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Record W2084266732 · doi:10.1177/146442070421800105

Finite element analysis of the design of an elastomeric foam seal for an automotive air filter

2004· article· en· W2084266732 on OpenAlexaff
Elizabeth Azhikannickal, Peter Wild

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part L Journal of Materials Design and Applications · 2004
Typearticle
Languageen
FieldEngineering
TopicAsphalt Pavement Performance Evaluation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSeal (emblem)TearingFinite element methodAutomotive industryClosure (psychology)Filter (signal processing)Materials scienceService lifeBeadMechanical engineeringStructural engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

A polyurethane foam sealing bead used in an automotive air filter is examined in this study. The sealing bead provides a seal between the air filter and the two components of the rigid split air filter housing. Tearing of the seal during service is a common mode of failure. Finite element analysis is used to identify the high strain regions in the seal at full closure of the housing that may result in tearing. Alternate sealing bead designs are developed and modelled in which failure is less likely to occur. Critical seal/housing design issues are identified, such as the maximum strains due to closure of the housing, closure forces and effects and manufacturing tolerances on seal performance. Key trade-offs between these design issues are also identified.

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)0.001
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: none
Teacher disagreement score0.637
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.259
Teacher spread0.231 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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