Innovative moisture/icing-resistant flush air data system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bombardier Aerospace contracted the Flight Research Laboratory of the National Research Council of Canada (NRC) to develop a Flush Air Data System (FADS) capable of operation following transit through adverse weather conditions for use on Bombardier's test aircraft. The NRC's existing FADS design was modified to incorporate water traps at the inlet of each of the four pressure ports to prevent moisture ingestion into the pressure lines. A heating system was designed to reduce moisture condensation in the pressure lines. After fabrication of the final prototype was completed, experimental bench tests were performed to demonstrate that the FADS had met the performance requirements for flight through adverse conditions and that the system was safe for flight. The FADS was then sent to Bombardier Flight Test Centre and installed on a Bombardier Global 5000 aircraft for flight testing. To evaluate the FADS performance, manoeuvres were performed where the FADS was exposed to adverse weather conditions; the FADS angle of attack and angle of sideslip measurements were unaffected by these conditions during the tests. The FADS was calibrated using NRC's GPS-based Simultaneous Calibration of Air Data Systems (SCADS) technique by developing angle of attack and angle of sideslip calibration coefficients. The calibration coefficients were then validated across the aircraft's flight envelope and weather requirements. From the results of the bench tests and flight tests, it was concluded that the new FADS was able to measure angles of attack and sideslip after flight through adverse weather conditions accurately. © NRC Canada 2012.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it