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Record W2618028574 · doi:10.11159/htff17.164

Impact of Sizing on the Temperature of Automotive Lighting Products

2017· article· en· W2618028574 on OpenAlexvenueno aff
Mustafa Emre Bayraktar, Mehmet Aktaş

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2017
Typearticle
Languageen
FieldMaterials Science
TopicTextile materials and evaluations
Canadian institutionsnot available
Fundersnot available
KeywordsSizingAutomotive industryAutomotive engineeringManufacturing engineeringComputer scienceMaterials scienceProcess engineeringEngineeringChemistryAerospace engineering

Abstract

fetched live from OpenAlex

In this study effect of geometrical size on temperature in automotive lighting units is experimentally and numerically investigated. In the experimental phase a product, which includes a P21W type bulb, with clear polycarbonate (PC) material in rectangular section is used. 5 thermocouples are used for temperature measurements. Numerical investigation is conducted with the same geometry and material properties by ANSYS CFX 12.1. In this investigation flow is assumed to be steady and laminar and 3 dimensional (3D) Navier-Stokes equations are used for calculation. It is observed that numerical results are consistent with experimental measurement in terms of temperatures. In the numerical studies where the effect of geometrical size on temperature is investigated distance between filamenthousing (z1), distance between filamenthousing side surface (y) and distance between filamentlens (x) are examined. It is observed that the greatest temperature decline is observed when z1 is increased. y is the least effective one on temperature among investigated parameters. The greatest temperature drop for lens is obtained by change in x.

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)0.001
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 score0.005
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.015
GPT teacher head0.255
Teacher spread0.241 · 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
Published2017
Admission routes1
Has abstractyes

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