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Record W2004831265 · doi:10.4271/2015-01-0637

Simulation of Vehicle Pothole Test and Techniques Used

2015· article· en· W2004831265 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

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldEngineering
TopicTransportation Safety and Impact Analysis
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsPothole (geology)Computer scienceTest (biology)SimulationGeology

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">During the service life, the impacts of vehicle against potholes result in damage for the wheel and suspension components. Knowing the internal forces generated in the suspension components during this event would helpful to design the critical components. Measurement of these loads in physical test is more costly and not feasible for new designs. There are several virtual tools and methods available to predict the loads during this event. Using the ABAQUS FE solver, the non-linear dynamic behavior could be captured accurately during the impact. The tire model plays an important role during this event by absorbing energy during the impact. The CAE tire model is validated with some physical tests results and it is used in the vehicle pothole impact simulation. In vehicle pothole physical test, the force and acceleration measurement are taken and compared with the CAE results. The effect of the tire pressure variations and the vehicle speed at pothole impact is also studied. This paper explains more about the modeling of tires, vehicle, simulation methodology and the correlation of the CAE results against the test values.</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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.257
Teacher spread0.239 · 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