MétaCan
Menu
Back to cohort

Optimal Sizing of a Hybrid Power System for Driving a Passenger Boat in Bangladesh

2020· article· en· W3126447163 on OpenAlexaff
Mohammad Abu Abdullah Al Mehedi, M. Tariq Iqbal

Bibliographic record

Venue2020 IEEE Electric Power and Energy Conference (EPEC) · 2020
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSizingAutomotive engineeringDiesel generatorDiesel fuelRenewable energyBattery (electricity)Environmental scienceBattery capacityFuel efficiencyMarine engineeringPower (physics)Electrical engineeringEngineering

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 transportation 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. 10kWh/day. A boat could be driven by a DC motor using electrical power generated using an onboard PV system, battery, and small-sized generator. The size of the vessel is length 12m, width 4. 8m, depth 0. 5m, and draft height 0. 04m with a carrying capacity of 20passenger for 8hours a day. The designed system consists of a 10.6 kWPV, 1.6kW rated small generator, onboard battery storage consists of 16 batteries, each rated 6V, 240Ah, and a 4SV DC motor rated 5kW300rpm with a speed controller. The paper includes system design details and sizing using HOMER Pro.

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)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.210
Teacher spread0.199 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venue2020 IEEE Electric Power and Energy Conference (EPEC)Same topicHybrid Renewable Energy SystemsFrench-language works237,207