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New Laser Based Method for Non-Intrusive Measurement of Available Energy Loss and Local Entropy Generation

2004· article· en· W3010506460 on OpenAlexaff
G.F. Naterer, O. B. Adeyinka

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsUniversity of ManitobaMemorial University of Newfoundland
Fundersnot available
KeywordsParticle image velocimetryDissipationTurbomachineryMechanicsEntropy (arrow of time)Entropy productionThermalRangingThermal energyMaterials scienceComputer scienceEnvironmental sciencePhysicsStatistical physicsMeteorologyThermodynamicsTurbulence

Abstract

fetched live from OpenAlex

This paper outlines a new experimental technique for measuring the instantaneous entropy production using a non-intrusive, laser based approach. Unlike point-wise methods which yield measured velocities at single points in space, the method of PIV (Particle Image Velocimetry) is used to derive the spatial velocity gradients over the entire problem domain. When combined with local temperatures and thermal irreversibilities, these velocity gradients can be used to determine the energy availability loss due to exergy destruction. This article focuses on frictional effects, which lead to irreversible degradation of mechanical energy into internal energy through viscous dissipation. Frictional effects are significant in various devices, ranging from power turbines, internal flow systems and other turbomachinery. In addition to measured data for validation of predictive models, such data can be used by designers to characterize and improve the energy efficiency of engineering devices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score0.298

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 head0.021
GPT teacher head0.226
Teacher spread0.205 · 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
GenreMethods

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
Published2004
Admission routes1
Has abstractyes

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