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Record W4389624797 · doi:10.3390/en16248017

Experimental Study of an Air-Conditioning System in an Electric Vehicle with R1234yf

2023· article· en· W4389624797 on OpenAlexaboutno aff
Jeonghyun Song, Seongyong Eom, Jae‐Seung Lee, Youngshin Chu, Jaewon Kim, Seohyun Choi, Minsung Choi, Gyungmin Choi, Yeseul Park

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantHVACCoefficient of performanceAir conditioningAir source heat pumpsGas compressorCoolantAutomotive engineeringNuclear engineeringIntercoolerWater chillerCooling capacityWater coolingHeat pumpEnvironmental scienceHeat exchangerEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

R134a, a vehicle refrigerant used in the vehicle heat pump system, is regulated according to the Montreal Protocol. Refrigerants such as R1234yf, R744, and R290 in vehicle heat pump systems are being investigated to identify their alternatives. Because developing a new system exclusively for new refrigerants is costly, an empirical test was conducted on the R1234yf refrigerant in a heat pump system designed for the R134a refrigerant in an actual vehicle system. The heating, cooling, and battery-cooling modes were tested for the amount of refrigerant charge, and operability tests were conducted for the compressor load; heating, ventilation, air conditioning (HVAC) air flow rate; coolant temperature; and flow rate of each mode. The optimal refrigerant charge in heating mode was 0.7 kg, and the optimal refrigerant charge in the cooling and battery-cooling modes was 0.9 kg. To yield the highest coefficient of performance of the system, the compressor load was 50%, the HVAC fan was 12 V, and the coolant flow rate was 10 LPM. The most efficient system operation was possible at a coolant temperature of 30 °C in the cooling and heating modes and at 20 °C in battery-cooling mode.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.233
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes1
Has abstractyes

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