Prediction of the static pressure generation for an electrohydrodynamic conduction pump
Bibliographic record
Abstract
Steady-state 2-D electrohydrodynamic pumping through bipolar hetero-charge conduction phenomenon has been investigated numerically. In order to validate the presented numerical algorithm, the pump geometry was chosen to be identical to the device whose experimental data is available in the literature. The concentrations of the positive and negative ions and the electric field distribution of the pump were calculated. The resulting static pressure generation of the pump was predicted for the refrigerant R-123 at different applied voltages ranging between 2 to 20kV DC. The comparison between the predicted static pressure generation and the previous experimental results in the absence of fluid flow shows a good agreement with a maximum deviation of 12.5% at 20 kV applied voltage. In the present work, the electrical conductivity of R-123 was assumed to be the most recent proposed value of σ=7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-11</sup> S/m. Assuming this value of electrical conductivity, the numerical model predicts the hetrocharge layer thicknesses in the order of gap spacing even at low applied voltage and weak field regime. By increasing the applied voltage, above ~15kV, the heterocharge layers of the opposite electrodes extend to the entire gap spacing and create an overlapping region. Assuming three orders of magnitude higher value of electrical conductivity for R-123 in the previous numerical studies, the thickness of the hetrocharge layer and pressure generation was underestimated by almost two orders of magnitude. Through extensive numerical experiments, the electrical conductivity of the working fluid was found to be an important parameter to determine the heterocharge layer thickness, electric body force, and predicted static pressure. The issues related to the scaling of the conduction pump when the heterocharge layers create an overlapping region in the reduced gap spacing are discussed.
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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".