Vario Comparison

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.

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.

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.

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

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.

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

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

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.

On Wednesday, 5 September 2018 15:22:14 UTC+10, 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

Actually what you have going on in your head is a pretty good stab at a Kalman filter. You are weighting the vario reading vs your backside and other cues to arrive at what you think is happening. It is workload intensive and all too often fails.

Those talking about about the effects of gusts should read my horizontal gust article on the website.
Very small horizontal velocity gradients cause large signals on a normal TE variometer. I give some examples there. The effect depends on the square of the True Air Speed so in South Africa, Australia and the western US where you may be at high altitude and cruising at 100KIAS + your TAS can be in the 120 to 140 KTAS range.
It is just as well gliders don't cruise at 200KTAS because the normal TE vario would be uselessly and apparently randomly moving between the stops.

When a glider enters a thermal the air coming from below changes the direction of the relative wind which increases the angle of attack which increases the lift and the glider starts going up. On entering strong thermals pitch stability of the glider will tend to maintain the trimmed AoA, hence the glider will tend to pitch nose down. The effect is short lived as the time constant of the response to vertical air changes is short. It depends on airspeed, wing loading and the slope of the lift curve of the wing. With modern gliders it is around 0.4 to 0.5 seconds at low speeds and around 0.2 to 0.25 seconds at high speeds.
I had to derive this and I later found the derivation in a book called "Airplane Response to Atmospheric Turbulence" by John C. Houbolt. Yep, that guy - the one who pushed the Lunar Orbit Rendevous for Apollo.
Now the tendency of a airplane to pitch nose down on entering rising air (which can momentarily stall the airplane if the lift is strong enough) can be a really GREAT way to kill yourself because as nearly everyone has been taught to fly attitude your first reaction is to pull the stick back to maintain the attitude. If the wing was stalled or nearly so you are now stalling or pulling deeper in to the stall. Do this while turning final with what looks like adequate airspeed and you could find yourself on the ground short of the runway wondering what just happened if you live through it. The same of course applies to thermalling at low altitude. Remember the stick controls angle of attack and in very short term vertical velocity changes in the atmosphere also change AoA. Anything else it apparently does is a consequence of the angle of attack change.

Mike Borgelt

Borgelt Instruments