Numerical heat transfer analysis of steam injection into subcooled water
Bibliographic record
Abstract
Steam direct contact condensation (DCC) into subcooled water may be encountered in different important industrial process applications, such as steam jet pumps, steam ejectors, pressurizers, and emergency cooling systems of nuclear reactor core. In this work, a numerical simulation study has been done for injection of steam into a tank full of subcooled water. In the simulations, the Eulerian multiphase flow in addition to a realizable k-epsilon turbulence model has been utilized. Moreover, a DCC model has been used for condensation capturing. Fluent software with a user-defined function (UDF) for the DCC model was employed for simulations. The results obtained from simulations were validated with experimental results, and a fair agreement was observed. This study considered the local Nusselt number (LNN) the most suitable parameter for investigating the heat transfer rate (HTR) at the computational cell level. Therefore, the contours of the LNN, its axial distribution, and radial distribution were studied with respect to the inlet pressure of injected steam, temperature of tank water, and location along the axis of the nozzle. The results reveal the fact that the value of the LNN reaches a maximum at the nozzle exit along the axis of the nozzle at 323 K tank water temperature. LNN decreases by increasing or decreasing the tank water temperature beyond 323 K. It is claimed that the heat transfer (HT) study at such a local scale has been conducted for the first time to the best of our knowledge, and it unfolds various crucial facts regarding steam-water interaction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".