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Record W2017778661 · doi:10.1243/095441005x30315

On Aerodynamic Modelling and Simulation of the Dynamic Interface

2005· article· en· W2017778661 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · 2005
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFlight envelopeFidelityFlight simulatorEnvelope (radar)Flight dynamicsComputer scienceSimulationAerodynamicsInterface (matter)Flight planningAeronauticsAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.203
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it