A Novel Vacuum-Insulated Dual-Wall Composite Pipe for Cold Environment Applications
Bibliographic record
Abstract
Transportation of multiphase flows in hydrocarbon mining and processing, such as in sub-sea flow lines, often requires the fluid to be heated to reduce viscosity and prevent solidification within the pipeline. However, conventional metallic pipelines used in this application are prone to external corrosion from the surrounding oceanic water and internal metal loss caused by corrosion and abrasive particles within the multiphase fluid. Fibre-reinforced polymeric composite pipes are an economical means for mitigating these shortcomings by providing increased corrosion and abrasion resistance. As such, the current paper examines the small-scale preliminary design of a novel vacuum-insulated dual-walled composite pipe as a means of overcoming the aforementioned limitations. The inner and outer walls are based on a recently developed intrinsically-bonded lined-composite pipe; the polymeric liner serves to: (1) prevent degradation of the vacuum created in the annulus between the internally-lined inner and externally-lined outer walls, and (2) help prevent abrasion/corrosion of the composite pipe. Furthermore, by utilizing filament-wound pipes, a health monitoring system can be readily incorporated into the composite structure via electrical or optical fibres. The aim of this article is to: (a) outline the pipe structure, and (b) describe the laboratory based manufacturing and testing protocols.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".