I lost my mavic pro, help me!

[sar104]

Ah - would you being willing to translate this post into layman's terms ??

I use one of the common scientific data analysis applications, Wavemetrics Igor Pro. Self-explanatory

I estimated the drag coefficients by comparing freefall data with standard geometric drag models. There are numerous data sets from flights where the motors were stopped or props were lost - that's the freefall data. There are also accepted drag coefficient equations for various geometric shapes. I used a combination of those to get the drag coefficients.

It's highly simplified in its assumption of isotropic behavior, but it seems to be close enough to predict trajectories with a somewhat useful level of fidelity. The model assumes that the drag coefficient is constant - independent of the orientation of the aircraft (isotropic). That's obviously not going to be fully correct, but works well enough.

Even with the simplest drag model included there is no analytic solution to the resulting differential equations of motion, and so those are solved with a first order numerical finite difference method. The differential equations of motion (in the form of F = ma - Newton's 2nd law) form cannot simply be rearranged and integrated to get velocity and position as a function of time (an analytic solution), and so an iterative, time-stepping (explicit) numerical method has to be used instead.

Unfortunately such methods require a reasonable level of comfort with at least college level physics and mathematics as well as somewhat costly software, and so I suspect that the audience for such a thing would be very small indeed. The methods are not easily understood by the layman.

 

[Mavic Mac]

I use one of the common scientific data analysis applications, Wavemetrics Igor Pro. Self-explanatory

I estimated the drag coefficients by comparing freefall data with standard geometric drag models. There are numerous data sets from flights where the motors were stopped or props were lost - that's the freefall data. There are also accepted drag coefficient equations for various geometric shapes. I used a combination of those to get the drag coefficients.

It's highly simplified in its assumption of isotropic behavior, but it seems to be close enough to predict trajectories with a somewhat useful level of fidelity. The model assumes that the drag coefficient is constant - independent of the orientation of the aircraft (isotropic). That's obviously not going to be fully correct, but works well enough.

Even with the simplest drag model included there is no analytic solution to the resulting differential equations of motion, and so those are solved with a first order numerical finite difference method. The differential equations of motion (in the form of F = ma - Newton's 2nd law) form cannot simply be rearranged and integrated to get velocity and position as a function of time (an analytic solution), and so an iterative, time-stepping (explicit) numerical method has to be used instead.

Unfortunately such methods require a reasonable level of comfort with at least college level physics and mathematics as well as somewhat costly software, and so I suspect that the audience for such a thing would be very small indeed. The methods are not easily understood by the layman.

I am willing to admit it I am one of the layman members on this forum and that I, along with others, are extremely appreciative of
1. Your scientific knowledge
2. And more importantly your willingness to help the other members with their problems !

 

[MPPilot]

@Marcelopr, getting back to the original question, any luck finding the drone? It looked like a lot of trees to search for. Having a green drone surly doesn't help.

 

[dannybgoode]

Unfortunately such methods require a reasonable level of comfort with at least college level physics and mathematics as well as somewhat costly software, and so I suspect that the audience for such a thing would be very small indeed. The methods are not easily understood by the layman.

Totally ot but this last explanation totally cracked me up . And agree with others - @sar104 you really do go above and beyond in helping others.

I hope never to need your input but I’ll sure be grateful for it if I ever do.