Pool Boiling Heat Transfer Characteristics of Propylene Glycol, Glycerol, and Their Mixtures
Bibliographic record
Abstract
Abstract Boiling phase change phenomena of propylene glycol (PG), glycerol (Gl), and their mixtures are of great interest in many engineering and industrial applications dealing with high heat flux systems. Examples include heating, ventilation, and air conditioning systems, corrosion controls, and food, pharmaceutical, entertainment, and vape industries. However, a systematic assessment of their heat transfer performance from a heating element to surrounding fluids remains obscure. In this study, heat transfer characteristics of pure PG, Gl, and three different PG–Gl mixtures, i.e., 30PG/70Gl, 50PG/50Gl, and 70PG/30Gl, are investigated by conducting pool boiling experiments. Pure PG and Gl show up to about 49% lower critical heat flux (CHF) than de-ionized water (DW). Gl has a higher heat transfer coefficient compared to PG or DW in the nucleate boiling regime, indicating an enhanced heat transfer performance. Mixing of PG and Gl significantly affects the solution's CHF and heat transfer coefficient due to changes in characteristic properties of a binary mixture. The boiling mechanism is further examined by high-speed imaging of bubble formation. This study provides comprehensive data, which are useful to operate PG- and/or Gl-based boiling equipment or processes more efficiently.
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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 | 0.000 | 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) | 0.002 | 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 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".