Bibliographic record
Abstract
A comprehensive study of using thermal energy storage (TES) tank was performed. In this report, the comprehensive literature review of various options of storing thermal energy in buildings was discussed. The objective of the project was to evaluate charging and discharging performance of a storage tank with and without phase change material (PCM) blocks. The general format of the energy balance for the storage tank considering losses, input energy, inlet and outlet mass flow rates, and PCM blocks was developed. Charging performance was analyzed by three different approaches. Initially, constant input thermal energy rate was considered to be delivered to the tank by different heat pumps from 4.5 kW to 9 kW heating capacity. Charging time, phase change process, and stored energy were analyzed under constant thermal energy rate input mode for charging process. Then, the effect of constant coil temperature on charging process was studied and results were compared to previous cases. Also, a solar assisted heat pump was implemented into the model to verify the effect of solar radiation on pre-heating the air for heat pump and how this process improves the overall charging progress of storage tank. Moreover, discharge procedure was discussed to evaluate various discharge modes based on different water draw flow rates. Based on this analyses developing a complete TRNSYS model of the tank is recommended in order to do whole building energy simulation.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".