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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Overview of NNSA Projects (Criticality Safety) – Savannah River Plutonium Processing Facility – Surplus Plutonium Disposition Project – Tritium Finishing Facility – Progress of Criticality Safety Analysis – Processes requiring CAAS
Caustic scrubbers are proposed to remove vaporized iodine from nuclear fuel reprocessing plants. Resulting caustic slurries of radioactive iodine ( 129 I) and other anions can be captured and immobilized into the cage structures of sodalite-type minerals. Here, mixed anion sodalites were hydrothermally synthesized from a simulated caustic scrubber solution containing hydroxide/water, carbonate, chlorine, bromine, iodine, nitrate, and nitrite ions with added kaolinite. Experiments were conducted with twenty-five different sets of process conditions. Resulting powders were characterized by quantitative X-ray diffraction (XRD) and multi-functional thermal analysis, including gravimetry, calorimetry, and mass spectrometry. Using statistical analysis, process variables were tailored to maximize conversion of kaolinite into cage silicates, immobilize caustic scrubber anions, and minimize amorphous content. Further, chemical analysis showed the incorporation of all the targeted ions into the solids. Detailed investigation of a batch characterized after washing (creating secondary waste) and one without washing but subsequent calcination is given. This work contributes to defining the constraints required for subsequent consolidation process steps of the waste form to avoid volatilization.
Soils along a salinity gradient from a coastal forested floodplain (Beaver Creek in Western Washington) were analyzed for their microbial community composition and community assembly processes. These files contain processed 16S rRNA amplicon sequences into OTU table, the corresponding tree file, metadata, community assembly process analysis and results, microbial phyla abundances, and codes evaluating BNTI indices and community assembly process estimates.
Estimating the discretization error of computational fluid dynamics (CFD) or other scientific codes as part of solution verification is often a non-trivial part of the analysis process. Methods can be complicated and may include assumptions/qualifications that need to be checked during analysis. CFD analysts, therefore, are likely to make errors when trying to conduct this necessary analysis on their own in not knowing about the best method for their problem, not correctly implementing a method, or in not have diagnostic tools to determine if the method was correctly applied.
RAVEN, under the support of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program [1], is advancing its capability to perform statistical analyses of stochastic dynamic systems. This is aligned with its mission to provide the tools needed by the Risk Informed Safety Margin Characterization (RISMC) path-lead [2] under the Department Of Energy (DOE) Light Water Reactor Sustainability program [3]. In particular this task is focused on the synergetic development with the RELAP-7 [4] code to advance the state of the art on the safety analysis of nuclear power plants (NPP). The investigation of the probabilistic evolution of accident scenarios for a complex system such as a nuclear power plant is not a trivial challenge. The complexity of the system to be modeled leads to demanding computational requirements even to simulate one of the many possible evolutions of an accident scenario (tens of CPU/hour). At the same time, the probabilistic analysis requires thousands of runs to investigate outcomes characterized by low probability and severe consequence (tail problem). The milestone reported in June of 2013 [5] described the capability of RAVEN to implement complex control logic and provide an adequate support for the exploration of the probabilistic space using a Monte Carlo sampling strategy. Unfortunately the Monte Carlo approach is ineffective with a problem of this complexity. In the following year of development, the RAVEN code has been extended with more sophisticated sampling strategies (grids, Latin Hypercube, and adaptive sampling). This milestone report illustrates the effectiveness of those methodologies in performing the assessment of the probability of core damage following the onset of a Station Black Out (SBO) situation in a boiling water reactor (BWR). The first part of the report provides an overview of the available probabilistic analysis capabilities, ranging from the different types of distributions available, possible sampling strategies, and post processing analysis capabilities. The first part of the report provides an extensive description of two major developments introduced this year: adaptive sampling for limit surface sampling and multi variate distributions. The document concludes with a description of the demo case (BWR-SBO) and a discussion of the results obtained.
The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.
Global infrasound monitoring provides a persistent means of detecting energetic bolide atmospheric entries, complementing optical observations and extending coverage over remote regions. We present a global assessment of the physical factors governing bolide infrasound detectability by correlating 623 bolide events reported by the Center for Near-Earth Object Studies between 2007 and 2025 with waveform data from the International Monitoring System. We identify 311 events with confirmed infrasound detections, corresponding to a detection rate of approximately 50%, substantially higher than inferred from earlier surveys, reflecting both the maturation of the global infrasound network and advances in automated, multi-frequency array processing. Analysis of flight parameters shows that infrasound detectability is selective rather than uniform across the bolide population. Detected events are preferentially associated with steeper entry angles and lower-altitude energy deposition, while shallow, high-altitude trajectories are less consistently observed. Very high-energy events remain detectable regardless of geometry, but for the more common lower-energy regime, observability depends on specific combinations of entry parameters and propagation conditions. This geometric dependence persists across comparable energy ranges and atmospheric conditions, indicating that entry angle exerts a primary control on detectability, with energy and propagation acting as secondary modulating factors. Furthermore, these results provide new physical constraints on bolide-atmosphere interactions and improve interpretation of global infrasound observations for planetary defense and atmospheric-entry studies.
ABSTRACT We present and characterize the galaxy shape catalogue from the first 3 yr of Dark Energy Survey (DES) observations, over an effective area of 4143 deg2 of the southern sky. We describe our data analysis process and our self-calibrating shear measurement pipeline metacalibration, which builds and improves upon the pipeline used in the DES Year 1 analysis in several aspects. The DES Year 3 weak-lensing shape catalogue consists of 100 204 026 galaxies, measured in the riz bands, resulting in a weighted source number density of neff = 5.59 gal arcmin−2 and corresponding shape noise σe = 0.261. We perform a battery of internal null tests on the catalogue, including tests on systematics related to the point spread function (PSF) modelling, spurious catalogue B-mode signals, catalogue contamination, and galaxy properties.
The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management
The National Ignition Facility uses a soft x-ray opacity spectrometer for x-ray spectral imaging in high-energy-density experiments. The increased demand for a better spectral resolution prompted the investigation into the Agfa D4 film. Characterization is already under way for the film. A Manson x-ray source using six different anodes was used to expose film to the linear optical density (OD) region. This is a continuation of the previous work, and the updated analysis process is communicated here. The identified uncertainties have been reduced with the updated steps that improve the results of the characterization process. In conclusion, when the Stanford Synchrotron Radiation Lightsource Beamline 16-2 was operational, the film was characterized at that source. Its beam offered a higher fluency with a lower exposure time needed to reach saturation. Results for both sources are compared in this paper.
Small-signal stability is an important concern in three-phase inverter-based ac power systems. The impedance-based approach based on the generalized Nyquist stability criterion (GNC) can analyze the stability related with the medium and high-frequency modes of the systems. However,. the GNC involves the right-half-plane (RHP) pole calculation of return-ratio transfer function matrices, which cannot be avoided for stability analysis of complicated ac power systems. Therefore, it necessitates the detailed internal control information of the inverters, which is not normally available for commercial inverters. To address this issue, this paper introduces the component connection method (CCM) in the frequency domain for stability analysis in the synchronous d-q frame, by proposing a method of deriving the impedance matrix of the connection networks of inverter-based ac power systems. Demonstration on a two-area system and a microgrid shows that: The CCM-enabled approach can avoid the RHP pole calculation of return-ratio matrices and enables the stability analysis by using only the impedances of system components, which could be measured without the need for the internal information. A stability analysis method based on d-q impedances, the CCM, and the determinant-based GNC is also proposed to further simplify the analysis process. Inverter controller parameters can be designed as stability regions in parameter spaces, by repetitively applying the proposed stability analysis method. Simulation and experimental results verify the validity of the proposed stability analysis method and the parameter design approach.
There is a growing interest in using rf transverse deflecting structures for a plethora of applications in the current and future high performance colliders. In this paper, we present the results of a proof of principle superconducting rf dipole, designed as a prototype for a 750 MHz crabbing corrector for the Medium Energy Electron-Ion Collider (MEIC), which has been successfully tested at 4.2 K and 2 K at the Jefferson Lab’s Vertical Testing Area (VTA). The analysis of its rf performance during cryogenic testing, along with Helium pressure sensitivity, Lorentz detuning, surface resistance, and multipacting processing analysis are presented in this work. Detailed calculations of losses at the port flanges are included for completeness of the cavity’s cryogenic performance studies.
AET-2 has expertise in process modeling, economics, business case analysis, risk assessment, Lean/Six Sigma tools, and decision analysis to provide timely decision support to LANS leading to continuous improvement. This capability is critical during the current tight budgetary environment as LANS pushes to identify potential areas of cost savings and efficiencies. An important arena is business systems and operations, where processes can impact most or all laboratory employees. Lab-wide efforts are needed to identify and eliminate inefficiencies to accomplish Director McMillan’s charge of “doing more with less.” LANS faces many critical and potentially expensive choices that require sound decision support to ensure success. AET-2 is available to provide this analysis support to expedite the decisions at hand.
We present the first application of the COSMOGLOBE analysis framework by analyzing nine-year WMAP time-ordered observations that uses similar machinery to that of BEYONDPLANCK for the Planck Low Frequency Instrument (LFI). We analyzed only the Q-band (41 GHz) data and report on the low-level analysis process based on uncalibrated time-ordered data to calibrated maps. Most of the existing BEYONDPLANCK pipeline may be reused for WMAP analysis with minimal changes to the existing codebase. The main modification is the implementation of the same preconditioned biconjugate gradient mapmaker used by the WMAP team. Producing a single WMAP Q1-band sample requires 22 CPU-hrs, which is slightly more than the cost of a Planck 44 GHz sample of 17 CPU-hrs; this demonstrates that a full end-to-end Bayesian processing of the WMAP data is computationally feasible. In general, our recovered maps are very similar to the maps released by the WMAP team, although with two notable differences. In terms of temperature, we find a ~2 μK quadrupole difference that most likely is caused by different gain modeling, while in polarization we find a distinct 2.5 μK signal that has been previously referred to as poorly measured modes by the WMAP team. In the COSMOGLOBE processing, this pattern arises from temperature-to-polarization leakage from the coupling between the CMB Solar dipole, transmission imbalance, and sidelobes. No traces of this pattern are found in either the frequency map or TOD residual map, suggesting that the current processing has succeeded in modeling these poorly measured modes within the assumed parametric model by using Planck information to break the sky-synchronous degeneracies inherent in the WMAP scanning strategy.
Black-box machine learning models are recognized as useful tools for prediction applications, but the algorithmic complexity of some models causes interpretation challenges. Explainability methods have been proposed to provide insight into these models, but there is little research focused on supervised modeling with functional data inputs. We argue that, especially in applications of high consequence, it is important to explicitly model the functional dependence in a black-box analysis to not obscure or misrepresent patterns in explanations. As such, we propose the V ariable importance E xplainable E lastic S hape A nalysis (VEESA) pipeline for training supervised machine learning models with functional inputs. The pipeline is an analysis process that includes the data preprocessing, modeling, and post-hoc explanations. The preprocessing is done using elastic functional principal components analysis, which accounts for vertical and horizontal variability in functional data and, ultimately, allows for explanations in the original data space that identify the important functional variability without bias due to correlated variables. Here, we demonstrate the pipeline on two high-consequence applications: explosives classification for national security and inkjet printer identification in forensic science. The applications exhibit the VEESA pipeline’s ability to provide an understanding of the characteristics of the functional data useful for prediction. Code for implementing the pipeline is available in the veesa R package (and supplemental python code).
Sensitivity analysis has been applied to complex models, such as performance assessment models, since at least the advent of computational tools to support such an approach to model evaluation. Initially, sensitivity analysis methods were applied to deterministic models and focused on changing one input variable at a time. Probabilistic modeling opened the door to evaluating changes in all input variables simultaneously. Standardized rank regression methods were primarily used in the last century to evaluate probabilistic models. These methods assume linearity in the rank response of the model to the inputs. At the very least this assumes monotonicity in the response, but for the most part the response needs to be near-linear. This is highly unlikely for performance assessment models that are based on differential equations, multiplication and division of variables that are usually not correlated, and conservatism that is not well understood. There are better statistical methods for evaluating highly non-linear and dynamic probabilistic models. Some of these methods were founded in the data mining industry. This century has seen a rapid development of the internet that led directly to the development of data mining techniques for massive databases. Although the goals were different, these techniques can be adapted to sensitivity analysis for complex probabilistic models, which, also, form large, or even massive, databases. These modern sensitivity analysis procedures assess the importance of every explanatory variable (input parameters) used in a probabilistic model. In practice, this means that for each endpoint of interest, every explanatory variable in the model is associated with a quantitative measure of importance. For a given explanatory variable, the importance value depends on the endpoint of interest. This global approach to sensitivity analysis allows all levels of interactions to be evaluated, so that the variation in the output can be measured at every level of possible interaction. The effects are collected together during sensitivity analysis processing, and then separated out to attribute the overall contribution of each input parameter to the model output. These global sensitivity analysis procedures have the ability to characterize non-linear and non-monotonic relationships between explanatory variables and endpoints of interest. This is critically important because of the need to characterize complex interactions among multiple explanatory variables in PA models that often have non-linear and non-monotonic effects. The options for sensitivity analysis are fully explored, including an evaluation of several methods when applying them to a well-known complex non-linear non-monotonic model. The results clearly demonstrate the relative benefit of using data mining methods over simple statistical approaches such as standardized rank regression. (authors)
The purpose of the Scope, Complexity, Options, Risks, Excursions (SCORE) model is to estimate the relative complexity of design variants of future warhead options, resulting in scores. SCORE factors extend this capability by providing estimates of complexity relative to a base system (i.e., all design options are normalized to one weapon system). First, a clearly defined set of scope elements for a warhead option is established. The complexity of each scope element is estimated by Subject Matter Experts (SMEs), including a level of uncertainty, relative to a specific reference system. When determining factors, complexity estimates for a scope element can be directly tied to the base system or chained together via comparable scope elements in a string of reference systems that ends with the base system. The SCORE analysis process is a growing multi-organizational Nuclear Security Enterprise (NSE) effort, under the management of the NA-12 led Enterprise Modeling and Analysis Consortium (EMAC). Historically, it has provided the data elicitation, integration, and computation needed to support the out-year Life Extension Program (LEP) cost estimates included in the Stockpile Stewardship Management Plan (SSMP).
The purpose of the Scope, Complexity, Options, Risks, Excursions (SCORE) model is to estimate the relative complexity of design variants of future warhead options. The results of this model allow those considering these options to understand the complexity tradeoffs between proposed warhead options. The core idea of SCORE is to divide a warhead option into a well- defined set of scope elements and then estimate the complexity of each scope element against a well understood reference system. The uncertainty associated with estimates can also be captured. A weighted summation of the relative complexity of each scope element is used to determine the total complexity of the proposed warhead option or portions of the warhead option (i.e., a National Work Breakdown Structure code). The SCORE analysis process is a growing multi-organizational Nuclear Security Enterprise (NSE) effort, under the management of the NA- 12 led Enterprise Modeling and Analysis Consortium (EMAC), that has provided the data elicitation, integration and computation needed to support the out-year Life Extension Program (LEP) cost estimates included in the Stockpile Stewardship Management Plan (SSMP).