Experimental Evaluation of Using Silica Aerogels as the Thermal Insulator for Combustor Liners
Bibliographic record
Abstract
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. 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 IR camera. 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. The measured evolution of temperature distribution confirmed thermal equilibriums for every test condition with transpiration cooling. The measured temperature distribution of metal liner demonstrated superior thermal insulation of aerogel blanket under the protection of cooling film with a temperature difference as high as 1580 K between combustion products temperature and the metal liner temperature on the back side. In addition, silica aerogel samples were examined before and after the combustion tests to understand their material degradation exposing to a typical gas turbine combustor environment using high-resolution scanning electron microscope (SEM). Test results suggest multiple degradation mechanisms to the silica aerogel blanket samples from the combustion tests. Improvements can be made to the silica aerogel blankets for a more resilient thermal insulator, for example, by replacing glass fibers in silica aerogels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".