A CFD ASSISTED CONTROL SYSTEM DESIGN FOR SUPERCRITICAL WATER COOLED REACTOR (Thesis format: Integrated Article)
Bibliographic record
Abstract
In this study, the methodology to construct a control system based on computational fluid dy-namics (CFD) simulations is developed for supercritical water cooled reactor (SCWR). The CFD model using Reynolds Stress Model (RSM) and k − ωS S T model is validated with the experimental cases of steady state and vertically up flowing supercritical water in circular tubes for normal heat transfer and deteriorated heat transfer (DHT) cases. This model is extended to simulate the transient thermal-hydraulic behaviour of supercritical fluid flow and heat transfer, and the results are also compared with the 1-D numerical model, THRUST. The DHT phe-nomenon is investigated using the turbulence kinetic energy (TKE) and velocity distribution and their effect on the heat transfer. A correlation is reported between the TKE and veloc-ity profiles and heat transfer phenomenon at supercritical condition. The non-dimensional buoyancy and acceleration parameters are also used to predict the occurrence of DHT in the supercritical water flow in circular tubes. In the process of developing a control system for Canadian version of SCWR, system iden-tification method is used to develop the linear dynamic models based on non-linear CFD sim-ulations. Considering the strong cross-coupling between the inputs and outputs of the SCWR, multiple input and multiple output (MIMO) system is decoupled and is converted to several single input and single output (SISO) systems using pre-compensator. Based on the decoupled SISO systems, loop compensator is developed for the control and stability of the reactor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".