Effect of Surface Roughness on Non-Equilibrium Condensation in a Laval Nozzle
Bibliographic record
Abstract
The present study focuses on the effects of different surface roughness of the walls of a Laval nozzle on the nonequilibrium condensation of steam. The study describes the result of a numerical investigation of wet steam flow in a low-pressure convergent-divergent nozzle using commercial computational fluid dynamics package ANSYS Fluent 16. A 2D computational domain is considered and is discretized in a structured mesh with finer grid near the nozzle walls to capture the effects of roughness in the supersonic flow of steam. The mathematical model describing the phase change, which involves the formation of liquid droplets in a homogeneous non-equilibrium condensation process, is based on the classical nucleation theory. The Eulerian-Eulerian approach for modeling the wet steam has been adopted. The SST k- model has been used for the accurate formulation of the flow physics in the near wall region. The computational results for the case with no surface roughness were validated with the experimental results available in the literature provided by Moses and Stein were found to be in very good agreement. The pressure distribution, nucleation rate, average droplet radius and mass flow rate were compared for different values of surface roughness of nozzle walls. It is found out from the simulations that the parameters studied have a dependence on the surface roughness of the nozzle walls. There is a shift in the point of the incipience of the droplets and also in the nucleation rate. A reduction in the average droplet radius and the nucleation rate with increase in surface roughness along with a reduction in the condensation shock strength of rough nozzles when compared with the nozzle with no surface roughness is observed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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 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".