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Record W2218815957 · doi:10.1115/1.4031459

Optimizing Tire Vertical Stiffness Based on Ride, Handling, Performance, and Fuel Consumption Criteria

2015· article· en· W2218815957 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFuel efficiencyStiffnessAutomotive engineeringVehicle dynamicsSuspension (topology)Automobile handlingEngineeringComputer scienceStructural engineeringMathematics

Abstract

fetched live from OpenAlex

Researchers mostly focus on the role of suspension system characteristics on vehicle dynamics. However tire characteristics are also influential on the vehicle dynamics behavior. In this paper, the effects of tire vertical stiffness on the ride, handling, accelerating/braking performance, and fuel consumption of a vehicle are analytically investigated. Furthermore, a method for determining the optimum vertical stiffness of tires is presented. For these purposes, first an appropriate mathematical criterion for the ride, handling, accelerating/braking performance, and fuel consumption is developed. Next, to achieve the optimum tire characteristic, a performance index, which contains all of the above-mentioned criteria, is defined and optimized. In the proposed performance index, the tire vertical stiffness is a design variable and its optimization provides a compromise among ride, handling, accelerating/braking performance, and fuel consumption of the vehicle. Last, the analytical optimization results are confirmed by performing precise numerical simulations.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.255
Teacher spread0.216 · 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