Bibliographic record
Abstract
The past decade has seen significant advancements in modelling and simulation of the dynamic interface. The goal of the initial work in this area was to reduce the costs associated with first-of-class flight trials, and to deal with the backlog of aircraft-ship combinations for which flight-clearance envelopes were minimal or non-existent. A decade ago, piloted simulation of the dynamic interface appeared to be the obvious way to overcome these deficiencies. Validated models of fixed-wing and rotorcraft were in existence, and work began to combine these models with prescribed weather/lighting conditions (wind, rain, snow, fog, night, etc.), ship visuals, and motion. It had been envisioned that through the use of high-fidelity flight simulation, a test pilot could rapidly and safely determine the flight envelope boundaries without resorting to, or at least minimizing, flight trials. During the past decade, significant advancements in simulation fidelity did transpire due to increased computational power, an improved understanding of airwakes, and enhanced simulation capabilities. The article describes some of the fundamental and applied research that contributed to the improved fidelity, much of it gained in a collaborative fashion. To date, modelling and simulation technologies have not advanced to the state where they can replace flight tests to derive flight-clearance envelopes, but they have approached the point where they can augment flight tests and serve in a training capacity. The accrual of a training benefit has recently emerged and is a significant, though unplanned, dividend from the efforts directed towards flight-envelope prediction. This article sets out to examine some of the strengths and deficiencies of the current capabilities, and provides a discussion of the way forward. Modelling and simulation of the dynamic interface are discussed in a broad context, wherein they are defined to include non-piloted, non-real-time activities. The article will provide a critical review of many of these efforts to date, focusing primarily on aerodynamic issues. The article also discusses the challenges which are present for rotary-wing operations, for both small and large ships. It compares the environment in both cases and how that impacts the simulation requirements.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".