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Record W4401824690 · doi:10.18280/i2m.230403

Ultrasonic Spike Signal Analysis for Subcooled Boiling Condensation Measurement

2024· article· fr· W4401824690 on OpenAlexvenueno aff
Thang Nguyen

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2024
Typearticle
Languagefr
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSubcoolingBoilingSpike (software development)CondensationSIGNAL (programming language)Ultrasonic sensorAcousticsMaterials scienceThermodynamicsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Condensation is a key factor in subcooled boiling, significantly affecting heat and mass transfer, which are critical in many industrial processes.In a previous study, we successfully obtained the condensation rate -the collapsing speed of vapor bubbles during subcooled boiling using the ultrasonic velocity profile method -UVP method, employing two primary ultrasonic frequencies.The ultrasonic sensor was driven by sinusoidal electrical burst signals, also called tone-bursts, with a specified center frequency.This study examines the application of spike excitation signals within the same method.Spike signals, commonly produced by pulsers, i.e. pulse generators, in the ultrasonic testing field, offer the potential to enhance the accuracy of the UVP method while can reduce the cost of the measurement system.Our investigation involved measuring and analyzing ultrasonic echoes in water under two conditions: ambient temperature and boiling.Subsequently, these signals were applied to measure air-water adiabatic bubbly flow, and the measurement accuracy was validated.Finally, subcooled boiling flow measurements were performed, and the associated uncertainties were thoroughly analyzed.

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.301
Teacher spread0.243 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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