Nuclear microreactors and thermal integration with hydrogen generation processes
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The nuclear industry is developing new advanced reactor technologies, and many companies are conceptualizing designs for microreactors, a class of nuclear reactor with a sub-20 MWth power output designed to be factory fabricated, easily transportable, and simple to control. Microreactors offer promising solutions to several use cases for which large-scale plants would not be suitable, and conventional power generation means, notably diesel electric generators, are expensive and logistically difficult. Many of the potential use cases are in isolated locations such as arctic communities, remote mines, and military installations. Therefore, the cost of microreactor deployment and traditional onsite operations pose a challenge that requires new technical solutions to address. One solution that has the potential to greatly improve economics is to operate and monitor microreactors remotely from a centralized location. Remote monitoring and operation are novel concepts to the nuclear industry and will greatly alter the tasks and responsibilities associated with current commercial nuclear power plant operators. As such, it is important to perform research on potential technological solutions and the impacts those solutions have on operations with end-goal of defining a safe and effective remote concept of operations. This paper proposes a framework for resilient remote operation of microreactors enabled by a novel digital twin implementation.
The rising interest in nuclear microreactors has highlighted the need for comprehensive technoeconomic assessments. However, the scarcity of publicly available designs and cost data has posed significant challenges. To address this issue, the Microreactor Optimization Using Simulation and Economics (MOUSE) tool is developed. MOUSE is a tool that integrates nuclear microreactor design with reactor economics. The design calculations encompass core simulations using the OpenMC Monte Carlo Particle Transport Code [romano2015], along with simplified balance of plant calculations. On the economic side, MOUSE provides detailed bottom-up cost estimates, calculating both the total capital cost and the levelized cost of energy for first-of-a-kind and nth-of-a-kind microreactors. The cost estimation correlations are developed using data from the MARVEL project and additional literature sources. MOUSE has released as an open-source tool on GitHub (MOUSE Tool). By combining design calculations with cost equations, MOUSE enables users to evaluate the impact of various technological consideration, advanced moderators, design changes, material/fuel changes, and geometry modifications—as well as economic parameters like interest rates and construction duration. This comprehensive framework can guide stakeholders towards technological solutions that enhance microreactor competitiveness. Additionally, powered by the WATTS toolkit [romano2022], MOUSE supports optimization studies, parametric analyses, and uncertainty calculations/propagation. Currently, preconceptual designs of three microreactor types are included in MOUSE: a liquid metal thermal microreactor (LTMR), gas cooled TRISO-fueled microreactor (GCMR) and heat-pipe TRISO fueled microreactor (HPMR). To showcase its ability, MOUSE was used to conduct detailed bottom-up cost estimates for the first of a kind (FOAK) and Nth of a kind (NOAK) of the following microreactors • A 20MWt LTMR that is built on the ongoing MARVEL demonstration at Idaho National Laboratory (INL) • A 15 MWt GCMR that was designed to be more representative of the typical commercial microreactor • A 7 MWt HPMR that was built on previous work (Choi 2024) The The reader should note that these three designs and corresponding cost estimates are examples to demonstrate the MOUSE capability. The designs are pre-conceptual, the reactor designs were not optimized, and the cost estimates were developed with incomplete information. Additionally, stakeholders might be interested in a variety of designs that may differ from the examples provided in this report. The MOUSE tool can also be used to study how design choices affect economics. To demonstrate its capability, MOUSE was used conduct parametric studies such as examining the economic impact of the reflector's material and thickness, the moderator's booster material and dimensions, fuel composition and enrichment, core size, and power level. Several insights were gained from these parametric studies.
Introduction There is a global goal to reduce greenhouse gas emissions by 43% by 2023. Nuclear microreactors, a subset of small modular reactors, offer a potential solution due to their compact size, transportability, and carbon-neutral power generation capabilities. Methods This study explores the feasibility of using heat from nuclear microreactors for bioconversion and agricultural processes, including transforming biomass into energy carriers and products such as syngas, bio-oil, and pasteurized milk. Operating requirements for gasification, pyrolysis, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, ethanol production, anaerobic digestion, and pasteurization were obtained through a literature review. A Brayton cycle model based on the eVinci TM microreactor was developed to assess the feasibility of powering these processes using nuclear microreactor heat. Results and Discussion Exergetic efficiency values for high-temperature processes ranged from 72% to 100%, whereas lower-temperature processes ranged from 2% to 53%. These efficiencies depend on the available source temperature for each microreactor design. There were trade-offs between producing net power and using process heat, particularly for high-temperature processes. Three heat exchanger locations were considered: before the turbine (600 ℃ ), between the turbine and regenerator (370 ℃ ), and after the regenerator (192 ℃ ). High-temperature processes like gasification require temperatures too high for feasibility. Middle temperature processes are better suited to a heat exchanger between the turbine and regenerator, while also operable before the turbine. Lower-temperature processes like pasteurization and anaerobic digestion can use waste heat after the regenerator and do not impact power production. These findings are valuable for optimizing nuclear microreactor heat use and aligning with global climate initiatives.
The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.
This report provides an experimental assessment of two different optical fiber–based acoustic sensors that are being investigated for application in nuclear microreactors to enhance structural health monitoring capabilities. Optical fibers are resilient in high-temperature and high-radiation environments, have a small sensor footprint, are immune to electromagnetic interference, and are capable of spatially distributed sensing. The two sensors investigated here are (1) Fabry–Pérot Cavities (FPCs) between two copper-coated fibers, embedded in nickel capillary tubes and (2) type-I fiber Bragg grating (FBG) arrays contained within metal capillary tubes. These sensors can be interrogated using low-coherence interferometry or swept wavelength interferometry, respectively, to measure the resonant frequencies of the components or systems to which these sensors are bonded. The FPC developed herein has been subjected to temperatures up to nearly 800°C while tack-welded to a tubular test specimen. Even at the highest temperatures, the measured resonant frequencies compared well with those obtained using an accelerometer that was bonded to an unheated portion of the specimen. The FBG array was tested in multiple bonding configurations to a tubular test specimen, all at room temperature, with the understanding that high temperature (i.e., type II) FBGs could be used to obtain similar data at high temperatures; the FBG array data were validated with noncontact laser vibrometry, which is being used at Los Alamos National Laboratory to relate acoustic signatures to component stresses and/or structural defects. The goal of this work is to identify the most promising techniques that are also compatible with operation in a microreactor environment.
Nuclear microreactors promise to open new markets for nuclear industry. This is due to their potential competitive cost in non-traditional market segments, e.g., mines, forward military basis, extraterrestrial surfaces and remote areas, and inherently safe characteristics that make them deployable where other power sources are not available or difficult to exploit. Transition metal dihydrides have been considered among the most promising candidates for moderating nuclear microreactors. In particular, yttrium hydride (YH\textsubscript{x}) has been selected for high temperature applications due to its high thermal stability and relatively high hydrogen retention at temperatures exceeding $870^\circ C$. One of the main issues associated with the use of hydrides is that, when exposed to temperature, stress, or concentration gradients, the hydrogen contained in the metallic matrix tends to redistribute and leak from the moderating elements, potentially leading to reactivity losses and power swings. The purpose of this paper is to gain a better physical understanding of the neutronic feedback associated with the hydrogen redistribution in YH\textsubscript{x}, by using Griffin and Bison. This feedback is inherently multiphysics, since the hydrogen distribution is strongly dependent on the moderator temperature spatial distribution. In particular, we want to understand the sign (+/-) of the neutronic feedback, its order of magnitude, and its underlying physical causes.
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Power Point presentation on Nuclear Reactor to children.
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Microreactors, or small, transportable reactors with a capacity of 20 MWTH or less, are sought to provide heat and power for myriad applications in remote areas, military installations, emergency operations, humanitarian missions, and disaster relief zones. These small, transportable reactor designs, while offering many advantages, are largely untested and unproven. A non-nuclear system and component testing capability is needed to demonstrate to regulators that these designs are safe and to convince customers that the systems are robust, reliable, and efficient. Idaho National Laboratory has constructed the microreactor agile non-nuclear experimental test bed (MAGNET) to assist with the development, demonstration, and validation of microreactor components and systems. MAGNET will support microreactor technology maturation to reduce uncertainty and risk relative to the operation and deployment of this unique class of systems. Stakeholders for this test bed include microreactor developers, energy users, and regulators. Regulators will be engaged early in the design and testing to expedite regulatory approval and licensing.
Nuclear microreactors carry the potential to open up new markets for the nuclear industry, as their expected cost competitiveness in non-traditional market segments (e.g., mines, military bases, extraterrestrial surfaces, and remote areas), and their inherent safety features make them deployable when other power sources are unavailable or difficult to exploit. For countries that have not traditionally participated in nuclear power, microreactors represent a clean energy solution [1]. However, their use, especially in non-weapons states, may entail challenges in terms of maintaining international nuclear safeguards [2]. Furthermore, the deployment locations where microreactors may prove most cost competitive would be difficult to access by state and International Atomic Energy Agency (IAEA) inspectors [3]. In addition to the isolated nature of potential deployment sites, the low-power characteristic of microreactors suggests that numerous microreactors would need to be deployed to meet energy demands. That, coupled with the unique physics of many current microreactor designs, opens up a new area of research with respect to nonproliferation and safeguards concerns [2]. Whereas traditional facilities are inspected as isolated cases when looking for signs of diversion or misuse; microreactors may need to be assessed in the context of the entire fleet to which they belong. International safeguards necessitate timely detection of any significant quantities (SQs) of material that are being diverted (e.g., 1 SQ of special nuclear material diverted over the course of a 1-year period) [4]. For low-enriched uranium, the IAEA defines 1 SQ as corresponding to 75 kg of 235U. The purpose of the present paper is to explore the detectability threshold for material diversion in microreactors by relying on critical control drum angles, excess reactivity, and the reactor lifetime as the selected operational parameters. For this assessment, a heat-pipe-cooled microreactor was regarded as the base design. While the conclusions reached in this paper are not readily extendable to the design of actual microreactors, the analysis herein enables conclusions to be drawn regarding the level of accuracy needed for reference calculations in order to detect diversion scenarios by utilizing the selected operational parameters (i.e., mainly control drum angles, critical insertion angle, and the reactor lifetime).
To improve the marketability of novel microreactor designs, there is a need for automated and optimal control of these reactors. This paper presents a methodology for performing multiobjective optimization of control drum operation for a microreactor under normal and off-nominal conditions. Here, two different case studies are used where the control drum configuration is optimized for the reactor to be critical with some desired power distribution that would satisfy peaking limits. A surrogate model for power distribution is developed based on a feedforward neural network. The process for determining weights for scalarization of the multiobjective optimization problem is also detailed. Six optimization algorithms: evolutionary strategies, differential evolution, grey wolf optimization, Harris hawks optimization, moth flame optimization and particle swarm optimization, are all applied to these cases and the results analyzed. Although all these algorithms will demonstrate optima-seeking behavior, for real-time control it is necessary to identify the best algorithm to efficiently provide reasonable optima without operator interference. The moth flame optimization algorithm was found to perform particularly well on both cases. Overall, it was found that the algorithms capable of supplying the best optima were also the most consistent. Finally, the found optima were verified with the original model used to train surrogates.
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Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.
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