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Record W2905874298

A SELF‐ADAPTIVE ENERGY DISSIPATION STRUCTURE FOR LOW SPEED IMPACT AND PEDESTRIAN PROTECTION

2018· article· en· W2905874298 on OpenAlexfundno aff
James Wu

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2018
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaFord Motor Company
KeywordsDissipationPedestrianEngineeringPhysicsTransport engineering
DOInot available

Abstract

fetched live from OpenAlex

Bumper design regulations for low-speed vehicular impacts (Part 581, Bumper Standard, formerly FMVSS 215) and high-speed pedestrian impacts (ECE 127/GTR 9) possess a contradictory set of requirements. Essentially, an ideal bumper structure should be stiff under low-velocity vehicular impacts while exhibiting a softer response when impacting pedestrians at elevated velocities to satisfy both regulations. A series of energy absorbing structures capable of passively adapting their mechanical responses based on the loading conditions were investigated using finite element modelling tools which were validated to a series of experimental testing observations. The preferred structure consisted of a rectangular array of trapezoidal cells which buckle under single-cell impacts while resisting lateral deformation and artificially increasing the overall stiffness under large area impacts. A geometric study of this dissipater demonstrated the potential to increase total energy absorption by 37.3% by adding thin-walled crossbars at the bases of adjacent cells. Additionally, a parametric study identified a combination between upper cell angle and the ratio between the wall thicknesses as the most critical parameter to consider when tailoring the overall mechanical response.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.951

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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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