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Record W1496810619

Design optimization of the of an automotive thermoacoustic air conditioning system

2007· article· en· W1496810619 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Thermodynamic Systems and Engines
Canadian institutionsConcordia University
Fundersnot available
KeywordsAutomotive industryAir conditioningRefrigerationAutomotive engineeringThermoacousticsPower (physics)EngineeringWaste heatAutomotive engineMechanical engineeringComputer scienceAcousticsAerospace engineeringHeat exchanger
DOInot available

Abstract

fetched live from OpenAlex

A comprehensive algorithm has been developed to design and optimize heat-driven thermoacoustic refrigeration (HDTAR)systems. This algorithm is applied to design and optimize a HDTAR capable of providing 30 w of cooling power at 2°C. The computer code DeltaE is used to simulate the device. The HDTAR has the total length of 1.25 m and the cross sectional area of 0.012m 2 . The results indicate that since HDTAR devices utilize the available waste heat from the automotive engine to cool down the automotive interior, even at relatively low efficiency these devices could still reduce the amount of fuel used in the conventional automotive air conditioning systems.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.349

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)0.0000.000

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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