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Record W323778421 · doi:10.4271/2015-01-2162

Progress towards a 3D Numerical Simulation of Ice Accretion on a Swept Wing using the Morphogenetic Approach

2015· article· en· W323778421 on OpenAlexaff
Krzysztof Szilder, Edward P. Lozowski

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2015
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversity of AlbertaNational Research Council Canada
Fundersnot available
KeywordsWingAerospace engineeringComputer scienceGeologySimulationEngineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">We have developed an original, three-dimensional icing modelling capability, called the “morphogenetic” approach, based on a discrete formulation and simulation of ice formation physics. Morphogenetic icing modelling improves on existing ice accretion models, in that it is capable of predicting simultaneous rime and glaze ice accretions and ice accretions with variable density and complex geometries. The objective of this paper is to show preliminary results of simulating complex three-dimensional features such as lobster tails and rime feathers forming on a swept wing. The results are encouraging. They show that the morphogenetic approach can predict realistically both the overall size and detailed structure of the ice accretion forming on a swept wing. Under cold ambient conditions, when drops freeze instantly upon impingement, the numerical ice structure has voids, which reduce its density. At warmer air temperatures, when drops do not freeze on impingement, larger cavities form, due to the shadowing process, along with the development of morphological features called lobster tails. Ice feathers also appear. As far as we know, no other numerical model can simulate these complex, three-dimensional ice structures that form on a swept wing.</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)0.001
metaresearch head score (Gemma)0.001
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 score0.957
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.274
Teacher spread0.240 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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