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Activity identification in modular construction using audio signals and machine learning
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Modular basis for potent SARS-CoV-2 neutralization by a prevalent VH1-2-derived antibody class
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Modular HIV-1 Capsid Assemblies Reveal Diverse Host-Capsid Recognition Mechanisms
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RANSBox: A zero-dimensional modular software package for Reynolds-averaged Navier-Stokes modeling
RANSBox is a zero-dimensional software package for Reynolds-averaged Navier-Stokes (RANS) modeling developed at Lawrence Livermore National Laboratory to support common implementation of RANS models across a variety of host codes with different numerical schemes and code bases. Herein this work describes the key features of RANSBox including “model-agnostic integration,” which allows new models to be implemented in RANSBox and quickly deployed to host codes without additional changes to the host code base. Three one-dimensional test problems with analytical asymptotic properties are described which can be used to verify correctness of RANSBox integration. Results for these problems are then compared across different RANS models in a single code and across different codes with a common RANS model. By using a common model implementation in RANSBox, host codes with different numerical schemes and formal orders of accuracy are demonstrated to predict the expected behavior for the three test problems.
Process design and analysis of a novel carbon-capture-ready process for flexible-load power generation: Modular pressurized air combustion
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Making coal relevant for small scale applications: Modular gasification for syngas/engine CHP applications in challenging environments
Small-scale coal gasification technology, coupled to a reciprocating engine generator, has the potential for making coal a cost competitive resource for meeting the flexible energy needs and resiliency requirements of utilities across the United States. To maintain grid stability and reliability, electrical generation must be regulated to match the load at the proper voltage and frequency. With the expansion of intermittent sources into the grid, such as wind and solar, frequency and voltage regulation become increasingly important and challenging. This work presents the results of a Front End Engineering Design (FEED) effort to detail the engineering and preliminary economics of a small-scale, air blown, fixed-bed gasification process, operating at near-atmospheric pressure, with gas cleanup to provide syngas and pyrolysis liquid fuels for use in reciprocating engine generators for combined heat and power at the University of Alaska Fairbanks. A very detailed assessment of capital and operating costs allows the evaluation of a levelized cost of electricity of 208.06 $/MWh, which can be reduced through the selling of by-products (steam for heating purposes and pyrolysis liquids to be used for power generation in an existing diesel engine). Here, a combined sensitivity analysis, based on the Monte Carlo approach, has been carried out to evaluate the effects of the uncertainties (capital and operating costs and plant annual availability) on the LCOE.
Shutdown dose rate analysis with the Shift Monte Carlo radiation transport code and modular verification workflow
Calculation of the shutdown dose rate is crucial for safe fusion reactor operations. The Rigorous-two-step (R2S) method is a method that requires connected neutron transport, activation, and gamma transport. Shift has integrated variance reduction with a deterministic solver Denovo, supports multiple geometry formats, and is scalable. These features make it an attractive transport solver choice for an R2S workflow. An R2S workflow for the Shift Monte Carlo code is developed and compared to the existing Oak Ridge National Laboratory Shutdown Dose Rate Code Suite (ORCS) workflow. Also, a Python framework for integrating two R2S workflows is developed to mix and match each step in the R2S workflow for improved collaboration and verification experience. In this study, results show that the Shift-Denovo R2S workflow and the ORCS workflow calculate the shutdown dose rate of the ITER Shutdown Dose Rate benchmark problem with an average relative error of 2.285%.
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.