Developing the machine learning models to estimate thermodynamic and transport properties of refrigerants
Bibliographic record
Abstract
The thermophysical properties of refrigerating systems should be accurately understood for designing low-temperature refrigeration cycles of economic acceptance. The thermal conductivity, density, velocity of sound, entropy, and enthalpy of various refrigerating systems from four differing classes, namely halocarbon, inorganic, hydrocarbon, and cryogenic fluids, were investigated here by the use of Multivariate Nonlinear Regression (MNR), Genetic Programming (GP), and Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (PSO-ANFIS). The development of a new and simple correlation was for the first time introduced to estimate saturated thermodynamic and transport properties of refrigerants without having in-depth knowledge on complicated parameters. Our research demonstrates that the PSO-ANFIS model is representative of an outstanding alternate for estimating the thermodynamic and transport properties of various refrigerating systems with a proper precision because Absolute Average Relative Errors (%AARD) for liqid and vapor thermal conductivity, liquid density, velocity of sound, entropy, and enthalpy were estimated as 3.0651, 7.9934, 1.0681, 1.2155, 1.8603, and 2.4835. This model is generally capable of estimating the thermodynamic and transport properties of various refrigerants with a superior accordance with data obtained experimentally.
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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".