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Record W1541918924

Evaporator Design for an Isokinetic Total Water Content Probe in a Naturally Aspirating Configuration

2007· article· en· W1541918924 on OpenAlexvenueno aff
David Buttsworth, Craig R. Davison, James MacLeod, J. W. Strapp

Bibliographic record

VenueNPARC · 2007
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporatorLiquid water contentEnvironmental scienceHeat transferMeteorologyMechanicsNuclear engineeringAerospace engineeringGas compressorEngineeringPhysicsCloud computingComputer science
DOInot available

Abstract

fetched live from OpenAlex

A number of recent aircraft turbofan power failure events have been linked to ice accretion in the initial compressor stages while the aircraft is traversing the anvil region of storm clouds. The water content in such cloud regions is not well known and the accuracy of most existing water content probes is likely to be poor under such conditions. A new cloud water content probe is being developed for airborne characterisation of such clouds and a critical feature of the probe is the evaporator. In this work we develop some analytical expressions to assist in the design and characterisation of the evaporator. In particular, we consider the issue of convective heat transfer to the ice and water particles moving with the air flow through the evaporator. For the particular evaporator design we are considering, it is shown that ice particles larger than 100 μm are unlikely to have sufficient residence time to evaporate if they remain suspended in the heated air. Although these larger ice particles are likely to impact on the evaporator walls so there is also an opportunity for direct conduction heating, the present analysis indicates that particles larger than 100 μm may not adhere to the walls. However, there are many uncertainties in the present analysis and experiments are needed to determine the actual performance of the evaporator.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.345

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 head0.047
GPT teacher head0.248
Teacher spread0.201 · 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
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

Citations4
Published2007
Admission routes1
Has abstractyes

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