Air-Source Heat Pump with Integrated Latent Heat Storage for Residential Buildings: Concept Development and Design Evaluation
Bibliographic record
Abstract
Heat pumps effectively support electrification and reduce energy consumption associated with building heating and cooling.Air-source heat pumps (ASHP) use the ambient air as a thermal source or sink for the heat pump (HP).ASHPs are the most common form of HP system in Canadian residential buildings due to their simplicity and lower initial cost.While the technology is recognized to play a key role in the path to building decarbonization, its widespread adoption will significantly increase peak demand for electricity grids, especially in regions where fossil fuel-based systems are to be replaced.Thermal storage technology can support the increased adoption of ASHPs in those regions by providing a more energy-flexible link between the building's thermal demand and the electrical grid.This study investigates a new method of integrating and applying a phase change material (PCM) as latent heat storage in an ASHP system for residential buildings.The approach employs direct heat exchange between the refrigerant and the PCM and indirect discharge of thermal energy into the room.This research describes the concept and evaluates its advantages.It also explores two suggested design configurations to integrate the storage unit into current ASHP systems.This research further evaluates the effect of those configurations on the storage unit's charging process via a numerical model developed to simulate heat transfer in the storage medium.
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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 | 0.001 | 0.001 |
| 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".