Bibliographic record
Abstract
Electric baseboard heaters have been widely used to provide convenient and economical heating in residential rooms. For old heaters that were installed many years ago, a secondary enclosure can be installed to cover the old heater for aesthetic and preventing scalding by the hot elements. The enclosure increases the thermal resistance for heating, which results in high temperature on the top surface of the enclosure. There is a significant lack of the information and work on the thermal design of enclosure for electric baseboard heaters. Developing an effective enclosure design that is able to keep the surface temperature within safe-to-touch range is the objective of this research. In this project, computational fluid dynamics simulation using COMSOL Multiphysics is the major methodology. Three approaches are taken to develop the new enclosure design. The first approach is to change material's thermal properties: thermal conductivity, surface emissivity. It was found that the surface temperature decreased with reducing the thermal conductivity of the material. Moreover, the surface temperature can be effectively reduced by using the high surface emissivity coating material on the outer surface and the low surface emissivity coating material on the inner surface. The second approach is to modify mechanical design to facilitate air flow. It was discovered that increasing the openings for air inlet and outlet can effectively reduce the surface temperature. The third approach is to use thermoelectric fan to provide passively-powered forced convection heat transfer. It was found that the thermoelectric fan can be used to not only enhance the airflow through the heater but also to reduce the top surface temperature of the enclosure. The three approaches will be combined to develop effective and safe enclosures for electric baseboard heaters.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".