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Cold Gas Dynamic Spray Technology: The Simulation of Aerodynamics of Flow

2019· article· en· W2964217678 on OpenAlex
Liang Cui, Andrew G. Gerber, Gobinda C. Saha

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueKey engineering materials · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of New Brunswick
FundersNew Brunswick Innovation Foundation
KeywordsAerodynamicsComputational fluid dynamicsSupersonic speedMechanical engineeringFlow (mathematics)Gas dynamic cold sprayPressure-sensitive paintMaterials scienceMechanicsAerospace engineeringEngineeringPhysicsNanotechnologyCoating

Abstract

fetched live from OpenAlex

Cold gas dynamic spray (CGDS) is a solid-state material additive manufacturing method where the particulate feedstock is accelerated under high pressure and relatively low temperature to supersonic condition to develop thin coatings or 3D freeform objects. In this paper, a literature review of the CGDS state-of-art, explanation of fundamentals of gas dynamic principles required to generate supersonic flow condition, and demonstration of a flow model based on computational flow dynamics (CFD) are presented. The focus of the preliminary 3D CFD model validation is the demonstration aerodynamics structures such as shocks that appear in the CGDS problem.

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.

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 score0.328
Threshold uncertainty score0.673

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 head0.003
GPT teacher head0.193
Teacher spread0.190 · 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