Minimizing Direct Operating Cost for Turbojet and \nTurboprop Aircraft in Cruise
Bibliographic record
Abstract
Canada’s greenhouse gas emissions increased by 20% between the years 1990 and 2014, and \nthe aviation industry is a large contributor to this increase. The optimization of fuel consumption \nis therefore of paramount importance. This thesis focuses on minimizing the direct operating cost \n(DOC) for a cruising turbojet and turboprop aircraft. The DOC is a trade-off of fuel costs and time \ncosts that are related by the cost index CI . By determining DOC-optimal trajectories, aircraft may \nbalance the need to arrive at their target destination in a timely fashion with the need to keep fuel \nemissions low. The main contribution of this thesis is a two-part approach to determining the DOCoptimal \ntrajectories of a cruising turbojet and turboprop aircraft. For a turbojet, the first part of the \nproposed methodology is the derivation of an analytic expression for the optimal speed in terms of \nposition and optimal initial speed, while the second part derives an analytic implicit definition of \nthe optimal initial speed. For a turboprop, the first part of the proposed methodology is concerned \nwith developing a suboptimal approximation for the DOC-optimal speed presented in terms of the \nweight of the aircraft and the optimal final speed. The second part presents a recursive algorithm \nby which the optimal final speed may be obtained. This thesis assumes that the aircraft cruises \nbelow its drag divergence Mach number at constant altitude. Numerical examples will illustrate the \nproposed methodologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".