MétaCan
Menu
Back to cohort

Modelling out-of-plane hydrodynamic hysteresis in horizontal submarine maneuvers with indicial responses

2025· article· en· W4412480831 on OpenAlexafffund
C.J. Marshall, Tiger Jeans, Andrew G. Gerber, R. Doyle

Bibliographic record

VenueOcean Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsDefence Research and Development CanadaUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaDefence Research and Development CanadaUniversity of New BrunswickNorth Atlantic Treaty Organization
KeywordsSubmarinePlane (geometry)HysteresisHorizontal planePhysicsMechanicsGeologyAerospace engineeringMarine engineeringGeodesyEngineeringGeometryMathematicsCondensed matter physics

Abstract

fetched live from OpenAlex

Predicting the transient hydrodynamic loads in submarine maneuvering simulations has traditionally been limited to quasi-steady modelling assumptions which are not well-founded in extreme maneuvering scenarios. Recent studies have shown that the time-history of the wake is significant for the out-of-plane hydrodynamics of a submarine in horizontal maneuvers. Despite efforts of modelling these effects with integral-type impulse response methods, known within the vehicle dynamics community as indicial theory, the complex separation and development of the wake surrounding the hull causes significant modelling errors in high frequency oscillation maneuvers. This study investigates characteristics of the time-domain hydrodynamic response of an SSK-class submarine, and a corresponding reduced-order model implementation. Model predictions for a set of small and large amplitude drift oscillation maneuvers are used to demonstrate the shortcomings of the current model formulation. A revised model designed to modify the high-frequency response of the vehicle is shown to significantly improve the qualitative and quantitative performances of the model in large amplitude oscillations.

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 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 score0.134
Threshold uncertainty score0.906

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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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 teacher head, 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueOcean EngineeringSame topicComputational Fluid Dynamics and AerodynamicsFrench-language works237,207