A Methodology to Predict Mobile Air-Conditioning System (MAC) Performance for Low GWP Drop-In Refrigerant Using 1D CAE Simulation Tool
Bibliographic record
Abstract
In developing nations, most passenger vehicles are equipped with mobile air conditioning (MAC) systems that work on Hydro Fluoro Carbons (HFC) based refrigerants. These refrigerants have a high global warming potential (GWP) and hence adversely affect the environment. According to the Kigali amendment to Montreal Protocol, Article-5 Group-2 countries including India must start phasing down HFCs from 2028 and replace them with low Global Warming Potential (GWP) refrigerants. One such class of low GWP refrigerant is Hydro Fluoro Olefins (HFO) In order to replace HFCs with HFOs in existing MAC systems, the various system performance parameters with the new refrigerant are required to be evaluated. Performance evaluation of MAC system is rendered quicker and cost-effective by deploying a digital simulation tool. There is good correlation and confidence established for MAC performance prediction with HFCs through 1D CAE. Further, to enable AC performance simulation with drop-in refrigerant through 1D CAE, a simulation methodology needs to be formulated to build correlation with physical test. This work comprises generating the physical test data by replacing the R-134a refrigerant in a test vehicle with low GWP R-1234yf drop-in refrigerant. The MAC system performance is validated at severe ambient condition (>40°C) and then compared with baseline performance with R-134a refrigerant. Preliminary work comprises performing first-cut simulation by replacing R-134a in the correlated model with R-1234yf and analyzing the gap between physical test data and 1D CAE outcome. A sensitivity analysis is carried out to understand the impact of different parameters like warm-up temperatures, duct heat gain values etc. on MAC performance. Simulation results obtained by tuning these parameters are found to correlate with physical test data by > 95% accuracy. With the correlated model, this simulation methodology is deployed for another vehicle to predict the MAC performance with drop-in refrigerant. The proposed methodology will help to understand the impact of drop-in refrigerants on present HFC-based MAC systems and enable us to provide feasible recommendations to meet the target MAC performance for intended climatic usage conditions well before prototyping and physical validation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".