Optimal design of hydraulic capsule pipelines transporting spherical capsules
Bibliographic record
Abstract
Abstract The scarcity of fossil fuels affects the efficiency of established modes of cargo transport within the transportation industry. Extensive research is being carried out on improving the efficiency of existing modes of cargo transport, as well as developing alternative means of transporting goods. One such alternative method is using energy contained within fluid flowing in pipelines to transfer goods from one place to another. The present study focuses on the use of advanced numerical modelling tools to simulate the flow within hydraulic capsule pipelines (HCPs) transporting spherical capsules with an aim of developing design equations. “Hydraulic capsule pipeline” refers to the transport of goods in hollow containers (“capsules”), typically spherical or cylindrical in shape, which are carried along the pipeline by water. HCPs are used in mineral industries and have potential for use in oil and gas sectors. A novel modelling technique was employed to investigate various geometric and flow conditions within HCPs. Both qualitative and quantitative flow analyses were carried out on the flow of spherical capsules in an HCP for both onshore and offshore applications. Furthermore, based on the least‐cost principle, an optimization methodology was developed for the design of single‐stage HCPs. The input to the optimization model is the solid throughput required from the system, and the outputs are the optimal diameter of the HCPs and the pumping requirements for the capsule transporting system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".