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Record W3147729832 · doi:10.22215/etd/2013-10027

Development of a Spacial Dynamic Handling and Securing Model for Shipboard Helicopters

2013· dissertation· en· W3147729832 on OpenAlexaff
Michael Leveille

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicVehicle Dynamics and Control Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeckMarine engineeringEngineeringRange (aeronautics)Aerospace engineeringShip motionsTraverseCockpitSimulationStructural engineeringHullGeologyGeodesy

Abstract

fetched live from OpenAlex

Maritime helicopters have the ability to greatly increase the range of influence and utility of naval vessels.For smaller ships in rough seas, flight deck motion becomes considerable and additional infrastructure is required for securing and traversing helicopters while on-deck.The current state-of-the-art in dynamic modelling of the on-deck helicopter/ship dynamic interface includes a fully spacial securing simulation and one capable of modelling planar traversing and manoeuvring operations.A fully spacial securing and manoeuvring simulation named SSMASH (Spacial Securing and Manoeuvring Analysis for Shipboard Helicopters) has been developed to provide complete analysis capability of the on-deck helicopter/ship dynamic interface.Specifically, a new capability to model helicopter response to manoeuvring events in the presence of flight deck motion is realized.The SSMASH simulation is modeled with twelve degrees-of-freedom and mass coupling between the helicopter body and wheel carriages.A five degree-of-freedom tire model is used to extend the model capability to include ground handling characteristics.The model has been validated against the state-of-the-art securing simulation Dynaface R and experimental data from land-based manoeuvring trials.Excellent correlation with the Dynaface R simulation is achieved, while good correlation with the experimental data is observed as well.Improvements to the modelling of grip-limited tire behaviour would likely improve agreement with experimental helicopter responses for some manoeuvres.iii Dedicated to my family, for teaching me that anything is possible.I would also like to thank my fellow researchers of the Applied Dynamics Group.Whether it meant reviewing and critiquing my modeling approach, troubleshooting coding problems, sharing L A T E X knowledge, or simply lending an ear, they were always available and willing to help.I wish you all the best for your own research efforts and careers.The relentless support, encouragement, and patience from my family and the love of my life, Natasha Skanes, has helped me work through the long nights and difficult times.I am grateful to have such people in my life.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations2
Published2013
Admission routes1
Has abstractyes

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