Thermodynamic Investigation of Solar-Assisted Heat Pumps for Water Heating Applications in Cold Climatic Conditions
Bibliographic record
Abstract
In this thesis, the annual energy use, exergy, environmental, and economic performance of a direct-expansion solar-assisted heat pump (DX-SAHP) and a dual-source (solar/air) indirect-expansion solar-assisted heat pump (IDX-SAHP) for water heating under cold climatic conditions of Calgary, Alberta, is investigated. Also, the thermodynamic performance of the DX-SAHP system operating with various low global warming potential (GWP) refrigerants is evaluated. A mathematical model based on the fundamentals of thermodynamics and heat transfer is developed, validated, and implemented in MATLAB®. CoolProp®, a MATLAB® addon, was used to determine the required refrigerant properties. Besides, a series of experiments were performed on a prototype DX-SAHP water heater in Calgary, Alberta. The results show that a DX-SAHP can supply domestic hot water for a typical Canadian household in Calgary throughout the year with the maximum and minimum monthly average COPs of 3.94 and 2.4, respectively. Compared to a conventional ASHP and an electric water heater, the energy consumption of the system was reduced by 25% and 62%, respectively. Moreover, using SAHP lowers CO2 emissions by 1283 kg/year when switching from gas water heaters to SAHPs, while the payback period of these systems is more than a decade. Among the investigated low GWP refrigerants, R1233zd achieves the highest COPs, whereas R32 shows the lowest COPs. The second-law analysis results indicate that the exergy efficiency of the heat pump water heaters reduces in summer. The ASHP obtained the highest monthly average exergy efficiencies, followed by the DX-SAHP and the dual-source IDX-SAHP. The test results of a 2.3 m2 DX-SAHP prototype in Calgary show that the system can heat 178 L of water on sunny days in winter and sunny and cloudy days in summer in 4.5, 3, and 3.5 hours, respectively. The average COP of the system varied from 3.0 to 3.44. However, the system showed a considerable degree of superheat during operation, which resulted in inefficient performance. The imperfect bonding between the collector plate and the serpentine tube is identified as the leading cause of this issue. This collector is being replaced with a professionally fabricated one in the next design.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".