Steady-State Radiolysis of Supercritical Water: Model Predictions and Validation
Bibliographic record
Abstract
Chemical kinetic models are being developed for the γ-radiolysis of subcritical and supercritical water (SCW) to estimate the concentrations of radiolytically produced oxidants. Many of the physical properties of water change sharply at the critical point. These properties control the chemical stability and transport behavior of the ions and radicals generated by the radiolysis of SCW. The effects of changes in the solvent properties of water on primary radiolytic processes and the subsequent aqueous reaction kinetics can be quite complicated and are not yet well understood. The approach used in this paper was to adapt an existing liquid water radiolysis model (LRM) that has already been validated for lower temperatures and a water vapor radiolysis model (VRM) validated for higher temperatures, but for lower pressures, to calculate radiolysis product speciation under conditions approaching the supercritical state. The results were then extrapolated to the supercritical regime by doing critical analysis of the input parameters. This exercise found that the vapor-like and liquid-like models make similar predictions under some conditions. This paper presents and discusses the LRM and VRM predictions for the concentrations of molecular radiolysis products, H2, O2, and H2O2 at two different irradiation times, 1 s and 1 hr, as a function of temperature ranging from 25°C to 400°C. The model simulation results are then compared with the concentrations of H2, O2, and H2O2 measured as a function of γ-irradiation time at 250°C. Model predictions on the effect of H2 addition on the radiolysis product concentrations at 400°C are presented and compared with the experimental results from the Beloyarsk Nuclear Power Plant (NPP).
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".