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Record W2515431628 · doi:10.1016/j.jestch.2016.07.015

Engineering design of plasma generation devices using Elmer finite element simulation methods

2016· article· en· W2515431628 on OpenAlex

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.

Bibliographic record

VenueEngineering Science and Technology an International Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsFinite element methodProcess (computing)LimitingSoftwareComputer scienceMechanical engineeringEngineeringEngineering design processPlasmaSystems engineeringStructural engineeringPhysicsProgramming language

Abstract

fetched live from OpenAlex

Plasma generation devices are important technology for many engineering disciplines. The process for acquiring experience for designing plasma devices requires practice, time, and the right tools. The practice and time depend on the individual and the access to the right tools can be a limiting factor to achieve experience and to get an idea on the possible risks. The use of Elmer finite element method (FEM) software for verifying plasma engineering design is presented as an accessible tool that can help modeling multi-physics and verifying plasma generation devices. Furthermore, Elmer FEM will be suitable for experienced engineer and can be used for determining the risks in a design or a process that use plasma. A physical experiment was conducted to demonstrate new features of plasma generation technology where results are compared with plasma simulation using Elmer FEM.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.399
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.316
Teacher spread0.284 · 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