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Record W3137761135 · doi:10.18280/jesa.540111

Fabrication and Working of a Compressed Air Vehicle

2021· article· en· W3137761135 on OpenAlexvenueno aff
Sampath Suranjan Salins, Asrar Ali Khan, Khaled Riyaz, Ismail Sami Mahmoud, Syed Naeem, Krishnamurthy H. Sachidananda

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2021
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsCompressed airAutomotive engineeringCompressed natural gasAutomotive industryVolume (thermodynamics)Power (physics)Bar (unit)Work (physics)Environmental scienceComputer scienceEngineeringMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The automotive industries are looking for many ways in order to reduce the weight of the vehicle. Also, light vehicles are better at handling and also efficiency of the vehicle is more. Heavy weighted vehicles as compared to light weighted vehicles produce a large amount of harmful gases like CO2 and SO2. There are many alternatives in order to reduce these harmful gases. One such alternative is using compressed air to power the vehicle. So, in this research work the main aim of the project is to design, fabricate and test a single seated vehicle that runs on an alternative source of energy, specifically compressed air. A pneumatic rotary engine having a maximum pressure of 7 bar is used in designing the above air compressed vehicle. In order to achieve speed of 30-40 Km/hr, two air cylinders are used in this design. From this study it can be concluded that using less dense and higher volume tank it is possible to increase the output velocity of the vehicle. The findings of this research can be used for further development of air compressed vehicle.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.241
Teacher spread0.220 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueJournal Européen des Systèmes AutomatisésSame topicHybrid Renewable Energy SystemsFrench-language works237,207