The TRIGA (Training, Research, Isotopes, General Atomics) reactor is a type of research reactor designed for training, medical isotope production, and various nuclear experiments. Known for its inherent safety features, TRIGA reactors utilize a unique fuel composition that allows for self-regulating power excursions, making them ideal for educational and experimental purposes.
My project focuses on the implementation of Digital Twin Technology on the TRIGA reactor to enhance operational efficiency and safety. A Digital Twin is a virtual replica that continuously updates and interacts with real-world data, allowing us to simulate and predict the dynamic behavior of the reactor. This is crucial for:
Real-time monitoring of reactor performance
Accurate prediction of dynamic responses under different operating conditions
Providing control guidance to operators for optimized decision-making
We have developed a system capable of real-time data acquisition from various sensors monitoring the reactor. Each night at midnight, data collected from the NETL TRIGA reactor is transferred to a cloud-based and supercomputer repository. Using neutronics simulations and machine learning algorithms, the system analyzes reactor behavior and communicates the findings to operators and relevant personnel.
To further enhance the digital twin system, our next steps include:
Anomaly Detection: Identifying and diagnosing irregularities in reactor behavior
Sparse Sensing: Optimizing sensor placement for efficient data collection
Prediction Model for Dynamics: Developing advanced models to forecast reactor responses under various conditions
By integrating digital twin technology, we aim to revolutionize TRIGA reactor operations, making them more intelligent, predictive, and operator-friendly.