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Record W6976591792 · doi:10.60692/emnd5-gd916

DEVELOPMENT OF ACTIVE AIR SUSPENSION SYSTEM FOR SMALL AGRICULTURAL VEHICLES

2020· article· en· W6976591792 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAir suspensionAir compressorSuspension (topology)Piston (optics)Ride qualityAxleTrailerGas compressorTractor

Abstract

fetched live from OpenAlex

Air ride suspension carries the load on each axle with a pressurized air bag just as a high pressure balloon.This system provides the smoothest and most shock free ride of any of the known vehicle suspension system.An air suspension includes a multiple air spring assemblies that each includes a piston airbag and a primary airbag mounted over the piston airbag.The primary and piston airbags each have a variable volume that is controlled independently of the other for active suspension control.The system automatically adjusts air pressure in the air bag so that the trailer always rides at the same height, whether lightly loaded or heavily loaded.The higher air bag pressure associated with higher trailer loads automatically provides a stiffer suspension which is required for a smooth ride.The lower air bag pressure for lightly loaded conditions automatically provides for a softer suspension, thus providing the same ride quality for all trailer loading conditions.Since each axle is independently supported by its own air bag, the air ride suspension is known as fully independent suspension system.The automatic control of the air bag pressure is accomplished by a solid state electronic control system specifically designed and packaged for vehicle use.This system continuously checks the ride height of the suspension and accordingly increases air pressure if the ride height is too low, by turning 'ON' an on-board air compressor.The air compressor stops automatically when the proper ride height is reached.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.356

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 head0.051
GPT teacher head0.207
Teacher spread0.156 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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