Improving thermo-optic properties of smart windows via coupling to radiative coolers
Bibliographic record
Abstract
In this work we first solve the radiative heat transfer problem in one dimension to perform a comparative analysis of the time-averaged performance of the partially transparent radiative windows and radiative coolers. In doing so, we clearly distinguish the design goals for the partially transparent windows and radiative coolers and provide optimal choice for the material parameters to realize these goals. Thus, radiative coolers are normally non-transparent in the visible, and the main goal is to design a cooler with the temperature of its dark side as low as possible relative to that of the atmosphere. For the radiative windows, however, their surfaces are necessarily partially transparent in the visible. In the cooling mode, the main question is rather about the maximal visible light transmission through the window at which the temperature on the window somber side does not exceed that of the atmosphere. We then demonstrate that transmission of the visible light through smart windows can be significantly increased (by as much as a factor of 2) without additional heating of the windows. This is accomplished via coupling the windows to the radiative coolers using transparent cooling liquid that flows inside of the window and radiative cooler structures. We also demonstrate that efficient heat exchange between radiative coolers and smart windows can be realized using small coolant velocities (sub-1 mm/s for ${\sim}{1}\;{\rm m}$∼1m large windows) or even using a purely passive gravitationally driven coolant flows between a hot smart window and a cold radiative cooler mounted on top of the window with only a minimal temperature differential (sub-1K) between the two. We believe that our simple models complemented with an in-depth comparative analysis of the standalone and coupled smart windows and radiative coolers can be of interest to a broad scientific community pursuing research in these disciplines.
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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".