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At least 991 records · Page 55

A Search for Signs of Life and Habitability on Europa

Europa is a key target in the search for life beyond the Earth because of consistent evidence that below the icy surface there is liquid water. Future missions to Europa could confirm the presence and nature of the ocean and determine the thickness of the ice layer. Confirming the presence of an ocean and determining the habitability of Europa are key astrobiology science objectives. Nevertheless, the highest priority objective for astrobiology will be a search for life. How could a search for life be accomplished on a near-term mission given the thick ice cover? One answer may lie in the surface materials. If Europa has an ocean, and if that ocean contains life, and if water from the ocean is carried up to the surface, then signs of life may be contained in organic material on the surface. Organics that derive from biological processes (dead organisms) are distinct from organics derived from non-biological processes in several aspects. First, biology is selective and specific in its use of molecules. For example, Earth life uses 20 left-handed amino acids. Second, biology can leave characteristic isotopic patterns. Third, biology often produces large complex molecules in high concentrations, for example lipids. Organic material that has been on the surface of Europa for long periods of time would be reprocessed by the strong radiation field probably erasing any signature of biological origin. Evidence of life in the ocean may be found on the surface of Europa if regions of the surface contained relatively recent material carried up from the ocean through cracks in the icy lithosphere. But organic material that has been on the surface of Europa for long periods of time would be reprocessed by the strong radiation field probably erasing any signature of biological origin. Thus, the detailed analysis required may not be possible via remote sensing but direct sampling of the material below the radiation processed upper meter is probably required.

Fonda, Mark↗

Analyzing Relationships Between Economic and Neighborhood-Related Social Determinants of Health and Intensive Care Unit Length of Stay for Critically Ill Children With Medical Complexity Presenting With Severe Sepsis

Objectives: Of the Social Determinants of Health (SDoH), we evaluated socioeconomic and neighborhood-related factors which may affect children with medical complexity (CMC) admitted to a Pediatric Intensive Care Unit (PICU) in Shelby County, Tennessee with severe sepsis and their association with PICU length of stay (LOS). We hypothesized that census tract-level socioeconomic and neighborhood factors were associated with prolonged PICU LOS in CMC admitted with severe sepsis in the underserved community. Methods: This single-center retrospective observational study included CMC living in Shelby County, Tennessee admitted to the ICU with severe sepsis over an 18-month period. Severe sepsis CMC patients were identified using an existing algorithm incorporated into the electronic medical record at a freestanding children's hospital. SDoH information was collected and analyzed using patient records and publicly available census-tract level data, with ICU length of stay as the primary outcome. Results: 83 encounters representing 73 patients were included in the analysis. The median PICU LOS was 9.04 days (IQR 3.99–20.35). The population was 53% male with a median age of 4.1 years (IQR 1.96–12.02). There were 57 Black/African American patients (68.7%) and 85.5% had public insurance. Based on census tract-level data, about half (49.4%) of the CMC severe sepsis population lived in census tracts classified as suffering from high social vulnerability. There were no statistically significant relationships between any socioeconomic and neighborhood level factors and PICU LOS. Conclusion: Pediatric CMC severe sepsis patients admitted to the PICU do not have prolonged lengths of ICU stay related to socioeconomic and neighborhood-level SDoH at our center. A larger sample with the use of individual-level screening would need to be evaluated for associations between social determinants of health and PICU outcomes of these patients.

60 APPLIED LIFE SCIENCES↗

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. Here, this paper introduces “molecular hypernetworks” (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The three-dimensional Multi-Block Advanced Grid Generation System (3DMAGGS)

As the size and complexity of three dimensional volume grids increases, there is a growing need for fast and efficient 3D volumetric elliptic grid solvers. Present day solvers are limited by computational speed and do not have all the capabilities such as interior volume grid clustering control, viscous grid clustering at the wall of a configuration, truncation error limiters, and convergence optimization residing in one code. A new volume grid generator, 3DMAGGS (Three-Dimensional Multi-Block Advanced Grid Generation System), which is based on the 3DGRAPE code, has evolved to meet these needs. This is a manual for the usage of 3DMAGGS and contains five sections, including the motivations and usage, a GRIDGEN interface, a grid quality analysis tool, a sample case for verifying correct operation of the code, and a comparison to both 3DGRAPE and GRIDGEN3D. Since it was derived from 3DGRAPE, this technical memorandum should be used in conjunction with the 3DGRAPE manual (NASA TM-102224).

Alter, Stephen J.↗

Sample Analysis at Mars Instrument Simulator

The Sample Analysis at Mars Instrument Simulator (SAMSIM) is a numerical model dedicated to plan and validate operations of the Sample Analysis at Mars (SAM) instrument on the surface of Mars. The SAM instrument suite, currently operating on the Mars Science Laboratory (MSL), is an analytical laboratory designed to investigate the chemical and isotopic composition of the atmosphere and volatiles extracted from solid samples. SAMSIM was developed using Matlab and Simulink libraries of MathWorks Inc. to provide MSL mission planners with accurate predictions of the instrument electrical, thermal, mechanical, and fluid responses to scripted commands. This tool is a first example of a multi-purpose, full-scale numerical modeling of a flight instrument with the purpose of supplementing or even eliminating entirely the need for a hardware engineer model during instrument development and operation. SAMSIM simulates the complex interactions that occur between the instrument Command and Data Handling unit (C&DH) and all subsystems during the execution of experiment sequences. A typical SAM experiment takes many hours to complete and involves hundreds of components. During the simulation, the electrical, mechanical, thermal, and gas dynamics states of each hardware component are accurately modeled and propagated within the simulation environment at faster than real time. This allows the simulation, in just a few minutes, of experiment sequences that takes many hours to execute on the real instrument. The SAMSIM model is divided into five distinct but interacting modules: software, mechanical, thermal, gas flow, and electrical modules. The software module simulates the instrument C&DH by executing a customized version of the instrument flight software in a Matlab environment. The inputs and outputs to this synthetic C&DH are mapped to virtual sensors and command lines that mimic in their structure and connectivity the layout of the instrument harnesses. This module executes, and thus validates, complex command scripts prior to their up-linking to the SAM instrument. As an output, this module generates synthetic data and message logs at a rate that is similar to the actual instrument.

Benna, Mehdi↗

Advanced Long-Term Environmental Monitoring Systems (ALTEMIS) Artificial Intelligence Data Management Plan

Across the Department of Energy’s Environmental and Legacy Management sites, complex groundwater plumes exist that will require long-term monitoring to ensure remedial actions that have been put in place remain effective decades into the future. The current monitoring paradigm predominantly consists of groundwater well sampling, whereby samples are collected, concentrations analyzed, and plume anomalies are detected after they have occurred. The Advanced Long Term Environmental Monitoring Systems (ALTEMIS) program is a multi-lab, multi-institution team of researchers that is deploying spatially integrative technologies (i.e., real-time in situ sensor networks), coupled with artificial intelligence and machine learning, to establish a more proactive monitoring paradigm. Within this approach, plume anomalies can be predicted, and corrective actions can be established prior to the occurrence, offering a more cost-effective and robust approach to long-term monitoring. The team has deployed a variety of different in situ sensing technologies at the Savannah River Site’s F-Area Hazardous Waste Management Facility around the F-Area Seepage Basins, which are unlined basins that received 7 billion liters of acidic low-level radioactive waste from the 1950s until the late 1980s. The technologies and techniques that the team is deploying are intended to ensure that the remedial actions that have been taken by the site remain effective decades into the future. Foundational to this approach is a robust, integrated data management and analysis plan to ensure accurate and timely reporting from the variety of sensor systems that are in place. This report will outline the data management plan that has been implemented by the ALTEMIS team at the Savannah River Site and will serve as a blueprint as the technology is translated to new sites across the DOE Complex.

54 ENVIRONMENTAL SCIENCES↗

Preliminary Analysis of Pyrite Reactivity Under Venusian Temperature and Atmosphere

Measurements of Venus surface chemistry suggest a basaltic composition with a predominantly CO2 atmosphere. In order to understand the reactivity of certain possible mineral species on the surface, previous simulation chambers conduct experiments at 1 atmosphere with a simplified CO2 atmosphere. Following this procedure, pyrite (FeS2) samples are used to estimate the reactivity of sulfide minerals under a Venusian atmosphere and climate. Sulfurous gas species have been identified and quantified in the Venusian atmosphere, and sulfurous gas and mineral species are known to be created through volcanism, which is suggested to still occur on the surface of Venus. This experimentation is necessary to constrain reactions that could occur between the surface and atmosphere of Venus to understand terrestrial geology in a thick and hot greenhouse atmosphere. Quantifying this reaction can lead to approximations necessary for further experimentation in more complex environments such as those in the GEER chamber at Glenn Research Center that can simulate pressure along with temperature and a more inclusive and representative Venusian atmosphere.

Venus surface↗

Persistent Structure and Frustrated Magnetism in High Entropy Rare-Earth Zirconates

The configurational complexity and distinct local atomic environments of high entropy oxides remain largely unexplored, leaving structure-property relationships and the hypothesis that the family offers rich tunability for applications ambiguous. This work investigates the influence of cation size and materials synthesis in determining the resulting structure and magnetic properties of a family of high entropy rare-earth zirconates (HEREZs, nominal composition RE 2 Zr 2 O 7 with RE = rare-earth element combinations including Eu, Gd, Tb, Dy, Ho, La, or Sc). Here, the structural characterization of the series is examined through synchrotron X-ray diffraction and pair distribution function analysis, and electron microscopy, demonstrating average defect-fluorite structures with considerable local disorder, in all samples. The surface morphology and particle sizes are found to vary significantly with preparation method, with irregular micron-sized particles formed by high temperature sintering routes, spherical nanoparticles resulting from chemical co-precipitation methods, and porous nanoparticle agglomerates resulting from polymer steric entrapment synthesis. In agreement with the disordered cation distribution found across all samples, magnetic measurements indicate that all synthesized HEREZs show frustrated magnetic behavior, as seen in a number of single-component RE 2 Zr 2 O 7 pyrochlore oxides. These findings advance the understanding of the local structure of high entropy oxides and demonstrate strategies for designing nanostructured morphologies in the class.

36 MATERIALS SCIENCE↗

CH 4 Activation over Perovskite Catalysts: True Density and Reactivity of Active Sites

The high thermal stability of perovskites has drawn attention toward their applications for catalytic CH 4 activation and conversion, typically occurring at high temperatures. The reaction rates of perovskite catalysts for CH 4 combustion, however, trail behind those of noble metal catalysts. Ways to optimize the performance of perovskite catalysts are destined to trial-and-error approaches unless their complex reconstructed surfaces are correlated with fundamental kinetic parameters. Discerning the intrinsic activity of surface catalytic sites and the density of those sites is crucial to rationally envision complex metal oxides with enhanced catalytic performance. Here, the present work presents a detailed kinetic analysis of catalytic CH 4 combustion over a set of seven perovskites (SrTiO 3 , SrZrO 3 , SrFeO 3 , LaFeO 3 , LaInO 3 , LaCoO 3 , LaMnO 3 ) with various surface terminations. Steady-state isotopic transient kinetic analysis was employed to measure turnover frequency (TOF) and density of surface intermediates (N) under operando conditions. Top surface characterization elucidated performance-structure relationships between near-monolayer surface composition and intrinsic reactivity of the catalysts. By using a chemical etching procedure to expose Fe-sites at the top surface of LaFeO 3 (LaFeO 3 , HNO 3 ), its TOF was increased 4-fold, compared with the unmodified sample, although N on the surface of LaFeO 3 , HNO 3 decreased. Density functional theory simulations corroborated that surface Fe-termination and La-Fe termination offer lower energetic barriers for CH 4 activation when compared with La-termination. In general, surface reconstruction is shown as a tool to tune TOF and N to improve reaction rates. This work fills a gap in current kinetic studies of perovskites through a careful assessment and discussion of the density and intrinsic reactivity of active sites for methane combustion over well-characterized reconstructed perovskite surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

New K-feldspar Pb isotope results for Mesozoic arc crust in the Pacific Northwest, U.S.A. and Canada: comparison with the Mojave-Salinia province of southern California and Implications for Baja-BC

Measurements of lead isotopic compositions in detrital K-feldspar have been increasingly used as a tool to assess sediment provenance. We compiled a database of previously published Pb isotope data from 700 bedrock K-feldspar samples and 1,423 age-corrected bedrock whole rock samples from western North American igneous and metamorphic bodies. Additionally, we report 66 new K-feldspar Pb isotope data for plutons throughout the Pacific Northwest region of the United States and British Columbia. Results show that the Pb isotope values of plutonic K-feldspar depend on the isotopically juvenile or evolved nature of underlying crust. Samples obtained from the mid Cretaceous – mid Eocene Coast Plutonic Complex, North Cascades, and Intermontane superterrane that occur west of the initial 87 Sr/ 86 Sr (Sri) = 0.706 isopleth exhibit a highly restricted 207 Pb/ 206 Pb and 208 Pb/ 206 Pb values centred upon 0.83 and 2.03, respectively. Conversely, rocks overlying older continental crust further east such as the Middle Jurassic – Late Cretaceous Omineca crystalline belt, Idaho batholith, and Boulder Batholith exhibit far greater variation of Pb isotope values that parallel the 100 Ma isochron calculated from a two-stage Pb evolution model. We demonstrate that Pb isotopic results from the Idaho and Boulder Batholith region can be used to define distinctive subregions for Pb isotopic provenance analysis, and compare these signatures to the Mojave-Salinian batholith of southern California and western Arizona, as these two areas have previously been proposed as source regions for extraregional sediment that was deposited within the Nanaimo Basin during the Campanian – Maastrichtian. Future Pb isotopic analysis of detrital K-feldspar from the Nanaimo Basin of southwestern British Columbia may effectively distinguish between potential extraregional sources separated by thousands of kilometres.

Coast Plutonic Complex↗

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗

A Backscatter Moessbauer Spectrometer (BaMS) for use on Mars

The use of Moessbauer spectroscopy for in situ analysis on the surface of Mars was proposed and the design and implementation of a backscatter Moessbauer spectrometer (BaMS) instrument suitable for planetary missions to the surfaces of Mars (MESUR), the Moon (Artemis and lunar outpost), asteroids, or other solid solar system objects is discussed. The BaMS instrument is designed to be capable of analysis of a sample for the mineralogy of its iron-bearing phases without any sample preparation. A requirement of lander missions to Mars is instrumentation for in situ mineralogical analyses. Such analyses provide data needed for primary characterization as to the type of surface materials present and by inference the processes that formed and subsequently modified them. For purposes of providing diagnostic information about naturally occurring materials, the element iron is particularly important because it is abundant and multivalent (primarily 0, +2, and +3 oxidation states). Knowledge of the oxidation state of iron and its distribution among iron-bearing mineralogies tightly constrains the types of materials present. The pivotal role of iron was already recognized in 1978 by COMPLEX, who recommended development of flight instruments that would identify mineralogy and the oxidation state of iron in planetary surface materials. The near-term U.S. strategy for the exploration of Mars is the MESUR (Mars Environmental SURvey) program, which entails emplacement of a network of small, long-lived surface landers. For the Moon, BaMS was recommended as part of a three-instrument landed payload for the Artemis missions, targeted for 1997. BaMS would prospect for ilmenite, an oxygen resource material, and provide data to assess the maturity of lunar soil. Because instrumental characteristics are low mass, low volume, and low power consumption, BaMS is suitable for implementation on even small landers and rovers, as are being envisioned in MESUR and Artemis concepts. In addition to providing highly diagnostic data, a BaMS analyzer is inherently simple and, as is highly desirable for remote operation, no sample preparation is required.

Agresti, D. G.↗

Integrated Modeling Approach for the Terrestrial Planet Finder Mission

Because of the complexity of the Terrestrial Planet Finder (TPF) design concepts, the project will rely heavily on the use of engineering and science simulations to predict on-orbit performance. Furthermore, current understanding of these missions indicates that the 3m to 8m class optical systems need to be as stable as picometers in wavefront and sub-milli arcsec in pointing. These extremely small requirements impose on the models a level of predictive accuracy heretofore never achieved, especially in the area of microgravity effects, material property accuracy, thermal solution convergence, and all other second order modeling effects typically ignored. New modeling tools and analysis paradigms are developed which emphasize computational accuracy and fully integrated analytical simulations. The process is demonstrated on sample problems using the TPF Coronagraph design concept. The TPF project is also planning a suite of testbeds through which various aspects of the models and simulations will be verified.

terrestrial planet finder↗

Experiments on Lunar Core Composition: Phase Equilibrium Analysis of A Multi-Element (Fe-Ni-S-C) System

Previous geochemical and geophysical experiments have proposed the presence of a small, metallic lunar core, but its composition is still being investigated. Knowledge of core composition can have a significant effect on understanding the thermal history of the Moon, the conditions surrounding the liquid-solid or liquid-liquid field, and siderophile element partitioning between mantle and core. However, experiments on complex bulk core compositions are very limited. One limitation comes from numerous studies that have only considered two or three element systems such as Fe-S or Fe-C, which do not supply a comprehensive understanding for complex systems such as Fe-Ni-S-Si-C. Recent geophysical data suggests the presence of up to 6% lighter elements. Reassessments of Apollo seismological analyses and samples have also shown the need to acquire more data for a broader range of pressures, temperatures, and compositions. This study considers a complex multi-element system (Fe-Ni-S-C) for a relevant pressure and temperature range to the Moon's core conditions.

Go, B. M.↗

Physics-Informed Sparse Gaussian Process for Probabilistic Stability Analysis of Large-Scale Power System with Dynamic PVs and Loads

This work proposes a physics-informed sparse Gaussian process (SGP) for probabilistic stability assessment of large-scale power systems in the presence of uncertain dynamic PVs and loads. The differential and algebraic equations considering uncertainties from dynamic PVs and loads are reformulated to a nonlinear mapping relationship that allows the application of SGP. Thanks to the nonparametric characteristic of Gaussian process, the proposed framework does not require distributions of uncertain inputs and this distinguishes it from existing approaches. As the original Gaussian process is not scalable to large-scale systems with high dimensional uncertain inputs, this paper develops the SGP with a stochastic variational inference technique. It leads to approximately two orders of complex reduction. A data pre-processing step is also introduced to tackle the coexistence of stable and unstable cases by sample clustering and constructing separate SGPs. The probabilistic transient stability index is analyzed to assess system stability under different uncertain dynamics loads and PVs. Comparisons are performed with the sampling-based, the polynomial chaos expansion-based, and traditional Gaussian process-based methods on the modified IEEE 118-bus and Texas 2000-bus systems under various scenarios, including different levels of uncertainties and the existence of nonlinear correlations among dynamic PVs. The impacts of data quality and quantity issues are also investigated. It is shown that the proposed SGP achieves significantly improved computational efficiency while maintaining high accuracy with a limited number of data.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Survey of baffle plate samples, specimen machining, and hydrogen measurements

This report outlines preliminary results on the survey and characterization of irradiated baffle plate, baffle former, and flux thimble tube specimens as part of the ongoing investigation into irradiation-induced embrittlement in austenitic stainless steels at ambient (room temperature) conditions. The specimens originated from commercial pressurized water reactor (PWR) components, covering displacement damage doses ranging from approximately 0.065 to 75 dpa. Initial scanning electron microscopy (SEM) surveys revealed that specimen surfaces exhibited fine machining marks and in-service-formed oxide layers on the side surfaces of the analyzed specimens. The oxide layers revealed specific features resembling localized "pitting-like" corrosion. An additional set of specimens representing in-service oxidation is being preserved for future microstructure analysis work. As believed, this will provide additional insights into long-term material degradation in PRWs. Specimen machining challenges emerged due to a complex failure of the electric discharge machining (EDM) system located in hot area. Despite partial restoration, issues persist with the EDM’s secondary power supply, necessitating the exploration of alternative EDM or computer numerical control (CNC) machining approaches to facilitate tensile specimen preparation. Currently, low speed saw cutting is in progress to prepare specimens for hydrogen measurements and general microstructure analysis. The near-term goals include completing tensile specimen machining for advanced mechanical testing and characterizing fracture mechanisms, and stress-corrosion cracking testing, ultimately aiming to identify and mitigate the ambient-condition intergranular cracking through targeted post-irradiation annealing strategies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

RAVEN User Manual

RAVEN is a generic software framework to perform parametric and probabilistic analysis based on the response of complex system codes. The initial development was aimed to provide dynamic risk analysis capabilities to the Thermo-Hydraulic code RELAP-7, currently under development at the Idaho National Laboratory (INL). Although the initial goal has been fully accomplished, RAVEN is now a multi-purpose probabilistic and uncertainty quantification platform, capable to agnostically communicate with any system code. This agnosticism includes providing Application Programming Interfaces (APIs). These APIs are used to allow RAVEN to interact with any code as long as all the parameters that need to be perturbed are accessible by inputs files or via python interfaces. RAVEN is capable of investigating the system response, and investigating the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused to- ward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. The development of RAVEN has started in 2012, when, within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the need to provide a modern risk evaluation framework became stronger. RAVEN principal assignment is to provide the necessary software and algorithms in order to employ the concept developed by the Risk Informed Safety Margin Characterization (RISMC) program. RISMC is one of the pathways defined within the Light Water Reactor Sustainability (LWRS) program. In the RISMC approach, the goal is not just the individuation of the frequency of an event potentially leading to a system failure, but the closeness (or not) to key safety-related events. Hence, the approach is interested in identifying and increasing the safety margins related to those events. A safety margin is a numerical value quantifying the probability that a safety metric (e.g. for an important process such as peak pressure in a pipe) is exceeded under certain conditions. The initial development of RAVEN has been focused on providing dynamic risk assessment capability to RELAP-7, currently under development at the INL and, likely, future replacement of the RELAP5-3D code. Most the capabilities that have been implemented having RELAP-7 as principal focus are easily deployable for other system codes. For this reason, several side activates are currently ongoing for coupling RAVEN with soft- ware such as RELAP5-3D, etc. The aim of this document is the explanation of the input requirements, focalizing on the input structure.

97 MATHEMATICS AND COMPUTING↗