Discrete-Element Model of Wear of Structural Elements of a Centrifugal Decanter for Improved Cleaning of Drilling Fluids
Bibliographic record
Abstract
Abstract It is shown that during drilling, cleaning of drilling solution using centrifugal decanters minimizes the presence of abrasive particles in the system and reduces mechanical wear of structural elements and pumping units. The results of a comparative analysis of the resource of the initial nozzle design and three proposed modifications, differing in surface geometry and orientation relative to the direction of rotation of the drum, are presented. A comprehensive assessment of the effect of design changes on the particle escape velocity and, as a result, on the wear rate was carried out using an analytical model for calculating the amount of wear during particle impact. A discrete-element model of the processes accompanying the operation of a centrifugal decanter (movement of drilling solution along the centrifuge screw) has been developed. The time before the formation of critical wear (destruction) of the nozzle surfaces is calculated. The influence of geometric and design parameters of centrifugal decanter nozzles on the efficiency of the drilling solution cleaning process has been studied. It is shown that reducing the height and diameter of the nozzles to 34 and 37 mm, respectively, increased the surface wear time by 1.5 times. An analytical evaluation of the resource of cylindrical nozzle surfaces for various design options has been performed. A significant increase in service life is achieved by changing the inclination of the cylindrical surfaces of the nozzles against the direction of rotation of the drum. The wear time of the nozzle surface increases 12.5 times.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".