Exergy Analysis and Sustainability Assessment of a Solar-Ground Based Heat Pump With Thermal Energy Storage
Bibliographic record
Abstract
In this study, both energy and exergy analyses and sustainability assessment of a thermal energy storage system with a solar-ground coupled heat pump installed in a 120 m2 house are performed. The actual operating data taken from the literature are utilized for model validation. The system considered here mainly consists of a solar collection system, an underground thermal storage system, an indoor air conditioning system, and a data collection system. First, energy analysis is employed to the system and its components, and the rates of energy input (solar radiation), energy storage, collector heat loss, and other heat loss are found to be 4.083 kW, 1.753 kW, 1.29 kW, and 1.04 kW for a 5 h working time, respectively, while the energy efficiency of the system is calculated to be 42.94%. Exergy analysis of the entire system is then conducted for various reference temperatures varying from 0°C to 25°C with a temperature interval of 5°C. As a result of this analysis, the rates of the maximum exergy input, exergy storage, and exergy losses are determined for a reference temperature of 0°C to be 0.585 kW, 0.24 kW, and 0.345 kW, respectively. Finally, the maximum exergy efficiency of the system is obtained to be 40.99% and the maximum sustainable development using sustainability index, which is a function of exergy efficiency, is calculated to be 1.6946 for a reference temperature of 0°C. Furthermore, the energy and exergy results are illustrated through Sankey (energy flow) and Grassmann (exergy loss and flow) diagrams.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".