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Record W2147722726 · doi:10.1504/ijvs.2007.015545

Incremental harmonic balance method for analysis of standard/smart vehicles-to-rigid barrier frontal collision

2007· article· en· W2147722726 on OpenAlexaff
Ahmed Elmarakbi, Jean W. Zu

Bibliographic record

VenueInternational Journal of Vehicle Safety · 2007
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCollisionCrashworthinessCrashDamperOffset (computer science)Harmonic balanceNonlinear systemStructural engineeringEngineeringComputer scienceSimulationControl theory (sociology)Finite element methodControl (management)PhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this paper is to investigate and to enhance crashworthiness of frontal barrier impact using a new idea of crash improvement. Two different types of smart front-end structure are proposed to support the function of the existing vehicle. The work described includes developing and analysing mathematical models of vehicle-to-rigid barrier full and offset frontal collision events for the two types of smart front-end structure. In these models, vehicle components are modelled by lumped masses and nonlinear springs. Moreover, the hydraulic cylinders are represented by non-linear damper elements. In this paper, the dynamic responses of the crash events are obtained with the aid of analytical approach using the Incremental Harmonic Balance Method (IHBM). In addition, the intrusion injury and occupant deceleration are used for interpreting the results. It is demonstrated from simulation results that significant improvements to both intrusion and deceleration injuries are obtained using the smart front-end structures.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
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 head0.020
GPT teacher head0.359
Teacher spread0.338 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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