Design and optimize a novel hydrocyclone by combining mechanistic and data-driven models
Bibliographic record
Abstract
New hydrocyclone designs can significantly enhance separation efficiency in applications such as water treatment and particle classification. Therefore, various hydrocyclone geometries with different inlet and cone configurations are explored through a validated mechanistic model, leading to a new cyclone design. The proposed design features a laminar spiral inlet and a straight-to-convex cone, achieving reductions of 44.2 % in separation sharpness and 58.4 % in water split. Its double-cone configuration reduces tangential velocities in the upper conical section, while the convex lower cone broadens the separation region, maintaining relatively high tangential velocities near the spigot. These effects reduce particle accumulation near the spigot and improve separation performance. Moreover, the laminar spiral inlet mitigates short-circuit flow near the vortex finder. To further enhance separation efficiency, the novel conical section is optimized by integrating mechanistic and data-driven models. Compared to the initial novel design, the optimized version exhibits an 18.2 % reduction in separation sharpness and a 16.2 % reduction in water split by optimizing four geometric variables characterizing the conical section. Internal flow field analysis confirms that the optimized configuration establishes favorable tangential velocities in the conical section, guiding fine and coarse particles along optimal paths and improving overall performance. • A novel design for the hydrocyclone is developed using the mechanistic model. • Mechanistic and data-driven models are combined to optimize the novel design. • The separation efficiency of the optimized novel design is improved significantly. • The laminar spiral inlet decreases the short-circuit flow near the vortex finder. • The longer and convex cone promotes the proper directional migration of particles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".