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Record W3043497792 · doi:10.1155/2020/5340814

A Simulation Model for a Hybrid-Electric Craft in Restricted Waters

2020· article· en· W3043497792 on OpenAlexvenueno aff
Francesco Mauro, Ubaldo la Monaca, Alberto Marinò, Vittorio Bucci

Bibliographic record

VenueJournal of Advanced Transportation · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
FundersEuropean Regional Development FundRegione Autonoma Friuli Venezia Giulia
KeywordsCraftPropulsionMarine engineeringWork (physics)EngineeringElectrically powered spacecraft propulsionTransport engineeringEnvironmental scienceSimulationAutomotive engineeringAerospace engineeringMechanical engineering

Abstract

fetched live from OpenAlex

Sustainable mobility is one of the most challenging issues for passenger transport inside environmental protected areas and ecologically fragile environments. To reduce the pollutant emissions, the adoption of electric or hybrid-electric solutions for crafts propulsion is a suitable option for green navigation. However, the operation in restricted basin leads also to specific critical issues for the vessel sailing, as dealing with shallow or restricted waters and transit under low air-gap bridges. The combination of these constraints with the adoption of a hybrid-electric propulsion system increases the design difficulties also for a small craft, requiring the use of advanced simulation models to assess the vessel performances. This work presents a simulation model for a small passenger craft that will operate in the Grado Lagoon. The model combines the hydrodynamic issues of manoeuvring and propulsion in restricted water with the simulation of the electric loads and capacity of the energy storage system installed onboard. The simulations performed with the developed simulation system are in accordance with data measured during trials on a prototype of the vessel. The developed model is a powerful tool for designers in order to rapidly assess the green capabilities of new projects since the early design stages.

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 score0.223
Threshold uncertainty score0.323

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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