Assessment of the performance of ultralow‐ <scp>GWP</scp> refrigerants in a two‐stage heat pump system using simulation and <scp>MCMD</scp>
Bibliographic record
Abstract
Abstract The Kigali Amendment's mandate to phase down high‐GWP refrigerants highlights the urgent need for sustainable alternatives in cold climate heat pumps, which are critical for delivering efficient heating in severely cold regions. This study evaluates ultralow‐GWP refrigerants—R‐290, R‐152a, R‐161, R‐1234yf, and R‐1234ze(E)—for application in a two‐stage vapor injection heat pump operating at outdoor temperatures as low as −20 °C and a condenser temperature of 52 °C. The methodology integrates modeling and simulation with thermodynamic and exergy analyses, total equivalent warming impact (TEWI) assessment, and a TOPSIS‐based multicriteria decision‐making (MCDM) approach. Results show that R‐152a achieves the highest COP (2.92) and the lowest TEWI (9078.78 kg CO 2 e), followed by R‐290 (COP = 2.81, TEWI = 9345.6 kg CO 2 e), while R‐161 shows the lowest COP (2.64). A trade‐off in volumetric heating capacity is observed: R‐152a delivers 3.2 MJ/m 3 versus 3.78 MJ/m 3 for R‐161. Exergy efficiency increases with ambient temperature; for example, R‐152a improves from 0.355 to 0.383 as temperature rises from −20 °C to 0 °C. The TOPSIS ranking confirms R‐152a as the top performer (score = 0.642), closely followed by R‐290 (0.637). All refrigerants evaluated show acceptable performance for cold‐climate use. Notably, R‐152a and R‐290 emerge as climate‐friendly, high‐performance candidates. Unlike prior studies that rely primarily on single‐criterion comparisons, this work introduces a novel integrated framework combining thermodynamic, exergy, environmental, and MCDM analyses to guide refrigerant selection for sustainable heating in cold climates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".