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Record W6908643846 · doi:10.26083/tuprints-00021692

Noise Source Localisation in a Low Speed Automotive Engine Cooling Module

2022· article· en· W6908643846 on OpenAlexaboutno aff

Bibliographic record

VenueTUbilio (Technical University of Darmstadt) · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsnot available
Fundersnot available
KeywordsNoise (video)Rotor (electric)Distortion (music)Mechanical fanJet engineAnnulus (botany)Plenum spaceCoolantHeat exchangerAutomotive engine

Abstract

fetched live from OpenAlex

The sound generated by rotating machines is a topic that is more and more considered. In fact, this noise is an environmental concern in many industrial fields. The rotating machine studied here is an automotive engine cooling fan system. The noise produced by these fans is a major contributor to the total radiated noise and in certain cases it dominates other sources like the engine or the tires. Usually, the fan is extracting the air through a heat exchanger where a coolant fluid is circulating through a network of tubing. The heat exchanger is usually located close to the fan on its suction side. This creates a turbulent flow and participates to the generation of broadband noise. As the module is located in the engine bay, the upstream flow is distorted which leads to a strong tonal noise contribution. The impact of those distortion on the blades or struts generates tonal noise. The article aims to evaluate the contribution of acoustic sources located on the rotor blades. The source localization for rotating machine should take into account the Doppler effect as the sources are rotating with the fan. Classical beamforming works but it leads to continuous annulus sources. Sitjsma et al. 1 propose to solve this problem by using a transfer function for a moving monopole source in uniform flow to reconstruct the signal in the time domain and develop an algorithm able to deal with rotating sources, Autord ROtating Source Identifier (ROSI). Using this method, the measurement of the sound pressure of rotating sources can be done using an array of microphones. The proper implementation of the method was verified using the benchmark from 2. In the present study, a 60-microphones logarithmic array was used and the reconstruction of sound pressure level was done in third octave bands. Measurements were done in different rotational velocity of the fan in the anechoic chamber of Sherbrooke's university (UdeS) without any other flow restriction than the heat exchanger itself. A measurement was done at full RPM with and without heat exchanger to compare if it is transparent acoustically. The Directivity measurements were conducted as well. Both method has been compared to the central microphone of the microphone array. The present paper is a part of a collaborative project between UdeS and Von Karman Institute (VKI). The major aim is to conduct an experimental investigation of the fan noise in two different facilities and to compare the results. 1 P, Sijtsma and S, Oerlemans and H, Holthusen. "Location of rotating sources by phased array measurements'', AIAA 2001-2167, 2001 2 Herold, G. "Microphone Array Benchmark b11: rotating points sources'', 2017

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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)0.0020.000

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 head0.007
GPT teacher head0.164
Teacher spread0.157 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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