Fan Zhang

I. A nuclear power plant (NPP) is a powerful carbon-free energy solution for the modern world’s power infrastructure. Digital upgrades to NPP systems bring benefits in operations, but also introduc...

I. Critical infrastructure plays a key role in modern society, encompassing a wide range of digital systems to support the operations and production of essential services such as food, water, energ...

I. Advanced reactors are rapidly moving from concept to demonstration and deployment. These systems are incorporating technology such as machine learning (ML)/artificial intelligence (AI), autonomo...

As nuclear facilities experience digital transformation and advanced reactor development, AI integration in cyber-physical security and other emerging technologies, such as autonomous robot operations, is increasingly developed. However, evaluation and deployment are challenged by the lack of dedicated virtual testbeds. The iFAN ecosystem is developed, a comprehensive digital twin framework with a realistic 3D environment with physics-based simulations. The iFAN ecosystem serves as a high-fidelity virtual testbed to develop AI methods for plant operation, cybersecurity, physical security, and robotic operation, as it provides real-time data exchange for pre-deployment verification. Core features include virtual reality, reinforcement learning, radiation simulation, and cyber-physical security. In addition, the paper investigates various applications through potential operational scenarios. The iFAN ecosystem provides a versatile and secure architecture for validating the next generation of autonomous and cyber-resilient nuclear operations.

With the proliferation of digital technologies in instrumentation and control systems comes an increased demand for improvements in cybersecurity. Developing strategies for preventing and detecting cyber-attacks is important in safety-critical systems such as nuclear power plants (NPPs). The application of machine learning (ML) to automate cyber-attack detection on operational technology (OT) networks is increasing significantly. To help facilitate the development of ML algorithms for cyber-attack detection, it is necessary to develop testbeds that are representative of the target networks on which the algorithms will be deployed. However, the design of cyber-attack detection methods using data from a specific network testbed can result in models that achieve high performance on that network but do not generalize well to others. The design of cyber-attack detection algorithms for NPP networks that are generalizable across networks is therefore essential to avoid significant performance degradation as well as to minimize disruptive testing on a target physical plant and improve the generalization. To solve this challenge, an NPP cybersecurity testbed called FANCY-X is developed, which is composed of two disjoint networks. One is entirely virtual, while the other is a hybrid network containing both virtual and physical components that emulate an NPP network. Cyber-attack detection cross-network generalizability can be evaluated by training on the virtual network and testing on the hybrid network. A high-fidelity two-loop pressurized water reactor (PWR) simulator is used to represent the underlying NPP, enabling the generation of realistic network activity. The FANCY-X testbed is validated through both same-network and cross-network evaluations.