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Modelling of SiC-Matrix Composite Formation by Thermal Gradient Chemical Vapour Infiltration

2004· article· en· W2016311735 on OpenAlex
В. И. Кулик, A.V. Kulik, M.S. Ramm, Yuri Makarov

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

VenueMaterials science forum · 2004
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsImpact
Fundersnot available
KeywordsChemical vapor infiltrationMaterials scienceComposite numberInfiltration (HVAC)Composite materialThermalTemperature gradientMatrix (chemical analysis)Thermodynamics

Abstract

fetched live from OpenAlex

Abstract. Mechanical properties of ceramics can be dramatically improved by embedding a reinforcement phase (particles, whiskers, or fibres), i.e. producing a Ceramic-Matrix Composite (CMC). An advanced technique for manufacturing the CMC is Chemical Vapour Infiltration (CVI). In this paper, we developed a 1D model describing the Thermal Gradient Chemical Vapour Infiltration (TG CVI) for a formation of a composite with the silicon carbide (SiC) matrix from methyltrichlorsilane (MTS). Within the model, the fibrous substrate (preform) is considered as a complex porous medium with two systems of parallel non-uniformly scaled pores oriented along the preform thickness. Longitudinal convection in the process is governed by the phase transitions due to the matrix material deposition. To allow for the mass exchange between the pore systems, transverse diffusion and convection are accounted for in the model. We analysed the influence of the TG CVI operating conditions, namely the precursor (MTS) concentration in the ambient gas, the pressure in the reactor, the susceptor temperature, and the temperature gradient in the preform on the quality of the composite and the process duration.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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