A lumped parameter model of turboprop aircraft operating on gravel runways
Bibliographic record
Abstract
The purpose of an aircraft Takeoff Performance Monitoring System (TOPMS) is to provide to the pilot information pertaining to the level of safety with which a takeoff is proceeding. The authors have developed a theoretical dynamic model to investigate the feasibility of using an observer system during the roll and takeoff phases of aircraft operation to provide to the pilot the information that is needed to manoeuver safely. This model was validated using a prototype device installed in a 19-passenger commercial turboprop aircraft. Unlike previous work in this field, this investigation focussed on various factors that are unique to the far-northern environment. Further, the Global Positioning System(GPS) was proposed as the sole source of kinematic information. This provided the possibility that a TOPMS could be devised that would require no additional ground-based installation. The results of a practical investigation 1 that was conducted to validate the theoretical model and signal processing technique appears in AIAA 2001-4170. The investigation showed that it was possible to predict the displacement of an aircraft to within 15 [m], the length of the test aircraft, in sufficient time to aid the pilot in decision-making.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".