MS Excel Spreadsheet Add-in for Thermodynamic Properties and Process Simulation of R152a
Bibliographic record
Abstract
Microsoft Excel add-in has been developed for the thermodynamic properties of refrigerant DiflouroEthane (R152a) an alternative refrigerant to R134a, which has no Ozone Depletion Potential. Thermodynamic properties’ equations for R152a were formulated from a popular equation of state. The equations were transformed into a computer program in Microsoft Excel-Visual Basic for Application in Integrated Development Environment as an Excel add-in. The add-in is able to compute the thermodynamic properties of R152a refrigerant - specific volume, internal energy, enthalpy, and entropy in the wet, subcooled and superheated-vapor regions. The calculated values are accurate compared to the standard reference properties tables for refrigerants. Computed properties’ data can easily be used in the Excel spreadsheet for process analysis, simulation and design of R152a refrigerating plants. The solution scheme and computer language adopted in this work are easy to apply and use as opposed to the available sophisticated and expensive computer software packages; and the MS Excel add-in presented in this paper would be useful to both practicing engineers and engineering students in the area. An application was illustrated by solving a typical problem in R152a refrigerating process analysis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.040 |
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".