Projections of Moments on a new Axis

In summary, the conversation is about moments in physics and a question regarding finding the moment about a new axis AA that is parallel to the y-axis. The formula being discussed is M = u (r x f), where r is the position vector from anywhere on the AA axis to anywhere on the line of the force and f is the components of the force. The conversation also clarifies that the moment about the axis along AA is a scalar, the u-component of the whole moment, and not a separate vector.
  • #1
tave
1
0
Hi all, this is my first post, so forgive me if this is in the wrong forum. (I believe it does belong here)

We are currently doing moments in physics and I have seen this in a few homework problems but can't seem to grasp it.

For example, in one question, we are given a new axis AA that is parallel to the y-axis but is 150j and -100k (150, y, 100) (infinitely long in the y axis?) and the question wants the moment about that new axis AA.

My thinking is that I can take the projection about the y-axis or something along those line (I believe my thinking is completely off here)

M = u (r x f)
where r would be the position vector from the origin to anywhere on the AA axis.
F is the components of the force
u being the unit vector, and where I'm completely clueless.

Again, sorry for the lack of diagram or question, because this is more of a general question that applies to moments in general and axis's that are not touching the origin
 
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  • #2
welcome to pf!

hi tave! welcome to pf! :smile:
tave said:
M = u (r x f)
where r would be the position vector from the origin to anywhere on the AA axis.
F is the components of the force
u being the unit vector

no, r is the position vector from anywhere on the AA axis to anywhere on the line of the force

also, it should be u.(r x f), with a dot, so that it's a scalar

the moment (about one particular point on the AA axis) is actually the whole vector r x f,

what we call "the moment about the axis along AA" is really a scalar, the u-component of the whole moment: u.(r x f) …

(when r x f happens be along u, it makes no difference, of course, whether we regard u.(r x f) as a scalar or a vector: when it isn't along u, it's highly unnatural to call u.(r x f) a separate vector)
 

Related to Projections of Moments on a new Axis

What is the concept of Projections of Moments on a new Axis?

Projections of Moments on a new Axis is a mathematical technique used to transform data from one coordinate system to another. It involves calculating the moments of the data along the new axis and then using those moments to create a new set of coordinates.

Why is Projections of Moments on a new Axis important in scientific research?

Projections of Moments on a new Axis is important in scientific research because it allows for the comparison of data from different coordinate systems. This is especially useful when analyzing complex data sets or when trying to find patterns or relationships between variables.

What are the benefits of using Projections of Moments on a new Axis compared to other transformation methods?

One of the main benefits of using Projections of Moments on a new Axis is that it preserves the moments of the data, which can provide important information about the distribution and variability of the data. Additionally, it is a more flexible and accurate method compared to other transformation techniques.

How is Projections of Moments on a new Axis calculated?

The calculation of Projections of Moments on a new Axis involves taking the original data and finding the moments of that data along the new axis. These moments are then used to create a new set of coordinates, which can be used to plot the data in the new coordinate system.

What are some applications of Projections of Moments on a new Axis in scientific research?

Projections of Moments on a new Axis has many applications in scientific research, particularly in fields such as statistics, physics, and engineering. It can be used to analyze data from experiments, to compare data from different sources, and to identify trends and patterns in complex data sets.

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