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Record W4317581775 · doi:10.2514/6.2023-1492

Interaction of a high energy laser with metals in reacting atmospheres

2023· article· en· W4317581775 on OpenAlexaff
Daniil Andrienko, Iain D. Boyd, Jaykob N. Maser, Steven M. Shepard

Bibliographic record

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsEvaporationMaterials scienceOxideVacuum evaporationMetalSurface energyPlumeLaserSurface (topology)Thin filmAtmospheric pressureThermodynamicsChemical physicsMechanicsComposite materialChemistryOpticsNanotechnologyMetallurgyPhysicsMeteorology

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-1492.vid The interaction of a high-energy laser with metals exposed to air is being investigated computationally and experimentally. Particular emphasis is made on a transition to regimes of oxidation, melting, and evaporation of a metal surface. The computational model includes a solution of the Euler equation describing plume formations coupled to a material model accounting for melting and evaporation. A comparison of melting dynamics under normal atmospheric pressure and under vacuum is made. The model includes the possibility of oxide film formation on the surface of a metal. It is found that a thin oxide film can substantially change the evaporation dynamics and surface temperature during the interaction of materials with a high-energy laser.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0090.001

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 head0.010
GPT teacher head0.229
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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