NIMBLE. A data management and visualization framework for large scale engineering and science VVUQ applications
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96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
SEARCH · Search NASA
Results for “Uncertainty visualization”
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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The one-dimensional power spectrum from the SDSS DR14 Lyα forests
In this paper, we present a measurement of the 1D Lyα forest flux power spectrum, using the complete Baryon Oscillation Spectroscopic Survey (BOSS) and first extended-BOSS (eBOSS) quasars at z qso > 2.1, corresponding to the fourteenth data release (DR14) of the Sloan Digital Sky Survey (SDSS). Our results cover thirteen bins in redshift from z Lyα = 2.2 to 4.6, and scales up to k = 0.02 (km/s) -1 . From a parent sample of 180,413 visually inspected spectra, we selected the 43,751 quasars with the best quality; this data set improves the previous result from the ninth data release (DR9), both in statistical precision (achieving a reduction by a factor of two) and in redshift coverage. We also present a thorough investigation of identified sources of systematic uncertainties that affect the measurement. Finally, the resulting 1D power spectrum of this work is in excellent agreement with the one from the BOSS DR9 data.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Risk Analysis Virtual ENvironment
RAVEN has 3 major functionalities: 1. Provides a Graphical User Interface for the pre- and post-processing of the RELAP-7 input and output. 2. Provides the capability to model nuclear power plants control logic for the RELAP-7 code and dynamic control of the accident scenario evolution. This capability is based on a software structure that realizes a direct connection between the RELAP-7 solver engine (MOOSE) and a python environment where the variables describing the plant status are accessible in a scripting environment. RAVEN support the generation of the probabilistic scenario control by supplying a wide range of probability and cumulative distribution functions and their inverse functions. 3. Provides a general environment to perform probability risk analysis for RELAP-7, RELAP-5 and any generic MOOSE based applications. The probabilistic analysis is performed by sampling the input space of the coupled code parameters and it is enhanced by using modern artificial intelligence algorithms that accelerate the identification of the areas of major risk (in the input parameter space). This environment also provides a graphical visualization capability to analyze the outcomes. Among other approaches, the classical Monte Carlo and Latin Hypercube sampling algorithms are available. For the acceleration of the convergence of the sampling methodologies, Support Vector Machines, Bayesian regression, and collocation stochastic polynomials chaos are implemented. The same methodologies here described could be used to solve optimization and uncertainties propagation problems using the RAVEN framework.
Rabiti, Cristian↗
MatPhase: Material phase prediction for Li-ion Battery Reconstruction using Hierarchical Curriculum Learning
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are used extensively in many industrial applications. These batteries consist of electrodes that are put together with heterogeneous material compositions. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Such reconstructions of material constituents help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. These images often suffer from low image contrast between multiple material constituents, hence making it difficult for humans to distinguish and characterize these constituents through visual inspection. A minor error in detecting distributions of the material constituents can lead to magnified errors in the calculated parameters of material properties (e.g., porosity). We present MatPhase, a novel hierarchical curriculum learning technique to address the complex task of estimating material constituent distribution in battery electrodes. MatPhase comprises three modules: (i) an uncertainty-aware global model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a local model to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator model to appropriately fuse the local and global effects towards obtaining the final distribution. On average, MatPhase improves prediction up to 8.5% relative to other sophisticated modeling pipelines and state-of-the-arts (SOTA) object detection models employed in the performance comparison.
Tabassum, Anika↗
The art and power of data imaging
Data imaging and visual data assessment are veritable gold mines in the scientist's quest to understand and accurately interpret numerical data. Graphical displays of various aspects of a dataset offer the analyst insight to the data that no mathematical computation or statistic can provide. It is difficult, at best, for even a skilled and observant statistician to understand the underlying structure of a dataset. Often, there is either too little data to get a good 'picture' of the structure that might be present or there is so much data that one cannot readily assimilate it. Of course, the latter problem (too much data) is, in reality, no problem at all given the abilities of modern computers and software systems to manage large amounts of data. Advances in computer technology and the advent of the global positioning satellite system have enabled scientists from many fields of endeavor to collect and view data in its spatial context. Visual images constructed from spatially referenced data reveal the inherent richness and structure in the data and lead to more informed conclusions. So powerful is data collected with spatial context that a relatively new branch of mathematical statistics, geo-spatial statistics, has emerged. Geo-spatial statistics seek to exploit this context rich data form to better understand the spatial and co-relationships that might exist, but would be otherwise hidden in tabular data or obscured with classic statistical approaches. This paper (and accompanying presentation) will show the power that visual data assessment possesses to understand radiological scanning data and to make confident and accurate decisions based on the data images. It will challenge the traditional mathematical concept of detection limits for scanning. It will demonstrate that more data, even if the individual datum comprising the dataset is of 'poorer quality' (i.e., has a larger uncertainty and, thus, a larger calculated minimum detection value), is significantly more powerful than a smaller dataset comprised of higher quality measurements. This presentation will cause the open-minded health physicist to rethink how they prescribe, collect, evaluate, and make decisions based upon radiological scan data. (authors)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Visualizing quasiparticles from quantum entanglement for general one-dimensional phases
In this work, we present a quantum information framework for the entanglement behavior of the low-energy quasiparticle (QP) excitations in various quantum phases in one-dimensional (1D) systems. We first establish an exact correspondence between the correlation matrix and the QP entanglement Hamiltonian for free fermions and find an extended in-gap state in the QP entanglement Hamiltonian as a consequence of the position uncertainty of the QP. A more general understanding of such an in-gap state can be extended to a Kramers theorem for the QP entanglement Hamiltonian, which also applies to strongly interacting systems. Further, we present a set of ubiquitous entanglement spectrum features, dubbed entanglement fragmentation, conditional mutual information, and measurement-induced nonlocal entanglement for QPs in 1D symmetry protected topological phases. Our result thus provides another framework to identify different phases of matter in terms of their QP entanglement.
1-dimensional spin chains↗
Stereoscopic detection of hot spots in superfluid 4 He (He II) for accelerator-cavity diagnosis
Superconducting radio-frequency (SRF) cavities cooled by superfluid 4He (He II) are building blocks of many modern particle accelerators due to their high quality factor. However, Joule heating from sub-millimeter surface defects on cavities can lead to cavity quenching, which limits the maximum acceleration gradient of the accelerators. Developing a non-contacting detection technology to accurately locate these surface hot spots is the key to improve the performance of SRF cavities and hence the accelerators. In a recent proof-of-concept experiment, we demonstrated that a molecular tagging velocimetry (MTV) technique based on the tracking of a He molecular tracer line created nearby a surface hot spot in He II can be utilized to locate the hot spot. In order to make this technique practically useful, here we describe our further development of a stereoscopic MTV setup for tracking the tracer line’s motion in three-dimensional (3D) space. We simulate a quench spot by applying a transient voltage pulse to a small heater mounted on a substrate plate. Images of the drifted tracer line, taken with two cameras from orthogonal directions, are used to reconstruct the line profile in 3D space. Here, a new algorithm for analyzing the 3D line profile is developed, which incorporates the finite size effect of the heater. We show that the center location of the heater can be reproduced on the substrate surface with an uncertainty of only a few hundred microns, thereby proving the practicability of this method.
43 PARTICLE ACCELERATORS↗
County Land-Use Regulations for Solar Energy Development in Colorado
We present a survey of county-level policies on ground-mounted solar development across Colorado, including both solar-specific ordinances as well as general land-use code that might be applicable in counties without solar-specific policies. This report provides an accessible reference for stakeholders interested in identifying counties with particular regulations or in analyzing the diversity of regulations across Colorado. We defined a set of search criteria to find information on solar definitions and classifications, permitting processes, and use-specific requirements in each of Colorado's 64 counties. With those criteria, we reviewed relevant ordinances, land-use code, and comprehensive and master plans. If any uncertainties were identified, we contacted county officials for clarification. The findings are categorized and mapped to illustrate the distribution of key policies adopted across Colorado's counties on the following topics: solar definitions, solar siting policy documentation, categorization of PV systems for permitting, 1041 permitting, solar on agricultural land, panel height restrictions, fencing requirements, vegetation management, visual impacts, decommissioning plans, and financial assurance for decommissioning. Additionally, we identify and discuss policies that might impact the deployment of agrivoltaics, a dual land use combining both agriculture and solar on the same land, which might not fit neatly in existing zoning definitions and solar-specific regulations.
14 SOLAR ENERGY↗
Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.
Tabassum, Anika↗
Integrating human factors issues into critical portable detection usage - 121
There are multiple situations where portable detection systems are used to quantify distributed radionuclides. Such applications include holdup measurements in decommissioning activities, other routine usage and safeguards applications. Human factors dominate equipment deployment and operation and have significant impact upon the resulting data collection and its quality. Equipment use includes simultaneous positioning and targeting the object/region being quantified, observation and reporting of incoming data, often in difficult conditions and often while dressed in protective equipment. We examine the human interface with the equipment and other human factors where balance, perception, situational awareness and personnel health level influence the physical control and positioning of the detector. All these aspects impact both the measurement outcome and the Total Measurement Uncertainty (TMU). In this work, we are developing approaches and solutions to: 1. Initiate an inclusive program to assess and evolve human factors aspects of the interface to provide real time feedback tailored to a specific field operator profile. 2. Develop a measurement system human interface on a 'bench top' detection system to allow for both audio and visual I/O for auditory attention and situational (objective and subjective) awareness across varied end user characteristics. 3. Assess and develop an enhanced health assessment plan to identify and monitor pertinent auditory and balance characteristics for operator(s) interface and physical control of the gamma detector. Use the plan to verify personnel ability for effective deployment. We take a multidisciplinary approach to maximize accuracy, efficiency, and safety for the end-user to optimize project and measurement performance. (authors)
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Low Emissions Burner Technology for Metal Processing Industry using Byproducts and Biomass Derived Liquid Fuels (Final Report)
This research and development efforts produced low-emission burner technology capable of operating on natural gas as well as crude glycerin and/or fatty acids generated in biodiesel plants. The research was conducted in three stages (1) Concept definition leading to the design and development of a small laboratory scale burner, (2) Scale-up to prototype burner design and development, and (3) Technology demonstration with field vefiication. The burner design relies upon the Flow Blurring (FB) fuel injection based on aerodynamically creating two-phase flow near the injector exit. The fuel tube and discharge orifice both of inside diameter D are separated by gap H. For H < 0.25D, the atomizing air bubbles into liquid fuel to create a two-phase flow near the tip of the fuel tube. Pressurized two-phase fuel-air mixture exits through the discharge orifice, which results in expansion and breakup of air bubbles yielding a spray with fine droplets. First, low-emission combustion of diesel, biodiesel and straight VO (soybean oil) was achieved by utilizing FB injector to yield fine sprays for these fuels with significantly different physical properties. Visual images for these baseline experiments conducted with heat release rate (HRR) of about 8 kW illustrate clean blue flames indicating premixed combustion for all three fuels. Radial profiles of the product gas temperature at the combustor exit overlap each other signifying that the combustion efficiency is independent of the fuel. At the combustor exit, the NOx emissions are within the measurement uncertainties, while CO emissions are slightly higher for straight VO as compared to diesel and biodiesel. Considering the large variations in physical and chemical properties of fuels considered, the small differences observed in CO and NOx emissions show promise for fuel-flexible, clean combustion systems. FB injector has proven to be very effective in atomizing fuels with very different physical properties, and it offers a path forward to utilize both fossil and alternative liquid fuels in the same combustion system. In particular, experiments show that straight VO can be cleanly combusted without the need for chemical processing or preheating steps, which can result in significant economic and environmental benefits. Next, low-emission combustion of glycerol/methane was achieved by utilizing FB injector to yield fine droplets of highly viscous glycerol. Heat released from methane combustion further improves glycerol pre-vaporization and thus its clean combustion. Methane addition results in an intensified reaction zone with locally high temperatures near the injector exit. Reduction in methane flow rate elongates the reaction zone, which leads to higher CO emissions and lower NOx emissions. Similarly, higher air to liquid (ALR) mass ratio improves atomization and fuel pre-vaporization and shifts the flame closer to the injector exit. In spite of these internal variations, all fuel mixes of glycerol with methane produced similar CO and NOx emissions at the combustor exit. Results show that FB concept provides low emissions with the flexibility to utilize gaseous and highly viscous liquid fuels, straight VO and glycerol, without preheating or preprocessing the fuels. Following these initial experiments in quartz combustor, we demonstrated that glycerol combustion can be stably sustained in a metal combustor. Phase Doppler Particle Analyzer (PDPA) measurements in glycerol/methane flames resulted in flow-weighted Sauter Mean Diameter (SMD) of 35 to 40 μm, depending upon the methane percentage. This study verified that lab-scale dual-fuel burner using FB injector can successfully atomize and combust glycerol and presumably other highly viscous liquid fuels at relatively low HRR (<10 kW). For industrial applications, a scaled-up glycerol burner design thus seemed feasible.
03 NATURAL GAS↗
Decision Making Under Uncertainty Human Subjects Data - Fire Evacuation Task
This dataset contains de-identified data from human subjects experiments, along with the images and code that were used to run the experiments (as a crowdsourced online study). In this study, participants were shown the probability of a house being in the burn zone of a wildfire. They were asked if they would stay in the house or evacuate in that scenario. The probability information was presented in different ways, including text and maps. The studies tested the impact of different visual cues on the participants' patterns of decisions.
Matzen, Laura E. [Sandia National Laboratories (SN↗
Preliminary Assessment of Tecplot Chorus for Analyzing Ensemble of CTH Simulations
The exploration of large parameter spaces in search of problem solution and uncertainty quantifcation produces very large ensembles of data. Processing ensemble data will continue to require more resources as simulation complexity and HPC platform throughput increase. More tools are needed to help provide rapid insight into these data sets to decrease manual processing time by the analyst and to increase knowledge the data can provide. One such tool is Tecplot Chorus, whose strengths are visualizing ensemble metadata and linked images. This report contains the analysis and conclusions from evaluating Tecplot Chorus with an example problem that is relevant to Sandia National Laboratories. This report documents a preliminary evaluation of Tecplot Chorus for analyzing ensemble data from CTH simulations. The project that funded this report and evaluation is also evaluating and guiding development with SNL’s Slycat. Slycat and Tecplot Chorus each have their strengths, weaknesses, and overlapping capabilities. It is quite likely that, as the scale of ensemble data increases, both of these tools (and possibly others) will be needed for different processing goals. This report will focus on Tecplot Chorus and its application to an example ensemble of data supplied by David J. Peterson and John P. Korbin; this example is of a flyer plate impact and weld study henceforth referred to as CTH Impact Example. This evaluation also defines a workflow for analysts that can help reduce the time and resources for processing ensemble data.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Enhancements to the RAVEN code in FY16
The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN has currently reached a good level of maturity in terms of deployed state-of-art and advanced capabilities. The main subject of this report is to show the activities that have been recently accomplished: • Implementation of ensemble modeling for time-series, and • initial implementation of model validation for surrogate models, and • advanced visualization capability for topology based data analysis The development of ensemble modeling for time-series has been performed in order to begin tackling the needs of those RISMC applications that need to communicate 1-Dimensional information (e.g. power histories, etc.) among different models. In this document the implementation details and an application example is reported. The second subject of this report is about the initial development of methods, within the RAVEN framework, to assess the validity of the predictive capabilities of surrogate models. Indeed, after the construction of a surrogate tight to a certain physical model, it is crucial to assess the goodness of its representation, in order to be confident with its prediction. In this initial activity, a cross-validation technique has been employed. This report will highlight the implementation details and proof its correct implementation by an application example. The final subject of this report is about the implementation of advanced visualization capability in RAVEN, for interactive data analysis. Indeed, RAVEN offers several post-processing capabilities that can structurally decompose data extracted from experimental results offering both data clustering/partitioning and dimensionality reduction techniques. A disadvantage of the workflow available in RAVEN is that it treats these as black box operations and the user is expected to know specific information about their data including the number of partitions to expect in some cases or the “correct” parameter settings for a particular algorithm. In order to overcome this limitation, it has been added an interactive user interface that can be run in RAVEN to explore different parameter settings on the fly specifically for the topological post-processor and allows the user to explore the data interactively. The design of this user interface is generalizable to other algorithms and can be used as a model to generate more dynamic and user-friendly visualization capabilities within RAVEN.
97 MATHEMATICS AND COMPUTING↗
Eastern North Atlantic (ENA) Aerosol Supplementary Site (S1) Data Analysis Report
We present strategies to identify time periods when local aerosol sources impact the data collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility in the Eastern North Atlantic (ENA). Data from the aerosol observing system (AOS) at the Central Facility and a temporary aerosol Supplementary Site (S1) were evaluated. S1 instrumentation was deployed within 0.75 km of the ENA Central Facility (C1) for approximately one year. Two time periods, one during the summer and one during the winter, were examined in support of the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) intensive operating periods (IOPs) in 2017 and 2018. As the criteria for identifying regionally representative data varies based on the specific needs of the user, we present and discuss different strategies using measured aerosol properties and their associated meteorological parameters to isolate periods in AOS data that were impacted by local aerosol at ENA. We identify high-concentration aerosol events at ENA using submicron number concentrations, size distributions, high-time-resolution measurements, and diurnal profiles. An improved understanding of local aerosol to select the optimal aerosol mask was obtained with an understanding of local meteorology during the different seasons. We developed a mathematical algorithm that employs AOS data to mask high-concentration aerosol events at ENA. We validated the ENA Aerosol Mask with aerosol data from C1 and S1 and associated meteorological data. The Supplementary Site was ideal to understand and constrain local aerosol sources within the region. In its absence, collocated data from the ENA AOS could be used to select appropriate parameters to develop an aerosol mask that would limit over- or underestimating the regional baseline aerosol data. When available, associated metadata, such as airport flight logs and visual identification with AOS cameras should also be used to validate the application of an aerosol mask at a new site. Our results confirm the importance of optimizing an aerosol mask prior its application. In the future, the application of an aerosol mask at ARM observatories and during mobile deployments could be used to increase the data quality of continuous AOS data and to reduce uncertainties when parameterizing regional aerosol processes and impacts on clouds for incorporation in models.
54 ENVIRONMENTAL SCIENCES↗
Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes
The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical consequences of battery particles’ evolving (de)attachment with the conductive matrix. Herein, we tackle this issue with a unique combination of multiscale experimental approaches, machine-learning-assisted statistical analysis, and experiment-informed mathematical modeling. Our results suggest that the degree of particle detachment is positively correlated with the charging rate and that smaller particles exhibit a higher degree of uncertainty in their detachment from the carbon/binder matrix. We further explore the feasibility and limitation of utilizing the reconstructed electron density as a proxy for the state-of-charge. Our findings highlight the importance of precisely quantifying the evolving nature of the battery electrode’s microstructure with statistical confidence, which is a key to maximize the utility of active particles towards higher battery capacity.
25 ENERGY STORAGE↗
A Review and Outlook on Experimental Advances and Innovations in Geological CO 2 Storage: Insights from Depleted Gas Reservoirs and Saline Aquifers
Geological storage of carbon dioxide (CO 2 ) in depleted gas reservoirs and deep saline aquifers is a key part of global decarbonization efforts. As carbon capture and storage advances toward commercial-scale deployment, the credibility and scalability of laboratory experiments are increasingly vital for guiding safe and effective field implementation. This review offers a comprehensive, cross-scale evaluation of experimental methodologies, including core flooding, high-pressure, high-temperature systems, microfluidic visualization, and emerging systems such as multilayer commingled/compartmentalized core flooding, 3D-printed micromodels, and AI-powered digital twins. These innovations are demonstrated to enhance representativeness, reproducibility, and real-time insight, thereby addressing the limitations of conventional workflows. A critical analysis of methodological gaps, such as inconsistent pressure–temperature conditions, oversimplified brine chemistry, and a lack of standardization, reveals experimental sources of scale translation errors and performance uncertainty. By comparing the unique challenges of depleted gas reservoirs (such as low water saturation and legacy well leakage) to those of saline aquifers (including pressure buildup and caprock integrity), this review identifies formation-specific priorities for experimental design. Novel contributions include a synthesis of best practices, integration strategies for model calibration, and recommendations for standardizing core handling, saturation procedures, and reporting protocols. Furthermore, this work serves as a guide for developing robust, field-relevant experimental strategies that can increase the deployment and regulatory acceptance of CO 2 storage technologies at scale.
58 GEOSCIENCES↗
A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data
Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.
Bayesian Networks · Structural Model Diagnostics ·↗