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Record W4287884926 · doi:10.4271/2022-01-0609

Modeling and Analysis of the Hysteresis Behavior of the Tensioner

2022· article· en· W4287884926 on OpenAlexaff
Haoran Wang, Lixiang Wan, Quanyu Ding, Wen‐Bin Shangguan

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2022
Typearticle
Languageen
FieldEngineering
TopicVibration and Dynamic Analysis
Canadian institutionsMD Precision (Canada)
Fundersnot available
KeywordsHysteresisControl theory (sociology)Computer scienceMaterials sciencePhysicsControl (management)Condensed matter physicsArtificial intelligence

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">The tensioner of the engine front end accessory drive system was taken as a study object, and the mechanical structure and working principle of the automatic tensioner were analyzed. The hysteresis behavior test of tensioner torque-angular displacement was carried out, and the effects of different excitation frequencies and excitation amplitudes on the hysteresis behavior of the tensioner were analyzed. According to the modified Dahl hysteresis model, the model parameters of the tensioner was identified. Based on the identified model parameters, the hysteresis behavior of the tensioner was calculated, and the calculation model accuracy was verified with the tested results. The influence of the hysteresis curve transition area exponent on the tensioner behavior was studied. The dynamic behavior of the engine front end accessory drive system was simulated using the simulation software. The effects of the hysteresis curve transition area exponent on the dynamic behavior of the engine front end accessory drive system were studied under the slow acceleration and the rapid acceleration. The method proposed herein facilitates the predicting the dynamic hysteresis behavior of the tensioner, which has the certain engineering significance for the design and the development of the tensioner and the engine front end accessory drive system.</div></div>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.220
Teacher spread0.209 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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