Aerogel Product Applications for High-Temperature Thermal Insulation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".