Velocity profile from 3d velocity space data

In summary, the conversation discusses finding a measure of the distribution of velocity from a dataset, specifically in the cartesian x, y, and z directions. The speaker has plotted the velocity data in 3D and noticed a probability decrease as 1/r^2. They are looking for a way to get the density of dots as a function of radius from the raw data. One suggestion is to use shell binning, where the bins are shells with equal volume in velocity space. The speaker has successfully used this method and is willing to share the results with others who may be facing a similar problem.
  • #1
jimbo_durham
13
0
Hi. I have a load of data objcts, each with velocity components in the cartesian x,y,z directions. I would like to find a measure of the distribution of velocity (assuming it is averaged in all directions - a good assumption). Ie i would like to end up with some 2d plot with velocity along the 'x' axis, and number density, propability amplitude etc up the 'y' axis.

I have plotted the velocity data in 3d to show the distribution in x,y,z. it is attached. It appears that the velocity falls off in probability as 1/r^2 or similar. ie there are lots of objects with velocitys in the low 10s of kms-1, and fewer objects in the high 100s kms-1. I have the raw data and need find the form of the function.

Basically, from the plot in the figure, i need the density of dots(representing velocity componants) as a function of radius. How can i do this from the raw data? i think this might be a binning problem, where the bins are shells with equal volume in velocity space, and the number of 'dots' which fall into the shell gives the form of the function i am after.

any better ideas?

thanks in advance
jimbo
 

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  • #2
solved. shell binning worked fine. alhough the volume of each shell was allowed to change, this was accounted for and the number density found as a function of radial distance from center, equating to a number density profile against actual velocity.

thanks to anyone who read this and was thinking of a solution for me
 
  • #3
ps if anyone wants to see the results/method if they are doing anything similar, i can post them
 
  • #4
jimbo_durham said:
ps if anyone wants to see the results/method if they are doing anything similar, i can post them

Dear Jimbo,
may be i have more complicated problem, but i believe your example would help me much, please send me the method and results to [email address deleted]

Best Regards,
Mohamed
 
Last edited by a moderator:

1. What is a velocity profile?

A velocity profile is a graphical representation of how the velocity of an object varies over time or distance. In the context of 3D velocity space data, it shows how the velocity of an object changes in three-dimensional space.

2. How is a velocity profile calculated?

To calculate a velocity profile from 3D velocity space data, the individual velocity components (x, y, and z) must first be measured or recorded over a period of time. Then, these values are plotted on a graph with time or distance on the x-axis and velocity on the y-axis. This results in a plot of the velocity profile, which can be analyzed to understand the motion of the object in 3D space.

3. What can a velocity profile tell us about an object's motion?

A velocity profile can provide important information about an object's motion, such as its speed, direction, and acceleration. It can also reveal patterns or changes in the object's velocity over time, which can help scientists understand the forces acting upon the object.

4. How is 3D velocity space data collected?

3D velocity space data can be collected using various methods, such as motion capture technology, Doppler radar, or by tracking the position of an object using cameras. The specific method used will depend on the type of object being studied and the goals of the research.

5. What are some applications of studying velocity profiles from 3D velocity space data?

Studying velocity profiles from 3D velocity space data can have many practical applications. For example, it can be used in sports science to analyze an athlete's movements and improve their performance. It can also be used in engineering to design more efficient and aerodynamic structures, or in meteorology to track and predict weather patterns.

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