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Record W4398766834 · doi:10.1201/9781003450252-112

Design, analysis and simulation of a hydro-pneumatic suspension system for a quarter car model with two degrees of freedom

2024· book-chapter· en· W4398766834 on OpenAlexaboutno aff
Kaushal Kedar, Prem Narayan Vishwakarma, Ajay Sharma

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Car modelSuspension (topology)Degrees of freedom (physics and chemistry)EngineeringAutomotive engineeringComputer scienceMathematicsPhysicsGeography

Abstract

fetched live from OpenAlex

Hydro pneumatic suspension enables smooth suspension as the hydraulic fluid to the bladder containing the dinitrogen gas transmits the force due to uneven road. In the present work, design has been developed in such a manner that the isolation is possible to maximum extent, as four chambers of hydro pneumatic systems have been incorporated for each wheel. The dimensions have been fixed with respect to the already existing accumulator. The spacing of four chambers is done keeping in mind the same dimension so that more space is not required. Chambers of hydro pneumatic systems have been incorporated for each wheel for which the piston rod has been designed such that the impact of uneven road will be shared among the four chambers equally for individual wheel and increasing the smoothness of the suspension. Following the development of the mathematical modelling, the differential equations of motion are obtained from the model. The simulation for the quarter automobile model has two degrees of freedom. The findings illustrate how the displacement of both the sprung and the unsprung mass varies with regard to the passage of time.

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: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0090.001

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 head0.023
GPT teacher head0.225
Teacher spread0.203 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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