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  #21  
Old September 4th 18, 01:11 AM posted to rec.aviation.soaring
2G
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On Tuesday, August 28, 2018 at 7:06:24 PM UTC-7, Mike Borgelt wrote:
On Tuesday, 28 August 2018 23:13:24 UTC+10, wrote:
Mike,

Congrats on the new gadget. It sounds great. Since you are here and talking about TE compensation, there is a dumb question that I've often wondered about.

In theory TE = MGH + 1/2MV**2

but ideally, should V be the plane speed vector with respect to the air or ground?

-Stu


The air. But that isn't as simple as you might think.

More on Dynamis on our website. See the article "Horizontal Gusts" and under products - Dynamis Variometer System.

I'll put up some more articles and maybe comment under "blog" on the website over the next few days. Also pricing.

I'd like to get the next few local systems installed and test flown before release to the wider world.

Mike


My guess is that you incorporated a vertical accelerometer (G meter) to differentiate between a lifting air mass and horizontal gusts (which don't produce vertical acceleration). I believe this has already been done in the Butterfly vario.

Tom
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  #22  
Old September 4th 18, 02:28 AM posted to rec.aviation.soaring
jfitch
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On Monday, September 3, 2018 at 5:11:28 PM UTC-7, 2G wrote:
On Tuesday, August 28, 2018 at 7:06:24 PM UTC-7, Mike Borgelt wrote:
On Tuesday, 28 August 2018 23:13:24 UTC+10, wrote:
Mike,

Congrats on the new gadget. It sounds great. Since you are here and talking about TE compensation, there is a dumb question that I've often wondered about.

In theory TE = MGH + 1/2MV**2

but ideally, should V be the plane speed vector with respect to the air or ground?

-Stu


The air. But that isn't as simple as you might think.

More on Dynamis on our website. See the article "Horizontal Gusts" and under products - Dynamis Variometer System.

I'll put up some more articles and maybe comment under "blog" on the website over the next few days. Also pricing.

I'd like to get the next few local systems installed and test flown before release to the wider world.

Mike


My guess is that you incorporated a vertical accelerometer (G meter) to differentiate between a lifting air mass and horizontal gusts (which don't produce vertical acceleration). I believe this has already been done in the Butterfly vario.

Tom


Unfortunately horizontal gusts definitely do produce a vertical acceleration, since lift is 0.5 * d * Cl * V^2. Because of the V^2 term, they produce quite a lot of vertical acceleration. A 10 knot gust at 60 knots airspeed will give you near 0.4G acceleration.

Butterfly does seem to have worked this out first though.
  #23  
Old September 4th 18, 05:07 PM posted to rec.aviation.soaring
George Haeh
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Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.
  #24  
Old September 4th 18, 06:59 PM posted to rec.aviation.soaring
Steve Koerner
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Posts: 390
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On Tuesday, September 4, 2018 at 9:07:09 AM UTC-7, George Haeh wrote:
Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.


More holistically, you run a Kalman filter using every sensor you have and then some. You treat the system as having three disturbances: two horizontal wind components plus vertical flow. You continuously calculate a solution yielding least square error for the six DOF system with those disturbances being of primary interest.
  #25  
Old September 4th 18, 08:49 PM posted to rec.aviation.soaring
Andy Blackburn[_3_]
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Posts: 507
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On Tuesday, September 4, 2018 at 10:59:26 AM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 9:07:09 AM UTC-7, George Haeh wrote:
Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.


More holistically, you run a Kalman filter using every sensor you have and then some. You treat the system as having three disturbances: two horizontal wind components plus vertical flow. You continuously calculate a solution yielding least square error for the six DOF system with those disturbances being of primary interest.


Making a trip in the wayback machine to my control theory and aerodynamics classes - apologies if I mess up the details...

If you have a dynamic model for the glider (a Kalman filter would typically require this) you may also be able to use the difference between activating the short period and phugoid dynamic pitch modes with respect to pitch rate. This difference is why a strong thermal has that seat of the pants surge that pitches the nose down instead of up - as you'd get with a gust under normal circumstances.

For all of this you'd get a better result if you also knew the control positions and Cm vs control position - primarily for the elevator. Also, we don't really have dynamic models for gliders though my guess is it would not be that difficult to measure with a reasonably instrumented glider and a couple of test flights. You might be able to get decent results with a generic model for a reasonably current generation racing glider (for instance), though model-specific parameters would of course be better. You're really just trying to filter out the gusts so you may not need anything all that precise to get an improvement, just Cm vs alpha and Cm vs V. Knowing the c.g. and weight will matter as well, but you might be able to calculate these effects.

I wonder if it's easier or harder to use machine learning to do this than a more deterministic least squares model...or if all of the above is overkill.

Andy Blackburn
9B
  #26  
Old September 4th 18, 10:40 PM posted to rec.aviation.soaring
Steve Koerner
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Posts: 390
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On Tuesday, September 4, 2018 at 12:49:36 PM UTC-7, Andy Blackburn wrote:
On Tuesday, September 4, 2018 at 10:59:26 AM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 9:07:09 AM UTC-7, George Haeh wrote:
Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.


More holistically, you run a Kalman filter using every sensor you have and then some. You treat the system as having three disturbances: two horizontal wind components plus vertical flow. You continuously calculate a solution yielding least square error for the six DOF system with those disturbances being of primary interest.


Making a trip in the wayback machine to my control theory and aerodynamics classes - apologies if I mess up the details...

If you have a dynamic model for the glider (a Kalman filter would typically require this) you may also be able to use the difference between activating the short period and phugoid dynamic pitch modes with respect to pitch rate. This difference is why a strong thermal has that seat of the pants surge that pitches the nose down instead of up - as you'd get with a gust under normal circumstances.

For all of this you'd get a better result if you also knew the control positions and Cm vs control position - primarily for the elevator. Also, we don't really have dynamic models for gliders though my guess is it would not be that difficult to measure with a reasonably instrumented glider and a couple of test flights. You might be able to get decent results with a generic model for a reasonably current generation racing glider (for instance), though model-specific parameters would of course be better. You're really just trying to filter out the gusts so you may not need anything all that precise to get an improvement, just Cm vs alpha and Cm vs V. Knowing the c.g. and weight will matter as well, but you might be able to calculate these effects.

I wonder if it's easier or harder to use machine learning to do this than a more deterministic least squares model...or if all of the above is overkill.

Andy Blackburn
9B


I agree Andy; to do a Kalman filter you'd want gyros, accelerometers and control position sensors. Position sensors are not hard though and their mapping might be learned for each installation with a smooth air test flight. The problem with Neural network AI is that you have to begin the process with a comprehensive training set. Probably Mike Borgelt has a simpler and better way to get to just the goods that we care about without a plane load of sensors. I'll be looking forward to hearing more about this.
  #27  
Old September 5th 18, 06:22 AM posted to rec.aviation.soaring
2G
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Posts: 495
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On Tuesday, September 4, 2018 at 2:40:05 PM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 12:49:36 PM UTC-7, Andy Blackburn wrote:
On Tuesday, September 4, 2018 at 10:59:26 AM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 9:07:09 AM UTC-7, George Haeh wrote:
Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.

More holistically, you run a Kalman filter using every sensor you have and then some. You treat the system as having three disturbances: two horizontal wind components plus vertical flow. You continuously calculate a solution yielding least square error for the six DOF system with those disturbances being of primary interest.


Making a trip in the wayback machine to my control theory and aerodynamics classes - apologies if I mess up the details...

If you have a dynamic model for the glider (a Kalman filter would typically require this) you may also be able to use the difference between activating the short period and phugoid dynamic pitch modes with respect to pitch rate. This difference is why a strong thermal has that seat of the pants surge that pitches the nose down instead of up - as you'd get with a gust under normal circumstances.

For all of this you'd get a better result if you also knew the control positions and Cm vs control position - primarily for the elevator. Also, we don't really have dynamic models for gliders though my guess is it would not be that difficult to measure with a reasonably instrumented glider and a couple of test flights. You might be able to get decent results with a generic model for a reasonably current generation racing glider (for instance), though model-specific parameters would of course be better. You're really just trying to filter out the gusts so you may not need anything all that precise to get an improvement, just Cm vs alpha and Cm vs V. Knowing the c.g. and weight will matter as well, but you might be able to calculate these effects.

I wonder if it's easier or harder to use machine learning to do this than a more deterministic least squares model...or if all of the above is overkill.

Andy Blackburn
9B


I agree Andy; to do a Kalman filter you'd want gyros, accelerometers and control position sensors. Position sensors are not hard though and their mapping might be learned for each installation with a smooth air test flight.. The problem with Neural network AI is that you have to begin the process with a comprehensive training set. Probably Mike Borgelt has a simpler and better way to get to just the goods that we care about without a plane load of sensors. I'll be looking forward to hearing more about this.


What I know is I don't have a Kalman filter going in my head: but I do have a butt which feels vertical acceleration. If it doesn't tell me I am going up, I discount the screaming vario.

Tom
  #28  
Old September 5th 18, 02:00 PM posted to rec.aviation.soaring
[email protected]
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Posts: 1,772
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On Wednesday, September 5, 2018 at 1:22:14 AM UTC-4, 2G wrote:
On Tuesday, September 4, 2018 at 2:40:05 PM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 12:49:36 PM UTC-7, Andy Blackburn wrote:
On Tuesday, September 4, 2018 at 10:59:26 AM UTC-7, Steve Koerner wrote:
On Tuesday, September 4, 2018 at 9:07:09 AM UTC-7, George Haeh wrote:
Lift from horizontal gusts can be filtered out with rates of dynamic pressure and attitude, perhaps with application of polar.

Lots of fun with math.

More holistically, you run a Kalman filter using every sensor you have and then some. You treat the system as having three disturbances: two horizontal wind components plus vertical flow. You continuously calculate a solution yielding least square error for the six DOF system with those disturbances being of primary interest.

Making a trip in the wayback machine to my control theory and aerodynamics classes - apologies if I mess up the details...

If you have a dynamic model for the glider (a Kalman filter would typically require this) you may also be able to use the difference between activating the short period and phugoid dynamic pitch modes with respect to pitch rate. This difference is why a strong thermal has that seat of the pants surge that pitches the nose down instead of up - as you'd get with a gust under normal circumstances.

For all of this you'd get a better result if you also knew the control positions and Cm vs control position - primarily for the elevator. Also, we don't really have dynamic models for gliders though my guess is it would not be that difficult to measure with a reasonably instrumented glider and a couple of test flights. You might be able to get decent results with a generic model for a reasonably current generation racing glider (for instance), though model-specific parameters would of course be better. You're really just trying to filter out the gusts so you may not need anything all that precise to get an improvement, just Cm vs alpha and Cm vs V. Knowing the c.g. and weight will matter as well, but you might be able to calculate these effects.

I wonder if it's easier or harder to use machine learning to do this than a more deterministic least squares model...or if all of the above is overkill.

Andy Blackburn
9B


I agree Andy; to do a Kalman filter you'd want gyros, accelerometers and control position sensors. Position sensors are not hard though and their mapping might be learned for each installation with a smooth air test flight. The problem with Neural network AI is that you have to begin the process with a comprehensive training set. Probably Mike Borgelt has a simpler and better way to get to just the goods that we care about without a plane load of sensors. I'll be looking forward to hearing more about this.


What I know is I don't have a Kalman filter going in my head: but I do have a butt which feels vertical acceleration. If it doesn't tell me I am going up, I discount the screaming vario.

Tom


What I "think" I've learned is that each day the thermals have a feel to them that takes attention to notice and adjust to. That feel also changes during the day. Variables include airmass vertical motion, the way mixing happens with altitude, horizontal gusts, vertical gusts, thermal size,and gradient within the thermal. This seems like a lot of variables to try to roll into a solution.
Horizontal gusts are a big complication and filtering obviously could help many pilots. That said, those gusts are also useful to know what may be coming.
It will be interesting to see what Mike has developed.
UH
  #29  
Old September 5th 18, 03:20 PM posted to rec.aviation.soaring
krasw
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Posts: 435
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keskiviikko 5. syyskuuta 2018 8.22.14 UTC+3 2G kirjoitti:

What I know is I don't have a Kalman filter going in my head: but I do have a butt which feels vertical acceleration. If it doesn't tell me I am going up, I discount the screaming vario.

Tom


The wing transforms horizontal gust into vertical, and your butt gets it wrong.
  #30  
Old September 5th 18, 05:44 PM posted to rec.aviation.soaring
Andy Blackburn[_3_]
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Posts: 507
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On Wednesday, September 5, 2018 at 9:20:53 AM UTC-5, krasw wrote:
keskiviikko 5. syyskuuta 2018 8.22.14 UTC+3 2G kirjoitti:

What I know is I don't have a Kalman filter going in my head: but I do have a butt which feels vertical acceleration. If it doesn't tell me I am going up, I discount the screaming vario.

Tom


The wing transforms horizontal gust into vertical, and your butt gets it wrong.


That's true if you only use your butt and not you're inner ear to sense the pitch rotation. A horizontal gust on the nose excites the phugoid (dCm/dV) and pitches the nose up. Vertical air movement excites the short period (dCm/dalpha) and pitches the nose down. A thermal you can climb in is likely to produce a more prolonged surge than a vertical gust. The exact magnitude of these effects depend on the specific aircraft aerodynamics and things like cg location.

Tom, you may not have a Kalman filter in your head, but you are a neural network - kind of by definition since your brain is made of connected neurons.. Pattern recognition is how we all interpret the "feel" of thermals. It helps a little if you can decompose some of the bigger dynamic effects, but there's a lot going on with lift, gusts and aircraft dynamics - as UH points out. I think a smart vario ought to be able to sort out some of these dynamic interactions better than simple total energy compensation. I figure with cheap gyros and accelerometers they would be doing a lot of this already, but I don't know how far it's gotten.

Again, apologies if I didn't completely accurately describe the engineering of aircraft dynamics. I think this is roughly correct.

Andy
9B
 




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