Conference Agenda

The growing demand for flexible power generation, particularly with small-scale advanced nuclear reactors integrated with other energy systems, requires load-following capabilities. These reactors must adjust to frequent power fluctuations, making advanced autonomous control systems essential for maintaining efficiency and safety. In this paper, we present a novel integrated hardware/software testbed designed to develop and evaluate advanced autonomous control strategies for small-scale advanced nuclear reactors. The testbed represents a shift from traditional nuclear power plant operations by incorporating machine learning-based digital twin technology. This allows nuclear systems to dynamically adjust their output, allowing load-following operation or integration with other renewable energy sources. The testbed comprises three main components. The first is a thermal-fluid facility with three loops and a control rod drive mechanism, simulating the operational characteristics of advanced nuclear reactors under various scenarios. The second component is the Control Process Automation (COPA) system based on the Open Process Automation Standard (O-PAS), providing a flexible control architecture using OPC-UA communications for seamless signal integration and access. The third component is a customizable control algorithm platform employing machine learning techniques such as Bayesian optimization and AI-Agent. This platform communicates with the thermal-fluid facility through the COPA system, enabling autonomous, real-time control adjustments based on varying power loads and operational conditions. Our results show that advanced autonomous control systems enhance load-following capabilities, improving adaptability and integration with renewable energy systems. This work paves the way for more resilient, efficient, and safer nuclear plant operations in an evolving energy landscape.

The eVinci™ Microreactor which is under development by Westinghouse Electric Company could bring a cost-competitive and reliable nuclear energy source to the world. The small size of the eVinci microreactor allows for transportability and rapid, on-site deployment.

Instead of a fluid-based primary coolant system normally seen in nuclear power plants, eVinci Microreactor adopts heat pipes to transfer heat from the reactor to the Primary Heat Exchanger (PHX). The heat pipe design enables passive core heat removal which eliminates numerous components needed in active coolant systems and makes the eVinci microreactor a pseudo “solid-state” reactor with minimal moving parts.

The eVinci Nuclear Test Reactor (NTR) is a nuclear test facility dedicated for eVinci microreactor’s development. The NTR will provide critical engineering information for analysis code validation to support commercial licensing. The NTR is a highly integrated design involving strong interactions among various systems and components. It can test the multidisciplinary physics governing how the nuclear power is generated, controlled, and converted to thermal energy. To support the NTR development timeline, a first-of-its-kind thermal analysis has been performed which created a full scale and integrated CFD model of fully coupled, multiple NTR thermal systems.

The high-fidelity CFD analysis notably improves the inputs being used for design completion of the NTR and helps to expediate the design progression of various NTR systems and components.

In this paper the development of the NTR integrated CFD model is presented. Results of the analysis are introduced. The potential applications of the model will be also discussed.

In advanced U.S. molten salt reactor (MSR) designs, inert gas, such as helium, can be introduced to remove fission products, but can also induce reactivity fluctuations by increasing the core void fraction. This challenge was first identified in the Molten Salt Reactor Experiment (MSRE) at Oak Ridge National Laboratory, where gas entrainment affected reactor physics. Gas-induced fluctuations remain a concern for MSR operation, necessitating accurate predictive modeling. The drift-flux model has been used extensively in thermal-hydraulic codes to predict two-phase flow dynamics, including void fraction, but rely on constitutive terms that are typically estimated using empirical correlations. These correlations are generally developed for vertical upward pipe flows in water–air systems, limiting their applicability to alternative fluids and flow orientations. This paper presents a machine learning (ML) methodology using a supervised learning approach to predict constitutive terms in the drift-flux model. Two supervised ML models were trained on computational fluid dynamics (CFD) data to predict the distribution parameter and mean local drift velocity. These models were combined to compute the mean drift velocity. The methodology was applied to vertical pipe flow simulations under varying thermal conditions. The ML predictions showed good agreement with CFD data, with most results falling within the expected time-dependent fluctuation bounds. The largest errors were observed near the gas injection region. Overall, this study demonstrates the feasibility of ML-based modeling for constitutive terms in two-phase flow and highlights the need for broader datasets and experimental validation.

Small Modular Reactors (SMRs) are garnering global attention as a transformative solution for sustainable and clean energy production. Compared to traditional large-scale nuclear power plants, SMRs face challenges such as potentially higher generation costs and limited construction and operation experience. The virtual small modular reactor (VSMR) platform has emerged as a groundbreaking approach to innovating nuclear reactor design and optimizing safety through advanced simulation and analysis. Recognizing its significance, the Korean government designated the VSMR platform project as one of the Global Top Strategic Research Working Group in June 2024, A key enabler for VSMR implementation is the acceleration of computational fluid dynamics (CFD) simulations. While state-of-the-art machine learning (ML) models have shown promise in accelerating unsteady CFD, critical gaps remain, including (1) applicability to complex geometries, (2) suitability for long-term simulations, and (3) the lack of benchmark studies targeting nuclear reactor applications. This study aims to address these gaps by constructing CFD datasets for helical coil steam generators, developing state-of-the-art ML models fitted to CFD simulations, and benchmarking their performance under the same conditions. To ensure scalability to complex geometries, three base ML algorithms were selected: deep neural operator (DeepONet), graph neural networks (GNN), and implicit neural representations (INR). Preliminary results confirm the successful training of these models within their respective algorithms and provide a comprehensive performance comparison. These benchmark studies are expected to inform future ML model development strategies and significantly advance its application in VSMR.

One of the preliminary steps in the design and safety analysis of nuclear reactor systems is the formulation of the Phenomena Identification and Ranking Table (PIRT). PIRT was introduced by the United States Nuclear Regulatory Commission as part of the Code Scaling Applicability and Uncertainty methodology. PIRT depends on experts’ inputs and insight, and its formulation is based on the joint consensus and agreement of a panel of experts. The outcome of the PIRT is significantly affected by the domain knowledge of the participating experts. Moreover, oratory skills, thinking patterns, and biases (such as confirmation bias and psychological biases) can also affect the outcome of the PIRT process.

PIRT requires systematic strategy and knowledge abstraction at different levels. Counterfactual reasoning and knowledge transfer from different disciplines are also needed depending on the application. In this work, we aim to leverage the recent developments in the area of multimodal Large Language Models (LLMs) to build an AI-driven multi-agent framework for the implementation of PIRT. Foundation models based on frontier LLMs, customized and adapted by finetuning, retrieval augmented generation and prompt engineering, are used as proxy experts to support knowledge abstraction, reasoning and information retrieval for PIRT formulation. As PIRT results are impacted by the thinking patterns and inherent biases of the participating experts, theory of mind perspective in LLM under different configurations of multi-agent collaborations are also tested and explored. The demonstration of the framework is presented using a case study on an advanced reactor application.

Nuclear reactor system involves multiple physical processes which are coupled. In general, the traditional design process depends on the linear iteration and the manual trial calculations, which faces the challenge of the “combinatorial explosion” issue caused by vast and complex problem space. In recent years, the rapid development of Artificial Intelligence (AI) technology has brought new insight into the paradigm innovation of nuclear reactor system design, which includes the following four fields: model construction and knowledge discovery, reduced-order accelerated solution, design parameter optimization, and generative AI-assisted design. Moreover, the optimization-driven design method can be considered as the key of intelligent design, which can support the realization of intelligent design at three levels: characteristic parameter, shape variations, and topological relationship. Also,the relevant application has been conducted in the reactor thermal-hydraulic field. The AI-integrated optimization design technology is expected to shift the nuclear reactor design concept towards “function-led design”, which can be extended or inherited in many fields and finally support the realization of generative and heuristic reactor design.