CFD Predictions of Efficiency for Non-Equilibrium Steam 2D Cascades
Bibliographic record
Abstract
Most non-equilibrium wet steam CFD analyses in the open literature have concentrated on predicting blade pressure loadings, with very few studies emphasizing turbine efficiencies. One of the few exceptions is the work of Gerber et al. [1]. In light of this, in this paper we present CFD predictions of isokinetic efficiency and Markov Loss coefficients and comparisons with measurements for the 2D cascades of White et al. [2] and Bakhtar et al. [3, 4]. Predictions were obtained using an Eulerian-Eulerian multiphase formulation, which is an extension of General Electric’s proprietary CFD turbomachinery code, TACOMA. The formulation is optimized to capture the thermodynamic loss. There is no slip between the droplets and the surrounding vapor. Comparisons with other experimental quantities are also presented as needed to ensure that the non-equilibrium wet steam physics is accurately captured. Although the non-equilibrium models used cannot capture all the loss components present in actual flows, our efficiency predictions are much closer to experimental data than those of equilibrium simulations or the Baumann rule.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".