Bibliographic record
Abstract
Population growth has significantly increased the energy consumption of the building sector, which is currently 32% of the total global energy demand. Energy use for residential and commercial heating and cooling is projected to strongly grow until 2050, increasing by 79% and 84%, respectively, compared to 2010 [1]. Development of high performance thermal insulation materials is crucial to saving space and energy, increasing comfort, and decreasing environmental impact, cost, and complexity. Aerogels are a promising high-performance type of thermal insulation for both stationary and mobile applications. The thermal performance of insulations is typically judged by their reported R-value (thermal resistance); however, this value may differ from the in-service R-value for reasons such as temperature and humidity variations as well as mechanical compression.In this research, the thermal performance of aerogel blanket super insulation is thoroughly studied under various operating conditions, i.e., temperature, compression, and humidity. The microstructure of commercially available aerogel blankets was characterized using microscopy, porosimetry and spectroscopy. A comprehensive set of accurate analytical models were developed and verified experimentally to predict the thermal and mechanical performance of aerogel blankets in dry and humid conditions. These models can be utilized to predict the thermal performance of the insulation for building envelopes and other large-scale applications. Furthermore, the design of such materials can be improved by performing an optimization study on the microstructural and morphological properties of aerogel blankets using the developed analytical models.The results of the aerogel blanket performance modeling and measurements indicated that mechanical load on the material, elevated temperature and high humidity decrease the R-value of aerogel blankets. These factors should be considered in the thermal insulation design and selection for an application to benefit the most from this super insulation material.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".