Performance Improvement Of Solar-Assisted Desiccant Cooling System By Changing Collector Type And Stage Number
Bibliographic record
Abstract
Solar energy systems have been recognized as a significant component of HVAC systems during the last two decades, providing thermal and electrical energy for a variety of applications.Meanwhile, solar-assisted cooling systems present a great opportunity to provide thermal comfort conditions in hot and humid climate weather.In this research, a solar-assisted desiccant cooling system is presented and its performance in the hot and humid climate of Jakarta, Indonesia is evaluated using the TRNSYS 18 software.To improve and optimize the efficiency of the system, the number of stages in the cycle is changed from one to three.Furthermore, as the novelty of the research, the effect of different types of solar collectors including Direct Absorption Solar Collector (DASC), Photovoltaic Thermal (PVT), and evacuated tube collector is investigated and the COP of the system is compared.According to the results, the suggested system with two sets of desiccant and heat wheels has a greater COP.Additionally, the system with one stage has a higher COP than the system with three stages.Furthermore, using PVT solar collectors is suggested for these systems as they can provide both thermal and electrical energy for the system.PVT collector increases the system's COP to 1.3, while the evacuated tube and direct absorption solar collectors lower it to 1.215 and 1.199, respectively.
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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) | 0.002 | 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".