Buoyancy assist adaptive charging and discharging thermal storage tank
Bibliographic record
Abstract
Abstract Thermal storage tanks are extensively used to supply domestic hot water for residential applications. Also, the thermal storage tanks are used in solar energy operated power plant. Since solar energy is intermittent, there is a need for a thermal storage system to accommodate the periods where the solar energy is not enough to keep the working fluid temperature at the desired operational levels. In this work, a novel design of a thermal storage tank is introduced and analyzed. The analyses are performed to understand the effect of the operating parameters on the performance of the proposed tank. The main idea of the tank is to control the fluid flow direction in the tank depending on the charging or discharging temperature without the aid of an external controlling system. The system relies on the buoyancy force to adjust the fluid flow direction. By this adaptive strategy, we can enhance the charging and discharge of the storage tank, which is one of the problems of the conventional storage systems. Lumped capacity method analyses are carried out to understand the temperature distribution of the system and the performance of the system. Sinusoidal variation of the fluid inlet temperature to the tank is assumed, to resemble the solar intensity variation. The fluid outlet temperature from the tank is monitored for a range of the controlling parameters. The results have demonstrated the effectiveness of the proposed system. Relatively large tank showed that the water in the storage tank could be kept at the average temperature level regardless of the fluctuations in the inlet temperature of the working fluid.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".