Computational Study of Critical Flow Discharge in Supercritical Water Cooled Reactors
Bibliographic record
Abstract
Supercritical Water-cooled Reactor (SCWR) is a Generation-IV nuclear reactor design that operates on a direct energy conversion cycle above the thermodynamic critical point of water (3740C and 22.1 MPa), and offers higher thermal efficiency and considerable design simplification. As an essential step in the design of SCWR safety systems, the accident behaviour of the reactor is evaluated to ensure that the safety systems can achieve safe shutdown for all the design basis accidents. Unfortunately, the computational tools and computer codes that are currently employed for safety analysis have little application in the supercritical region, and faces significant challenges in simulating the transitions from subcritical to supercritical conditions. This thesis examines the predictive capabilities of Computational Fluid Dynamics (CFD) code STAR-CCM+ by evaluating critical flow (or choked flow) due to accidental release of coolant from supercritical fluid systems. The biggest challenge of this research is that the current version of STAR-CCM+ does not support supercritical simulations because the steam tables included in the package are only limited to the subcritical subset of the thermodynamic fluid properties. The research was carried out in two stages. In the first stage, the CFD code STAR-CCM+ was customized to simulate supercritical conditions by, (i) Generating updated steam tables to include subcritical and supercritical fluid properties and using more pressure and temperature points in the pseudo critical region (22 – 25 MPa, 645 -660 K) to handle the rapid changes in the fluid properties, and (ii) Implementing a multi-dimensional steam table interpolation scheme to access the fluid property data at any thermodynamic state during the simulation. In the second stage, the customized CFD code was extensively evaluated by simulating several accidental release scenarios from supercritical conditions using rounded-edge and sharp-edge nozzles and the model results were validated with experimental data. To overcome the solution stability (or convergence) issues encountered during the supercritical simulations, a fine tuning procedure was proposed that guaranteed convergence for all the case studies considered in this thesis. The simulation results revealed that the CFD model produced results that were in good agreement with experimental data and only about 10% prediction error was noticed for most cases considered in the thesis. Considering the sensitivity of the CFD model for upstream temperatures and pressures, these results appear to be quite reasonable. From the computational experience gained in this research , we believe that the CFD code STAR-CCM+ is a very useful tool to perform thermal hydraulic simulations for supercritical systems. However, an appropriate customization and extensive validation of the code is required before it can be exclusively used for safety analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".