• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.855-865
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• 2024

Commercial deployment of nuclear microreactors presents an opportunity for the industry to rethink its approach to manufacturing, siting, operation and maintenance, and fuel cycle management as certain principles used in grid-scale nuclear projects are not applicable to a decentralized microreactor economy. The success of this nascent industry is dependent on its ability to reduce infrastructure, logistical, regulatory and lifecycle costs. A utility-like 'Central Facility' that consolidates the services required and responsibilities borne by vendors into one or a few centralized locations will be necessary to support the deployment of a fleet of microreactors. This paper discusses the requirements for a Central Facility, its implications on the cost structures of owners and suppliers of microreactors, and the impact of the facility for the broader microreactor industry. In addition, this paper discusses the pre-requisites for eligibility as well as the opportunities for a Central Facility host site. While there are many suitable locations for such a capability across the U.S., this paper considers a facility co-located with the Vogtle Nuclear Power Plant and Savannah River Sites to illustrate how a Central Facility can leverage the existing infrastructure and stimulate a local ecosystem.

• Nuclear Engineering and Technology
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• v.54 no.4
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• pp.1482-1494
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• 2022

Power conversion cycles (Subcritical Steam, Supercritical Steam, Open Air Brayton, Recuperated Air Brayton, Combined Cycle, Closed Brayton Supercritical CO₂ (sCO₂), and Stirling) are evaluated for land-based nuclear microreactors based on technical maturity, system efficiency, size, cost and maintainability, safety implications, and siting considerations. Based upon these criteria, Air Brayton systems were selected for further evaluation. A brief history of the development and applications of Brayton power systems is given, followed by a description of how these thermal-to-electrical energy conversion systems might be integrated with a nuclear microreactor. Modeling is performed for optimized cycles operating at 3 MW(e) with turbine inlet temperatures of 500 ℃, 650 ℃ and 850 ℃, corresponding to: a) sodium fast, b) molten salt or heat pipe, and c) helium or sodium thermal reactors, coupled with three types of Brayton power conversion units (PCUs): 1) simple open-cycle gas turbine, 2) recuperated open-cycle gas turbine, and 3) recuperated and intercooled open-cycle gas turbine. Aeroderivative turboshaft engines employing the simple Brayton cycle and two industrial gas turbine engines employing recuperated air Brayton cycles are also analyzed. These engines offer mature technology that can facilitate near-term deployment with a modest improvement in efficiency.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.