Condenser operating conditions effects on dielectric liquid boiling and condensation heat transfer in closed systems
Bibliographic record
Abstract
Localized boiling and enclosed condensation of a dielectric liquid in a thermosyphon can be used as an alternative to traditional air cooling for high performance microprocessors. A small quantity of dielectric fluid boils at the surface of the microprocessor and is condensed by a heat exchanger, which removes the heat from the system. Here, the heat exchanger was a water-fed cold plate cooling a round fins heat sink. Such a thermosyphon can remove large heat fluxes and increase the energy efficiency of data centers. An experimental setup was built to measure the impact of various operating parameters on the thermal resistances of the thermosyphon. The thermal resistances that were considered were the boiling resistance between the heater and the dielectric fluid, the condensation resistance between the dielectric fluid and the cold water feeding the cold plate, and their sum (the total thermal resistance). The operating parameters tested were the cold plate inlet flow rate (0.2 L/min and 1 L/min), the cold plate inlet temperature (15°C and 24°C) and the filling ratio of the enclosure (33%, 66% and 88%). It was observed that changing the filling ratio did not have a significant impact on the thermal resistances of the thermosyphon. Increasing the cold plate inlet flow rate from 0.2 L/min to 1 L/min and decreasing the cold plate inlet temperature from 24°C to 15°C both led to significantly lower total thermal resistance. However, increasing the flow rate lowered the rate at which the pressure increased with the amount of heat removed from the system, while this rate did not change when decreasing the cold plate inlet temperature. These observations could be properly explained by a simple analytical model, with a single free parameter (increasing the thermal resistance of the heat sink for the lowest cold plate flow rates). With the free parameter adjusted to the data, the model had a root-mean square error of 1.8°C and a maximum error of 6°C, over all measured conditions. This study demonstrates how changing the filling ratio or the cold plate inlet temperature and flow rate have an impact on the pressure, the temperature and the thermal resistances inside the thermosyphon. This is a first step in evaluating the energetic and economic efficiency of the thermosyphon solution for its future integration in data centers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".