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This set of slides describes challenges and solutions for MOOSE-based multiphysics simulations. It was presented to the NASA Digital Transformation Working Group.
Presentation on a conceptual study of combining a small modular nuclear reactor (SMR) with a solid oxide fuel cell, solid oxide electrolysis cell, and a gas turbine as an integrated energy system with high operational flexibility and rapid load following. Virtually presented at the MILLENNIUM CLEAN and SUSTAINABLE POWER workshop 2025, University of Genoa, Italy.
Authorized by the Nuclear Energy Innovation Capabilities Act (NEICA), the National Reactor Innovation Center (NRIC) provides private sector technology developers access to strategic infrastructures and assets for commercial nuclear energy research, development, demonstration, and deployment activities. The mission is to support a timely and cost-effective path to the licensing and commercialization of new nuclear energy systems. To meet these needs, NRIC is developing two reactor demonstration test beds at Idaho National Laboratory (INL), the ZPPR Test bed (ZTB) and the EBR-II Test bed (ETB). ZTB will support the demonstration of systems that operate at less than 500 kWt. The baseline objective is for the ZPPR Cell to act as a confinement structure capable of siting reactors that utilize Safeguards Category 1 material for operations. The major areas addressed in the pre-conceptual design include: • Installation of an access door • Electrical Power • Heat Removal • Ventilation in the Cell • Reactor Installation Along with the design for ZTB, a concept of operations (COP) has also been developed. The COP is intended to facilitate a common understanding of ideas, challenges, and issues. As systems continue to evolve in complexity System Engineers and Project Directors will utilize and update the COP to develop and sustain a common vision of the system for stakeholders.
Many engineers and scientists researching modeling and analysis methods for nuclear energy advancement are focused on proof-of-concept and early-stage development for new reactor designs. Since instrumentation and control (I&C) systems are traditionally developed later in the systems engineering lifecycle, researchers may be unfamiliar with modern digital technology used in these I&C systems. It is even more likely that they are unfamiliar with the cyber risks associated with them. This paper provides a brief overview of nuclear digital I&C systems before examining the importance of cyber risk management at nuclear reactors and the benefits of including Cyber-Informed Engineering throughout the systems engineering lifecycle. The primary goal is for engineers and scientists to develop a mindset that incorporates cybersecurity as another discipline alongside safety when designing and developing new technologies, regardless of technology readiness level.
In order to effect large-scale return of extraterrestrial resources to Earth orbit, it is desirable to optimize the propulsion system to maximize the mass of payload returned per unit energy expended. This optimization problem is different from the conventional rocket propulsion optimization. A rocket propulsion system consists of an energy source plus reaction mass. In a conventional chemical rocket, the energy source and the reaction mass are the same. For the transportation system required, however, the best system performance is achieved if the reaction mass used is from a locally available source. In general, the energy source and the reaction mass will be separate. One such rocket system is the nuclear thermal rocket, in which the energy source is a reactor and the reaction mass a fluid which is heated by the reactor and exhausted. Another energy-limited rocket system is the hydrogen/oxygen rocket where H2/O2 fuel is produced by electrolysis of water using a solar array or a nuclear reactor. The problem is to choose the optimum specific impulse (or equivalently exhaust velocity) to minimize the amount of energy required to produce a given mission delta-v in the payload. The somewhat surprising result is that the optimum specific impulse is not the maximum possible value, but is proportional to the mission delta-v. In general terms, at the beginning of the mission it is optimum to use a very low specific impulse and expend a lot of reaction mass, since this is the most energy efficient way to transfer momentum. However, as the mission progresses, it becomes important to minimize the amount of reaction mass expelled, since energy is wasted moving the reaction mass. Thus, the optimum specific impulse will increase with the mission delta-v. Optimum I(sub sp) is derived for maximum payload return per energy expended for both the case of fixed and variable I(sub sp) engines. Sample missions analyzed include return of water payloads from the moons of Mars and of Saturn.
With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.
This document is the user manual for the Hybrid Repository that is a part of the Integrated Energy Systems program. The Hybrid Repository is a collection of physical modelica models used for the creation of transient integrated energy systems.
As exploration and utilization of space proceeds through the 1970s, 1980s, and beyond, spacecraft in earth orbit will become increasingly larger, spacecraft will travel deeper into space, and space activities will involve more complex operations. These trends require increasing amounts of energy for power and propulsion. The role to be played by nuclear energy is presented, including plans for deep space missions using radioisotope generators, the reactor power systems for earth orbiting stations and satellites, and the role of nuclear propulsion in space transportation.
Navigating through the world of Artificial Intelligence (AI) in nuclear reactors and their Instrumentation and Control (I&C) systems demands a careful, deliberate journey. AI’s capability to manage massive datasets and streamline control systems has indeed carved out a significant role in various sectors, including nuclear energy. However, while AI, and particularly Large Language Models (LLMs), bring a lot to the table in terms of operational efficiency and anomaly detection, they also expose the sector to a new breed of cybersecurity threats, like Inference Attacks, Adversarial Attacks, and Trojan Attacks. This guide is designed to be a straightforward manual, diving deep into the intertwining worlds of AI and cybersecurity within nuclear reactors, and tailoring insights for three crucial audiences: I&C Vendors/Developers, Nuclear Regulators, and Nuclear Reactor Operators and Cyber Defense Teams. (1) Section 2, directed at I&C Vendors/Developers, will provide a clear and focused look at several cybersecurity attacks, offering practical recommendations and detailed scenarios related to AI cybersecurity. This section isn’t just about identifying problems but also about giving solid, usable solutions. (2) Section 3, meant for Nuclear Regulators, gets straight to the point about regulations, policy suggestions, and guidelines that are needed to lay down a robust, secure, and ethical foundation for the application of AI in nuclear operations. The focus is on making sure that everything adheres to international standards and laws while being practicable and clear-cut. (3) Section 4, aimed at Nuclear Reactor Operators and Cyber Defense Teams, offers an exhaustive exploration and technical reports, with clear recommendations and scenario analyses vital to protect operational environments and guarantee the secure application of AI in nuclear reactor operations. The goal is simple: as we step into an era where AI becomes a fundamental element of our technological and energy infrastructures, this guide is here to act as a clear, direct handbook, ensuring that AI is implemented within the nuclear sector in a manner that is secure, responsible, and practical. It’s about striking a balance – optimizing the undeniable benefits offered by AI while securing and shielding against potential cyber threats as we move through this new and complex landscape.
Over the past decade, the use of artificial intelligence techniques in the field of health-monitoring has gained significant interest, especially for structures such as building and bridges. However, applications to industrial systems such as equipment-piping systems in nuclear plants have not been explored. Here, in this paper, it is shown that the existing techniques developed for buildings and bridges cannot be extended directly to equipment-piping systems as the response of such systems is governed by multiple localized modes unlike that in buildings and bridges. This paper proposes a new approach that consists of three key aspects: (i) a novel vector of degradation-sensitive features extracted from measured data, (ii) using a deep Artificial Neural Network (ANN) for diagnosis of degradation location and degradation severity, and (iii) consideration of uncertainty in degradation severity when training the ANN. Degradation in piping-equipment systems can occur due to flow-accelerated erosion and corrosion. These locations can potentially exhibit damage such as localized yielding or initiation of cracking due to an external event such as an earthquake. Moreover, such locations can at times go undetected by current inspection techniques. Therefore, a robust framework is needed for detection of degradation after a seismic event. This manuscript proposes a proof-of-concept framework, which utilizes data collected from sensors to generate a deep ANN database for predicting degraded locations and severity in a piping-equipment system. Degradation severity is classified as minor, moderate, and severe. In the suggested methodology, a novel vector of degradation-sensitive features is extracted from the sensor data to train the ANN. A simple piping-equipment system is selected to demonstrate feature extraction as a means to simplify pattern recognition, explore the design and parameters of an ANN, and develop a sensor placement strategy. The effectiveness of the proposed framework is demonstrated on a realistic primary safety system of a two-loop nuclear reactor. It is shown that the proposed post-hazard condition assessment framework is able to detect degraded locations along with the severity levels, including minor degradation, with considerably higher accuracy.
Molten chloride salts are being given strong consideration for use in heat transfer and storage in concentrating solar power (CSP) systems as well as in some nuclear reactor applications. Containment of the molten salt, particularly at the highest temperatures, is a major material concern and has received considerable study. Another material issue is the pumps that will be required to move the molten salt along with the bearing materials that will be required to have sufficient corrosion resistance as well as wear resistance in the high temperature salt. A pair of coordinated studies in our laboratory has addressed the corrosion, and the wear issues of candidate bearing materials including the selection of candidate materials as well as their performance in a molten sodium chloride-potassium chloride-magnesium chloride salt environment. This work addresses the selection of candidate materials and their chemical compatibility with the molten salt. The studies have identified material pairs that have suitable properties for use as bearings that would be immersed in molten chloride salt.
The IES program has identified specific industries that are high priority for characterizing nuclear thermal integration. Foundational reference integration designs are needed both for research reference and communication with industry.
The energy economy is continually evolving in response to socio-political factors in the nature of primary energy sources, their conversions to useful forms, such as electricity and heat, and their utilization in different sectors. Nuclear energy has a crucial role to play in the evolution of energy economy due to its clean and non-carbon-emitting characteristics. A techno-economic analysis was undertaken to establish the viability of selling heat along with electricity for an advanced 100 MWth small modular reactor (SMR) and four nuclear hybrid energy system (NHES) configurations featuring the SMR paired with chemical heat pump (ChHP) systems providing a thermal output ranging from 1 to 50 MWth. Net present value, payback period, discounted cash flow rate of return, and levelized cost of energy were evaluated for these systems for different regions of U.S. reflecting a range of electricity and thermal energy costs. The analysis indicated that selling heat to high temperature industrial processes showed profitable outcomes compared to the sale of only electricity. Higher carbon taxes improved the economic parameters of the NHES alternatives significantly. Providing heat to high temperature industries could be very beneficial, helping to cut down the greenhouse gases emission by reducing the fossil fuel consumption.
Turbo-MHD cycle technology of nuclear electric power systems with high temperature reactor for space and terrestrial applications
A preliminary shield design for a nuclear power system utilizing a SNAP-8 reactor for space base application is presented. A representative space base configuration was selected to set the geometry constraints imposed on the design. The base utilizes two independent power packages each with a reactor operating at 600 kwt and each producing about 50 kwe. The crew compartment is located about 200 feet from each reactor and is large enough in extent to intercept a total shadow angle of 60 deg measured about the center line of each reactor.
Air pollution resulting from the use of fossil fuels is discussed. Phenomena relating to the emission of CO2 such as the greenhouse effect and multiplier effect are explored. Particulate release is also discussed. The following recommendations are made for the elimination of fossil fuel combustion products in the United States: development of nuclear breeder reactors, use of solar energy systems, exploration of energy alternatives such as geothermal and fusion, and the substitution of coal for gas and oil use.
Basic guidelines are presented for a nuclear space power system which utilizes heat pipes to transport thermal power from a fast nuclear reactor to an out of core thermionic converter array. Design parameters are discussed for the nuclear reactor, heat pipes, thermionic converters, shields (neutron and gamma), waste heat rejection systems, and the electrical bus bar-cable system required to transport the high current/low voltage power to the processing equipment. Dimensions are compatible with shuttle payload bay constraints.
TAP2 thermal-analysis program was developed as part of research on finite element methodology for thermal analysis of convectively cooled structures, such as scramjet engines and hypersonic aircraft. Program simplifies computations when both structural and thermal analyses are required and is suited for thermal analysis of nuclear reactors and solar-panel heating systems.