A Methodology for Optimizing Startup-Shutdown Transients for Pressure Vessel Integrity
Bibliographic record
Abstract
A robust methodology for the optimization of thermal start up and shut down cyclic loading of a Generation IV supercritical-water cooled reactor (SCWR) core is presented in the conceptual development stage. The goal is understand the design space and identify design issues that deserve additional analysis, not to finalize the design. Parameterization of the geometry of different parts of the plenum supported automatic mesh generation of the parts. This enabled the Design of Optimal Experiments to be automated for a prescribed design space to compute the sensitivity of the defined objective functions to each design parameter including mesh parameters. With this software framework, the optimized profile of temperature and pressure for start up and shut down cycles was investigated with respect to an objective function to minimize the effective plastic strain over a number of cycles. In particular, the optimized value of effective plastic strain at saturated pressure and temperature conditions was determined. The intent was to demonstrate a capability to do design by analysis for pressure vessels, i.e., design based on a 3D nonlinear coupled holistic macroscopic thermal/stress analyses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".