Ultrasonic Measurements of Local Particle Velocity and Concentration Within the Casing of a Centrifugal Pump
Bibliographic record
Abstract
In the design of slurry transport equipment used in the mining and dredging industries, the effects of solid particle velocity and concentration on hydraulic performance and wear need to be considered. Two ultrasonic techniques have been used to investigate slurry flows through a centrifugal pump casing: a local particle concentration measurement technique (Furlan et al., 2012) and a pulsed ultrasonic Doppler Velocimetry (PUDV) technique (Hanjiang, 2003, Garman, 2015). Local particle velocities and concentrations have been obtained in a flow of soda lime glass particles (diameter of 195 μm) and water through the casing of a centrifugal slurry pump operating close to the best efficiency point using the two ultrasound techniques. For the concentration measurements, the acoustic properties of slurry flows such as sonic velocity, backscatter, and attenuation are correlated to the volume fraction of solid particles. The algorithm utilizes measurements obtained from homogeneous vertical pipe flow fields as calibration data in order to obtain experimental concentration profiles in the non-homogenous flow regimes which are encountered in the pump casing. The PUDV technique correlates the Doppler shift in frequency associated with the movement of particles towards or away from the transducer. A two measurement (angle) technique is applied within the pump casing in order to account for the components of particle velocity which are orthogonal to the casing side wall. The techniques are utilized to obtain concentration and velocity profiles within the pump casing for overall average loop particle concentrations ranging from 7–11 % by volume. The experimental results are compared with the concentration and velocity fields that are predicted by in-house finite element computational fluid dynamics (CFD) codes (Pagalthivarthi and Visintainer, 2009) which are used to predict wear in centrifugal slurry pump wet end components. Reasonable agreement is observed for both the concentration and velocity fields. Specifically, measurements indicate that there is a reduction of in-situ concentration and hence a corresponding radial acceleration of the particles with respect to the fluid occurring within the impeller which has also been predicted by computational predictions of flow through the impeller (Pagalthivarthi et al., 2013). Additionally, the prediction of the existence of secondary flow patterns by the casing computational code has been supported with the velocity measurements.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".