Particle image velocimetry (<scp>PIV</scp>) experimental investigation of flow structures and dynamics produced by a centripetal turbine
Bibliographic record
Abstract
Abstract Turbines are capable of producing powerful radial discharge flow, which is why they are widely applied in such fields as chemical, petroleum, pharmaceutical, and so on. The centripetal turbine (CT) represents a new type of optimized turbine structure based on the Rushton turbine (RT). In order to reveal the characteristics of turbulence, centripetal and Rushton turbines were designed. The flow fields of impellers were measured by two‐dimensional particle image velocimetry (PIV), in combination with the time‐averaged method, phase‐resolved method, and proper orthogonal decomposition (POD) method for comparative analysis. Within the measured area, the time‐averaged velocity and phase‐resolved velocity of the CT is smaller compared to the RT. The numeric range of fluctuation velocity for the two types of impeller is basically the same, but the high‐value area of CT is more widely distributed. The swirling strength of the CT is significantly smaller than that of the RT. Besides, the wake structure is significantly weakened, which is conducive to reducing power consumption. The POD triple decomposition method proves effective in decoupling the instantaneous flow field into mean part, coherent part, and turbulence part. In each phase of blade passage, the turbulence part energy of the CT is considerably higher. For different blade passages, the cyclic variation of the CT is more significant. The flow field generated by the CT exhibits stronger eddy diffusion, which is conducive to mixing or reaction. It provides a basis for understanding the turbulent flow characteristics of CTs and impeller optimization.
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 | 0.000 |
| 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".