Effective R-value of enclosed reflective space for different building applications
Bibliographic record
Abstract
Many parts of the building envelopes contain enclosed airspaces. Also, the insulating glass units in fenestration systems, such as curtain walls, windows, and skylight devices, contain enclosed spaces that are normally filled with air or heavy gas such as argon, xenon, or krypton. The thermal resistance (R-value) of an enclosed space depends mainly on the type of the filling gas, emissivity of all surfaces that bound the space, the size and orientation of the space, the direction of heat flow through the space, and the respective temperatures of all surfaces that define the space. Assessing the energy performance of building envelopes and fenestration systems, subjected to different climatic conditions, requires accurate determination of the R-values of the enclosed spaces. In this study, a comprehensive review is conducted on the thermal performance of enclosed airspaces for different building applications. This review includes the computational and experimental methods for determining the effective R-value of enclosed reflective airspaces. Also, the different parameters that affect the thermal performance of enclosed airspaces are discussed. These parameters include the following: (a) dimensions, (b) inclination angles, (c) directions of heat flow, (d) emissivity of all surfaces that bound the space, and (e) operating conditions. Moreover, numerical simulations are conducted using a previously developed and validated model to investigate the effect of the inclination angle, direction of heat transfer, and the coating emissivity on the R-values of enclosed spaces when they are filled with different types of gases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".