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This procedure provides guidance for reporting and resolving Nuclear Material Accounting and Control (NMAC) irregularity.
There is an imminent need for existing United States nuclear power plants to reduce their operating and maintenance costs to remain economically viable. Digital technology provides significant opportunity for the existing nuclear power plant fleet to transform that way in which work is accomplished to reduce costs and allow the fleet to remain economically competitive. However, a careful understanding of the human-technology integration is needed to ensure the continued safe and reliable operation of these existing plants with new digital capabilities. This work presents interim findings in applying human factors engineering to a safety-significant digital upgrade for a United States nuclear power plant, following the new Alternative Review Process in the recently revised Digital Instrumentation and Control Interim Staff Guidance Licensing Process, Revision 2. The interim results described in this work provides an industry perspective, based on ongoing work, to recent work published from Vazquez, Green, and Desaulniers (2022).
Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe–C system that couples a CALPHAD-based chemical free energy with demagnetization-field magnetostatics and microelasticity. Here, the model reproduces single- and multi-particle evolution during the α → γ inverse transformation at 1023 K under external fields up to 20 T, including ellipsoidal morphologies observed experimentally at 8 T. Chemically driven growth is isotropic; a magnetic interaction introduces an anisotropic driving force that elongates γ precipitates along the field into ellipsoids, while elastic coherency promotes faceting, yielding elongated cuboidal or “brick-like” particles under combined magneto-elastic coupling. Growth kinetics increase with C content, and decrease with field strength and misfit strain. Multi-particle simulations reveal dipolar interaction-mediated coalescence for field-parallel neighbors and ripening for field-perpendicular neighbors. Incorporating field-dependent diffusivity from experiment slows kinetics as expected; a first-principles-motivated anisotropic diffusivity correction is estimated to be small (<2%). These results establish a process-structure link for magnetically assisted heat treatments of Fe–C alloys and provide guidance for microstructure control via chemo-magneto-mechanical synergism.
High-speed laser welding is a crucial process to enhance the production rate of fuel cell fabrication. However, exceeding a critical welding speed leads to defect formation such as humps and root concavities, which degrade weld quality and mechanical performance. This study employed shielding gas and spot reduction approaches to suppress these defects. The relationships between individual defect types and their influence on weld strength were systematically examined. Moreover, in situ high-speed synchrotron X-ray imaging was employed to comprehend the mechanisms of humping mitigation by shielding gas through analysis of instantaneous keyhole and melt pool dynamics. The observations reveal that shielding gas mitigates humping by shortening the molten pool through enhanced surface heat convection and by altering surface tension to prevent melt flow collisions. The effects of shielding gas and spot reduction were further incorporated into the dimensionless humping index (π h ) that describes the humping formation tendency. These findings provide mechanistic insights and quantitative guidance for defect control strategies in high-speed laser welding.
Achieving self-consistent performance predictions for ITER requires integrated modeling of core transport and divertor power exhaust under realistic impurity conditions. We present results from a systematic power-flow and impurity-content study for the ITER 15 MA baseline scenario constrained directly by existing SOLPS-ITER neon-seeded divertor solutions. Using the OMFIT STEP workflow, stationary temperature and density profiles are predicted with TGYRO for $1.5 \unicode{x2A7D} Z_\textrm{eff} \unicode{x2A7D} 2.5$, and the corresponding power crossing the separatrix $P_\textrm{sep}$ is evaluated. We find that $P_\textrm{sep}$ varies by more than a factor of 1.7 across this scan and matches the ${\sim}100$ MW SOLPS-ITER prediction when $Z_\textrm{eff} \simeq 1.6$ or when auxiliary heating is reduced to ${\sim}75\%$ of nominal. Rotation-sensitivity studies show that plausible variations in toroidal flow magnitude modify $P_\textrm{sep}$ by $\lesssim 20\%$, while AURORA modeling confirms that charge-exchange radiation inside the separatrix is dynamically negligible under predicted ITER neutral densities. These results identify a restricted compatibility window, $Z_\textrm{eff} \approx 1.6$ –1.75 and $0.75 \lesssim f_{P_\textrm{aux}} \unicode{x2A7D} 1.0$, in which core transport predictions remain aligned with neon-seeded divertor protection targets. This self-consistent, model-constrained framework provides actionable guidance for impurity control and auxiliary-heating scheduling in early ITER operation and supports future whole-device scenario optimization.
Epitaxial lattice matching is an important condition for the formation of coherent interfaces with low defect densities. However, lattice-matched substrates with the same crystal structure as the active layer are often not available, suggesting opportunities for utilizing heterostructural interfaces. For example, at high Al contents that are interesting for ultrawide-gap applications in power electronics, Al x Ga 1-x N semiconductor alloys in the (0001) orientation of the wurtzite (wz) structure become lattice-matched to (111)-oriented rocksalt (rs) TaC substrates. To predict the expected interface atomic structures under different synthesis conditions, we perform high-throughput density-functional-theory calculations, using an algorithm for systematic sampling of the possible stacking sequences of the atomic layers on the in-plane hexagonal lattice. The approach considers octahedral, tetrahedral, and prismatic coordination motifs, and is generally applicable for the modeling of commensurate rs/wz heterostructural interfaces. Our results provide guidance for synthesis control of substrate-film bonding and the polarity of ultrawide-gap Al x Ga 1-x N alloys on TaC substrates.
Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.
Cyber-Informed Engineering (CIE) addresses the reality that cyber attacks on engineered systems can have consequences far beyond data loss or disruption of digital networks. When control systems are compromised, safety, reliability, and performance of the physical process itself may be threatened. This presentation discusses engineered controls of 7 categories and the CIE database of controls that provides clear examples and guidance for defining and applying engineered controls in CIE. It explains what engineered controls are, how they differ from information security measures, and how they are integrated into system design.
This paper validates the efficacy of an artificial intelligence (AI)-based photovoltaic (PV) plant control and optimization approach in enabling PV plants as accountable grid reliability service providers. The validation is performed in a realistic laboratory controller-hardware-in-the-loop environment, leveraging accurate PV plant modeling and standard industrial communication protocols. Through simulations that account for diverse weather conditions and active control scenarios, the results highlight the superior performance of the AI-based solution in comparison to a state-of-the-art reference-control grouping-based approach. Such a finding contributes to mitigating the risk of overcurtailment and uninstructed deviations of active PV plant controls, and offers practical guidance for its field deployment. Furthermore, it establishes a standardized testing framework for comparing various PV active control strategies.
This document provides guidance for retrofitting existing fluorescent troffer fixtures with LEDs, as well as information about lighting controls. It outlines the pros and cons of three different retrofit options: Tubular LEDs, Retrofit Kits, and Luminaires.
As nuclear reactors transition from analog to digital technology, the benefits of enhanced operational capabilities and improved efficiencies are potentially offset by cyber risks. Cyber-Informed Engineering (CIE) is an approach that can be used by engineers and staff to characterize and reduce new cyber risks in digital instrumentation and control systems. CIE provides guidance that can be applied throughout the entire systems engineering lifecycle, from conceptual design to decommissioning. In addition to outlining the use of CIE in nuclear reactor applications, this chapter provides a brief primer on nuclear reactor instrumentation and control and the associated cyber risks in existing light water reactors as well as the digital technology that will likely be used in future reactor designs and applications.
The performance and life of Li-ion battery cathode materials are determined by both the composition (crystal structure and transition metal ratio) and the morphology (particle size, size distribution, and surface area). Careful control of these two aspects is the key to long lasting, high-energy batteries that can undergo fast charge. Developing such cathodes requires manipulation of the synthesis conditions, namely the coprecipitation process to develop the precursor and a calcination step to lithiate and convert it to a transition metal oxide. In this paper, we utilize a combination of controlled synthesis, microscopic and spectroscopic characterization, and multi-scale mathematical modeling to shed light on the synthesis of cathode precursors. The complex interplay between the various chemical reactions in the co-precipitation process is studied to provide experimentalists with guidance on achieving composition control during synthesis. Further, the formation of a variety of morphologies of the primary particles and the driving force for agglomeration is mathematically described, for the first time, based on an energy minimization approach. Results suggest that the presence of Ni and/or Co significantly lowers the reaction rate constant compared to Mn, resulting in agglomerated growth in the former and single crystal growth in the latter. Modeling studies are used to provide a phase map describing the synthesis conditions needed to control the secondary particle size and corresponding size distribution. Finally, this paper represents an important step in developing a computationally guided approach to the synthesis of battery cathode materials.
Lithium metal anode promises the highest theoretical energy density and may enable high energy designs such as lithium–sulfur and lithium–air batteries. However, stable lithium plating and stripping remains a challenge in all electrolyte systems including liquids, polymers, and ceramic electrolytes. In this perspective, we examine literature studies of lithium morphologies in solid polymer and gel polymer systems and compare that with well-studied liquid electrolytes. In solid polymer electrolytes, current density and mechanical properties are both governing parameters for lithium morphology, differing from conventional liquid electrolytes. Stable lithium electrodeposition may be accomplished by a polymer electrolyte with good stiffness operating at significantly lower current densities than its limiting current density, which is defined by the Sand equation. In gel polymer electrolytes, the reported lithium morphology is more similar to that in liquid electrolytes, suggesting similar nucleation and growth mechanisms. Based on experimental evidence and theoretical guidance, current strategies to control lithium morphology in solid polymer and gel polymer electrolytes are summarized. The limitations of these strategies are discussed. In particular, we note the knowledge gap in understanding the solid electrolyte interphase in solid polymer systems and the critical role it can play in regulating lithium morphologies.
Environmental composition is a major, though poorly understood, determinant of microbiome dynamics. Here we ask whether general principles govern how microbial community growth yield and diversity scale with an increasing number of environmental molecules. By assembling hundreds of synthetic consortia in vitro, we find that growth yield can remain constant or increase in a non-additive manner with environmental complexity. Conversely, taxonomic diversity is often much lower than expected. To better understand these deviations, we formulate metrics for epistatic interactions between environments and use them to compare our results to communities simulated with experimentally-parametrized consumer resource models. We find that key metabolic and ecological factors, including species similarity, degree of specialization, and metabolic interactions, modulate the observed non-additivity and govern the response of communities to combinations of resource pools. Our results demonstrate that environmental complexity alone is not sufficient for maintaining community diversity, and provide practical guidance for designing and controlling microbial ecosystems.
Background, In early 2020, following the start of the coronavirus disease 2019 (COVID-19) pandemic, institutions of higher education (IHEs) across the United States rapidly pivoted to online learning to reduce the risk of on-campus virus transmission. We explored IHEs’ use of this and other nonpharmaceutical interventions (NPIs) during the subsequent pandemic-affected academic year 2020–2021. Methods, From December 2020 to June 2021, we collected publicly available data from official webpages of 847 IHEs, including all public (n = 547) and a stratified random sample of private four-year institutions (n = 300). Abstracted data included NPIs deployed during the academic year such as changes to the calendar, learning environment, housing, common areas, and dining; COVID-19 testing; and facemask protocols. We performed weighted analysis to assess congruence with the October 29, 2020, US Centers for Disease Control and Prevention (CDC) guidance for IHEs. For IHEs offering ≥50% of courses in person, we used weighted multivariable linear regression to explore the association between IHE characteristics and the summated number of implemented NPIs. Results, Overall, 20% of IHEs implemented all CDC-recommended NPIs. The most frequently utilized NPI was learning environment changes (91%), practiced as one or more of the following modalities: distance or hybrid learning opportunities (98%), 6-ft spacing (60%), and reduced class sizes (51%). Additionally, 88% of IHEs specified facemask protocols, 78% physically changed common areas, and 67% offered COVID-19 testing. Among the 33% of IHEs offering ≥50% of courses in person, having < 1000 students was associated with having implemented fewer NPIs than IHEs with ≥ 1000 students. Conclusions, Only 1 in 5 IHEs implemented all CDC recommendations, while a majority implemented a subset, most commonly changes to the classroom, facemask protocols, and COVID-19 testing. IHE enrollment size and location were associated with degree of NPI implementation. Additional research is needed to assess adherence to NPI implementation in IHE settings.
A greenhouse gas (GHG) inventory is a systematic approach to account for the production and release of certain gases generated by an institution from various emission sources. The gases of interest are those that climate science has identified as related to anthropogenic global climate change. This document presents an inventory of GHGs generated during Fiscal Year (FY) 2021 by Idaho National Laboratory (INL)—a Department of Energy (DOE) sponsored entity located in southeastern Idaho. In recent years, concern has grown about the environmental impact of GHGs. This, together with a desire to decrease harmful environmental impacts, would be enough to encourage the calculation of an inventory of the total GHGs generated at INL. Additionally, INL has a desire to see how its emissions compare with similar institutions, including other DOE national laboratories. Executive Order 13834 requires that federal agencies and institutions track and report GHG emissions where required. INL’s GHG inventory was calculated according to methodologies identified in federal GHG guidance documents using operational control boundaries. It measures emissions generated in three scopes: (1) INL emissions produced directly by stationary or mobile combustion and by fugitive emissions, (2) the share of emissions generated by entities from which INL purchased electrical power, and (3) indirect or shared emissions generated by outsourced activities that benefit INL (occurring outside INL’s organizational boundaries but are a consequence of INL’s activities). This inventory found that INL generated 81,185.05 metric tons (MT) of CO 2 equivalent (CO 2 e) emissions during FY 2021. The following conclusions were made from looking at the results of the individual contributors to INL’s FY 2021 GHG inventory: Electricity (including the associated transmission and distribution losses) is the largest contributor to INL’s GHG inventory, with over 50% of the CO 2 e emissions; Other sources with high emissions were mobile combustion (fleet fuels), employee commuting, stationary combustion (facility fuels), and waste disposal (fugitive emissions from the onsite landfill); Sources with low emissions were waste disposal (contracted disposal), fugitive emissions from refrigerants, wastewater treatment (onsite and contracted), and business ground travel (in personal and rental vehicles). This report details the methods behind quantifying INL’s GHG inventory and discusses lessons learned on better practices by which information important to tracking GHGs can be tracked and recorded. It is important to note that because this report differentiates between those portions of INL that are managed and operated by Battelle Energy Alliance, LLC (BEA) and those managed by other contractors, it includes only INL’s activities overseen by BEA. It is assumed that other contractors will provide similar reporting for those activities they manage, where appropriate.
A greenhouse gas (GHG) inventory is a systematic approach to account for the production and release of certain gases generated by an institution from various emission sources. The gases of interest are those that climate science has identified as related to anthropogenic global climate change. This document presents an inventory of GHGs generated during fiscal year (FY) 2022 by Idaho National Laboratory (INL)—a Department of Energy (DOE) sponsored entity located in southeastern Idaho. In recent years, concern has grown about the environmental impact of GHGs. This, together with a desire to decrease harmful environmental impacts, would be enough to encourage the calculation of an inventory of the total GHGs generated at INL. Additionally, INL has a desire to see how its emissions compare with similar institutions, including other DOE national laboratories. Executive Order 14057 requires that federal agencies and institutions track and report GHG emissions where required. INL’s GHG inventory was calculated according to methodologies identified in federal GHG guidance documents using operational control boundaries. It measures emissions generated in three scopes: (1) INL emissions produced directly by stationary or mobile combustion and by fugitive emissions, (2) the share of emissions generated by entities from which INL purchased electrical power, and (3) indirect or shared emissions generated by outsourced activities that benefit INL (occurring outside INL’s organizational boundaries, but are a consequence of INL’s activities). This inventory found that INL generated 75,572.42 metric tons (MT) of CO2 equivalent (CO2e) emissions during FY 2022. The following conclusions were made from looking at the results of the individual contributors to INL’s FY 2022 GHG inventory: • Electricity (including the associated transmission and distribution losses) is the largest contributor to INL’s GHG inventory, with over 50% of the CO2e emissions. • Other sources with high emissions were mobile combustion (fleet fuels), employee commuting, stationary combustion (facility fuels), and waste disposal (fugitive emissions from the onsite landfill). • Sources with low emissions were waste disposal (contracted disposal), fugitive emissions from refrigerants, wastewater treatment (onsite and contracted), and business ground travel (in personal and rental vehicles). This report details the methods behind quantifying INL’s GHG inventory and discusses lessons learned on better practices by which information important to tracking GHGs can be tracked and recorded. It is important to note that because this report differentiates between those portions of INL that are managed and operated by Battelle Energy Alliance, LLC (BEA) and those managed by other contractors, it includes only INL’s activities overseen by BEA. It is assumed that other contractors will provide similar reporting for those activities they manage, where appropriate.