Implementation of Fault Tolerant Control Scheme for Point Kinetic Reactor Model With Temperature Feedback in Matlab
Bibliographic record
Abstract
As a highly safety-critical system, it is insufficient for the Nuclear Power Plant (NPP) Instrumentation and Control (I&C) system to simply rely on a conventional control schemes or controllers which only satisfy stability and performance specification to the perturbation of the nominal plant. Since the current operating or newly-built I&C systems are based on transferring or adapting modern high performance electronic devices, it provides the hardware foundation and possibility to incorporate more advanced control systems into nuclear systems to achieve higher safety and stable performance, even in unexpected faulty situations. Active Fault Tolerant Control (FTC) is one of the choices for such advanced control. Active FTC encompasses the following components: 1. nominal controller design, 2. sensors and actuators fault detection and isolation, and 3. fault estimation and fault accommodation. In this research, approaches for each component are integrated into an active FTC scheme. Following this, the active FTC scheme is applied to a point kinetic rector model with fuel temperature and coolant temperature effect to reactivity. Simulation results show that the active FTC scheme designed in this research can effectively track the global power set point, even under situations with single fault from actuator or sensors.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".