DYNAMIC CALIBRATION OF THE NOSEBOOM SENSORS OF THE FLYING HELICOPTER SIMULATOR
Bibliographic record
Abstract
For modern highly or fully automated helicopters, both the airspeed and the airflow angles have to be \ndetermined as accurately as possible. Research projects dealing with enhancements of in-flight simulation, \nmodel following or automatic trajectory control - among them the current DLR ALLFlight (Assisted Low Level \nFlight and Landing on Unprepared Landing Site) project – need accurate knowledge of the actual helicopter \nstate. Due to rotor downwash, helicopter airspeed measurement in the low speed range via fuselage \nmounted pitot tubes is inherently prone to errors. The EC-135 Flying Helicopter Simulator (FHS) of DLR is \ntherefore equipped with noseboom mounted sensors to enable measurements relatively unperturbed by \nrotor downwash effects. This paper describes the calibration of the pitot system and the airflow angle \nmeasurement vanes of this noseboom. A variant of the Simultaneous Calibration of Aircraft Data System \n(SCADS) technique is applied which uses wind box maneuvers to reduce wind influence during the \ncalibration process. Similar to the flight tests performed at the National Research Council (NRC), Canada, \nposition error correction (PEC) tower flyby maneuvers are used to verify the results obtained via the SCADS \nwind box technique. The calculated velocity independent correction factors obtained from the SCADS \ntechnique are compared to those from classical flight path reconstruction technique.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".