Vertical flight path segments sets for aircraft flight plan prediction and optimisation
Bibliographic record
Abstract
ABSTRACT The paper presents a method for constructing a set of vertical flight path segments, that would compose an aircraft's vertical flight envelope, by using an aircraft performance model. This method is intended to be used for aircraft flight plan prediction and optimisation algorithms. The goal is to reduce the volume of recurring segment performance computations currently required for flight plan prediction or optimisation. The method presented in this paper applies to a free-flight scenario. The flight-path segments composing the vertical flight envelope belong to one of the unrestricted climb, constant-speed level flight, step-climb and continuous descent segments, performed at the consigned climb, cruise and descent speed schedules and at the consigned air temperature values. The method employs an aircraft model using linear interpolation tables. Nine test scenarios were utilised to assess the performances of the resulting flight envelopes as a function of the number of cruise altitudes and descent flight paths. The set of evaluated performance parameters includes the range of total flight times and still-air flight distances, and the vertical profiles describing the minimum and maximum flight times, and still-air flight distances. The advantages of the proposed method are multiple. First, it eliminates the need for repetitive aircraft performance computations of identical vertical flight plan segments, and provides the means for quick retrieval of the corresponding performance data for use in the construction of a full flight plan. Second, the vertical flight path look-up structure and the vertical flight-path graph describe a set of vertical flight paths that consider an aircraft's and flight plan's configuration parameters and cover its maximum flight envelope. Third, the look-up structure and the graph provide the means for rapid and clear identification of the available options for constructing a flight-plan segment, as well as for detecting the points associated with changes in the flight phases, including climb, cruise, step-climb and descent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".