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Record W4282982932 · doi:10.4271/2022-01-0935

Study of Installation Effects on Automotive Cooling Fan Noise

2022· article· en· W4282982932 on OpenAlexaff
Yidan Lai, Chenyang Weng, Yu-Yu Lu, Mikael Karlsson, Mats Åbom, Magnus Knutsson

Bibliographic record

VenueSAE International Journal of Advances and Current Practices in Mobility · 2022
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsVolvo (Canada)
Fundersnot available
KeywordsMechanical fanNoise (video)Automotive engineeringAutomotive industryFan coil unitEngineeringAcousticsMechanical engineeringComputer scienceAerospace engineeringHeat exchangerPhysics

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">Vehicle electrification is one of the biggest trends in the automotive industry. Without the presence of combustion engine, which is the main noise source on conventional vehicles, noise from other components becomes more perceivable; among these components, the cooling fan is one of the major noise sources, especially during battery charging. The design of cooling fan modules is usually carried out in the early stage before building prototype vehicles. Therefore, understanding the installation effects of the cooling fan on the radiated sound is essential to secure good customer satisfaction. In this study, three different measurement setups of cooling fans are carried out: free field, wall mounted, and in-vehicle measurement. Four cooling fan prototypes with different fan blade designs are used in each measurement. Correlations of these measurements are investigated through comparisons of the measurement results. The installation effects are identified through spectrum difference between free field and in-vehicle measurement. A spectral decomposition method is implemented to enable the separation of source strength and propagation effect in the results. One variable is introduced to represent the installation effects of vehicles tested in the present study.</div></div>

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.289

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 communication0.0000.001
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 head0.019
GPT teacher head0.339
Teacher spread0.320 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueSAE International Journal of Advances and Current Practices in MobilitySame topicVehicle Noise and Vibration ControlFrench-language works237,207