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Record W3127266915 · doi:10.24018/ejece.2021.5.1.294

Optimal Design, Dynamic Modeling and Analysis of a Hybrid Power System for a Catamarans Boat in Bangladesh

2021· article· en· W3127266915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAutomotive engineeringDiesel generatorDiesel fuelRenewable energySizingBattery (electricity)Environmental scienceMarine engineeringEngineeringPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

Bangladesh is a land of rivers, canals, and lakes where water transportation is an essential means of transport. The country use boats as one of the leading resources of a carrier in its widespread inland waterways. Most of the currently used boats use only diesel for fuel. Appropriate use of renewable energy sources, particularly solar energy with diesel generators, could reduce diesel consumption. In this paper, a typical boat energy requirement was calculated to be 42.10 kWh/day. A boat could be driven by a DC motor using electrical power generated using an onboard PV system, battery, and a small generator. The carrying capacity of the vessel is 20passenger for 8hours a day. The designed system consists of a 10.6 kW PV, 1.6kW rated small gas generator, onboard battery storage consists of 16 batteries, each placed 6V, 333 Ah, and a 48 V DC motor rated 5 kW 3000 rpm with a speed controller. The paper includes system design details and sizing using HOMER Pro and dynamic simulation using MATLAB Simulink.

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.

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)0.001
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: none
Teacher disagreement score0.487
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.182
Teacher spread0.176 · 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