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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Tessera: Open source software for accelerated data science

Extracting useful, actionable information from data can be a formidable challenge for the safeguards, nonproliferation, and arms control verification communities. Data scientists are often on the “front-lines” of making sense of complex and large datasets. They require flexible tools that make it easy to rapidly reformat large datasets, interactively explore and visualize data, develop statistical algorithms, and validate their approaches—and they need to perform these activities with minimal lines of code. Existing commercial software solutions often lack extensibility and the flexibility required to address the nuances of the demanding and dynamic environments where data scientists work. To address this need, Pacific Northwest National Laboratory developed Tessera, an open source software suite designed to enable data scientists to interactively perform their craft at the terabyte scale. Tessera automatically manages the complicated tasks of distributed storage and computation, empowering data scientists to do what they do best: tackling critical research and mission objectives by deriving insight from data. We illustrate the use of Tessera with an example analysis of computer network data.

Sego, Landon H.↗
DOE OSTI2012

Final Progress Report: Internship at Los Alamos National Laboratory

Originally I was tasked fluidized bed modeling, however, I changed projects. While still working with ANSYS Fluent, I performed a study of particle tracks in glove boxes. This is useful from a Health-Physics perspective, dealing respirable particles that can be hazardous to the human body. I iteratively tested different amounts of turbulent particles in a steady-state flow. The goal of this testing was to discover how Fluent handles built-in Rosin-Rammler distributions for particle injections. I worked on the health physics flow problems and distribution analysis under the direction of two mentors, Bruce Letellier and Dave Decroix. I set up and ran particle injection calculations using Fluent. I tried different combinations of input parameters to produce sets of 500,000, 1 million, and 1.5 million particles to determine what a good test case would be for future experiments. I performed a variety of tasks in my work as an Undergraduate Student Intern at LANL this summer, and learned how to use a powerful CFD application in addition to expanding my skills in MATLAB. I enjoyed my work at LANL and hope to be able to use the experience here to further my career in the future working in a security-conscious environment. My mentors provided guidance and help with all of my projects and I am grateful for the opportunity to work at Los Alamos National Laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2011

Membrane Purification Cell for Aluminum Recycling

Recycling mixed aluminum scrap usually requires adding primary aluminum to the scrap stream as a diluent to reduce the concentration of non-aluminum constituents used in aluminum alloys. Since primary aluminum production requires approximately 10 times more energy than melting scrap, the bulk of the energy and carbon dioxide emissions for recycling are associated with using primary aluminum as a diluent. Eliminating the need for using primary aluminum as a diluent would dramatically reduce energy requirements, decrease carbon dioxide emissions, and increase scrap utilization in recycling. Electrorefining can be used to extract pure aluminum from mixed scrap. Some example applications include producing primary grade aluminum from specific scrap streams such as consumer packaging and mixed alloy saw chips, and recycling multi-alloy products such as brazing sheet. Electrorefining can also be used to extract valuable alloying elements such as Li from Al-Li mixed scrap. This project was aimed at developing an electrorefining process for purifying aluminum to reduce energy consumption and emissions by 75% compared to conventional technology. An electrolytic molten aluminum purification process, utilizing a horizontal membrane cell anode, was designed, constructed, operated and validated. The electrorefining technology could also be used to produce ultra-high purity aluminum for advanced materials applications. The technical objectives for this project were to: - Validate the membrane cell concept with a lab-scale electrorefining cell; - Determine if previously identified voltage increase issue for chloride electrolytes holds for a fluoride-based electrolyte system; - Assess the probability that voltage change issues can be solved; and - Conduct a market and economic analysis to assess commercial feasibility. The process was tested using three different binary alloy compositions (Al-2.0 wt.% Cu, Al-4.7 wt.% Si, Al-0.6 wt.% Fe) and a brazing sheet scrap composition (Al-2.8 wt.% Si-0.7 wt.% Fe-0.8 wt.% Mn),. Purification factors (defined as the initial impurity concentration divided by the final impurity concentration) of greater than 20 were achieved for silicon, iron, copper, and manganese. Cell performance was measured using its current and voltage characteristics and composition analysis of the anode, cathode, and electrolytes. The various cells were autopsied as part of the study. Three electrolyte systems tested were: LiCl-10 wt. % AlCl3, LiCl-10 wt. % AlCl3-5 wt.% AlF3 and LiF-10 wt.% AlF3. An extended four-day run with the LiCl-10 wt.% AlCl3-5 wt.% AlF3 electrolyte system was stable for the entire duration of the experiment, running at energy requirements about one third of the Hoopes and the conventional Hall-Heroult process. Three different anode membranes were investigated with respect to their purification performance and survivability: a woven graphite cloth with 0.05 cm nominal thickness & > 90 % porosity, a drilled rigid membrane with nominal porosity of 33%, and another drilled rigid graphite membrane with increased thickness. The latter rigid drilled graphite was selected as the most promising membrane design. The economic viability of the membrane cell to purify scrap is sensitive to primary & scrap aluminum prices, and the cost of electricity. In particular, it is sensitive to the differential between scrap and primary aluminum price which is highly variable and dependent on the scrap source. In order to be economically viable, any scrap post-processing technology in the U.S. market must have a total operating cost well below the scrap price differential of $0.20-$0.40 per lb to the London Metal Exchange (LME), a margin of 65%-85% of the LME price. The cost to operate the membrane cell is estimated to be < $0.24/lb of purified aluminum. The energy cost is estimated to be $0.05/lb of purified aluminum with the remaining costs being repair and maintenance, electrolyte, labor, taxes and depreciation. The bench-scale work on membrane purification cell process has demonstrated technological advantages and substantial energy and investment savings against other electrolytic processes. However, in order to realize commercial reality, the following items need to be fully investigated: 1. Further evaluation of a pure fluoride electrolyte. 2. Investigate alternative non conductive, more mechanically robust and chemically inert membrane candidates. 3. Optimized membrane cell design to understand contribution of fluid flow patterns and the mass transfer conditions. 4. Improve current efficiency and total metallic aluminum recovery from the cell. All Tasks and Milestones were completed successfully.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

A new classification scheme of plastic wastes based upon recycling labels

Highlights: • PET, HPDE or PP types of plastics are considered. • An automated classification of plastic bottles based on the feature extraction and classification methods is performed. • The decision mechanism consists of PCA, Kernel PCA, FLDA, SVD and Laplacian Eigenmaps methods. • SVM is selected to achieve the classification task and majority voting technique is used. - Abstract: Since recycling of materials is widely assumed to be environmentally and economically beneficial, reliable sorting and processing of waste packaging materials such as plastics is very important for recycling with high efficiency. An automated system that can quickly categorize these materials is certainly needed for obtaining maximum classification while maintaining high throughput. In this paper, first of all, the photographs of the plastic bottles have been taken and several preprocessing steps were carried out. The first preprocessing step is to extract the plastic area of a bottle from the background. Then, the morphological image operations are implemented. These operations are edge detection, noise removal, hole removing, image enhancement, and image segmentation. These morphological operations can be generally defined in terms of the combinations of erosion and dilation. The effect of bottle color as well as label are eliminated using these operations. Secondly, the pixel-wise intensity values of the plastic bottle images have been used together with the most popular subspace and statistical feature extraction methods to construct the feature vectors in this study. Only three types of plastics are considered due to higher existence ratio of them than the other plastic types in the world. The decision mechanism consists of five different feature extraction methods including as Principal Component Analysis (PCA), Kernel PCA (KPCA), Fisher’s Linear Discriminant Analysis (FLDA), Singular Value Decomposition (SVD) and Laplacian Eigenmaps (LEMAP) and uses a simple experimental setup with a camera and homogenous backlighting. Due to the giving global solution for a classification problem, Support Vector Machine (SVM) is selected to achieve the classification task and majority voting technique is used as the decision mechanism. This technique equally weights each classification result and assigns the given plastic object to the class that the most classification results agree on. The proposed classification scheme provides high accuracy rate, and also it is able to run in real-time applications. It can automatically classify the plastic bottle types with approximately 90% recognition accuracy. Besides this, the proposed methodology yields approximately 96% classification rate for the separation of PET or non-PET plastic types. It also gives 92% accuracy for the categorization of non-PET plastic types into HPDE or PP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Large Language Models (LLMs) for Energy Systems Research

The integration of Large Language Models (LLMs) in energy systems research promises transformative results, as demonstrated in this work, particularly in the realms of information retrieval and legal document analysis. We have developed a chat-based interface, specifically designed to query an extensive corpus of technical reports from the National Renewable Energy Laboratory (NREL). This interface capitalizes on the natural language processing capabilities of LLMs, providing future consumers of NREL research with a user-friendly platform to access and extract valuable information from technical documents, thus enhancing the dissemination of research to the public. In addition to information retrieval, we have employed LLMs to extract renewable energy siting ordinances from a variety of legal documents, a task traditionally driven by significant human labor. This automated extraction not only supports the ongoing development of the high-impact NREL siting ordinance database but also ensures the database's accuracy and comprehensiveness. Crucially, we have augmented the performance of LLMs through the integration of a decision tree framework, resulting in a substantial improvement in extraction accuracy. Comparative analysis with manual efforts has shown that this approach not only rivals but also significantly surpasses human accuracy, heralding increased reliability in legal document analysis for energy systems research. To democratize access to these advancements and foster collaborative research, we introduce the "Energy Language Model" (ELM), an open-source software package. ELM encapsulates the methodologies and tools developed in this work, providing researchers and practitioners with a robust toolkit to conduct similar analyses within their respective domains. Through these contributions, this work underscores the immense potential of LLMs in revolutionizing energy systems research, improving accuracy, efficiency, and accessibility in the field.

automation↗
DOE OSTI2015

Idaho National Laboratory’s Analysis of ARRA-Funded Plug-in Electric Vehicle and Charging Infrastructure Projects: Final Report

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s (DOE) Idaho National Laboratory (INL), is the lead laboratory for U.S. Department of Energy’s Advanced Vehicle Testing Activity (AVTA). INL’s conduct of the AVTA resulted in a significant base of knowledge and experience in the area of testing light-duty vehicles that reduced transportation-related petroleum consumption. Due to this experience, INL was tasked by DOE to develop agreements with companies that were the recipients of The American Recovery and Reinvestment Act of 2009 (ARRA) grants, that would allow INL to collect raw data from light-duty vehicles and charging infrastructure. INL developed non-disclosure agreements (NDAs) with several companies and their partners that resulted in INL being able to receive raw data via server-to-server connections from the partner companies. This raw data allowed INL to independently conduct data quality checks, perform analysis, and report publicly to DOE, partners, and stakeholders, how drivers used both new vehicle technologies and the deployed charging infrastructure. The ultimate goal was not the deployment of vehicles and charging infrastructure, cut rather to create real-world laboratories of vehicles, charging infrastructure and drivers that would aid in the design of future electric drive transportation systems. The five projects that INL collected data from and their partners are: • ChargePoint America - Plug-in Electric Vehicle Charging Infrastructure Demonstration • Chrysler Ram PHEV Pickup - Vehicle Demonstration • General Motors Chevrolet Volt - Vehicle Demonstration • The EV Project - Plug-in Electric Vehicle Charging Infrastructure Demonstration • EPRI / Via Motors PHEVs – Vehicle Demonstration The document serves to benchmark the performance science involved the execution, analysis and reporting for the five above projects that provided lessons learned based on driver’s use of the vehicles and recharging decisions made. Data is reported for the use of more than 25,000 vehicles and charging units.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Using the JCC-71 neutron coincidence collar as a benchmark for detector characterization with PTR-32 list mode data acquisition

Augmenting or interchanging current international safeguards shift registerbased data acquisition systems with list mode data acquisition systems gives nondestructive assay (NDA) systems greater versatility. Neutron list mode data analysis offers comparable analytical results to the more widely used shift register analysis in nuclear material quantification applications and offers several diagnostic tools that are specifically beneficial to in-laboratory characterization and calibration measurements. These benefits include identification of non-ideal behavior, optimization of operational parameters from a single measurement, and an improved understanding of the physics-based behavior of NDA systems for a more precise system representation and more confident assay results. In this work the advantages of using list mode data acquisition for detector characterization are demonstrated experimentally. Two commercial-off-the-shelf International Atomic Energy Agency-supported technologies are used for a typical detector characterization procedure and their performance is compared. Specifically, a 3 He-based Canberra Industries JCC-71 Neutron Coincidence Collar is characterized using the Hungarian Institute of Isotopes’ Pulse Train Recorder-32 (PTR-32) list mode data acquisition system, and the results are compared to those obtained using the standard Canberra Industries JSR-15 model shift register. The quantitative results from the two systems are in agreement, which demonstrates that the PTR-32 is a technically viable alternative to conventional shift register electronics for this task. A suitable procedure for full instrument characterization is described, and the added benefits of list mode for characterization and data collection are discussed. Furthermore, this is an important step toward establishing a procedure for the routine use of list mode data acquisition and analysis for neutron NDA system characterization in safeguards field applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Determination of a Generic Human Error Probability Distribution, Part 1: A Dynamic Formulation of SPAR-H

In almost all major nuclear power plants (NPP) events include a significant human error component. As an example, in Fukushima Daiichi (2011) nuclear meltdown, the human actions related to the high-pressure injection cooling strongly affected accident evolution. Furthermore, in the Three Mile Island accident (1979) (which was initiated by a stuck open valve and thus was triggered a loss of coolant accident) the unit operators made decisions based upon false assumptions that contributed negatively to the accident. Given these premises, the ability to accurately model human interactions with plant evolution and, in particular, dynamically calculate human error probability (HEP) plays a major role in a probabilistic risk analysis (PRA). Human reliability analysis (HRA) identifies and models the causes of human errors, and quantifies HEP by applying different methodologies. HRA methods identify errors and reflect on the understanding of error-prone people and systems. There are a number of HRA methods available that assist in identifying the vulnerabilities in the system and provide guidance in determining the HEP. HEP is the probability of a human failure event (HFE), and quantification of HEPs under various situations is utilized in PRA of large complex systems. One of the more prominent HRA methods is the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method that was developed to estimate HEPs for the use in the Standardized Plant Analysis Risk (SPAR) PRA models employed in U.S. NPPs. SPAR-H is implemented in U.S. NPPs by regulators at the U.S. Nuclear Regulatory Commission (NRC) to determine the HEP based on expert estimation through the use of calculation worksheets. In SPAR-H, the nominal HEPs (NHEPs) are multiplied by performance shaping factors (PSF) multiplier values to obtain the overall error probability. PSFs encompass the human components, individual characteristics, their environment, and aspects of the task. With PSFs, the goal is to capture all factors that can affect human performance. In SPAR-H there are eight PSFs used, each having respective levels corresponding to multipliers: Available Time, Stress/Stressors, Complexity, Experience/Training, Procedures, Ergonomics/Human Machine Interface, Fitness for Duty, and Work Processes. The PSF multipliers indicate enhanced, nominal, or degraded human performance. The SPAR-H method estimates the HEP through multiplying a NHEP with PSFs. Hence, SPAR-H is a traditional HRA method that assesses human performance at a fixed, or static, point in time. In a dynamic HRA framework, human performance is assessed on a continuous time basis. SPAR-H can be used within a dynamic PRA framework through the use of simulation and stochastic models. The goal of dynamic HRA is to build a more realistic model of the human-system interaction to enhance calculation of reliability, risk and safety analysis. The progression of events in dynamic SPAR-H considers that some PSFs may experience gradual change, while other change can be sudden due to individualistic differences or rapid plant status changes. The PSF changes are caused by contextual changes in the scenario. For example, as more time passes, the realization that a task should be completed sooner may cause the operator more stress. In another example, a sudden relay of information about sickness of a family member may alter the operator's general stress level and fitness for duty. Each PSF has a different dynamic nature; PSFs like stress and complexity change more quickly over the course of a task than experience and training. While the experience of an operator changes over the course of attaining the objective, the amount that this added experience changes the HEP is very minimal. In contrast, the stress, complexity, and procedures PSFs may change quite a bit over the course of task completion.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

PATHA: Performance Analysis Tool for HPC Applications

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. In order to address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. Furthermore, it utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). This study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Application of distance correction to ChemCam laser-induced breakdown spectroscopy measurements

Laser-induced breakdown spectroscopy (LIBS) provides chemical information from atomic, ionic, and molecular emissions from which geochemical composition can be deciphered. Analysis of LIBS spectra in cases where targets are observed at different distances, as is the case for the ChemCam instrument on the Mars rover Curiosity, which performs analyses at distances between 2 and 7.4 m is not a simple task. Previously, we showed that spectral distance correction based on a proxy spectroscopic standard created from first-shot dust observations on Mars targets ameliorates the distance bias in multivariate-based elemental-composition predictions of laboratory data. In this work, we correct an expanded set of neutral and ionic spectral emissions for distance bias in the ChemCam data set. By using and testing different selection criteria to generate multiple proxy standards, we find a correction that minimizes the difference in spectral intensity measured at two different distances and increases spectral reproducibility. When the quantitative performance of distance correction is assessed, there is improvement for SiO 2 , Al 2 O 3 , CaO, FeOT, Na 2 O, K 2 O, that is, for most of the major rock forming elements, and for the total major-element weight percent predicted. But, for MgO the method does not provide improvements while for TiO 2 , it yields inconsistent results. Additionally, we observed that many emission lines do not behave consistently with distance, evidenced from laboratory analogue measurements and ChemCam data. This limits the effectiveness of the method.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Detecting new physics as novelty — Complementarity matters

Novelty detection is a task of machine learning that aims at detecting novel events without a prior knowledge. In particular, its techniques can be applied to detect unexpected signals from new phenomena at colliders. In this paper, we develop an analysis scheme that exploits the complementarity, originally studied in ref. [1], between isolation-based and clustering-based novelty evaluators. This approach can significantly improve the performance and overall applicability of novelty detection at colliders, which we demonstrate using a variety of two dimensional Gaussian samples mimicking collider events. As a further proof of principle, we subsequently apply this scheme to the detection of two significantly different signals at the LHC featuring a $t\bar{t}γγ$ final state: $t\bar{t}h$, giving a narrow resonance in the diphoton mass spectrum, and gravity-mediated supersymmetry, resulting in broad distributions at high transverse momentum. Compared to existing dedicated searches at the LHC, the sensitivities for detecting both signals are found to be encouraging.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Intra- and inter-observer variability in breast tumour bed contouring and the controversial role of surgical clips

The purpose of this study was to evaluate whether the visualization of surgical clips (SCs) on the same set of planning computed tomography (CT) of breast cancer (BC) patients influences agreement on tumour bed (TB) delineation. Planning CT (CT{sub orig}) of 47 BC patients with SCs to visualize the TB was processed in order to blur SCs and create a virtual CT (CT{sub mod}). Four radiation oncologists (ROs, 2 juniors and 2 seniors) contoured TB on both the CT sets. Centre of mass distance (CMD), percentage overlap as Dice similarity coefficient (DSC), surface distance as average Hausdorff distance (AHD) and TB volume size were analysed. The intra-observer variability when contouring TB with and without SCs was statistically significant (p-values = 0.016, 0.0002 and ≪ 0.001 for CMD, AHD and DSC, respectively). Junior ROs showed worse reproducibility compared to seniors. The median DSC was < 0.7. The inter-observer variability with and without SCs was statistically significant (p < 0.001) for all metrics, with an increase of 48.7% in DSC and decrease of 50.7% and 57.1% in CMD and AHD, respectively, as relative median values, when SCs were visible. Regarding TB volumes, when SCs were visible, the intra-observer analysis revealed that 3/4 ROs delineated larger volumes, especially juniors. The inter-observer analysis showed that, in presence of visible SCs, the difference in TB volume among all the ROs fell from statistically significant to borderline significance (p = 0.052). TB contouring is confirmed to be an observer-dependent task. SCs decreased the intra and inter-observer variability but the overall agreement between ROs remained low.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Towards Scalable Optimal Sequence Homology Detection

Abstract—The field of bioinformatics and computational biol- ogy is experiencing a data revolution — experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. This has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a funda- mental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large- scale optimal homology detection on massively parallel super- computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 × 107 optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

Daily, Jeffrey A.↗
DOE OSTI2012

Meta Parallel Coordinates For Visualizing Features in Large, High-Dimensional, Time-Varying Data

Managing computational complexity and designing effective visual representations are two important challenges for the visualization of large, complex, high-dimensional datasets. Parallel coordinates are an effective technique for visualizing high- dimensional data, but do not scale well to very large datasets. The addition of the temporal dimension leads to more uncertainty due to clutter on screen. Consequently, this poses a significant challenge for visually finding trends and patterns that maximize insight about the underlying time-varying properties of the data. To address these problems, we present meta parallel coordinates, a parallel coordinates display that is guided by perceptually mo- tivated visual metrics. These metrics describe the visual structures typically found in parallel coordinates and thus aid the user’s analysis by providing meaningful views of the data. Since they are computed in screen space, our metrics are computationally more efficient than data-based metrics. Our choice of metrics is driven by the different analytical tasks that a user typically wants to perform with time-varying multivariate data. In particular, we have worked with domain scientists who performed simulations of bioremediation experiments, and use their data and results to demonstrate the usefulness of our approach

Dasgupta, Aritra↗
DOE OSTI2025

A Typology of Decision-Making Tasks for Visualization

Despite decision-making being a vital goal of data visualization, little work has been done to differentiate decision-making tasks within the field. While visualization task taxonomies and typologies exist, they often focus on more granular analytical tasks that are too low-level to describe large complex decisions, which can make it difficult to reason about and design decision-support tools. In this paper, we contribute a typology of decision-making tasks that were iteratively refined from a list of design goals distilled from a literature review. Our typology is concise and consists of only three tasks: CHOOSE, ACTIVATE, and CREATE. Although decision types originating in other disciplines exist, we provide definitions for these tasks that are suitable for the visualization community. Our proposed typology offers two benefits. First, the ability to compose and hierarchically organize the tasks enables flexible and clear descriptions of decisions with varying levels of complexities. Second, the typology encourages productive discourse between visualization designers and domain experts by abstracting the intricacies of data, thereby promoting clarity and rigorous analysis of decision-making processes. We demonstrate the benefits of our typology through four case studies, and present an evaluation of the typology from semi-structured interviews with experienced members of the visualization community who have contributed to developing or publishing decision support systems for domain experts. Our interviewees used our typology to delineate the decision-making processes supported by their systems, demonstrating its descriptive capacity and effectiveness. Finally, we present preliminary findings on the usefulness of our typology for visualization design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Development of Technology Readiness Level (TRL) Metrics and Risk Measures

This is an internal project milestone report to document the CCSI Element 7 team's progress on developing Technology Readiness Level (TRL) metrics and risk measures. In this report, we provide a brief overview of the current technology readiness assessment research, document the development of technology readiness levels (TRLs) specific to carbon capture technologies, describe the risk measures and uncertainty quantification approaches used in our research, and conclude by discussing the next steps that the CCSI Task 7 team aims to accomplish.

carbon capture simulation↗
DOE OSTI2021

User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software (V.1.0)

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the initial software release as ADDER v1.0.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software (V.1.01)

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.0.1. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗