Study on the System Design of a Solar Assisted Ground Heat Pump System Using Dynamic Simulation
Bibliographic record
Abstract
Recently, the use of hybrid systems using multiple heat sources in buildings to ensure a stable energy supply and improve the system performance has gained attention. Among them, a heat pump system using both solar and ground heat was developed and various system configurations have been introduced. However, establishing a suitable design method for the solar-assisted ground heat pump (SAGHP) system including a thermal storage tank is complicated and there are few quantitative studies on the detailed system configurations. Therefore, this study developed three SAGHP system design methods considering the design factors focused on the thermal storage tank. Using dynamic energy simulation code (TRNSYS 17), individual performance analysis models were developed and long-term quantitative analysis was carried out to suggest optimum design and operation methods. As a result, it was found that SYSTEM 2 which is a hybrid system with heat storage tank for only a solar system showed the highest average heat source temperature of 14.81 °C, which is about 11 °C higher than minimum temperature in SYSTEM 3. Furthermore, the best coefficient of performance (COP) values of heat pump and system were 5.23 and 4.32 in SYSYEM 2, using high and stable solar heat from a thermal storage tank. Moreover, this paper considered five different geographical and climatic locations and the SAGHP system worked efficiently in having high solar radiation and cool climate zones and the system COP was 4.51 in the case of Winnipeg (Canada) where the highest heating demand is required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".