Characterization of Heat Transfer Rates in an Air to Phase Change Material Thermal Storage Unit for Integration in Air Handling Units
Bibliographic record
Abstract
Space heating accounts for 55% of the total energy demand in the commercial sector in Canada.Improvement in energy efficiency and energy storage in this area can have a significant impact on total energy demand.Phase change materials (PCM) have been shown to be a viable medium for thermal energy storage having larger storage capacity per mass than conventional sensible heat storage materials.For this thesis, an air-loop was designed, constructed and instrumented to characterize the heat transfer rates and energy storage potential of a small scale PCM thermal energy storage (TES) unit.The air-loop was designed to provide the necessary inlet conditions to the PCM TES unit.The storage unit was custom made and housed multiple aluminum flat plates (450 mm x 300 mm x 10 mm) filled with PCM (RT44HC).The plates were aligned in two rows of 29.A gap of 11 mm existed between each plate to allow air (the heat transfer fluid (HTF)) to pass.Overall, three variables were studied for the characterization: the HTF flow rates, the initial temperature of the PCM and the HTF inlet temperature.An empirical model was created in TRNSYS with the values determined from the characterization of the PCM TES.This model with data from a real commercial building AHU was used to simulate a PCM TES integrated into an AHU using the integration method of strategic heating with the goal of reducing peak power during start-up.This was but one of multiple methods of PCM TES integration into an AHU.The simulation results were compared to the AHU without PCM TES to determine whether any energy improvements were achieved.It was determined that by adding the PCM TES the start-up peak power could be reduced by 35 kW.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".