Surface Patterning of Stainless Steel in Prevention of Fouling in Heat Transfer Equipment
Bibliographic record
Abstract
Fouling of surfaces is a major challenge in design and operation of many industrial heat transfer equipment. Fouling causes significant energy, material and production losses, which increase the environmental impact and decrease economic profitability of processes. Even small improvements in prevention of fouling would lead to significant savings in a wide range of heat transfer applications. In this study, crystallization fouling of aqueous calcium carbonate solutions on a heated stainless steel surface is used to investigate the prevention of fouling in heat transfer equipment by physical surface modifications. Fouling behaviour of different surface patterns are studied experimentally in a laboratory scale fouling test apparatus. CFD modelling is used to study hydrodynamic and thermal conditions near surfaces with different patterns. In addition, the effect of surface pattern on the removal of particles is studied numerically. Surface patterning is found to affect the hydrodynamic and thermal conditions near the wall, and therefore to change the conditions for fouling layer build-up and removal, when compared to a flat heat transfer surface. The most promising surface pattern includes curved shapes, and it seems to create flow conditions in which improved convective heat transfer decreases the driving force for crystallization fouling. In addition, curved surfaces increase the shear forces at the wall, which prevents adhesion of the foulants to the surface and increases resuspension.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".