3D numerical study of wire-cylinder precipitator for collecting ultrafine particles from diesel exhaust
Bibliographic record
Abstract
A precipitator section is modeled numerically in 3D to determine the collection efficiency for conductive diesel exhaust particulates. It consists of a circular tube and a wire electrode mounted at the center of the tube, supplied with a negative high dc voltage, while the tube is electrically grounded. The analytical solutions of Poisson and current continuity equations are implemented to obtain the ionic space charge density and electric potential distributions in the channel. Commercial CFD FLUENT software is used to solve the k-ε turbulent flow model, while also considering the electrical body forces. Particle charging and motion equations are solved using Discrete Phase Model (DPM) feature of the FLUENT and programming User Defined Functions (UDFs). Particles are assumed to be charged by combined field and diffusion charging mechanisms. Effects of some electrical characteristics of diesel exhaust particulates, such as charge-to-mass ratio and particle migration velocity, on collection efficiency are assessed. Patterns of particle deposition along the channel are evaluated and compared for different particle sizes. Numerical modeling of the 3D EHD flow pattern induced by corona discharge is demonstrated in the cross section of the tube when the corona wire is slightly off-center (eccentric) in an arbitrary direction.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".