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Record W4285084004 · doi:10.1142/s0219455423500232

Comprehensive Crashworthiness Studies of Novel Alternately-Assembled Multi-Frusta Structures for Energy Absorption Applications

2022· article· en· W4285084004 on OpenAlexaff
Weiren Zhang, Fan Yang, Siyuan Zhang, Yijie Bian, Peng Wang, S. A. Meguid

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2022
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversity of Toronto
FundersState Key Laboratory of Mechanics and Control of Mechanical Structures
KeywordsFrustumCrashworthinessMaterials scienceAbsorption (acoustics)Square (algebra)Composite materialStructural engineeringFinite element methodMechanical engineeringEngineeringGeometry

Abstract

fetched live from OpenAlex

In this paper, we propose a new multicellular design of assembled multi-frusta with alternate orientations and varied taper angles for effective energy absorption applications. Extensive crush test experiments, comprehensive finite-element simulations and analytical modeling were carried out to evaluate the energy absorption performance of the newly proposed multicellular assemblies. The performances of these assemblies are calibrated against conventional assembly of uniform tubes. Two aspects of the work were parametrically examined. The first was concerned with the effect of the frusta taper angle, while the second was concerned with the multi-frusta layout on the energy absorption of the proposed multi-frusta assembled structures. The results reveal that the face centered square layout with an appropriately selected taper angle possesses the optimal high specific energy absorption, low peak force, and smooth crushing force curve, which makes it a crashworthy device.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.030
GPT teacher head0.294
Teacher spread0.263 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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