A directional, partial derivative of a scalar product?

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SUMMARY

The discussion focuses on calculating the directional derivative of the dot product of two vector fields, a(x,y,z) and b(x,y,z), represented as f = a•b. The goal is to express this derivative along vector a while only accounting for changes in vector b, specifically using the notation ## \vec a \cdot (\nabla \vec b)##. The conversation highlights the distinction between ## \vec a \cdot (\nabla \vec b)## and ## (\vec a \cdot \nabla) \vec b##, clarifying that the former may yield non-zero results under certain conditions, even when vector a has only x-components and vector b has only y-components.

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Let's say I have two vector fields a(x,y,z) and b(x,y,z).

Let's say I have a scalar field f equal to ab.

I want to find a clean-looking, simple way to express the directional derivative of this dot product along a, considering only changes in b.

Ideally, I would like to be able to express this without invoking unit vectors and without || 's, while still using vector notation.
 
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Henryk said:
## \vec a \cdot (\nabla \vec b)##

If vectors in a had x-components only, and vectors in b had y-components only, wouldn't ## \vec a \cdot (\nabla \vec b)## still return values with y-components only provided that b varies with x, as opposed to zero, which ab would be equal to everywhere?

Edit: Maybe I am conflating ## \vec a \cdot (\nabla \vec b)## with ## (\vec a \cdot \nabla) \vec b##

Edit2: Also, It seems like you gave the gradient of the dot product of ab, considering only changes in b. I was wondering about the directional derivative of ab along a, considering only changes in b.
 
Last edited:
My mistake
 

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