Experimental and Numerical Investigation of a Needle-Ring Type of Ionic Wind Generator Acting Alone and as a Flow Enhancement Device
Bibliographic record
Abstract
Abstract Experimental and numerical investigations are conducted to determine the operational characteristics of a needle-ring type of ionic wind generator for the case when it acts alone and for the case when it is used as a device assisting or enhancing the flow of a primary flow generator. The experimental study is conducted using a test facility designed according to the ANSI/AMCA 210-16 standard to determine the pressure rise, volume flowrate, and energy efficiency. The entire ionic wind generation and testing process is modeled by a hybrid multiphysics scheme for comparison with experimental results and to investigate details of the experiment that could not be measured. The performance of the ionic wind generator is numerically predicted by alternatively solving the fluid field equations using a finite volume computational fluid dynamic scheme and the electric field equation using a finite element simulation. The segregated steps are conducted sequentially in a loop system to reach a specified convergence level to obtain a steady-state solution. The experimental and numerical characteristic curves are in good agreement, and the associated static efficiencies are found to be much smaller compared to conventional fans. When the primary flow is in the same direction as the ion flow, the recovery efficiencies of the ionic wind generator are found to increase by supplying a larger primary flowrate.
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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 | 0.001 | 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".