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Techno-economic sensitivity analysis for combined design and operation of a small modular reactor hybrid energy system
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Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model
Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.
On piston engines as hydrocarbon gas reformers for modular, distributed chemical production
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Stochastic-based noise exposure assessment in modular and off-site construction
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Deployment of portable, modular gas samplers as part of an atmospheric tracer experiment
Underground nuclear explosions release noble gases into the atmosphere that can be detected to support international monitoring efforts. Atmospheric transport models help predict the movement of these gases over long distances, but struggle to predict the movement in the atmosphere local to the release. A field experiment was designed to monitor the movement of 127 Xe within a 5-km radius. Four gas samplers were deployed as part of this experiment to collect atmospheric samples at various distances from the release point. In conclusion, these samples were then analyzed in a near-field lab using a NaI detector and in an off-site lab using gamma-gamma coincidence and beta-gamma coincidence counting.
Nuclear waste attributes of near-term deployable small modular reactors
The nuclear waste attributes of near-term deployable SMRs were assessed using established nuclear waste metrics, which are the DU mass, SNF mass, volume, activity, decay heat, radiotoxicity, and decommissioning LLW volumes. Metrics normalized per unit electricity generation were compared to a reference large PWR. Three SMRs, VOYGR, Natrium, and Xe-100, were selected because they represent a range of reactor and fuel technologies and are active designs deployable by the decade’s end. The SMR nuclear waste attributes show both some similarities to the PWR and some significant differences caused by reactor-specific design features. The DU mass is equivalent to or slightly higher than the PWR. Back-end waste attributes for SNF disposition vary, but the differences have a limited impact on long-term repository isolation. SMR designs can vary significantly in SNF volume (and thus heat generation density). However, these differences are amenable to design optimization for handling, storage, transportation, and disposal technologies. Nuclear waste attributes from decommissioning vary depending on design and decommissioning technology choices. Given the analysis results in this study and assuming appropriate waste management system and operational optimization, there appear to be no major challenges to managing SMR nuclear wastes compared to the reference PWR.
MELCOR severe accident analysis for a natural circulation small modular reactor
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The STRONgrid library: A modular and extensible software library for IEEE C37.118.2 compliant synchrophasor data mediation
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Plenum-to-plenum natural convection heat transfer within a scaled-down prismatic modular reactor facility
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Ndr/Lats Kinases Bind Specific Mob-Family Coactivators through a Conserved and Modular Interface
Abstract not provided
Spin and Phonon Design in Modular Arrays of Molecular Qubits
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