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Record W4295096234 · doi:10.14447/jnmes.v25i3.a03

Computational Investigation of Vertical Axis Wind Turbine in Hydrogen Gas Generation Using PEM Electrolysis

2022· article· en· W4295096234 on OpenAlexvenueno aff
Abhishek Agarwal

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2022
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsVertical axis wind turbineComputational fluid dynamicsTurbineWind powerMarine engineeringElectricity generationEnvironmental scienceMechanical engineeringEngineeringPower (physics)Aerospace engineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Both offshore wind and hydrogen generation are increasingly seen as central to global decarbonization. The objective of current research is to investigate the effect of wind turbine height of Vertical Axis Wind Turbine (VAWT) on hydrogen generation. The numerical investigation of VAWT is conducted using techniques of Computational Fluid Dynamics. The VAWT design is developed in Solidworks design software and CFD analysis is conducted using ANSYS CFX software. The CFD analysis conducted on VAWT aided to determine the torque generated from it at 10m/s wind velocity determining the system impacts and ability of electrolyzer technology to accommodate the varying input from wind turbine. The research findings have shown that height of VAWT blade has significant effect on power generation. The power generation from VAWT increases with increase in blade height. The maximum hydrogen mass is generated for 850mm height wind turbine i.e., 2.09Kg. The external wind flow conditions have significant effect on power generation from VAWT and therefore the effect of varying air flow conditions needs to be investigated.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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 designBench or experimental
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

Citations13
Published2022
Admission routes1
Has abstractyes

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