An Experimental Investigation of Flow Phenomena in a Multistage Micro-Tesla Valve
Bibliographic record
Abstract
Abstract The Tesla–diode valve, with no moving parts, allows restricted flow in one direction. It has many potential applications in different industrial situations. Despite the application of the valve and the importance of the effect of flow phenomena on the Tesla valve's performance, very few studies have experimentally investigated the motion of flow within the Tesla valve. This study aims to contribute to this growing area of research on the performance of Tesla valves by demonstrating the flow phenomena and the flow conditions needed to be used in numerical studies. In this work, the effect of direction of the flow and Reynolds number on the flow phenomena generated in a Tesla–diode valve is studied. Particle shadowgraph velocimetry (PSV) is utilized to investigate and visualize the velocity field. The results of this study confirm some of the phenomena that have been observed using numerical simulations. It also highlights the flow phenomena leading to an increase in the diodicity by an increase in the number of Tesla loops in the valve. An important observation often ignored in numerical simulation is the presence of unsteady behavior and vortex shedding for higher Reynolds number flows.
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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 | 0.000 | 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 | 0.001 | 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".