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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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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Pretreatment of Crystalline Silicotitanate (CST) and development of a digestion standard to support Tank Closure Cesium Removal (TCCR)

In support of Savannah River Remediation’s (SRR) deployment of an ion exchange process to remove radioactive cesium from waste supernate, referred to as the Tank Closure Cesium Removal (TCCR) process, SRNL was tasked with validating the field protocol for pretreating the crystalline silicotitanate (CST) material to be used in the columns, preparing CST for use in in-tank batch contact testing, as well as developing a digestion standard to verify complete dissolution of the CST prior to characterization after each in-tank batch contact test. A procedure (SRNL L29 Manual, ITS-0229) was developed to document the protocol for pretreating CST utilizing conditions similar to what will be performed in the field. Analysis of material pretreated following the procedure confirmed similar conversion to the Na+ form of the resin as had been obtained previously. Portions of the batch of material pretreated according to the developed protocol were also used for development of a digestion standard. Results from digestion and analysis of this standard were used to establish a set of control limits for the measured Ti, Zr, and Nb concentrations in digested CST material. An aliquot of this standard CST material will be digested and analyzed alongside each in-tank batch contact sample, and the measured concentrations of Ti, Zr, and Nb will be compared against the established control limits to confirm complete digestion has occurred.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Analyses to support waste disposition of SNS Inner Reflector Plug

The Spallation Neutron Source Inner Reflector Plug (IRP) is scheduled for replacement. It reached end of life-time because of moderator neutron poison and decoupler burn-out, which is used for shaping neutron pulses. The replacement of IRP is a complex task due to its location in the area receiving high irradiation, being under significant amount of shielding, and size, which requires removal in segments. In order to support the disposal of spent IRP, waste characterization analyses are performed. These analyses include an accurate estimate of the radionuclide inventory and the residual dose rate. Using this data, the package class is determined for the and appropriate containers for temporary storage and subsequent transport are assigned. Analyses are performed in four steps: a radiation transport analysis of the radiation environment for producing the flux and isotope production information using MCNPX, a multi-cell transmutation analysis based n CINDER90, the generation of decay gamma source terms, and the gamma transport analyses of the decay gammas arriving at residual dose rates again using MCNPX. . Due to the complexity of the geometry of the lower segment, transport analyses for the residual dose rate require elaborate work for the source terms definition.

Popova, Irina I.↗
DOE OSTI2019

Neutronic Design and Analysis of the Holos-Quad Concept

HolosGen, LLC is proposing a highly innovative micro-reactor concept targeting both civilian and military applications. It consists of an advanced helium-cooled gas reactor using a turbojet-type turbine and compressor to achieve a highly condensed reactor that fits into a commercial ISO container. The Holos-Quad concept being considered for civilian applications will generate 22MWt, using four Subcritical Power Modules (SPMs) that fit into one 40-foot ISO container. It is a high-temperature gas-cooled reactor concept using TRISO fuel distributed in graphite hexagonal blocks, cooled with helium gas at 7MPa and a high outlet temperature. In FY2019, HolosGen, LLC received an ARPA-E MEITNER grant to team up with the Argonne National Laboratory (ANL) design team to demonstrate the feasibility of the Holos-Quad concept in terms of neutronics. In particular, the evaluation study of the Holos design features and alternative options for reactivity control was considered. Through these tasks, the ANL design team has been working on demonstrating the feasibility of the Holos-Quad concept in terms of neutronics to help advance the core design of the Holos-Quad. A rigorous design approach was employed for the Holos-Quad in order to ensure that the input space is fully investigated, and that the best solutions are considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the competitive design objectives: its economic performance is maximized with longer lifetime and its transportability to remote locations is maximized with reduced weight. The design problem also required to identify the operational constraints (in terms of thermal hydraulics, reactivity feedback and shutdown margins) and the input parameters that could be varied. In the second step, a sensitivity analysis was performed to enable preliminary investigation of the input space to identify the correlations among input and output variables. It allowed, for instance, to find input parameters that have relatively poor impact on the core performance and that could be removed from the optimization problem to accelerate its convergence. This sensitivity analysis showed that the design problem defined is highly constrained since only 117 solutions out of 1,000 simulated cores met all the design constraints. The design optimization was performed in the third step employing a genetic algorithm to effectively explore this highly constrained input space and find global optimal solutions. More than 2,500 cores were fully evaluated to converge to a Pareto Frontier of optimum solutions providing best compromise between the life-time target and the total core weight, as shown in Figure 1. The main benefit of this approach was to reduce the human effort and to be able to automatically re-run the optimization problem when changing the design problem. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗
DOE OSTI2023

Methods for Analysis and Quantification of Power System Resilience

This paper summarizes the report prepared by an IEEE PES Task Force. Resilience is a fairly new technical concept for power systems, and it is important to precisely delineate this concept for actual applications. As a critical infrastructure, power systems have to be prepared to survive rare but extreme incidents (natural catastrophes, extreme weather events, physical/cyber-attacks, equipment failure cascades, etc.) to guarantee power supply to the electricity-dependent economy and society. Thus, resilience needs to be integrated into planning and operational assessment to design and operate adequately resilient power systems. Quantification of resilience as a key performance indicator is important, together with costs and reliability. Quantification can analyze existing power systems and identify resilience improvements in future power systems. Given that a 100% resilient system is not economic (or even technically achievable), the degree of resilience should be transparent and comprehensible. Several gaps are identified to indicate further needs for research and development.

42 ENGINEERING↗
DOE OSTI2017

Thermal Analysis of a Pipe Over-pack Container with a Combustible Waste Matrix for Los Alamos National Laboratory - 17307

Los Alamos National Laboratory (LANL) Technical Area 55 (TA 55) utilizes several different container types to store and transfer special nuclear material and waste for numerous programs. The Nuclear Process Infrastructure - Infrastructure Operations Group (NPI-2) analyzes and certifies containers to meet the specifications for distinct processes and storage. Engineering analysis performed on these containers include but are not limited to: leak testing, filter efficiency testing, pressure drop test, drop testing, water penetration testing, polymer (o-ring) hardness testing, and thermal steady state testing. Of all of these tests, thermal steady-state testing is most crucial to ensure critical components for specific containers do not fail under an internal heat payload. This testing provides data for setting heat payload limits for different containers. The Pipe Over-pack Container (POC) is a vented carbon steel container with a removable lid designed for storing or transferring nuclear waste. Additionally, TA 55 has been tasked to manage and store all of its Transuranic (TRU) waste for the foreseeable future. Being able to place more material into a single container conserves the physical storage space available at TA 55. The Pipe Over pack Handling and Operations Manual requires each user to ensure that the maximum temperature of each component in the POC is not exceeded. Conducting a thermal steady-state test with a combustible waste matrix on the POC can benefit stakeholders on increasing the material limits of the container. The results of these measurements will establish new wattage limits to meet LANL's transportation requirements. The current LANL Transportation Safety Document (TSD) limits the amount of heat source plutonium to 10 grams per POC, corresponding to approximately 5 Watts of heat. The result of this test was provided to the Packaging and Transportation (OS-PT) group to evaluate new wattage limits that can satisfy the needs of TA 55, while maintaining a safe packaging configuration. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Jensen–Shannon divergence based novel loss functions for Bayesian neural networks

Bayesian neural networks (BNNs) are state-of-the-art machine learning methods that can naturally regularize and systematically quantify uncertainties using their stochastic parameters. Kullback–Leibler (KL) divergence-based variational inference used in BNNs suffer from unstable optimization and challenges in approximating light-tailed posteriors due to the unbounded nature of the KL divergence. To resolve these issues, we formulate a novel loss function for BNNs based on a new modification to the generalized Jensen–Shannon (JS) divergence, which is bounded. In addition, we propose a Geometric JS divergence-based loss, which is computationally efficient since it can be evaluated analytically. We found that the JS divergence-based variational inference is intractable, and hence employed a constrained optimization framework to formulate these losses. Our theoretical analysis and empirical experiments on multiple regression and classification data sets suggest that the proposed losses perform better than the KL divergence-based loss, especially when the data sets are noisy or biased. Specifically, there are approximately 5% and 8% improvements in accuracy for a noise-added CIFAR-10 dataset and a regression dataset, respectively. There is about 13% reduction in false negative predictions of a biased histopathology dataset. Additionally, we quantify and compare the uncertainty metrics for the regression and classification tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Joint Genome Institute Analysis Workflow Service (JAWS) v2.7

The U.S. Department of Energy Joint Genome Institute (JGI) has developed the JGI Analysis Workflow Service (JAWS) as a distributed framework to run computational workflows across diverse high-performance computing (HPC) and cloud environments. JAWS enhances the reusability, scalability, and robustness of scientific workflows by orchestrating data movement, code execution, and results retrieval across multiple DOE facilities. At its core, JAWS integrates the Cromwell workflow engine to run workflows expressed in the Workflow Description Language (WDL), ensuring portability and interoperability. To provide consistent runtime environments, JAWS employs container technologies such as Shifter, Apptainer, and Docker. Workflow tasks are managed via HTCondor on HPC backends, while Globus ensures secure and efficient data transfer between sites. JAWS is deployed as a multi-site workflow manager across national laboratory computing facilities, with dedicated instances supporting community projects such as the National Microbiome Data Collaborative (NMDC) and KBase. This distributed, service-oriented architecture enables users to "write once, run anywhere," providing scalable, production-quality workflow execution.

Kirton, Edward↗
DOE OSTI2024

Assessment of ROI for Workforce Development Efforts National & Homeland Security

The IDEAL Professional Engagement project at Idaho National Laboratory (INL) aims to enhance the laboratory's workforce diversity and inclusivity efforts, focusing on the U.S. National and Homeland Security mission areas. This project involves researching and evaluating opportunities for INL to engage in various professional events, particularly cyber conferences, to support recruitment and professional development. The project involved several key tasks: compiling comprehensive information on national laboratories and their mission statements, developing a deep understanding of INL’s role and efforts in national and homeland security, and identifying and engaging key stakeholders. Interviews were conducted with a set of targeted questions, and the findings were analyzed to identify common themes, insights, and actionable recommendations. Additionally, relevant upcoming cyber conferences were identified, various sponsorship levels and their associated benefits were evaluated, and the recruitment potential of these conferences was assessed. A detailed cost analysis was performed, including registration fees and travel expenses, and a cost-benefit analysis was conducted to evaluate the financial viability and potential return on investment (ROI) of conference participation and sponsorship. Based on the research and analysis, actionable recommendations for conference participation and sponsorship were formulated, ensuring alignment with INL’s mission and diversity goals. Preliminary results include a comprehensive list of relevant cyber conferences, a detailed cost analysis, and a set of actionable recommendations for future conference participation and sponsorship. The analysis highlights the financial requirements and geographical distribution of these conferences, providing valuable insights for INL's engagement strategies. This project underscores the importance of strategic engagement in professional events to attract and develop a diverse and skilled workforce, ultimately supporting INL’s mission areas in U.S. National and Homeland Security.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
DOE OSTI2019

Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification m acro F -score o ver the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2024

CMLM (Co-Optimized Machine-Learned Manifolds) [SWR-23-41]

Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).

Perry, Bruce↗
DOE OSTI2022

Stirred-Reactor Coupon Analysis: An International Round Robin Study

The objective of this task was to determine the precision of the SRCA technique when used to determine the dilute condition corrosion rate. To this end, an interlaboratory round robin study was conducted per the instructions in ASTM Practice E691 to measure the precision with which the SRCA test method can be conducted. Twelve independent labs from eleven different institutions each evaluated four glass compositions in three different conditions. The ASTM procedures recommend at least 6 labs participate in a round robin testing the same 3 materials in the same conditions to determine precision. In this case, 12 labs each performed 12 independent tests. This was only possible thanks to the multi-glass testing capability of the SRCA test. A total of 108 duplicate pairs were used to calculate the repeatability of the tests, with the same glass tested in the same vessel, producing as identical conditions as possible for the replicates. These test results were quite tightly clustered, with a median difference from the average value of the pair of only 2.9%. Based on the calculations outlined in ASTM E177-20 and a measured standard deviation of 4.74%, the intralaboratory repeatability limit (r) was calculated to be within 13.3% of the expected value with 95% confidence level. The reproducibility limit (R) of the test was examined using all 277 data points from the round robin. Because of the differences in dissolution rates due to pH variability and intrinsically for the 12 conditions tested, the reproducibility limits for each condition and overall were calculated from the percent relative residual value for each test. The SRCA test is expected to be reproducible within 64% of the expected value with a 95% confidence level.

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

Multiphysics Simulations of MSRE with NEAMS Thermal Hydraulics Tools

This report documents the benchmarks being developed and simulations performed using tools and codes developed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, utilizing MSRE experimental data. In FY23, three main work scopes were investigated under the NEAMS MSR work package at ANL. The first scope investigated the Griffin-SAM coupling model for simulating the pump startup transient experiment of MSRE. The analyses start with a simple model (single-channel, single-lattice), gradually adding more details (multi-channel, full-core) into the model. The results show that the reactivity loss curve is very sensitive to the axial boundary conditions and the radial core discretization. The simple model can predict a similar reactivity trend as that of the more sophisticated model, which is likely due to error cancellation. Accurately modeling the axial boundary condition may further improve the reactivity trend but would require significant efforts to generate the mesh of the MSRE inlet and upper plenum. The core channel radial discretization for the Griffin-SAM coupled model also depends on the flow distribution. Given the complex geometry in the inlet plenum, the flow distribution needed to be calculated from CFD analysis, which was performed using the NekRS code. This analysis employed a MSRE CAD model developed by Copenhagen Atomics. The CAD model was disassembled to keep the inlet plenum region only, which was subsequently cleaned and modified so that the mesh generated is under the memory limit. The results are merged to a few radial regions to show that the flow rate is highest in the central region. This would be useful for future improvement of the Griffin-SAM coupling model of the MSRE core. The last task investigated is tritium transport modeling using the standalone SAM code. This task aimed to initiate the effort to demonstrate and validate the tritium transport model implemented in SAM. The preliminary investigation employed an MSRE model consisting of the primary loop. Three tritium transport pathways were examined including the retention in the graphite, the permeation through the HX tube wall, and the removal from the off-gas system. The results compare well with the MSRE data, but improvements are still needed on the initial conditions (i.e., the present state may not have reached equilibrium), the boundary conditions, the off-gas system modeling, and a better numerical strategy to reach the equilibrium state.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Video Summarization Using Deep Action Recognition Features and Robust Principal Components Analysis

In an instance where desired pre-defined actions, behaviors, or other categories are known a priori, various video classification and recognition models can be trained to discover those classifications and their location within the video. Absent that information, one might still be tasked with identifying interesting portions within a video, a process which—if done manually—is onerous and time-consuming as it requires manual inspection of the video itself. Recognizing high-level interesting segments within a whole video has been a general area of interest due to the ubiquity of video data. However the size of the data makes storage, retrieval, and inspection of large collections of videos cumbersome. This problem motivates the task of generating shortened clips highlighting the primary content of a video, relieving the burden of having to watch the entire video. This paper presents an unsupervised method of creating shortened clips of videos, enabling the rapid review of the most interesting content within a video. Our method uses features extracted from pre-trained action recognition models as input to online moving window robust principal component analysis to generate summaries. The procedure is tested on a publicly available video summarization dataset and demonstrates comparable performance to state-of-the-art in an un-augmented setting while requiring no training.

Claborne, Daniel M.↗
DOE OSTI2022

Optimization Simulations for a Gamma-Ray Calibration Standard for a Cyclic Neutron Activation Analysis Pneumatic System at the Penn State Breazeale Reactor

For new experimental setups, the initial testing and calibrations can be expensive and time consuming without prior optimization. However, these issues can be mitigated using realistic modeling and simulation studies of the proposed system a priori. Specifically, an experiment can be performed virtually using realistic simulations and the expected results evaluated to inform adjustments of the experimental setup to achieve optimum results. At the Penn State Breazeale Reactor, a new pneumatic transfer system has been developed for the detection and characterization of short-lived fission fragments to enhance and complement existing nuclear data. The system accomplishes this task by transporting samples cyclically between an assortment of gamma-ray/neutron detectors and the reactor-core vicinity with sub-second transit times. The measured neutron flux paired with simulations of detector response using Geant4 and a custom module for estimating cascade summing effects and corrections were used to select sample-material masses for system characterization. With the optimized sample masses, an experimental counting plan for characterizing the repeatability of the newly developed pneumatic system was developed. Finally, the predictions made with these simulations allowed for optimization of the irradiation sample characteristics and gamma-ray detection system.

cyclic neutron activation, gamma-ray spectroscopy↗
DOE OSTI2023

Fission neutron activations of stainless steel and its corresponding elemental components

Characterizing neutron activation of stainless steel is a critical task for many applications, and yet little activation data is available for this alloy. To address the need, a series of neutron activation experiments have been performed using the Flattop critical assembly at the National Criticality Experiments Research Center. Several experimental conditions were probed for complete understanding of the neutron environment, including foil composition and position within the assembly. The experimental results reported here will aid in the production of stainless steel benchmarks for a range of nuclear data applications including those used in nuclear forensics analysis.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Time Dilated Bundt Cake Analysis of PV Output [Poster]

We present a novel method for modeling time-dependent statistics in the power signal generated by a photovoltaic (PV) system. Our white-box machine learning method is interpretable and auditable, based on principles of multiperiodic basis functions and convex optimization. Our proposed method of time dilating the daily signal to remove night time values results in a novel representation of PV power signals, evocative of a ‘Bundt cake’. The proposed model describes the marginal distribution of power output as a function of date and time. The resulting probabilistic model of a PV system can be used to perform a variety of tasks, and here, we demonstrate the application of clear sky detection.

14 SOLAR ENERGY↗