Determining Airspeed Without a Pitot-Static System for Use in a General Aviation Flight Data Recorder Using an Ardu-Pilot
Bibliographic record
Abstract
The research was to create the necessary algorithms for airspeed determination without the use of a pitot-static system or weather data. The motivation for this study is due to the large cost associated with integrating into aircraft systems and the uncertainty associated with meteorology. The primary piece of hardware used was the Pixhawk or the Ardu-Pilot Mega (APM) 2.5 by Arduino. The Pixhawk and the APM were powered by either the aircraft's 12V power supply or a small battery pack meant for cellphones. Both the Internal logs and the telemetry logs were used in the data acquisition process but focus was placed on the telemetry logs due to ease of data manipulation. The equations used to form the algorithm include the definition of the coefficient of lift, an estimation of the lift-curve slope and various other equations. There were preliminary ground tests performed as well as several flight tests. The flight tests primary consisted of scenic flights and had no aerobatic maneuvers such as stall or spins. The results showed 10% errors on a turbulent day and 5% errors on a low-turbulence day. It is possible that the difference in errors was due to turbulence but further investigation is required. The results show promise in steady flight but more work must be done to apply it to stall scenarios. Flaps were not taken into consideration. As it currently stands, the airspeed algorithm would be a good addition to a fuel burn estimation application located on an electronic flight bag.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".