Bibliographic record
Abstract
Society’s use of fossil fuels has led to increasingly high levels of CO2 in the atmosphere. These levels have been linked to global average temperature rises, and increases in the severity and frequency of major weather events. To combat these effects, nations around the world have committed to reducing their CO2 emissions, and transition to renewable energy. This thesis focuses on the development of a novel solar heating system, which combines a hybrid solar panel and cascade heat pump. The thesis begins by presenting a high-level literature review of solar and heat pump technologies, followed by the initial design development of the system. Two design iterations are presented, illustrating that the final design was selected because it exhibits improved peak heat output, and reduced sensitivity to panel temperature. Next, a manuscript-based chapter is presented that focuses on utilizing the proposed solar heating system for water distillation. Case studies are presented that compare the performance of the proposed system with a solar still at four different locations. The final conclusion from these studies is that using the proposed system offers area-based performance improvements of 780% compared to a basic solar still. A second manuscript-based study is then presented, which focuses on utilizing the proposed solar heating system for domestic hot water production. Additional case studies are detailed that compare the proposed system to an evacuated tube design, and a single heat pump. The conclusions from these studies are that the proposed system exceeds the performance of the evacuated tube system by up to 64%, and that the proposed system is most beneficial during seasons with higher average dry-bulb temperatures, and increased solar irradiation. A final manuscript-based study is then presented, which focuses on a methodology for improving alternate mode thermal performance estimates for hybrid solar panels. The conclusion from this study is that the proposed methodology can successfully estimate thermal performance within 5% of actual values. Each of these studies contributes to the project goal of developing a novel solar energy heating system, which can be further developed to reduce global CO2 emissions, and reduce the effects of climate change.
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) | 0.000 | 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) | 0.004 | 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".