Fluorescent lamp cold environment performance improvement
Bibliographic record
Abstract
Three approaches to improving the efficacy and start-up time of commercially available cold cathode fluorescent lamps under cold ambient conditions are evaluated in this paper: heating the tube on one side; heating the tube on two sides; heating the circumference of the outer tube. The internal power density generated by ion bombardment of the cathode and collisions in the plasma is indirectly obtained by matching simulated axial wall temperatures with those obtained by experiment at room temperature. The estimated power density is used to evaluate the temporal evolution of the axial surface wall temperature under different ambient conditions immediately after a cold start. All simulation results have been obtained using PHOENICS, a computational fluid flow program. Experiments have shown a good correlation between the time taken for the light output to reach an acceptable value and the time taken for the inner tube cold spot to reach a temperature of 0/spl deg/C. These results have been used to estimate heating time for different heater geometries and heater powers. The study shows that the conduction of heat to the outer glass envelope is the limiting process that controls the start-up time. Modest improvement in start-up time can be achieved by increasing heating power or by moving the heater to the side of the lamp. However, only a heater which entirely surrounds the lamp is capable of substantially decreasing the start-up time.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".