IN-TUBE CONVECTIVE CONDENSATION UNDER AC HIGH-VOLTAGE ELECTRIC FIELDS
Bibliographic record
Abstract
The effects of alternating high-voltage electric fields on heat transfer and pressure drop for tube-side condensation of flowing refrigerant HFC-134a have been investigated. Experiments were performed in a horizontal, single-pass, countercurrent heat exchanger with a rod electrode placed along the center of the tube. Tests were performed with a sine and square wave voltage signals over a range of frequencies, peak-to-peak voltages, and direct current (DC) offset voltage, for a fixed mass flux of 100 kg/m2s, inlet quality of 70%, and heat flux of 10 kW/m2. The heat transfer coefficient was enhanced by a factor up to 2.7 with a similar increase in the pressure drop. An increase in the DC offset voltage and/or the peak-to-peak voltage increased the effective voltage of the applied alternating current (AC) signal, with a consequent increase in both heat transfer and pressure drop. The effect of frequency on heat transfer and pressure drop is strongly influenced by the DC offset voltage and the peak-to-peak voltage of the applied signal. In general, the heat transfer enhancement and pressure drop penalty increased with an increase of frequency at the low-frequency range. The effect of frequency is less prominent as the frequency is increased and has little effect in the high-frequency range.
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 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 | 0.001 |
| 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) | 0.001 | 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".