MétaCan
Menu
Back to cohort
Record W1966222528 · doi:10.1109/mesa.2014.6935601

Modeling and simulation of a hybrid electric propulsion system of a green ship

2014· article· en· W1966222528 on OpenAlexafffund
Tiffany Jaster, Andrew Rowe, Zuomin Dong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversity of Victoria
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsPropulsionElectrically powered spacecraft propulsionPropellerController (irrigation)DSPACEAutomotive engineeringEngineeringMATLABControl engineeringComputer scienceMarine engineeringAerospace engineering

Abstract

fetched live from OpenAlex

In this work, the hybrid electric propulsion system of a marine vehicle was modeled in MATLAB Simulink and SimPowerSystems. Models of each of the propulsion components were developed and incorporated into a complete system propulsion model. A rule-based supervisory mode controller was constructed which specifies the combination of onboard power sources to be used throughout the mission cycle. The hybrid electric propulsion and control model was simulated on a dSPACE hardware-in-the-loop platform. For each simulation, the energy storage system state of charge, HEV mode, propeller motor drive speed set point, and hotel load were specified. This study forms the foundation for further research in ship hybrid electric propulsion system design and power management.

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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.206
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

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

Citations21
Published2014
Admission routes2
Has abstractyes

Explore more

Same topicMaritime Transport Emissions and EfficiencyFrench-language works237,207