Design Optimization of Radiative Cooling System Based on Intelligent Algorithm: Combination of Simulated Annealing and Decision Tree
Why this work is in the frame
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Bibliographic record
Abstract
As global climate change and energy issues become increasingly serious, radiative cooling technology has attracted widespread attention as an environmentally friendly and efficient passive cooling method. This study combines the simulated annealing algorithm and the decision tree algorithm to optimize the design of the radiative cooling system. The simulated annealing algorithm optimizes design parameters (such as reflectivity, emissivity, and thermal conductivity) through global search, while the decision tree algorithm provides feedback for the optimization process by predicting the cooling effects of different design schemes in real time. Experimental results show that this method significantly improves the cooling efficiency of the radiative cooling system and exhibits excellent performance under different parameter conditions. By comparing with other algorithms, the combination of simulated annealing and decision tree shows its unique advantages in multi-objective optimization. This study provides a new optimization idea for the application of radiative cooling technology and has broad practical application potential.
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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 it