Dynamic Human Reliability Analysis Using the EMRALD Dynamic Risk Assessment Tool
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Park, Jooyoung↗
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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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Formal Experimentation and Analysis of Handheld RFID Readers as a Tool for Nuclear Material Accounting
Commercial, off-the-shelf Radio Frequency Identification (RFID) systems have been successfully deployed for inventory tracking in numerous industries, but their viability in the tracking of complex environments containing nuclear material is less understood. Of primary interest in this setting is a RFID tracking system for nuclear material accounting which can reliably identify as many tags as possible with high accuracy, while also reducing an operator’s exposure to radiation. In this work, we develop a formal statistical approach to identify relevant handheld RFID reader settings which optimize tagging performance. To achieve this goal, we design a full factorial split-plot experiment for a static shelf configuration scene with 50 randomly placed nuclear material containers affixed with RFID tags. We use Bayesian inference to fit a second-order response surface model which expresses the probability of a successful match for each container as a function of the experimental factors. Such effects are allowed to vary across individual containers and the containers’ population in its entirety to estimate overall effects. Uncertainties of estimates and predictions are quantified via their corresponding posterior distributions. Following extensive model checking and validation, the fitted model is used to identify experimental factors which maximize matching probabilities at both the container-level and for the full shelf configuration scene. We also analyze sensitivity of performance to relevant factors.
25 ENERGY STORAGE↗
SIGNALS A Flexible Tool for Radiometry, Sensor Modeling, and Signal Analysis
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Kramer, Cove Jade [Sandia National Laboratories (S↗
MAUD Interface Tool Kit (MILK)
Materials Analysis Using Diffraction (MAUD) is an open source Rietveld refinement program which fits diffraction models to diffraction spectra allowing quantitative diffraction analysis. The scripting interface developed here using python facilitates the choice of parameters to refine during the Rietveld fitting process and provides a simple MPI framework for running MAUD instances in parallel. The scripting language facilitates batch, custom, and reproducible Rietveld refinements of large datasets.
Savage, Daniel↗
Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.
Chan, Jay↗
Managing Marine Energy Risks for Project Success
This presentation reviews recommended practices for marine energy risk management based on NLR's recent risk management framework publication (https://www.nrel.gov/docs/fy24osti/90212.pdf). This presentation will include a demonstration of risk management processes and techniques that everyone in the marine energy industry can use to successfully meet their project objectives. This presentation will include a description of methods to identify and manage risks that are specific to marine energy, while demonstrating this through tools such as risk registers, failure modes effects and criticality analysis (FMECA), and more tools that are currently being developed. The goal of this presentation is for the participants to have knowledge and access to tools to help them manage the risks specific to their marine energy projects.
13 HYDRO ENERGY↗
Used Nuclear Fuel Management Using the Next Generation System Analysis Model
The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.
Craig, Brian [Argonne National Laboratory (ANL)]↗
Beyond the Hype: An Evaluation of Commercially Available Machine-Learning-Based Malware Detectors
There is a lack of scientific testing of commercially available malware detectors, especially those that boast accurate classification of never-before-seen (i.e., zero-day) files using machine learning (ML). Consequently, efficacy of malware detectors is opaque, inhibiting end users from making informed decisions and researchers from targeting gaps in current detectors. In this paper, we present a scientific evaluation of four prominent commercial malware detection tools to assist an organization with two primary questions: To what extent do ML-based tools accurately classify previously and never-before-seen files? Is purchasing a network-level malware detector worth the cost? To investigate, we tested each tool against 3,536 total files (2,554 or 72% malicious, 982 or 28% benign) of a variety of file types, including hundreds of malicious zero-days, polyglots, and APT-style files, delivered on multiple protocols. We present statistical results on detection time and accuracy, consider complementary analysis (using multiple tools together), and provide two novel applications of the recent cost-benefit evaluation procedure of Iannacone & Bridges. Although the ML-based tools are more effective at detecting zero-day files and executables, the signature-based tool might still be an overall better option. Both network-based tools provide substantial (simulated) savings when paired with either host tool, yet both show poor detection rates on protocols other than HTTP or SMTP. Our results show that all four tools have near-perfect precision but alarmingly low recall, especially on file types other than executables and office files—37% of malware, including all polyglot files, were undetected. Priorities for researchers and takeaways for end users are given. Code for future use of the cost model is provided.
97 MATHEMATICS AND COMPUTING↗
Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis
Abstract Background Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. Results To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13 C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13 C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. Conclusions Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.
59 BASIC BIOLOGICAL SCIENCES↗
Overview of the Inflation Reduction Act of 2022 (IRA) Home Energy Rebate Tool
The IRA Home Energy Rebate Tool (Tool) provides if/then analysis on the impact of two provisions of the Inflation Reduction Act (IRA); 50121 - Home Energy Performance-Based Whole-House Rebates (Home Efficiency Rebates or HOMES Program) and 50122 - High-Efficiency Electric Home Rebate Program (Home Electrification and Appliance Rebates or High-Efficiency Electric Appliances Rebates Program). The Tool can generate realistic scenarios based on the legislative text. The results of the Tool should not be interpreted as forecasts or predictions of what will happen for individual households, States or the nation. This overview provides a high level summary of the data inputs and methods.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Development and Implementation of a New AI-Based Tool to Support Fast Reactor Software Model Generation and Validation
This report summarizes FY26 work to develop Maggie, an artificial intelligence-based assistant designed to support software model generation and validation activities for fast reactor analysis codes. The project established a modular, code-agnostic software architecture that separates reusable agent capabilities from code-specific knowledge and tools, with initial implementation focused on the FRP-supported fast reactor safety analysis code SAS4A/SASSYS1 (SAS). A curated SAS-specific knowledge base was assembled from the code manual, training materials, historical analysis reports, and representative input files, and was integrated through retrieval-augmented generation to ground Maggie’s responses in authoritative sources. Maggie was deployed on the internal Argonne network, where it demonstrated practical user-facing capability as a chatbot for answering natural language questions about SAS and retrieving relevant technical information. Demonstration cases also showed that Maggie can generate useful snippets of SAS input for selected modeling tasks, while highlighting current limitations in reliability and consistency for more complex input generation tasks. Overall, the FY26 effort established the technical foundation for an AI-assisted capability intended to improve the efficiency, consistency, and accessibility of fast reactor software model development at Argonne and, with further improvements, to support eventual use by the broader fast reactor community, including industry users of FRP-supported analysis tools.
Thomas, Rachel [Argonne National Laboratory (ANL),↗
GCAM Base Year Update
In FY21 we worked to update the Global Change Analysis Model (GCAM) to match historical energy consumption and technology cost trends and near-term economic growth expectations using available historical data out to 2019. We have done this in a way that builds the capacity to more easily update the model in the future to make GCAM a robust and readily updatable tool for decarbonization analysis.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas
This paper provides a comprehensive review of the TRANSP code, a sophisticated tool for interpretive and predictive analysis of tokamak plasmas, detailing its major capabilities and features. It describes the equations for particle, power, and momentum balance analysis, as well as the poloidal field diffusion equations. The paper outlines the spatial and time grids used in TRANSP and details the equilibrium assumptions and solvers. Various models for heating and current drive and radiation, including updates to the NUBEAM model, are discussed. The handling of large-scale events such as sawtooth crashes and pellet injections is examined, along with the predictive capabilities for advancing plasma profiles. The integration of TRANSP with the ITER Integrated Modeling and Analysis Suite (IMAS) is highlighted, demonstrating enhanced data access and analysis capabilities. Additionally, the paper discusses best practices and continuous integration techniques to enhance TRANSP's robustness. The suite of TRANSP tools, designed for efficient data analysis and simulation, further supports the optimization of tokamak operations and coupling with other tokamak codes. Continuous development and support ensure that TRANSP remains a major code for the analysis of experimental data for controlled thermonuclear fusion, maintaining its critical role in supporting the optimization of tokamak operations and advancing fusion research.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Numerical Simulations of Carbon Dioxide Storage Efficiency in Heterogeneous Reservoir Models
The U.S. Department of Energy’s National Energy Technology Laboratory (DOE-NETL) has been developing methods and tools (the online Carbon Dioxide Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN) tool) to estimate carbon dioxide (CO2) storage potential in subsurface reservoirs. The CO2 storage efficiency terms are input in the tool to calculate storage potential in targeted reservoirs. In this effort, two CO2 storage efficiency terms were evaluated: volumetric displacement ( E V ) and microscopic displacement ( E d ). The first term deals with efficiency of CO2 propagation into an accessible reservoir volume, while the second term evaluates effectiveness of native fluid displacement with CO2. The interpreted well logs and core sample measurements were applied to create the heterogeneous reservoir models including geostatistical realizations of porosity and intrinsic permeability fields. Supercritical CO2 was injected over the course of 30 years into brine-saturated reservoir models for clastics, limestone, and dolomite lithologies and deltaic fluvial, aeolian, shallow marine, and reef depositional environments by means of varying reservoir parameters and injection scenarios. The reservoir models providing vertically heterogeneous petrophysical properties and designated as “layered reservoir models” (with homogeneous parameters along each layer of the model) were not determined to be a transition between the homogeneous and heterogeneous models in respect to storage efficiency. Another finding shows that high-efficiency factors do not necessarily mean increased CO2 storage; they rather indicate that the available volume and pore space are more fully utilized. The CO2 storage efficiency factors were evaluated dynamically at the select time points using P 10 ‐ P 50 ‐ P 90 percentiles. The results of this study show that the P 10 ‐ P 90 distribution for volumetric efficiency is wider when compared to the microscopic efficiency. It was found that where dominant buoyancy forces drive the plume to the top of a target formation, the volumetric efficiency is low. Tighter sandstone and carbonate formations show prevalence of capillary forces and better utilization of reservoir volume.
Myshakin, Evgeniy M.↗
Gene network centrality analysis identifies key regulators coordinating day-night metabolic transitions in Synechococcus elongatus PCC 7942 despite limited accuracy in predicting direct regulator-gene interactions
Synechococcus elongatus PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO 2 -to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO 2 .
59 BASIC BIOLOGICAL SCIENCES↗
Natural transformation as a tool in Acinetobacter baylyi : Streamlined engineering and mutational analysis
Natural transformation and homologous recombination in a soil bacterium, Acinetobacter baylyi ADP1, occur with exceptionally high efficiency. These genetic features can be harnessed to address a wide variety of fundamental and applied scientific topics. Recent advances in synthetic biology and laboratory evolution have led to renewed appreciation for the use of A. baylyi as a model organism. To complement several review articles that highlight new tool sets, this chapter focuses on simple protocols and examples of transformation assays that facilitate genetic analysis and engineering. Whole genome sequencing often reveals extensive genetic variation among closely related isolates that can confound the association of genotypic and phenotypic changes. In A. baylyi, such associations can be deciphered in unique ways by directly transforming cells with linear DNA fragments. The resulting allelic replacement, which occurs at high frequency, rapidly generates desired mutants via targeted chromosomal editing. Diverse screening and selection methods can be used to test hypotheses and streamline experimental strategies to reveal the significance of specific DNA sequences. Moreover, large procedural variations are well tolerated, and techniques can be readily adapted for new purposes. Furthermore, one goal of highlighting natural transformation methodology in A. baylyi is to expand the community of researchers using this versatile bacterial host.
59 BASIC BIOLOGICAL SCIENCES↗
USE OF COGNITIVE WORK ANALYSIS IN DEVELOPING A NUCLEAR POWER PLANT NEW STATE VISION
The United States nuclear industry is in need of identifying and implementing a new strategy that will lower operating and maintenance costs while maintaining safety for existing plants. The industry must also have a clear and strategic vision of their transformative new state that focuses on ways in which technologies can be integrated to maximize the benefits of technology and people. While there are ongoing efforts in this area, this work discusses how the use of cognitive work analysis may further support these current efforts. This paper provides an overview of cognitive work analysis, as well as the state of current new state development efforts. The use of cognitive work analysis, as a tool to enhance existing practices, is presented in ensuring that the development of a new state vision is systematic and complete.
99 GENERAL AND MISCELLANEOUS↗
Material Removal and Surface Integrity Analysis of Ti6Al4V Alloy after Polishing by Flexible Tools with Different Rigidity
Ti6Al4V alloy has been widely used in many fields, such as aerospace and medicine, due to its excellent biocompatibility and mechanical properties. Most high-performance components made of Ti6Al4V alloy usually need to be polished to produce their specific functional requirements. However, due to the material properties of Ti6Al4V, its polishing process still requires significant development. Therefore, this study aimed to investigate the performance of polishing Ti6Al4V by using tools with different rigidities. Two kinds of bonnet tool were used, namely a pure rubber (PR) bonnet and a semirigid (SR) bonnet. The characterization of material removal and surface integrity after polishing was conducted through a series of experiments on a 6-DOF robotic polishing device. The results demonstrate that both bonnet tools successfully produce nanometric level surface roughness. Moreover, the material removal rate of the SR bonnet tool is significantly higher than that of the PR bonnet, which is consistent with the material removal characteristics of glass polishing in previous research. In addition, the presented analysis on key polishing parameters and surface integrity lays the theoretical foundation for the polishing process of titanium alloy in different application fields.
36 MATERIALS SCIENCE↗