Subchannel Analysis of Wire Wrapped SCWR Assembly
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Bibliographic record
Abstract
Application of wire wrap spacers in SCWR can reduce pressure drop and obtain better mixing capability. As a consequence, the required coolant pumping power is decreased and the coolant temperature profile inside the fuel bundle is flattened which will obviously decrease the peak cladding temperature. The distributed resistance model for wire wrap was developed and implemented in ATHAS subchannel analysis code. The HPLWR wire wrapped assembly was analyzed. The results show that: (1) the assembly with wire wrap can obtain a more uniform coolant temperature profile than the grid spaced assembly, which will result in a lower peak cladding temperature; (2) the pressure drop in a wire wrapped assembly is less than that in a grid spaced assembly, which can reduce the operating power of pump effectively; (3) the wire wrap pitch has significant effect on the flow in the assembly. Smaller<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mtext>w</mml:mtext><mml:mtext>i</mml:mtext><mml:mtext>r</mml:mtext><mml:mtext>e</mml:mtext></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mtext>r</mml:mtext><mml:mtext>o</mml:mtext><mml:mtext>d</mml:mtext></mml:mrow></mml:msub></mml:math>will result in stronger cross flow a more uniform coolant temperature profile, and also a higher pressure drop.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it