A comprehensive study on using underside infrared reflectors to enhance the performance of radiative cooling structures
Bibliographic record
Abstract
Radiative cooling is a sustainable and cost-effective technology that provides cooling by emitting thermal radiation into outer space. While radiative cooling is typically limited to sky-facing surfaces, this research explores the integration of underside reflectors to radiatively cool downward-facing surfaces. Herein, numerical analysis employing Monte Carlo ray tracing is carried out to complete a comprehensive study on the effects of heat gain from the surrounding air, the sun, and incident atmospheric radiation on the cooling power of structures that are radiatively cooled from both their top and bottom surfaces. Different configurations are investigated, wherein radiatively cooled materials reside above flat or parabolic reflectors. The results show underside infrared reflectors significantly increase the cooling power of radiatively cooled surfaces while lowering their temperature well below that of their surroundings. For example, if the effects of the solar irradiance and nonradiative heat transfer with the surroundings are neglected, the temperature of a radiatively cooled material is reduced from 289 K to 277 K when the emissive properties of the surface residing beneath it are changed from a black body to a flat broadband reflector. If the underside reflector is replaced by a parabolic IR reflector, the temperature of the radiatively cooled material can further be reduced to 272 K. Furthermore, when an ideal specularly reflective parabolic surface is used as the underlying reflector, a remarkably low steady state temperature of 229 K can be achieved. Our results also show that the temperature of radiatively cooled structures can be decreased by using parabolic reflectors with smaller diameters when their focal distances are reduced. This is advantageous for minimizing the area needed to achieve effective sub-ambient cooling. Thus, the results from this work have important implications for the design of better-performing radiatively cooled structures that occupy less area, which is highly advantageous for achieving passive cooling in an urban environment.
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 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".