Effect of foulant on temperature and steam quality profiles in once-through steam generator tubes
Bibliographic record
Abstract
• Novel 3D computational fluid dynamics model of a complete OTSG pass described. • For first time, examine how foulant impacts temperature and steam quality along pass. • Fouling interferes with heat transfer leading to temperature spikes in the tube wall. • Model yields basis for tube failure caused by temperature spikes linked to fouling. • Erosion likely occurs most in the bends of the OTSG. Once-through steam generators (OTSGs) are commonly used in the oil sands industry for steam generation for in-situ thermal recovery of extra heavy oil. These are expensive operations with challenges associated with the deposition of foulants, arising from solids and hydrocarbons in the boiler feed water, on the inner tube wall that leads to a decreased heat transfer rate. The foulant deposition also causes elevated tube-wall temperatures, which may lead to tube failure. This study examines the impact of foulant on the tube-wall temperature profile and vapor-to-liquid ratio in an OTSG. A three-dimensional finite volume model of multiphase flow and heat transfer in a single pass of a field-scale OTSG unit is described. Detailed flow behavior and heat transfer characteristics of water and steam from the CFD model were compared to field data. Thereafter, fouling was added to the model to explore overheating and erosion zones in the OTSG tubes. Velocity, temperature distribution, and steam generation in the OTSG tubes in the convection section and radiant section were investigated for areas prone overheating. It was found that the CFD model simulation results closely matched the bulk fluid temperature of the field unit. In the presence of fouling, the outer skin temperature from the model was well within the range of the thermocouple data from the field unit, enabling the application of the model to identify the foulant thickness based on the outer skin temperature. Lastly, the model demonstrated that the bends are the most likely sites for erosion within the pass.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".