Economic and Climate Advantages: Secondary-Loop Motor Vehicle Air Conditioners (MACs)
Bibliographic record
Abstract
This paper and presentation compare the thermal, economic and climate performance of existing direct expansion motor vehicle air conditioners (DX-MACs) using hydrofluorocarbon (HFC)-134a (global warming potential (GWP) =1300) with secondary-loop MACs (SL-MACs) using hydrofluoroolefin (HFO)-1234yf (GWP < 1) and HFC-152a (GWP = 138), both of which satisfy the European Union (EU) and Japan F-gas regulations and are listed as acceptable by the US Environmental Protection Agency (US EPA). In addition to a technical review of the SL-MAC system, the paper includes a part-by-part system manufacturing cost comparison and itemized ownership cost comparison taking into account fuel savings and reduced maintenance. The paper is timely because the Kigali Amendment to the Montreal Protocol on Substances that Deplete the Ozone Layer now requires both developed and developing countries to phase down the production and consumption of HFCs and at the same time encourages increases in energy efficiency. The developed country transition to HFO-1234yf is well underway, with the EU already requiring refrigerants with GWP less than 150, Japan requiring GWP < 150 after 2023, and the US EPA offering credits toward fuel efficiency standards for low GWP refrigerants. SL-MAC technology uses a refrigerant contained in the engine compartment to chill coolant that is circulated in the passenger compartment. The design uses a smaller refrigerant charge with fewer connections, which reduces refrigerant leak rates and lowers the frequency and cost of service. The thermal coolant ballast saves energy by allowing the compressor to turn off during acceleration and to engage during deceleration, with inertia converted to cold without carbon emissions. The thermal ballast prolongs passenger comfort during idle stop. A timeline of MAC history is also included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".