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Record W1633148336 · doi:10.4271/2001-01-3211

Development of the MADYMO CRABI-6 Dummy Head Model for Side Impact Simulation

2001· article· en· W1633148336 on OpenAlexaff
Michał Woźniak, Shankar Kalyanasundaram, Craig Newland, Mark Terrell

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2001
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsTransport Canada
Fundersnot available
KeywordsHead (geology)Side impactComputer scienceSimulationEngineering drawingEngineeringStructural engineeringGeology

Abstract

fetched live from OpenAlex

This paper describes the development of a validated MADYMO CRABI-6 dummy model for the purpose of simulating lateral impact to the CRABI-6 dummy. An investigation of the current CRABI-6 model revealed that the contact interaction properties of its head are significantly flawed. Without further development, such a model produces incorrect injury values and flawed dummy dynamics. Laboratory tests to characterise the lateral dummy head response were designed and conducted. Using mathematical methods, a MADYMO model was developed to simulate the laboratory tests and validated against laboratory results. Both injury prediction and time-history responses were used as a basis for comparison. Good correlation between the computer model and laboratory tests was obtained, resulting in a model capable of predicting side impact injury responses for the CRABI-6 dummy.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.042
GPT teacher head0.329
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2001
Admission routes1
Has abstractyes

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