Numerical procedure for scaling up pressure loss from mini flow loop tests
Bibliographic record
Abstract
Although in the literature friction factors have been developed specifically for Newtonian and non-Newtonian fluids to predict pressure loss during pipeline flow, their use for cemented paste backfills (CPB) still needs to be validated. For backfilling system feasibility studies, the flowability, pump selection and pumping requirement can be assessed through flow loop tests using full diameter (Dfull) pipeline arrangement. At the laboratory scale, only small flow loop tests using small diameter (Dsmall = Dloop) pipeline arrangement can be conducted. However, as the pressure loss (p/L) is closely dependent on the pipeline inner diameter (Di), p/L measured from a small flow loop test must be correctly scaled to the field pipeline diameter (Dfield = Dfull). The objective of this paper is to present a numerical simulations-based procedure for scaling up pressure loss from small flow loop tests. For this purpose, small flow loop tests were conducted using a 27.9 m-long pipeline circuit arrangement. The small pipe’s inner diameter (Dloop) was 0.0318 m. The pipeline circuit was instrumented with temperature probes (thermocouple) and a differential pressure meter for monitoring the evolution of the CPB temperature and pressure loss, respectively. After calibrating the non-isothermal pipe flow model in COMSOL Multiphysics® 5.2 software using temperature and pressure loss data gathered from the small flow loop tests, numerical simulations of flow loop tests were conducted to consider various filled inner diameters (Di) of pipes from 0.05 to 0.2 m) while keeping the rheological and thermal properties of the CPB unchanged. Results indicate a negative power law relationship between the pressure loss ratio and the inner diameter ratio (Di/Dloop). Work is still underway to verify if this relationship applies for different CPB mix recipes and different temperature conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".