Generation and Validation of CFD-Based ROMs for Real-Time Temperature Control in the Main Control Room of Nuclear Power Plants
This study develops and validates a Reduced Order Model (ROM) integrated with Digital Twin technology for real-time temperature control in the Main Control Room (MCR) of a nuclear power plant. Utilizing Computational Fluid Dynamics (CFD) simulations, we obtained detailed three-dimensional thermal fl...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/17/24/6406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This study develops and validates a Reduced Order Model (ROM) integrated with Digital Twin technology for real-time temperature control in the Main Control Room (MCR) of a nuclear power plant. Utilizing Computational Fluid Dynamics (CFD) simulations, we obtained detailed three-dimensional thermal flow distributions under various operating conditions. A ROM was generated using machine learning techniques based on 94 CFD cases, achieving a mean temperature error of 0.35%. The ROM was further validated against two excluded CFD cases, demonstrating high correlation coefficients (R > 0.84) and low error metrics, confirming its accuracy and reliability. Integrating the ROM with the Heating, Ventilating, and Air Conditioning (HVAC) system, we conducted a two-month simulation, showing effective maintenance of MCR temperature within predefined criteria through adaptive HVAC control. This integration significantly enhances operational efficiency and safety by enabling real-time monitoring and control while reducing computational costs and time associated with full-scale CFD analyses. Despite promising results, the study acknowledges limitations related to ROM’s dependency on training data quality and the need for more comprehensive validation under diverse and unforeseen conditions. Future research will focus on expanding the ROM’s applicability, incorporating advanced machine learning methods, and conducting pilot tests in actual nuclear plant environments to further optimize the Digital Twin-based control system. |
|---|---|
| ISSN: | 1996-1073 |