Acil soğutucu tedarik sisteminin candu soğutucu dağıtma haznesi üzerindeki etkisinin deneysel ve hesaplamalı olarak araştırılması.
Bibliographic record
Abstract
Inlet headers in the primary heat transport system(PHTS) of CANDU type reactors, are used to collect the coolant coming from the steam generators and distribute them into the reactor core via several feeders. During a postulated loss of coolant accident (LOCA), depressurization and vapor supplement into the core may occur, which results a deterioration in the heat transfer from fuel to the coolant. When a depressurization occurs, “Emergency Coolant Injection(ECI)” system in the PHTS in CANDU reactors, is automatically become active and supply coolant is fed into the reactor core via the inlet header and feeders. . This study is focused on the experimental and computational investigation of the ECI effect during a LOCA in a CANDU inlet header. The experiments were carried out in METU Two-Phase Flow Test Facility which consists of a scaled CANDU inlet header having 5 connected feeders. The same tests were simulated with a one dimensional two-fluid computer code, CATHENA, developed by Atomic Energy of Canada Limited(AECL). The average void fraction and the two phase mass flowrate data measured in the experiments are compared with the results obtained from CATHENA simulation. Although a few mismatched points exist, the results coming from two different studies are mostly matching reasonably. Lack of three-dimensional modeling for headers in CATHENA and experimental errors are thought to be the reasons for these dismatches.
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.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".