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Record W2315617554 · doi:10.2514/6.2008-8950

Development of a Landing Drag Chute System for Very Light Jets

2008· article· en· W2315617554 on OpenAlexaff
Randy Thomas, David G. Thomas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsParachute
Fundersnot available
KeywordsRunwayNonStopDragAerospace engineeringAeronauticsSAFEREngineeringAviationJet (fluid)Automotive engineeringMarine engineeringComputer science

Abstract

fetched live from OpenAlex

The recent emergence and proliferation of very light jets will revolutionize the general and corporate aviation markets with new innovative technologies to reduce operational costs, increase efficiency and operational flight capabilities. These jets will be operating from smaller municipal airports with shorter runway lengths. To allow safer operations from shorter runways, light weight high performance landing brake parachute systems have been developed by the author for use in very light jets. This paper describes the design and development of a landing drag chute system for the all carbon fiber construction Viper Jet Mark II aircraft. Design principles and configuration are derived from pervious parachute systems fight qualified for high speed deployments on rockets flown to space and back, as well as design heritage of the Global Flyer jet Drag chute system flown nonstop around the world in 2005 and 2006 by Steve Fossett to set 3 new aviation world records. The design principles and concepts outlined in this paper can be adapted and modified for use in other very light jet aircraft currently on the market.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.177
Teacher spread0.163 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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