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Record W4241834585 · doi:10.33737/gpps20-tc-46

Experimental Evaluation of Using Silica Aerogels as the Thermal Insulator for Combustor Liners

2020· article· en· W4241834585 on OpenAlexaff
Yeongmin Pyo, Taylor Robertson, Sean Yun, Zekai Hong

Bibliographic record

VenueProceedings · 2020
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsAerogelCombustorMaterials scienceComposite materialCombustionThermal insulationEmissivityBlanketThermal conductivityCeramicOpticsChemistry

Abstract

fetched live from OpenAlex

An experimental study was conducted for evaluating the feasibility of using silica aerogel as thermal insulator for combustor liners. Aerogels are a superior material for minimizing heat flux to the metal structure of the combustion liner due to their low thermal conductivity. In this study, a conical natural gas fired swirling-flame combustor was utilized for reproducing the combustion environment that's typical to a gas turbine combustor. The silica aerogel blanket was attached to the inner side of a perforated combustor liner. Temperature distribution on the outer side of the combustion liner was measured using a calibrated thermal infrared (IR) camera. To minimize the uncertainty in steel surface emissivity due to surface oxidation at elevated temperatures, designated areas on the metal surface were coated with a ceramic cement whose emissivity was carefully determined. To create a protective cooling film over the aerogel surface, cooling air was supplied from the back side of the perforated metal liner and was allowed to penetrate the silica aerogel blanket to be discharged to the combustor. As the combustor was operated at a fixed equivalence ratio of 0.83, cooling air flow rates were varied to evaluate the effectiveness of transpiration cooling on the aerogel blanket as various cooling flow rates.

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)0.000
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 score0.157
Threshold uncertainty score0.365

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 head0.098
GPT teacher head0.327
Teacher spread0.229 · 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.

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
Published2020
Admission routes1
Has abstractyes

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