A Hardware-in-the-Loop Simulation Platform for the Verification and Validation of Safety Control Systems
Bibliographic record
Abstract
Verification and validation (V&V) of safety control system quality and performance is required prior to installing control system hardware within nuclear power plants (NPPs). Thus, the objective of the hardware-in-the-loop (HIL) platform introduced in this paper is to verify the functionality of these safety control systems. The developed platform provides a flexible simulated testing environment which enables synchronized coupling between the real and simulated world. Within the platform, National Instruments (NI) data acquisition (DAQ) hardware provides an interface between a programmable electronic system under test (SUT) and a simulation computer. Further, NI LabVIEW resides on this remote DAQ workstation for signal conversion and routing between Ethernet and standard industrial signals as well as for user interface. The platform is applied to the testing of a simplified implementation of Canadian Deuterium Uranium (CANDU) shutdown system no. 1 (SDS1) which monitors only the steam generator level of the simulated NPP. CANDU NPP simulation is performed on a Darlington NPP desktop training simulator provided by Ontario Power Generation (OPG). Simplified SDS1 logic is implemented on an Invensys Tricon v9 programmable logic controller (PLC) to test the performance of both the safety controller and the implemented logic. Prior to HIL simulation, platform availability of over 95% is achieved for the configuration used during the V&V of the PLC. Comparison of HIL simulation results to benchmark simulations shows good operational performance of the PLC following a postulated initiating event (PIE).
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".