Bibliographic record
Abstract
The contaminant wear of any pump depends on the operational and the contaminant severity as well as the inherent contaminant sensitivity of the pump. In order to evaluate the contaminant sensitivity of a pump it is necessary to conduct a carefully controlled test. The primary result obtained from this test is a lumped parameter called the contaminant wear sensitivity coefficient, which represents the degree of contaminant tolerance. A contaminant sensitivity theory has been developed which states that for every contaminant particle that passes through a pumping chamber, the pump looses a finite portion of its flow delivery potential. The degraded flow rate equals the sum of the product obtained by using both the wear sensitivity coefficient and the particle exposure rate for all particles sizes to which the pump has been exposed. The wear sensitivity coefficient is simply the volume of the pumping potential that the pump looses per particle exposed. The particle exposure rate equals the product of the flow rate and the particle concentration in the fluid. An analytical treatment of the contaminant sensitivity concept is called the Omega Theory. This paper first reviews the contaminant sensitivity test and discusses the various test parameters. In addition, the paper presents the analytical model that permits computer techniques to be applied in deriving and manipulating contaminant sensitivity coefficients obtained from testing in order to construct the contaminant tolerance profile (Omega Life) and calculate the service life for a specific pump. Furthermore, the paper provides a means by which the field service life can be estimated based upon the standard test.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".