Unit Tangent Vector in a Scalar Field

In summary: If you want to work in higher dimensions you need to use a higher order tensor.Hope this helps!In summary, you are trying to calculate the unit tangent to a scalar field using the gradient of the field. However, you are having difficulty visualizing the gradient and are instead using a linear combination of basis vectors to solve for the unit tangent.
  • #1
auditt241
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Hello,
I am attempting to calculate unit normal and tangent vectors for a scalar field I have, Φ(x,y). For my unit normal, I simply used:
[itex]\hat{n}=\frac{\nabla \phi}{|\nabla \phi|}[/itex]​
However, I'm struggling with using this approach to calculate the unit tangent. I need to express it in terms of the gradient of the scalar field but I am having a hard time visualizing this.
Thanks!
 
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  • #2
Take a linear combination of basis vectors with unknown coefficients. Equate its dot product with ## \hat n ## to zero. You should be able to find two independent vectors as solutions. Those will span the tangent space to each point.
 
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Likes Geofleur
  • #3
Shyan said:
Take a linear combination of basis vectors with unknown coefficients. Equate its dot product with ## \hat n ## to zero. You should be able to find two independent vectors as solutions. Those will span the tangent space to each point.
Thanks for your response! I think I tried this: I used [itex] \hat{n} \cdot \hat{T} = 0 [/itex], writing each in terms of an x- and y-component, and then solving for the x-component and y-component of the unit tangent [itex] \hat{T} [/itex]. My [itex] T_x [/itex] and [itex] T_y [/itex] are written only in terms of unit normal components [itex] n_x [/itex] and [itex] n_y [/itex] (by defining [itex] \sqrt{T_x^2 + T_y^2} = 1 [/itex]. When I do this however, and plot my unit tensor field, I don't get clean, tangential vectors. Some are tangential, but they can be a bit of a mess. I am doing this in FEA software, could some of my issues be due to my mesh size? Or am I going about it the wrong way?
 
  • #4
One obvious problem I see, is that you're assuming the problem is two dimensional, Its not! You should work in three dimensions. Otherwise ##\hat n\cdot \vec T=0## and ##|\vec T|=1## will completely determine the components and you'll get only one vector as a solution which we know isn't right.
In fact you should've known this from the start because you are considering a function of two variables which can only be a surface in three dimensions.
 

FAQ: Unit Tangent Vector in a Scalar Field

What is a unit tangent vector in a scalar field?

A unit tangent vector in a scalar field is a vector that is tangent to the surface of the scalar field at a specific point and has a magnitude of 1. It is used to represent the direction of the steepest increase or decrease in the scalar field.

How is a unit tangent vector calculated in a scalar field?

To calculate a unit tangent vector in a scalar field, you first need to find the gradient of the scalar field at a given point. Then, normalize the gradient vector to have a magnitude of 1, which will give you the unit tangent vector.

What is the significance of the unit tangent vector in a scalar field?

The unit tangent vector in a scalar field is important because it represents the direction of maximum change in the scalar field. This information is useful in various applications, such as optimization and motion planning.

How does the unit tangent vector relate to the slope of a scalar field?

The unit tangent vector is perpendicular to the contours of the scalar field, and its direction is in the direction of the steepest increase or decrease in the field. The magnitude of the unit tangent vector is equal to the slope of the scalar field in that direction.

Can a unit tangent vector be negative in a scalar field?

Yes, a unit tangent vector can be negative in a scalar field. This can happen when the direction of maximum change is in the opposite direction of the positive direction of the coordinate system being used. The magnitude of the vector will still be 1, but its direction will be negative.

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