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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Symmetry reduction of tensor networks in many-body theory: I. Automated symbolic evaluation of SU(2) algebra

Abstract The ongoing progress in (nuclear) many-body theory is accompanied by an ever-rising increase in complexity of the underlying formalisms used to solve the stationary Schrödinger equation. The associated working equations at play in state-of-the-art ab initio nuclear many-body methods can be analytically reduced with respect to angular-momentum, i.e. SU (2), quantum numbers whenever they are effectively employed in a symmetry-restricted context. The corresponding procedure constitutes a tedious and error-prone but yet an integral part of the implementation of those many-body frameworks. Indeed, this symmetry reduction is a key step to advance modern simulations to higher accuracy since the use of symmetry-adapted tensors can decrease the computational complexity by orders of magnitude. While attempts have been made in the past to automate the (anti-) commutation rules linked to Fermionic and Bosonic algebras at play in the derivation of the working equations, there is no systematic account to achieve the same goal for their symmetry reduction. In this work, the first version of an automated tool performing graph-theory-based angular-momentum reduction is presented. Taking the symmetry-unrestricted expressions of a generic tensor network as an input, the code provides their angular-momentum-reduced form in an error-safe way in a matter of seconds. Several state-of-the-art many-body methods serve as examples to demonstrate the generality of the approach and to highlight the potential impact on the many-body community.

Tichai, A.↗
DOE OSTI2023

A Data Processing Pipeline for Adversarial Socio-Technical Network Analysis

With the rapid adoption of emerging technologies, there is a need to catalog and model sociotechnical interdependencies that have been historically used to influence the operation of Critical Infrastructure networks including the impacts of mergers and acquisitions, hostile takeovers, and foreign investment. Our research intends to address this need with two primary contributions. First, we have developed a data curation and processing pipeline to generate sociotechnical networks extracted from a variety of data sources including SEC filings and infrastructure asset databases. The pipeline, implemented in Apache Airflow, extracts and normalizes the representation of entities and relations, specified within ontologies. Our intent is to provide an extensible, machine-actionable approach to quickly communicate such models, reproduce previous results, and adapt them to new, unanticipated situations. Second, networks produced by our pipeline enable the development of graph-theoretic metrics that consider the properties of network components in addition to its topology. Metadata associated with network components---whether semantic, temporal, or geospatial---affects the alignment of generated networks with assumptions underlying complexity metrics. Validation of generated networks relative to component types defined by an ontology, may allow the research community to adapt metrics to the semantics of the domains being studied. Generated networks may be processed as knowledge, dynamic, or spatial graphs and enables a variety of analyses including automated reasoning and measures of network complexity. Automated reasoning views extracted entities and relations as a knowledge graph; this enables application of inference rules that represent historically-attested adversarial business methods and applies that behavior to a specific geographic context. Measures of network complexity, including degree distribution, reachability analyses, temporal analysis, and community detection can be adapted to indicate adversarial organizational influence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Cyber Framework for Steering and Measurements Collection Over Instrument-Computing Ecosystems

We propose a framework to develop cyber solutions to support the remote steering of science instruments and measurements collection over instrument-computing ecosystems. It is based on provisioning separate data and control connections at the network level, and developing software modules consisting of Python wrappers for instrument commands and Pyro server-client codes that make them available across the ecosystem network. We demonstrate automated measurement transfers and remote steering operations in a microscopy use case for materials research over an ecosystem of Nion microscopes and computing platforms connected over site networks. The proposed framework is currently under further refinement and being adopted to science workflows with automated remote experiments steering for autonomous chemistry laboratories and smart energy grid simulations.

Al Najjar, Anees↗
DOE OSTI2024

Active learning of neural network potentials for rare events

Developing an automated active learning framework for Neural Network Potentials, focusing on accurately simulating bond-breaking in hexane chains through steered molecular dynamics sampling and assessing model transferability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .

2D class averages↗
DOE OSTI2026

Meeting the Challenges of Modern Transit through the Integration of Traditional Transit, On-Demand Micro-Transit Services and Vehicle Automation - A Case Study in Chandler, Arizona

Current technology trends of vehicle automation, electrification and on-demand transit are providing new tools to develop effective public mobility services, yet need to be balanced with traditional modes to serve the spectrum of demand effectively and efficiently. Automated vehicle transportation network company (TNC) services, better known as 'Robotaxis' are beginning to proliferate and scale across the US, with Waymo as the lead commercial entity. Chandler, AZ was the first fully automated deployment of Waymo technology. Chandler was also one of the first municipalities in Arizona to roll out micro-transit service to its citizens in 2022. At present the Chandler Flex micro-transit program is beginning to partner with Waymo to augment micro-transit services during peak demands, another first in the nation. However, the mix of services in Chandler relies not only on new technology, but also on traditional modes including fixed route transit and para-transit services. These are blended and balanced such that both old and new modes are applied within the context in which each excel. This presentation will provide background on the Chandler, AZ public mobility services, and the performance metrics that govern their use and selection within the spectrum of development and population density as well as socio-demographics within Chandler. Additionally, the behavioral response of the use of unmanned, automated vehicles employed to augment micro-transit is novel, the first in the nation - and initial feedback from public transit constituents in Chandler will be shared.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Automating Log Synthesis and Visualization with Python and Splunk

The goal of this project is to automate log analysis by utilizing Splunk, Bash, and Python together. Simplifying the monitoring and analysis of network traffic was the main goal. In order to accomplish this, a Bash script was created to use 'tcpdump' to automate network sniffing. It also included a 24-hour file rotation mechanism to effectively manage the pcap files that were generated. After that, a Python script was written to read these pcap files and retrieve pertinent data about network traffic. After processing the collected data, Splunk is used to summarize the important metrics and visualize said information with relevant graphs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Cross-Facility Orchestration of Electrochemistry Experiments and Computations

Instrument-computing ecosystems supporting automated electrochemical workflows typically require the integration of disparate instruments such as syringe pump, fraction collector, and potentiostat, all connected to an electrochemical cell. These specialized instruments with custom software and interfaces are not typically designed for network integration and remote automation. We developed a networked ecosystem of these instruments and computing platforms, which includes software to enable automated workflow orchestration from remote computers. Specifically, we developed Python wrappers of APIs and custom Pyro client-server modules to support remote operation of these instruments over the ecosystem network. Herein, we describe a specific workflow for generating and validating voltammogram (I-V) measurements of an electrolyte solution pumped into the electrochemical cell. We demonstrate the orchestration of this workflow which is composed using a Jupyter notebook and executed on a remote computer.

Al Najjar, Anees↗
DOE OSTI2023

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Annotation of Photosynthetic Systems (RAPS): automated algorithm to generate genome-scale metabolic networks from algal genomes

Algae have mounting potential to serve as platform strains for engineering efforts, but tools for these species lag behind traditional model systems like E. coli and yeast. Metabolic models are one such tool that would enhance our ability to rationally engineer algae but current automated algorithms to build models do not perform well for algal genomes. We introduce a novel software pipeline, Rapid Annotation of Photosynthetic Systems (RAPS), which leverages manual curation efforts of published models to create high quality first draft metabolic networks for new species. We compared models produced by our pipeline to published models and found that these models are able to capture more genes than the published models and are more predictive of experimentally determined growth rate. We used RAPS to automate the creation of 8 first draft metabolic models of new species to enable the further study of metabolism in these species.

09 BIOMASS FUELS↗
DOE OSTI2023

Comparing Automated Posterior Estimation Techniques for Modeling Strong Lenses In Ground-based Survey Data

Current and future ground-based cosmological surveys, such as the Dark Energy Survey (DES), and the Vera Rubin Observatory Legacy Survey of Space and Time (LSST), are predicted to discover thousands to tens of thousands of strong gravitational lenses. The large number of strong lenses discoverable in future surveys will make strong lensing a highly competitive and complementary cosmic probe. However, conventional lens modeling techniques are unable to scale up to the sheer number of lenses that will be discovered through upcoming surveys. Therefore, the use of automated lens analysis techniques is necessary. We demonstrate that machine learning methods can be used to automate the inference of informative model posteriors of strong lensing systems in ground-based surveys with credible uncertainty estimation. We present two Simulation-Based Inference (SBI) approaches for lens parameter estimation of galaxy-galaxy lenses. We demonstrate applications of Neural Posteriors Estima tors (NPEs) and Bayesian Neural Network (BNNs) to automate the inference of a 12-parameter lensing system for DES-like ground-based imaging data. We apply a suite of diagnostics (e.g., posterior coverage and SBC) to validate the performance of our methods. We find that NPEs outperform the BNN, producing posterior distributions that are for the most part both more accurate and more precise; in particular, several source-light model parameters are systematically biased in the BNN implementation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗
DOE OSTI2020

Automated Realistic Testbed Synthesis for Power System Communication Networks based on Graph Metrics

Communication networks are integral to modern power grid operations and are becoming increasingly critical as grid dynamics speed up and as more controls become closed-loop in form. Focusing on the interdependence between the physical grid and the communication system, we identify several key characteristics and typical graph properties based on analysis of a real communication system for a power grid. Moreover, an automated process was provided for synthetic testbed in the NS-3 simulator for the power system test case, and its network characteristics have been further derived for power system monitoring and control applications

Graph Analysis, Synthetic Testbed, communication n↗
DOE OSTI2022

A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance

Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Applying Automated Detectors for Regional Infrasound Signals to Global Networks: A Case Study Using the 2022 Hunga Tonga Volcanic Eruption

The January 15, 2022 Tonga Hunga volcanic eruption was the largest event to be recorded across the International Monitoring System infrasound network. Signals from the eruption were identified at all 53 operational stations by International Data Centre analysts. This report contains descriptors for signal detection bulletins produced by infrasound researchers at Sandia National Laboratories and Los Alamos National Laboratory. Manual detection bulletins were produced by laboratory staff. Automated detection bulletins were produced using automated infrasound processing tools developed at both laboratories. Initial results are provided to evaluate the utility of extending tools developed for regional infrasound event analysis to global-scale events.

58 GEOSCIENCES↗
DOE OSTI2022

Protein sequence design with a learned potential

The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

ABSTRACT The Canada–France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ∼5000 deg2 of the sky, representing a first-rate opportunity to identify recently merged galaxies. Because of the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 per cent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ∼6 per cent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini–M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

Bickley, Robert W.↗