Improving radiative cooling performance by implementing underside IRreflectors
Bibliographic record
Abstract
The rapid development of urbanization and global warming have led to a dramatic increase in global energy demands for cooling.Conventional cooling systems are energy intensive, cause significant amounts of greenhouse gas (GHG) emissions, and add to the urban heat island effect, which further increases cooling demands.It has been estimated that about 40% of primary energy is used in buildings, most of which is used to operate heating, ventilation, and air conditioning systems.Radiative cooling (RC) is an effective passive cooling technique that does not require any energy input during operation and thus can mitigate GHG emissions released by conventional cooling devices.RC technology takes advantage of the fact that the atmosphere is highly transparent over the spectral range from 8 -13 m, which is referred to as the atmospheric window.In RC technology a sky-facing surface with a high emissivity over the atmospheric window can conduct radiative heat exchange with outer space which is at a temperature of 3 K.However, limited sky-facing area, especially in multistorey buildings, prevents widespread use of RC technologies.Presently RC systems focus on radiative cooling from the top sky-facing surfaces while other surfaces, especially the bottom surfaces, do not contribute to the RC effect.The objective of the research presented herein is to boost the performance of RC structures by placing an IR reflector beneath their bottom surfaces such that they can be cooled from the bottom as well as the upper sky-facing side.In this study, the cooling performance of a RC structure is simulated for three different cases wherein a different surface resides beneath the RC structure for each case.In Case 1 the underlying surface has the properties of a blackbody.In Case 2 the underlying surface is a 1m-by-1m flat reflector and in Case 3 the underlying surface is an infinitely wide underlying reflector.This work provides a wide-ranging analytical assessment of the RC performance for the above-mentioned three cases.The effects of the spectral reflective properties of the underlying reflector, solar absorbance in the RC structure, and convective heat loss to the surroundings on the cooling power of the RC structure are investigated.Numerical analysis shows that for an ambient temperature of 300 K under ideal conditions, in the absence of incident solar energy and convective heat transfer with the surroundings, the steady-state temperatures for Cases 2 and 3 are 245 K and 244 K, respectively, which are substantially lower than that for Case 1, which is 277 K.
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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 | 0.001 |
| 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".