Highly Efficient Euler-Euler Approach For Condensing Steam Flows In Turbomachines
Bibliographic record
Abstract
In steam turbines, insight into the aero-thermal interactions within the complex transient 3D multi-phase flow must be gained for effective optimization. This goal can be achieved by means of efficient and fast computational techniques capable of accounting for the appearing challenging phenomena like real gas effects and non-equilibrium condensation. To this aim, we present a density-based CFD solver library for condensing wet-steam flows implemented in the OpenFOAM CFD framework. The multi-phase flow is solved by means of two efficient mono-dispersed Euler-Euler models. Here, the numerical fluxes are computed by the AUSM+ scheme with a 2ndorder MUSCL reconstruction. For the presented steady state calculations, an explicit local time stepping with a 4th order Runge-Kuttaschemeis used. The non-equilibrium condensation effects are modelled based on the classical theory of droplet nucleation and droplet growth. For highly efficient and accurate computations, the thermodynamic properties of steam and water are obtained by means of the Spline Based Table Lookup(SBTL)method. This tabulation approach is optimized for the presented density-based solution methods and the potential is revealed comparing to Young's virial state equation. Surface tension, molecular viscosity and thermal conductivity coefficient are computed based on the IAPWS formulation. Quasi 3D (q3D) results of the Euler equations are shown for three different Laval-nozzle test cases compared to measurements. The computational speed of the different models and state equations is evaluated, where the source-term model in combination with SBTL shows best performance. On this basis, further laminar and turbulent q3D and 3D results are presented for the Moore nozzle test case B. Here, the agreement between numerical predictions and experiments is improved, because the calculations do account for boundary layer effects which are present in the experimental configuration. These developments represent the first steps towards high performance, high-fidelity simulations of condensing steam flows in low pressure turbines by means of large eddy simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".