CFD Modeling of Film Condensation from a Steam-Air Mixture in Vertical Channels
Bibliographic record
Abstract
Steam condensation in the presence of a noncondensable gas is of vital importance for passive cooling containment systems. The noncondensable gas causes a significant reduction in the condensation rate and heat transfer across the containment, which is important for postulated loss-of-coolant accidents in a nuclear reactor.In this work, computational fluid dynamics models of condensation and the adjacent single-phase steam-air mixture flow are developed for laminar and turbulent flow in vertical channels by two distinct wall condensation modeling approaches using the commercial code STAR-CCM+. The first is the fluid film model available in STAR-CCM+, which solves liquid layer governing equations with connections to the adjacent gas mixture flow. The second is a user-defined wall condensation model that neglects the fluid film and instead accounts for mass, momentum, and heat transfer via user-defined volumetric sink terms adjacent to the cold wall.The condensation models are assessed by first comparing the calculated results with the numerical solution of laminar flow, solved using a complete two-phase model that solves parabolic equations based on conservation of mass, momentum, energy, and species for each phase. Next, the results of a two-dimensional analysis are compared with COPAIN experiments and existing numerical solutions from three-dimensional analyses. The comparisons include new, detailed results that have not been reported in previous analyses of a COPAIN case. These new results include local field profiles of velocity, temperature, and air mass fraction, and local mass flux.
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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 | 0.001 |
| 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".