CFD Methodology to Determine the Hydrodynamic Roughness of a Surface with Application to Viscous Oil Coatings
Bibliographic record
Abstract
Water-lubricated pipe flow technology is an economic alternative for the long-distance transportation of viscous oils, such as heavy oil and bitumen. In the industrial-scale application of this technology, a thin oil film is always observed to coat the pipe wall. The natural process of wall coating during the lubrication is often referred to as wall fouling. A wall-fouling layer produces ultrahigh values of hydrodynamic roughness (∼1 mm), which have not been studied sufficiently to date. In this work, the hydrodynamic effects of a viscous wall-coating layer were experimentally investigated. A customized flow cell was used for the purpose. The equivalent sand grain (hydrodynamic) roughness was determined using a methodology involving computational fluid dynamics (CFD) simulations. The hydrodynamic roughness was also determined from the measured topology (physical roughness) of the surface. Additional verification of the method was obtained by applying it to analyze the hydrodynamic roughness produced by sandpapers and biofouling layers. The primary outcome of the present study is the validation and application of a CFD-based methodology to quantify the hydrodynamic roughness produced by any surface, including viscous oil coatings and biofouled surfaces. Additionally, it has been shown that the hydrodynamic roughness of a viscous oil coating, for the range of conditions tested here, is much more dependent on the coating thickness than on the Reynolds number. This has significant implications for the modeling of lubricated pipeline flows involving heavy oil and water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.001 | 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".