Two-Phase Heat Transfer Performance of Ethylene Glycol-Water Binary Mixture in Nucleate Pool Boiling
Bibliographic record
Abstract
Modifying the base fluid water with addition of organic & inorganic solvents, surfactants, suspensions, and organic salt solutions is a prominent passive strategy for enhancing two-phase heat transfer, playing a crucial role in thermal management across industrial and commercial applications.This approach has attracted substantial research interest in boiling heat transfer due to its potential to overcome the boiling crisis phenomena [1], [2].The present study explores the effects of ethylene glycol (EG) in EG-water binary mixtures on two-phase heat transfer behaviour under nucleate pool boiling conditions.The primary objective is to understand how variations in the molecular weight and concentration of EG influence single bubble dynamics during pool boiling at saturation temperature and atmospheric pressure.The pool boiling experiments were conducted using three different molecular weights of EG: Mono (M = 62.07 g/mol), EG-400 (M = 400 g/mol), and EG-1500 (M = 1500 g/mol), at concentrations of 2%, 5%, and 10% by weight in deionized water as a bulk fluid.A custom designed stainless steel boiling chamber (internal volume = 400 cm) was employed to carry out the desired boiling experiments, which has the provision maintaining bulk fluid saturation temperature and atmospheric pressure.An Indium Tin Oxide (ITO)-coated glass substrate was employed as the superheating heating surface for generation of the isolated single vapour bubble, under a controlled heat flux supplied by a DC power source.The isolated vapour bubble ebullition cycle was recorded in real time frame through a Rainbow Schlieren Deflectometry-A non-intrusive optical imaging technique integrated with high-speed colour imaging camera in real time frame [3].Key parameters including bubble departure diameter, departure frequency, dynamic contact angle, bubble base diameter, and local temperature variation at the vapour-liquid interface were extracted via image analysis.The results demonstrate that increasing the both EG concentration and molecular weight suppresses thermal hydrodynamic instabilities near the heating surface.
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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".