A Combined Numerical and Experimental Assessment of Air and Dust Flow in a Low-Reynolds Number Valve Including Modifications to Prevent Valve Seal Contamination
Bibliographic record
Abstract
High concentrations of particulate matter in air lead to deposition at the sealing radius of self-sealing valves involving direct intake from the environment. Dust deposition at and near this radius causes an increase in leakage flow when the valve is closed. In this paper, the mechanism which results in dust deposition in such valves is investigated and a new valve design which reduces leakage flow is developed and experimentally assessed. ANSYS Fluent 15.0 is used to numerically model the laminar flow assuming axisymmetry. Particle paths are predicted using Discrete Phase Modeling (DPM) as a post-processing step. Experimentally, dust deposition, mass flow at the operating pressure differential, and leakage flow rate are measured. The numerical and experimental results are utilized together to gain insight into the particles’ behavior. One of the key outcomes of this work is a post-processing technique which allows the numerical and experimental particle deposition results to be quantitatively compared. This supports the utlity of the numerical approach as locations of high concentrations of particle impacts in the numerical simulations are associated with locations of dense dust deposition in the experiments. High concentrations of particles at and near the sealing radius are observed to lead to increased leakage flow. Therefore, the impact of high concentrations of particles in this region is to be avoided. Utilizing this insight, the valve geometry is modified to reduce the amount of dust deposited in the region of the sealing radius. In the modified design, leakage flow is decreased by up to 93%, with a maximum of 2.1% reduction in flow rate margin relative to valve specifications.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".