Nuclear energy is increasingly being considered for such targeted energy applications as data centers in light of their high capacity factors and low carbon emissions. This paper focuses on assessing the tradeoffs between economies of scale versus mass production to identify promising reactor sizes to meet data center demands. A framework is then built using the best cost estimates from the literature to identify ideal reactor power sizes for the needs of the given data center. Results should not be taken to be deterministic but highlight the variability of ideal reactor power output against the required demand. While certain advocates claim that with the gigawatts of clean, firm energy needed, large plants are ideal, others advocate for SMRs that can be deployed in large quantities and reap the benefits from learning effects. The findings of this study showcase that identifying the optimal size for a reactor is likely more nuanced and dependent on the application and its requirements. Overall, the study does show potential economic promise for coupling nuclear reactors to data centers and industrial heat applications under certain key conditions and assumptions.

In the Nuclear Power Plant (NPP) control room, the operators’ performance in emergencies is impacted by the need to monitor many indicators on the control room boards, the limited time to interact with dynamic events, and the incompleteness of the operator’s knowledge. Recent research has been directed toward increasing the level of automation in the NPP system by employing modern AI techniques that support the operator’s decisions. In previous work, the authors have employed a novel AI-guided declarative approach (namely, Answer Set Programming (ASP)) to represent and reason with human qualitative knowledge. This represented knowledge is structured to form a reasoning-based operator support system that assists the operator and compensates for any knowledge incompleteness by performing reasoning to diagnose failures and recommend executing actions in real time. A general ASP code structure has been proposed and tested against simple scenarios, e.g., diagnosis of pump failures that result in loss of flow transients and generating the needed plans for resolving the issue of stuck valves in the secondary loop. In this work, we investigate the potential of the previously proposed ASP structure by applying ASP to a realistic case study of the Three Mile Island, Unit 2 (TMI-2) accident event sequence (in particular, the first 142 minutes). The TMI scenario presents many challenges for a reasoning system, including a large number of variables, the complexity of the scenario, and the misleading readings. The capability of the ASP-based reasoning system is tested for diagnosis and recommending actions throughout the scenario. This paper is the first work to test and demonstrate the capability of an automated reasoning system by applying it to a realistic nuclear accident scenario, such as the TMI-2 accident.

Large increases in nuclear generating capacity require cost analyses that connect reactor deployment, fuel cycle infrastructure, and time-dependent cost assumptions. This work presents the Workflow for Integrated Simulation and Economics (WISE), a Python-based workflow for evaluating dynamic nuclear fuel cycle costs from simulation outputs. WISE converts reactor deployment and material-flow histories into annual process requirements, determines non-reactor facility deployment, and aggregates operations and maintenance (O&M) costs, non-reactor facility and reactor overnight capital costs (OCCs), cumulative cost, and levelized cost of electricity (LCOE). The workflow is applied over 2025–2100 to two once-through (OT) scenarios, OT-Large and OT-SMR, and one Recycling scenario under a common nuclear generating capacity trajectory consistent with expansion to approximately 400 GW in the United States by 2050. The OT scenarios require fresh UO₂ fuel supply and direct used nuclear fuel disposal, while Recycling shifts part of the fuel cycle after midcentury toward recovered material, hot metal fuel fabrication, and high-level waste management. Using approximate representative data for this demonstration, by 2100, cumulative undiscounted total costs are $7.7 trillion, $9.2 trillion, and $9.6 trillion for OT-Large, OT-SMR, and Recycling, respectively, in 2025 USD. Reactor OCC is the dominant cost component in all scenarios. This work shows that WISE provides a consistent basis for evaluating how reactor and fuel cycle assumptions affect infrastructure requirements and costs.