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Record W2888774724 · doi:10.1109/mesa.2018.8449163

Modeling and Simulation of Hybrid Electric Ships with AC Power Bus - A Case Study

2018· article· en· W2888774724 on OpenAlexafffund
Hubert Hongbo Zhu, Zuomin Dong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaTransport Canada
KeywordsAutomotive engineeringDiesel generatorElectric power systemHybrid powerEnergy storageElectrically powered spacecraft propulsionPower (physics)EngineeringPropulsionComputer scienceDiesel fuel

Abstract

fetched live from OpenAlex

All-electric ships can provide a significant reduction in fuel consumption, maintenance, and emission as well as improved reliability and responsiveness. Today many electrified ships operate on an AC power bus. Due to the complexity of the system, effective modeling and simulation tools are essential for the design, analysis, optimization and evaluation of the hybrid electric propulsion system. This paper discusses a dynamic model of shipboard AC hybrid power system, which is composed of seven major components: three diesel-generator sets, a battery Energy Storage System (ESS), an AC power sources synchronizer and a bi-directional DC/AC power converter, using a ferry ship as the modeling platform. The power converter is modeled as a nonlinear dynamic average-value model to suit system-level studies. Each of component models is parameterized using data sheet provided information from the manufacturers. A rule-based supervisory controller is proposed to coordinate power sharing among diesel-generator sets and the ESS. Using the acquired load profile of the ferry, simulation results obtained using the introduced modeling tool present power sharing solutions among four power sources in four operation modes with voltage and frequency stabilization of the system AC bus.

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 score0.246
Threshold uncertainty score0.242

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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations7
Published2018
Admission routes2
Has abstractyes

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