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Record W1988058638 · doi:10.1115/detc2010-28375

Optimization of Asymmetric Damper Parameters of an Automotive Suspension for Minimal Camber Angle Variations

2010· article· en· W1988058638 on OpenAlexaff
Krishna Prasad Balike, Subhash Rakheja, Ion Stiharu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVibration Control and Rheological Fluids
Canadian institutionsConcordia University
Fundersnot available
KeywordsDamperStructural engineeringJackingKinematicsControl theory (sociology)Suspension (topology)EngineeringCamber (aerodynamics)Automotive engineeringComputer scienceMathematicsPhysics

Abstract

fetched live from OpenAlex

Asymmetric dampers invariably employed in automotive suspensions are known to cause ‘damper jacking’. The influence of the damper jacking on the suspension kinematic responses, particularly variations in the camber angle, are generally ignored while synthesizing a damper. This study presents influences of damper asymmetry on the camber angle variations of a double wishbone type of suspension together with the dynamic responses under measured urban road inputs. Simulation studies employing a kineto-dynamic quarter-car model comprising a bilinear damper revealed increase in the camber angle variations with an increase in the damper asymmetry, while this increment showed nonlinear relation with the suspension deflection. This study further investigates synthesis of an optimal two-stage asymmetric damper to yield a compromise between the conflicting performance measures. A composite performance index comprising the ride comfort and road holding measures with limit constraint on camber angle variation is formulated to seek optimal damper parameters. The results are presented so as to yield design guidance for synthesis of asymmetric dampers.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.212

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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