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Record W4307263582 · doi:10.3390/en15207792

Aerogel Product Applications for High-Temperature Thermal Insulation

2022· article· en· W4307263582 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

VenueEnergies · 2022
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAerogelThermal insulationBasalt fiberMaterials sciencePipe insulationComposite materialThermalFiberTemperature gradientFlue gasThermal conductivityVacuum insulated panelWaste managementEngineeringThermodynamics

Abstract

fetched live from OpenAlex

This paper presents the results of theoretical and experimental studies to determine the optimal thickness of thermal insulation from basalt fiber and aerogel products for pipelines at temperatures of 300 and 600 °C. We carried out a comparison of the key thermophysical characteristics of the claimed heat-insulating materials. We performed a thermal imaging survey of the furnace chimney, insulated with basalt fiber and aerogel, while controlling the temperature of the flue gases by establishing the required ratio of the flow rate of natural gas and oxidizer. The temperature gradient along the thickness of the thermal insulation was obtained using a numerical tool developed in ANSYS. The results show that aerogel surpasses basalt fiber in all key thermophysical characteristics. At the same time, the only barrier to widespread industrial production and use of aerogel in the high-temperature thermal insulation segment is its market cost, which is still several times higher than that of basalt fiber in terms of an equivalent performance.

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 score0.110
Threshold uncertainty score0.623

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)0.0010.000

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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