Thermal management of honeycomb heat sink filled with phase change material for smart lighting applications
Bibliographic record
Abstract
Recently, thermal management of LEDs lamps has become increasingly essential due to the widespread integration of LEDs in smart lighting applications. In this work, we focus on the thermal analysis of convective heat transfer using a honeycomb heat sink designed for LEDs lamp cooling. Three different heat sink geometries were examined: an aluminum-filled honeycomb radiator, a hollow honeycomb radiator, and a hollow honeycomb radiator incorporating a phase change material (PCM) layer. The results obtained from numerical simulations using COMSOL Multiphysics® showed that the third heat sink geometry, when employed in short-duration lighting applications, led to a 25% reduction in temperature for a 20W power lamp. We also determined the optimal operational time, during which the temperature drop is maximum. Moreover, we observed that the integration of a PCM-filled honeycomb radiator in cyclic lighting applications (involving on/off cycles and high/low power settings) significantly mitigates temperature rise in the lamp by leveraging the PCM's heat storage capacity. This approach effectively prevents thermal shocks, ensures prolonged LEDs performance, and contributes to energy savings in the lighting sector. By addressing the thermal management challenges associated with LEDs lamps through innovative heat sink designs and the utilization of PCM, our research offers valuable insights for enhancing the overall performance and efficiency of LED lighting systems.
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 | 0.000 |
| 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".