Numerical investigation of heat transfer in air channels for thermal energy storage applications in buildings
Bibliographic record
Abstract
Energy demand has significantly increased in building sectors over the past decade because of population growth, increased indoor activities, and increased energy requirements for controlling the internal environment due to global climate change. The residential sector accounts for 32% of the total energy consumption. Space and water heating account for most of the fossil fuel use in residential buildings. Several studies have been done to optimize the design of heat transfer fins and channels in space and water heating systems to improve their efficiency. In this study the effects of the cross-sectional shape of air-channels on heat transfer from the channels to the air is investigated. Air channels with circular, triangular, rectangular and sinusoidal shaped cross sections are considered. Further, heat transfer to air flowing through beds comprising these air channels are also investigated. Numerical analyses is performed to investigate the dependency of the outlet temperatures to optimize the shape of the air channels and beds. Results show the sinusoidal channel and bed have better performance in relation to the outlet temperature. Considering channels with a length of 100 mm, a surface area of 1770 m2, and a wall-to-air heat transfer rate of 790 W/m2, the air outlet temperature for the sinusoidal channel is about 57°C higher than that of the circular channel. Considering beds with a height of 5 mm and wall-to-air heat transfer rate of 790 W/m2, the outlet temperature of the sinusoidal bed is about 10°C higher than the outlet temperature for a rectangular bed with similar surface area. The results are applicable to the design of thermal energy storage units that could be used directly or indirectly (such as adsorbent beds for thermal energy storages) to supply heat to air within HVAC systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".