Numerical Investigation of Nanofluid’s Heat Transfer Performance in Passive Residual Heat Removing System of AP1000 Nuclear Reactor
Bibliographic record
Abstract
The Passive Heat Removal system (PHRS) is designed to remove the residual heat from the core in case of a station blackout, failure of emergency core cooling system, or failure of feedwater supply through the Passive Residual Heat Removal Heat Exchanger (PRHR HX). PRHR HX consists of a C-shaped tube bundle as a heat exchanger and the In-Containment Refueling Water Storage Tank (IRWST) as a heat sink. A temperature distribution of this passive heat removal system of an AP1000 Reactor is generated using COMSOL Multiphysics and the heat transfer coefficient is calculated to illustrate the effectiveness of the PHRS. A comparison of the heat transfer coefficient between the IRWST filled with water and nanofluid has been generated using the PRHR HX design. Thermophysical properties of nanofluids have been calculated in the process of calculating the heat transfer coefficient. Numerical results show the difference in temperature reduction of Al2O3, TiO2, and Ag as opposed to water in the IRWST. Time-dependent heat conduction of water and nanofluid results contribute to the effective analysis of passive heat removal systems and provide information for the safe operation of AP1000 reactors. By the end of 2024/2025, two VVER-1200 power stations with a combined capacity of 2400 MW will be operating in Bangladesh. For safety and licensing reasons, heat transfer simulation of VVER-1200 can be performed using COMSOL software.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".