1 and n. Next, we can use the fact that Q is an orthogonal tensor to show that Q preserves the length of vectors. This means that ||m|| = ||n|| = 1. Using this information, we can rewrite the given equation m\bulletn = cos\theta as follows:

m\bulletn = cos\theta = ||m||||n||cos\theta = 1\cdot1\cdotcos\theta = cos\theta

Now, let's consider the expansion of Qe1:

Qe1 = (1cos\theta + (1-cos\theta)e1\otimese1 - (e2\otimese3-e3\otimese2)sin\theta)e1
= e1cos\theta + (e1\bullet e1)(1-cos\theta)e1 - (e1\bullet e2)(e2\otimese3-e3\otimese2)sin\theta
= e1cos\theta + (1-cos\theta)e1 - (e1\bullet e2)(e2\otimese3-e3\otimese2)sin\theta
= e1cos\theta + (1-cos\theta)e1 - (e1\bullet Qn)(Qe2\otimes Qe3-Qe3\otimes Qe2)sin\theta
= e1cos\theta + (1-cos\theta)e1 - (e1\bullet m)(Qe2\otimes Qe3-Qe3\otimes Qe2)sin\theta
= e1cos\theta + (1-cos\theta)e1 - 0(Qe2\otimes Qe3-Qe3\otimes Qe2)sin\theta
= e1cos\theta + (1-cos\
 

FAQ: How Is the Rodrigues Rotation Formula Derived in Tensor Notation?

What is an orthogonal tensor?

An orthogonal tensor is a mathematical object that represents a linear transformation that preserves the length and angles of vectors. In other words, it is a matrix that does not change the shape or orientation of a geometric object.

How is an orthogonal tensor defined mathematically?

An orthogonal tensor is defined as a square matrix A whose columns and rows are orthogonal unit vectors, meaning that their dot product is equal to 0 and their length is equal to 1. This can be represented as ATA = I, where AT is the transpose of A and I is the identity matrix.

What is the significance of orthogonal tensors in mathematics?

Orthogonal tensors have numerous applications in mathematics, physics, and engineering. They are used to describe rotations, reflections, and other transformations in n-dimensional space. They also play a crucial role in solving systems of linear equations and in the study of eigenvalues and eigenvectors.

How can the orthogonality of tensors be proved?

The orthogonality of tensors can be proved using various methods, such as the Gram-Schmidt process, QR decomposition, or singular value decomposition. These methods involve manipulating the elements of the tensor to show that its columns and rows are orthogonal unit vectors.

What are some real-life applications of orthogonal tensors?

Orthogonal tensors have practical applications in fields such as computer graphics, image processing, and signal processing. They are also used in the construction of 3D models, robotics, and machine learning algorithms. Additionally, they are essential in the analysis of stress and strain in materials and structures.

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