Experimental modeling of refrigerants at high ambient temperature
Bibliographic record
Abstract
High ambient temperature is well known to have an adverse impact on air conditioners; it lowers their cooling capacity along with their coefficient of performance. Moreover, air conditioning plays a significant role in damaging the environment as it contributes to both global warming and ozone depletion. In the past few decades, the Montreal agreement was introduced to protect the ozone layer from substances that contribute to its depletion. Developing countries are now facing the challenges of phasing out HCFCs such as R22 in the air conditioning sector. The aim of this study is to find an alternative refrigerant that can replace R22 after its phase out. The new refrigerant must have low environmental impacts and should be able to withstand operation in high ambient temperature countries. In this study, the hydrocarbon refrigerant R290 and the hydrofluorocarbon R410A were selected as alternatives. Furthermore, experimental testing was carried out on the baseline refrigerant R22 and R410A using a 7kW ducted split unit at three outdoor conditions: 35oC, 48oC, and 52oC. From the experimental results, a model based on R22 and R410A was developed using Engineering Equation Solver (EES). The model was successful in verifying the experimental results within a 15% error range, and was tested again with external data from (Eltalouny & Nielsen, 2016) [10]. After confirming its validity, the model was simulated to predict the performance of the alternative refrigerant R290 under the same experimental conditions and input data. To test the model’s validity on R290, results from (Eltalouny & Nielsen, 2016) were used. The model succeeded in predicting the COP and power consumption. The refrigerants demonstrated deterioration in their performance at elevated temperatures compared to their performance at a typical operating regime. With proper safety measures, and enhancement to the compressor efficiency, R290 can be considered as a promising alternative.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".