FIRST ATLAS DOMESTIC STANDARD PROBLEM (DSP-01) FOR THE CODE ASSESSMENT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
KAERI has been operating an integral effect test facility, ATLAS (Advanced Thermal-Hydraulic Test Loop for Accident Simulation), for accident simulations of advanced PWRs. Regarding integral effect tests, a database for major design basis accidents has been accumulated and a Domestic Standard Problem (DSP) exercise using the ATLAS has been proposed and successfully performed. The ATLAS DSP aims at the effective utilization of an integral effect database obtained from the ATLAS, the establishment of a cooperative framework in the domestic nuclear industry, better understanding of thermal hydraulic phenomena, and an investigation of the potential limitations of the existing best-estimate safety analysis codes. For the first ATLAS DSP exercise (DSP-01), integral effect test data for a 100% DVI line break accident of the APR1400 was selected by considering its technical importance and by incorporating comments from participants. Twelve domestic organizations joined in this DSP-01 exercise. Finally, ten of these organizations submitted their calculation results. This ATLAS DSP-01 exercise progressed as an open calculation; the integral effect test data was delivered to the participants prior to the code calculations. The MARS-KS was favored by most participants but the RELAP5/MOD3.3 code was also used by a few participants. This paper presents all the information of the DSP-01 exercise as well as the comparison results between the calculations and the test data. Lessons learned from the first DSP-01 are presented and recommendations for code users as well as for developers are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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