Beyond NMF: Advanced Signal Processing and Machine Learning Methodologies for Hyperspectral Analysis in EELS
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Optical identification of liquid droplets, aerosols, or thin films is important for many applications. While reference spectra are sometimes available for such measurements, they are not always applicable to the observed spectrum or the given sample morphology. Reference spectra for many forms can be modeled, however, if the n/k vectors (real and imaginary refractive indices) are available. In previous work we have reported protocols to determine the n/k vectors for dozens of liquids, primarily in the mid-infrared (MIR) spectral range from 7500 to 400 cm –1 . In this work we extend the spectral range into the near-infrared (NIR) region, demonstrating a method to measure and merge the data sets to create composite n/k data ranging from 10 000 to 400 cm –1 (1.0 to 25 µm) with absorbance fidelity spanning over four orders of magnitude, and vastly improved signal-to-noise in the NIR. The precision of the composite data is evaluated for three different liquids, focusing primarily on the steps for converting the raw absorbance spectra to k values. The variability in both MIR and NIR data as well as in the final n/k vectors is also investigated for several liquids. For typical liquids, the overall variability (reported as 2σ) in the final n and k-vectors is determined to be ∼0.4% and 3%, respectively. Finally, the derived n/k data are used to calculate absorbance spectra for aerosol droplets, showing marginal variability due to the typical measurement errors in the final n/k vectors.
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This data catalog contains information related to the Training Site Analysis for the Geothermica project "DE-risking Exploration of geothermal Plays in magmatic ENvironments (DEEPEN)." The DEEPEN project aims to reduce exploration risk for geothermal fluids in magmatic systems by developing improved an improved framework for interpretation of exploration data using the Play Fairway Analysis (PFA) methodology. The Training Site Analysis performed for DEEPEN leverages existing datasets to develop a customized PFA approach to exploration for multiple geothermal resource types in magmatic systems (conventional hydrothermal resources, supercritical fluid and superheated steam resources, and superhot EGS resources). This data catalog contains links to publicly available data files related to 8 training sites in the United States. US training sites are: the Cascades/Aleutians PFA project; the Hawaii PFA project, the Oregon Cascades PFA project, the Snake River Plain, Idaho PFA project, the Washington State PFA project, Newberry Volcano, Coso Geothermal Field, and the Geysers Geothermal field. This database contains an overview of these training sites, data sources, and links to publicly available exploration datasets. For the five PFA projects, details on exploration data related to PFA components (heat, fluid, permeability, sometimes seal) are provided, including a summary of data weighting methodologies.
Comprehensive analysis of the molecular weight distribution of raw and catalytic fast pyrolysis oils remains a key technical hurdle to understanding oil quality and multiple methods may be necessary to accurately represent all components present.
Just over a decade ago, a truly outside-of-the-box approach to isotope ratio mass spectrometry (IRMS) was undertaken between research groups at Clemson University and the Pacific Northwest National Laboratory. The original motivation dealt with projections as to whether or not microplasmas could be developed into practical elemental ionization sources, perhaps for transportable analysis applications. In particular, the use of the liquid sampling–atmospheric pressure glow discharge (LS-APGD) was pursued. Its interfacing to an ultra-high resolution Orbitrap platform, proved not only facile, but opened up a wealth of potential applications. Here, we lay out a historical, tutorial description of the interfacing and the evolution of the methodology regarding IRMS of uranium. Practical challenges and opportunities are described, which hopefully provide guidance to further applications in high resolution IRMS. It is hoped that, while detailed and lengthy, the didactic nature of the presentation provides experimental insights and tips, and also serves as an homage to our very good friend, Professor Gary M. Hieftje, whose scientific inspiration and comradery have been immeasurably important in our own careers.
This paper presents a modeling methodology for realistic distribution system simulation and analysis. The methodology involves three major approaches: utility model conversion, feeder load modeling, and feeder model validation. The feeder models obtained from the utility are converted to the format that is more flexible for analysis and algorithm development. The load profiles down to each node are modeled in detail using advanced metering infrastructure data and supervisory control and data acquisition (SCADA) system measured load data. Then the distribution system models are validated by comparing the simulated feeder-head voltage results and SCADA measured voltage data. To better understand the bulk system operations and the interactions between transmission and distribution systems, the modeled realistic distribution systems are integrated into a transmission-and-distribution cosimulation framework to perform the system simulation from the bulk system down to each node in the distribution system.
Per 10 CFR 50 Appendix G, the pressure-temperature (P-T) limits curves and minimum temperature must be established to provide adequate margins for ferritic pressure-retaining components of the reactor coolant pressure boundary; this is to protect against brittle failure during any normal operating conditions, including anticipated operational occurrences and system hydrostatic tests, to which the pressure boundary may be subjected over its service lifetime. Specifically, ASME Code Section XI Appendix G procedures must be used for P-T limits calculation considering the pressure and temperature at various operating transient conditions. However, the elastic fracture mechanics solutions in Section XI are only suitable for cylindrical reactor pressure vessel (RPV) beltline without geometric discontinuities. Hence, these solutions are not suitable for postulated flaws near the core support blocks attached to the NuScale RPV inside surface, which is part of the beltline. As a result, NuScale has used finite element analysis to calculate thermal stress and stress intensity factor for the postulated flaws. The solutions using finite-element analysis have been validated using the formulations for straight cylinders for both axial and circumferential flaws. In addition, special considerations are given to the RPV beltline nil-ductility transition temperature (RTNDT) due to neutron irradiation occurring at lower temperatures than conventional plants. The paper summarizes the methodology and finite-element models used to develop P-T limits curves for NuScale RPV at the end of its 60-year design life.
Herein this paper describes a system-level view of a fully automated transit system comprising a fleet of automated vehicles (AVs) in driverless operation, each with an SAE level 4 Automated Driving System, along with its related safety infrastructure and other system equipment. This AV system-level control is compared to the automatic train control system used in automated guideway transit technology, particularly that of communications-based train control (CBTC). Drawing from the safety principles, analysis methods, and risk assessments of CBTC systems, comparable functional subsystem definitions are proposed for AV fleets in driverless operation. With the prospect of multiple AV fleets operating within a single automated mobility district, the criticality of protecting roadway junctions requires an approach like that of automated fixed-guideway transit systems, in which a guideway switch zone "interlocking" at each junction location deconflicts railway traffic, affirming safe passage. The analogous AV protection safety subsystem is defined as fail-safe equipment that monitors roadway intersections and junctions, communicates traffic signal status, perceives and communicates alerts and signals to AV connected vehicles concerning potential unsafe conditions, and performs related primary safety functions. Conclusions are drawn that the AV protection roadway intersection functions must be performed by local roadside equipment dedicated to protecting each roadway intersection and junction. Further, it is concluded that the communications technology connecting the infrastructure with the vehicle to perform this vital, fail-safe protection should meet specific functional and performance criteria.
Presented here is a modular methodology for time-domain stochastic seismic wave propagation analysis. Presented methodology is designed to analyse uncertain seismic motions as an input, propagating through uncertain material. Traditional approach for uncertain wave propagation relies on models that include deep bedrock, local soil site, and their random process and random field information. Such models can become quite large and computationally intractable. The modular approach proposed herein features two step approach that allows separate consideration of the deep bedrock and local site along with corresponding random field information. In this work, the first step considers an auxiliary stochastic motions problem in the bedrock. Stochastic local site response can then be simulated in a reduced domain within certain depth from the surface. Application of uncertain seismic motions at depth, for local uncertain site response is done using stochastic effective forces developed through the Domain Reduction Method. By using Hermite polynomial chaos expansion to represent the non-Gaussian random field of material parameters and non-stationary random process of seismic motion, the proposed modular methodology is formulated using intrusive stochastic Galerkin approach, as seen in the Stochastic Elastic–Plastic Finite Element Method (SEPFEM). Developed modular methodology is illustrated using a 1-D stochastic seismic wave propagation analysis with three cases, and simulation results are also verified with results from conventional approach.
This presentation is on the comparative analysis of standard and advanced USL methodologies. Each method has a different way to address/account for experimental biases and uncertainties. This study showed why bias and uncertainty predictions by these popular methods differ significantly in some cases. In order to establish confidence to our validation efforts, especially with new reactor concepts, in designing new critical experiments and claiming experimental coverage for our existing applications, we need to understand capabilities of our methods further.
Reactor noise is a nondestructive approach to measure neutron/gamma fluctuations. Extract kinetic parameters such the prompt neutron decay constant (a). Used in applications such as safeguards and reactor physics. This work address the impact on precision from frequency domain analysis methodology.
Reactor noise is a nondestructive approach to measure neutron/gamma fluctuations. Extract kinetic parameters such the prompt neutron decay constant (a). Used in applications such as safeguards and reactor physics. This work address the impact on precision from frequency domain analysis methodology.
Underground explosions generate potentially detectable signatures, including energy waves that travel through the Earth’s subsurface (i.e., seismic waves), low-frequency sound waves (i.e., infrasound and hydroacoustic waves), and radioactive gases and/or particles that might leak from the test cavity (if the event was nuclear). There can also be intelligence indicators of a test, such as observations of modified patterns of life and activity at a suspected test site. If all of these detectable signatures and intelligence indicators are present and self-consistent, then analysts have high confidence in classifying a signature generating event as an explosion. However, because only partial information about an event is likely to be available, determining whether an event was natural (e.g., an earthquake or landslide) or manmade (e.g., a chemical or nuclear explosion) is much more challenging. This primer describes how one category of event signatures—seismic signatures—can augment event analyses. While universities and government organizations have generated detailed technical descriptions of seismic analytic techniques, we seek to translate seismic event analysis for a broad, non-technical audience. When the geologic conditions near an event are well-characterized, seismic data can be used to calculate critical information, such as event location and depth, with relatively high accuracy. Moreover, specific features within seismic datasets can help determine whether an event was an explosion. However, a key challenge in seismic analysis is that geologic site conditions are often poorly characterized, complicating the ability to discern the true nature of the event. To overcome this challenge, geologists answer a series of questions (discussed in section 1) to guide seismic event analysis and determine the most probable nature of an event. As more information is gathered during each analytic step, confidence grows regarding the nature of the event. Section 2 addresses uncertainties in seismic analysis and the vital nature of high-fidelity geologic data for accurate seismic event analysis.
This study critically evaluates human reliability analysis (HRA) methodologies applicable to regulatory probabilistic safety assessment (PSA) model, with a particular focus on their role in supporting the significance determination process (SDP) in nuclear safety assessment. Firstly, three widely utilized HRA methods – IDHEAS-ECA, SPAR-H, and ASEP/THERP – were qualitatively and quantitatively assessed. Qualitative assessments were conducted using attributes from the NEA/CSNI/R(2015)1 report, while quantitative evaluations employed regression and correlation analyses to compare predicted human error probabilities (HEPs) against empirical data. Results reveal distinct strengths, for example, IDHEAS-ECA’s robust predictive accuracy and K-HRA’s alignment with operational practices. In addition, dependency analysis and recovery analysis were critically evaluated. For dependency analysis, the methods’ handling of inter-task dependencies and their impact on HEPs were examined, while recovery analysis highlighted strategies for mitigating failure events. Furthermore, strategies were proposed to evaluate performance-shaping factors under conditions of reduced human performance, such as stress, fatigue, or cognitive overload, addressing specific challenges faced in SDP evaluations. Human errors from KINS’s operational performance information system event reports were evaluated as a case study. This study identifies gaps and provides actionable insights to ensure their validity and applicability in SDP HRA applications. This paper is a part of research conducted by KINS, and it should be noted that this result does not represent the regulatory position of KINS.
To holistically address building sustainability, Argonne National Laboratory has expanded its Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) life-cycle model with a new GREET Building Module. This report documents life-cycle analysis (LCA) methodology and foreground data that Argonne National Laboratory compiles and develops to address embodied greenhouse gas (GHG) emissions and energy impacts of a wide range of envelope and structural building materials for new construction and retrofits. The methodology and data form the backbone of the GREET Building Module. This research effort focuses on developing consistent LCA methodology that conforms to building LCA standards such as the EN 15978 to address embodied GHG emissions and energy impacts of building materials/technologies. We document detailed foreground data for selected building materials and building components that are common for building construction. To test the LCA methodology and the GREET Building Module, this report includes case studies of insulation materials and wall panels for residential building retrofit. We have developed a separate document as a User Guide for understanding and applying the GREET Building Module to conduct detailed, process-level LCA of embodied carbon and energy impacts of emerging building materials and technology solutions that of interest to the Building Technologies Office (BTO) of the US Department of Energy, researchers, and industry stakeholders.
The intent of this project was to determine appropriate methodologies for analysis of lithium isotopes for 1) quasi-online monitoring of isotope separations, and 2) high-precision characterization of end-products, including lithium fluoride and lithium-beryllium fluoride (FLiBe). Traditionally, isotopic analysis requires the use of expensive, sophisticated instrumentation with often relatively long analytical timelines. We determined that the use of cheaper, smaller, and easier-to-operate instruments are sufficient for rapid analysis of lithium isotopes to monitor isotope separation efficiencies. In addition, we found that the analytical precision afforded by these lower-cost instruments is comparable to the more sophisticated (i.e., more expensive) instrumentation when the isotopic composition of lithium becomes highly depleted in 6Li. This finding will ultimately help lower costs of manufacturing of depleted lithium that is required for operation of certain designs of Generation IV nuclear reactors.
This research report (1) describes the process followed and products developed during the SR I&C Pilot Project Initial Scoping Phase, and (2) captures lessons learned. Exelon Generation and LWRS collaborated to develop a Digital Transformation Strategy as part of a larger Advanced Concept of Operations. The proposed SR I&C Pilot Upgrade provides a foundation stone for this Digital Transformation that will improve plant safety, reliability, and operational performance while lowering plant Total Cost of Ownership (TCO). Initial Scoping Phase activities for this Pilot Project have been performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process (SDP), NISP-EN-04, Standard Digital Engineering Process (SDEP), and Electric Power Research Institute (EPRI) Report 3002011816, Digital Engineering Guide (DEG). Completing Initial Scoping Phase Engineering and Operations, Licensing, and Project Management Activities was necessary to sufficiently bound the scope, schedule, and estimated cost of the Project to enable utility management to authorize moving into the Conceptual Design Phase. A significant finding of the Business Case Analysis (BCA) methodology developed and applied as part of this effort was that the growth rate of material costs for sustaining the operation of obsolete SR I&C equipment is accelerating. This directly contributed to the Project Economic Analysis created to justify continuing the Project. Project Initial Scoping Phase lessons learned have also been captured to assist the larger industry in understanding the Digital Transformation Strategy and SR I&C Pilot Project Initial Scoping Phase efforts. This is in keeping with the public/private partnership that has been established between the Department of Energy (DOE) and Exelon for this effort with engagement from the NRC. By addressing first-of-a-kind (FOAK) risks and capturing lessons learned, the SR I&C Pilot Upgrade Project addresses technical, regulatory, and business risks to enable subsequent implementers of similar upgrade.