CFD Study on S-CO2 Radial Turbine Experiment for Low Temperature and Pressure Conditions
Bibliographic record
Abstract
Abstract Supercritical carbon dioxide (S-CO2) Brayton cycles have emerged as a promising solution for power generation systems, owing to their high thermal efficiency across a wide range of heat source temperatures. These systems are particularly well-suited for small and micro-reactors due to their robust load-following capabilities. Radial turbines remain an optimal choice for power outputs in the range of several megawatts, and numerous research groups have conducted computational fluid dynamics (CFD) studies to evaluate their performance. However, the validation of these CFD analyses has predominantly relied on one-dimensional design codes rather than experimental data, which limits their reliability. At the Korea Advanced Institute of Science and Technology (KAIST), a radial inflow turbine is being operated in the Autonomous Brayton Cycle (ABC) Test Loop to produce a detailed performance map, including metrics such as pressure ratio, power output, and pressure difference. This study aims to validate previously proposed turbulence models by comparing CFD simulations with experimental data obtained from the test loop. The simulations are conducted using Ansys CFX 2024R2, with S-CO2 properties directly imported from the NIST REFPROP database to ensure reasonable accuracy. The CFD analysis employed a CAD model of the radial turbine used in recent experiments. The results showed a discrepancy of approximately 10% in pressure when compared with the experimental data.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".