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At least 685 records · Page 38

Assessing the reliability of the NIST library during routine GC-MS analyses: Structure and spectral data corroboration for 5,5-diphenyl-1,3-dioxolan-4-one during a recent OPCW proficiency test

In the field of analytical chemistry, it is the nature of the available instrumentation that makes a specific technique uniquely suited for answering a specific challenge. Often times, instruments equipped with ultrasensitive array of detectors and state-of-the-art separation components have served to highlight their invaluable role in the field. Nonetheless, it is their ultimate ability to predict the structure of an unknown material on the basis of mass spectral data that exponentially magnifies their value. Their efficacy in achieving this prediction is largely based on the size of their internal mass spectral library that conveniently can be accessed by using the software. The library can thus exhaustively correlate an unknown compound's mass spectrum to known compounds in a database and provide the analytical chemist with a probable structure for the unknown analyte. During routine analysis, it is customary that after the appropriate collection of the compound's mass spectrum, the next step involves its comparison with a compound in the instrument's National Institute of Standards and Technology (NIST) library.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

An ERCOT test system for market design studies

An open source test system is developed that permits the dynamic modeling of centrally-managed wholesale power markets operating over highvoltage transmission grids. In default mode, the test system models basic operations in the Electric Reliability Council of Texas (ERCOT): namely, centrally-managed day-ahead and real-time markets operating over successive days, with congestion handled by locational marginal pricing. These basic operational features characterize all seven U.S. energy regions organized as centrally-managed wholesale power markets. Modeled participants include dispatchable generators, load-serving entities, and non-dispatchable generation such as unrmed wind and solar power. Users can congure a broad variety of parameters to study basic market and grid features under alternative system conditions. Users can also easily extend the test system's Java/Python software classes to study modied or newly envisioned market and grid features. Finally, the test system is integrated with a high-level simulation framework that permits it to function as a software component within larger systems, such as multi-country systems or integrated transmission and distribution systems. Finally, detailed test cases with 8-bus and 200-bus transmission grids are reported to illustrate these test system capabilities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗

Microstructural behavior of iron and bismuth added Sn-1Ag-Cu solder under elevated temperature aging

An extensive study was done to investigate the microstructural behavior of iron (Fe) and bismuth (Bi) added Sn-1Ag-0.5Cu (SAC105) under severe thermal aging conditions. The isothermal aging was done at 200 °C for 100 h, 200 h, and 300 h. Optical microscopy with cross-polarized light revealed that the grain size significantly reduces with Fe/Bi addition to the base alloy SAC105 and remains literally the same after thermal aging. The micrographs of field emission scanning electron microscopy (FESEM) with backscattered electron detector and their further analysis via imageJ software indicated that Fe/Bi added SAC105 showed a significant reduction in the IMCs size (Ag{sub 3}Sn and Cu{sub 6}Sn{sub 5}), especially the Cu{sub 6}Sn{sub 5} IMCs, as well as β-Sn matrix and a refinement in the microstructure, which is due to the presence of Bi in the alloys. Moreover, their microstructure remains much more stable under severe thermal aging conditions, which is because of the presence of both Fe and Bi in the alloy. The microstructural behavior suggests that Fe/Bi modified SAC105 would have much improved reliability under severe thermal environments. These modified alloys also have relatively low melting temperature and low cost.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

General approaches for shear-correcting coordinate transformations in Bragg coherent diffraction imaging. Part I

This two-part article series provides a generalized description of the scattering geometry of Bragg coherent diffraction imaging (BCDI) experiments, the shear distortion effects inherent in the 3D image obtained from presently used methods and strategies to mitigate this distortion. Part I starts from fundamental considerations to present the general real-space coordinate transformation required to correct this shear, in a compact operator formulation that easily lends itself to implementation with available software packages. Such a transformation, applied as a final post-processing step following phase retrieval, is crucial for arriving at an undistorted, correctly oriented and physically meaningful image of the 3D crystalline scatterer. As the relevance of BCDI grows in the field of materials characterization, the available sparse literature that addresses the geometric theory of BCDI and the subsequent analysis methods are generalized here. This geometrical aspect, specific to coherent Bragg diffraction and absent in 2D transmission CDI experiments, gains particular importance when it comes to spatially resolved characterization of 3D crystalline materials in a reliable nondestructive manner. This series of articles describes this theory, from the diffraction in Bragg geometry to the corrections needed to obtain a properly rendered digital image of the 3D scatterer. Part I of this series provides the experimental BCDI community with the general form of the 3D real-space distortions in the phase-retrieved object, along with the necessary post-retrieval correction method. Part II builds upon the geometric theory developed in Part I with the formalism to correct the shear distortions directly on an orthogonal grid within the phase-retrieval algorithm itself, allowing more physically realistic constraints to be applied. Taken together, Parts I and II provide the X-ray science community with a set of generalized BCDI shear-correction techniques crucial to the final rendering of a 3D crystalline scatterer and for the development of new BCDI methods and experiments.

36 MATERIALS SCIENCE↗

Technical Specification Surveillance Interval Extension of Digital Equipment in Nuclear Power Plants: Review and Research. Light Water Reactor Sustainability Program report

The nuclear industry has been slow to incorporate digital technology into nuclear plant designs for several reasons, including: The high cost of design change modifications to implement them, versus simply replacing them with like for like technology when the components fail; Digital technology qualification issues, particularly in safety related applications that are susceptible to software common cause failure. This is a concern both for current operating plants as well as new builds; and, General familiarity and comfort with the existing analog technology on the part of the nuclear plant engineering staff, in spite of the superior performance and reduced maintenance costs of the digital replacements. At the same time, the nuclear industry is under significant cost pressure in the electric marketplace due to the abundance of natural gas generation and renewables. The industry would benefit by investment in new technologies that could lower future operating costs while addressing obsolescence and reliability issues of the current analog technologies. However, the industry has been unable to formulate the business cases to take advantage of these labor savings and production reliability improvements. This paper discusses another potential cost savings benefit of a transition to digital technology, extending the surveillance intervals of technical specification required equipment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗

Assessment of Microstructure Prediction Capabilities for Powder Bed Fusion Stainless Steel 316

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

36 MATERIALS SCIENCE↗

FENIX: Towards a Fully Integrated Multiphysics Framework for Plasma Facing Component Modeling

Computational tools have a crucial role to play in accelerating the deployment of fusion as a clean, reliable, abundant, and sustainable energy source. Multiphysics, high-fidelity simulation capabilities can help model, study, and predict intricate interactions between materials performance, plasma exposure, neutron irradiation, and engineering processes. As such, they can assist in the resolution of scientific and engineering challenges underpinning design, construction, and commission of fusion power plants. To address these needs, ongoing efforts are leveraging the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework and delivering new computational tools for the fusion community. These tools inherit crucial attributes from MOOSE. They are open-source, modular, integrated with nuclear industry-standard software quality assurance processes, and enable multiphysics, multi-fidelity, fully integrated, zero- to three-dimensional, and massively parallel simulations. After a short overview of these capabilities, we will present the development of Fusion ENergy Integrated multiphys-X (FENIX), a MOOSE-based application designed to enable plasma facing component design and performance evaluation. Throughout their lifetime, plasma facing components are exposed to extreme thermal loads, repeated thermal shocks, and irradiation by plasma ions, neutral particles, and high-energy neutrons. Consequently, designing a plasma facing component with acceptable lifetime degradation is extremely challenging. FENIX aims to model the multiphysics environment in which plasma facing components evolve to accelerate their design studies. To that end, FENIX couples existing MOOSE capabilities such as heat transfer, thermomechanics, and thermal hydraulics, with tritium transport via the MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8), with neutronics via the MOOSE-based high-fidelity neutron-photon transport and fluid dynamics code Cardinal, and finally with Particle-in-Cell plasma simulation capabilities being developed in this project. In this study, we present the current FENIX capabilities and preliminary results of its application to model the Tritium Plasma Experiment set up at Idaho National Laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Uncertainty Quantification in GADRAS Inverse Modeling

The Gamma Detector Response and Analysis Software (GADRAS) package includes an inverse modeling tool that is helpful in identifying characteristics of unknown radioactive materials. Traditionally, uncertainties in this analysis were derived solely from measurement data quality and the fit of synthetic spectra. This paper aims to rigorously quantify additional sources of uncertainty, focusing on uncertainties arising from measurements being analyzed, Detector Response Function (DRF) characterization, and DRF extrapolation. Applying these findings to the BeRPBall benchmark data set, we demonstrated the impact of these uncertainties on plutonium and polyethylene estimates. The results underscore the importance of incorporating diverse uncertainty sources to enhance the accuracy and reliability of GADRAS’s inverse modeling capabilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

WavePropaGator : interactive framework for X-ray free-electron laser optics design and simulations

This article describes the WavePropaGator (WPG) package, a new interactive software framework for coherent and partially coherent X-ray wavefront propagation simulations. The package has been developed at European XFEL for users at the existing and emerging free-electron laser (FEL) facilities, as well as at the third-generation synchrotron sources and future diffraction-limited storage rings. The WPG addresses the needs of beamline scientists and user groups to facilitate the design, optimization and improvement of X-ray optics to meet their experimental requirements. The package uses the Synchrotron Radiation Workshop (SRW) C/C++ library and its Python binding for numerical wavefront propagation simulations. The framework runs reliably under Linux, Microsoft Windows 7 and Apple Mac OS X and is distributed under an open-source license. The available tools allow for varying source parameters and optics layouts and visualizing the results interactively. The wavefront history structure can be used for tracking changes in every particular wavefront during propagation. The batch propagation mode enables processing of multiple wavefronts in workflow mode. The paper presents a general description of the package and gives some recent application examples, including modeling of full X-ray FEL beamlines and start-to-end simulation of experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Modeling of the effectiveness of novel edge seal designs for fast, low‐Cap‐Ex manufacturing

Abstract In the manufacturing of photovoltaic (PV) modules, the lamination process can take up to 20 min to complete. In this work, new lamination processes are being developed, and have been prototyped, which hope to be able to cut this time down to as little as 30 s. This could provide significant savings in the cost of lamination equipment, floor space, and energy. PV modules are expected to have a lifespan exceeding 20 to 30 years. For moisture‐sensitive PV technologies, the edge seal between the two layers of glass can be the weakest point of its reliability. There is an inherent challenge when evaluating edge seal materials due to their low permeation rates. As part of Colorado State University's Photovoltaic Research and Development 2, work at the National Renewable Energy Laboratory has developed models to evaluate edge seal configurations in glass‐glass PV modules. Here, this new manufacturing process is evaluated for long‐term moisture durability. Different edge seal design options within glass–glass PV modules are explored. Most of these designs are targeting a superstrate on glass configuration, e.g CdTe, but some designs could be used on conventional crystalline Si cells. Using COMSOL finite element simulation software, we investigated the edge seal and interlayer design configurations containing silicone perimeter edge adhesive, desiccated polyisobutylene‐based edge seal, air, and polyolefin while integrating climate conditions equivalent to a hot and humid environment such as Miami, Florida. We found optimized configurations that will allow the module to prevent moisture ingress over 50 years minimizing the amount of time and material used while utilizing polymers that are easily dispensed.

14 SOLAR ENERGY↗

Preliminary Results on Process Modeling Tools for Determining Variability in Additively Manufactured Stainless Steel 316 Parts

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the distinct characteristics of additive manufacturing (AM) materials, stemming from their unique processing history, microstructure, and properties, pose significant challenges for the qualification and certification of nuclear components. These challenges primarily arise from component-scale variations in microstructure and properties influenced by local process conditions and geometry, which affect thermal history, melt pool dynamics, and microstructure evolution. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents preliminary results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Initial results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing the challenges inherent in process modeling of AM components and outlines a plan for future development needs.

36 MATERIALS SCIENCE↗

Methodological approach to the sensitivity analysis of failure effects in modern digital I and C systems - 136

Modern I and C systems of nuclear power plants increasingly use digital equipment, software-based applications for I and C functions and different types and topologies of communication networks as well. The structure, operation and communication of digital safety-related I and C systems are strongly influenced by the implemented automatic fault detection and fault treatment procedures as barriers against failure propagation. Thus, these I and C systems can change their operating states automatically during the operation time, e.g. due to the detection and correction of certain failure types. Reasonably because of these dynamic properties of digital I and C systems the use of classical fault tree and event tree methods solely for the reliability analysis of modern I and C systems is less suitable. These methods basically apply static models and do not consider the temporal changes of the operating state of the I and C system or equipment appropriately. In this paper, a recently developed approach for the sensitivity analysis of digital I and C systems will be introduced that allows to investigate the dynamic behavior of such systems in cases of internal faults and to quantify the effects on the system reliability. The development and testing of the analysis methodology is carried out using models of generic I and C systems with different typical architectures of stepwise increased complexity. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

HPC for the EEC: Industrial Trade Tools for the Aging Energy Infrastructure

The petrochemical and refining sectors are challenged with reliably delivering safer and cleaner energy to US consumers, while meeting an ever-growing global demand. The petrochemical and refining infrastructure in the USA is aging, and corrosion and damage mechanisms are constant threats to mechanical integrity, safety, and profitability. However, governments and industry stakeholders are reluctant to replace or upgrade the existing infrastructure due to the immense cost. To better understand and mitigate the risks of aging energy infrastructure, strong technical analysis capabilities, combined with optimized monitoring and decision making, is critical. Today, this is accomplished via complex simulations and data analysis. To this end, advanced High-Performance-Computing (HPC) software will be leveraged and integrated in easy-to-use industrial trade tools, to lower the barrier for new users, increase the ease of access for experienced users, and allow for smarter decisions to be made in the midstream and downstream energy sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (User's Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a users manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

Accurate Electron-Phonon Interactions from Advanced Density Functional Theory

Electron-phonon coupling (EPC) is key to understanding charge transport, band renormalization, and superconductivity in energy materials, including correlated transition-metal oxides, ferroelectric perovskites, optoelectronic semiconductors, and phonon-mediated superconductors. Although first-principles density functional theory (DFT)-based EPC calculations are used widely, their predictive power is limited by the accuracy, transferability, and efficiency of the underlying exchange-correlation functionals. These limitations become exacerbated in complex 𝑑- and 𝑓-electron materials, where beyond-DFT approaches and additional corrections, such as the Hubbard 𝑈, are commonly invoked. Here, using the examples of Co⁢O and Ni⁢O, we show how the 𝑟 2 SCAN density functional correctly captures strong EPC effects in transition-metal oxides without requiring the introduction of Hubbard 𝑈 parameters. We also find that 𝑟 2 SCAN successfully describes the subtle interplay between ionic and covalent bonding, and strong EPC effects in the low-temperature rhombohedral phase of the prototypical ferroelectric Ba⁢Ti⁢O 3 , without requiring Hubbard 𝑈 and intersite 𝑉 corrections. We further demonstrate the ability of 𝑟 2 SCAN to accurately model the EPC of the main-group semiconductor Ga⁢As and the phonon-mediated superconducting properties of Mg⁢B 2 , with reliable electronic bands and phonons. Our study establishes 𝑟 2 SCAN-based EPC as a transferable, parameter-free, and computationally efficient framework for predictive material-specific modeling of EPC in energy materials, and opens a practical route toward high-throughput screening of superconductors, thermoelectrics, optoelectronic semiconductors, and oxide electrodes and catalysts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :

The Dakota (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗