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Record W2019506359 · doi:10.4271/2013-01-0856

Prediction of HVAC System Aero/Acoustic Noise Generation and Propagation using CFD

2013· article· en· W2019506359 on OpenAlexaff
Mayur Sah, Kumar Srinivasan, Fred Mendonça, Nachiket Pai

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2013
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsChrysler (Canada)
Fundersnot available
KeywordsComputational fluid dynamicsHVACAcousticsNoise (video)Computer scienceAeroacousticsAerospace engineeringEngineeringPhysicsMechanical engineeringArtificial intelligenceAir conditioningSound pressure

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">With the advent of quieter powertrain and improved cabin acoustic sealing, there is an increased focus on noise generated in the HVAC unit and climate control ducting system. With improved insulation from exterior noise sources such as wind & road noise, HVAC noise is more perceptible by the occupants and is a key quality indicator for new generation vehicles. This has increased the use of simulations tools to predict HVAC noise during the virtual development phase of new vehicle programs. With packaging space being premium under the instrument panel, changes to address noise issues are expensive and often impractical. The current methodology includes the best practices in simulation accumulated from prior aero acoustics validation studies on fans, ducts, flaps and plenum volume discharge. The paper details the acoustic noise generation and propagation in the near field downstream of an automotive HVAC unit in conjunction with ducting system. The simulation process is based on Detached Eddy Simulation (DES) with compressibility. The overall sound pressure level at a nearfield cabin location is compared with test data. Good agreement was achieved between simulation and test results.</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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.206
Teacher spread0.193 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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