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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Large Scale Bilevel Optimization for N-K SCOPF Using Adversarial Robustness

Ensuring a secure dispatch against multiple simultaneous outages has long been desired to maintain grid security in the presence of severe events, such as extreme weather phenomena. Traditionally denoted as N-k security constrained optimal power flow (N-k SCOPF), this problem is intractable to solve due to its size being combinatorial in the number of simultaneous outages and due to the non-convex nature of the AC network constraints. This hinders the use of N-k SCOPF for operating realistic-scale systems. In this paper, we introduce a methodology to scalably solve an AC-feasible dispatch that improves security over k simultaneous outages. Our methodology poses N-k SCOPF as a bilevel optimization problem and solves it using an adversarial robustness approach. We develop new efficient methods to solve each level of the bilevel optimization by employing knowledge of the physics of the underlying system. This yields significant improvements in speed and convergence that enable us to address the N-k SCOPF problem at scale. We demonstrate the effectiveness of our method by conducting a comprehensive analysis of an N-3 SCOPF for a 500-bus network. Furthermore, we emphasize the ability of our physics-driven techniques to handle larger systems by successfully scaling up to 12,000 buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Cause identification of electromagnetic transient events using spatiotemporal feature learning

This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.

42 ENGINEERING↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

MIONet: Learning Multiple-Input Operators via Tensor Product

As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space; i.e., the input of the operator is a single function. Here, for the first time, we study the operator regression via neural networks for multiple-input operators defined on the product of Banach spaces. We first prove a universal approximation theorem of continuous multiple-input operators. We also provide a detailed theoretical analysis including the approximation error, which provides guidance for the design of the network architecture. Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators. MIONet consists of several branch nets for encoding the input functions and a trunk net for encoding the domain of the output function. Here, we demonstrate that MIONet can learn solution operators involving systems governed by ordinary and partial differential equations. In our computational examples, we also show that we can endow MIONet with prior knowledge of the underlying system, such as linearity and periodicity, to further improve accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Toward Real-Time Analysis of Synchrotron Micro-Tomography Data: Accelerating Experimental Workflows with AI and HPC

ynchrotron light sources are routinely used to perform imaging experiments. In this paper, we review the relevant computational stages, identify bottlenecks, and highlight future opportunities to streamline data acquisition for experimental microscopy workflows. We demonstrate our preliminary exploration with an end-to-end scientific workflow on Summit based on micro-computed tomography data. Computational elements include: 1) reconstruction of volumetric image data; 2) denoising with deep neural networks; and 3) non-local means based segmentation and quantitative analysis.

Mcclure, James↗
DOE OSTI2026

Ice-nucleating particles (INPs) concentrations from SAIL-Net

This data set contains ice-nucleating particle (INP) concentration spectra collected during the SAIL-Net sampling period, which complemented the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River watershed near Crested Butte, Colorado. SAIL-Net was a distributed aerosol measurement network designed to investigate aerosol variability across complex mountainous terrain. The data set includes samples from multiple SAIL-Net sites, including AOS, Gothic, Snodgrass, Pumphouse, Irwin, and Top. INP concentrations are reported as a function of freezing temperature, together with confidence limits, sampling times, site location, elevation, sampled air volume, and treatment information. These data provide an analysis-ready record of the INPs across the SAIL-Net network.

activation temperature↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2024

Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks

Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and physics-based models provides information about spatially resolved neutron scattering length density (NSLD) profiles. Recent improvements in data acquisition systems have allowed acquiring thousands of NR curves in a couple of hours, which has led to a need for automated data analysis tools to interpret NR measurements in real-time. Here, we present a machine learning analysis workflow that uses a series of models, based on a convolutional neural network (CNN), to learn the relation between the NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly from NRs. The usefulness of our CNN-based models is demonstrated by constructing NSLDs from NRs of several films containing homopolymer polyzwitterions and diblock copolymers mixed with different types of salts. Comparisons of the NSLDs with those constructed using ad hoc multi-layer models reveal a very good agreement, suggesting the potential of CNN-based models for real-time automated data analysis of NRs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

Online social networks (OSNs) have become a powerful tool to study collective human responses to extreme events such as earthquakes. Most previous research concentrated on a single platform and utilized users’ behaviors on a single platform to study people’s general responses. In this study, we explore the characteristics of people’s behaviors on different OSNs and conduct a cross-platform analysis of public responses to earthquakes. Our findings support the Uses and Gratification theory that users on Reddit and Twitter are engaging with platforms that they may feel best reflect their sense of self. Using the 2019 Ridgecrest earthquakes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissions and 44,333 comments) to answer the following research questions: (1) What were the similarities and differences between public responses on Twitter and Reddit? (2) Considering the different mechanisms of Twitter and Reddit, what unique information of public responses can we learn from Reddit as compared with Twitter? By answering these research questions, we aim to bridge the gap of cross-platform public responses research towards natural hazards. Our study evinces that the users on the two different platforms have both different topics of interest and different sentiments towards the same earthquake, which indicates the necessity of investigating cross-platform OSNs to reveal a more comprehensive picture of people’s general public responses towards certain disasters. Our analysis also finds that r/conspiracy subreddit is one of the major venues where people discuss the 2019 Ridgecrest earthquakes on Reddit and different misinformation/conspiracies spread on Twitter and Reddit platforms (e.g., “Big one is coming” on Twitter and “Nuclear test” on Reddit).

58 GEOSCIENCES↗
DOE OSTI2023

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Rounding Error Analysis of Mixed Precision Block Householder QR Algorithms

Although mixed precision arithmetic has recently garnered interest for training dense neural networks, many other applications could benefit from the speedups and lower storage cost if applied appropriately. The growing interest in employing mixed precision computations motivates the need for rounding error analysis that properly handles behavior from mixed precision arithmetic. We develop mixed precision variants of existing Householder QR algorithms and show error analyses supported by numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Collaborative Computing Support for Analysis Facilities Exploiting Software as Infrastructure Techniques

Prior to the public release of Kubernetes it was difficult to conduct joint development of elaborate analysis facilities due to the highly non-homogeneous nature of hardware and network topology across compute facilities. However, since the advent of systems like Kubernetes and OpenShift, which provide declarative interfaces for building fault-tolerant and self-healing deployments of networked software, it is possible for multiple institutes to collaborate more effectively since resource details are abstracted away through various forms of hardware and software virtualization. In this whitepaper we will outline the development of two analysis facilities: "Coffea-casa" at University of Nebraska Lincoln and the "Elastic Analysis Facility" at Fermilab, and how utilizing platform abstraction has improved the development of common software for each of these facilities, and future development plans made possible by this methodology.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In Vivo Thermodynamic Analysis of Glycolysis in Clostridium thermocellum and Thermoanaerobacterium saccharolyticum Using 13 C and 2 H Tracers

Clostridium thermocellum and Thermoanaerobacterium saccharolyticum are thermophilic anaerobic bacteria with complementary metabolic capabilities that utilize distinct glycolytic pathways for the conversion of cellulosic sugars to biofuels. We integrated quantitative metabolomics with 2 H and 13 C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the central metabolic networks of these two microbes. We found that the glycolytic pathway in C. thermocellum operates remarkably close to thermodynamic equilibrium, with an overall drop in Gibbs free energy 5-fold lower than that of T. saccharolyticum or anaerobically grown Escherichia coli . The limited thermodynamic driving force of glycolysis in C. thermocellum could be attributed in large part to the small free energy of the phosphofructokinase reaction producing fructose bisphosphate. The ethanol fermentation pathway was also substantially more reversible in C. thermocellummthan in T. saccharolyticum. These observations help explain the comparatively low ethanol titers of C. thermocellum and suggest engineering interventions that can be used to increase its ethanol productivity and glycolytic rate. In addition to thermodynamic analysis, we used our isotope tracer data to reconstruct the T. saccharolyticum central metabolic network, revealing exclusive use of the Embden-Meyerhof-Parnas (EMP) pathway for glycolysis, a bifurcated tricarboxylic acid (TCA) cycle, and a sedoheptulose bisphosphate bypass active within the pentose phosphate pathway. Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae , the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Active Thermochemical Tables: Enthalpies of Formation of Bromo- and Iodo-Methanes, Ethenes and Ethynes

Here, the thermochemistry of halocarbon species containing iodine and bromine is examined through an extensive interplay between new Feller–Peterson–Dixon (FPD) style composite methods and a detailed analysis of all available experimental and theoretical determinations using the thermochemical network that underlies the Active Thermochemical Tables (ATcT). From the computational viewpoint, a slower convergence of the components of composite thermochemistry methods is observed relative to species that solely contain first row elements, leading to a higher computational expense for achieving comparable levels of accuracy. Potential systematic sources of computational uncertainty are investigated, and, not surprisingly, spin-orbit coupling is found to be a critical component, particularly for iodine containing molecular species. The ATcT analysis of available experimental and theoretical determinations indicates that prior theoretical determinations have significantly larger uncertainties than originally reported, particularly in cases where molecular spin-orbit effects were ignored. Accurate and reliable heats of formation are reported for 38 halogen containing systems, based on combining the current computations with previous experimental and theoretical work via the ATcT approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Three-dimensional imaging of grain boundaries via quantitative fluorescence X-ray tomography analysis

Abstract Three-dimensional visualization of material composition within multiple grains and across complex networks of grain boundaries at nanoscales can provide new insight into the structure evolution and emerging functional properties of the material for diverse applications. Here, using nanoscale scanning X-ray fluorescence tomography, coupled with an advanced self-absorption correction algorithm developed in this work, we analyze the three-dimensional gain distributions and compositions in a Ce 0.8 Gd 0.2 O 2-δ -CoFe 2 O 4 mixed ionic-electronic conductor system with high accuracy and statistical significance. Our systematic investigation reveals an additional emergent phase and uncovers highly intriguing composition stability ranges for the multiple material phases within this system. The presented visualization of composition variations across complex interfaces, supported by our quantitative composition analysis, discloses mechanistic pathways of the diverse phase transformations occurring in the material synthesis, providing insights for the optimization of transport properties in the mixed ionic-electronic conductor system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Localized High-Concentration Electrolytes Get More Localized

Liquid electrolytes in batteries are typically treated as macroscopically homogeneous ionic transport media, despite having complex chemical composition and atomistic solvation structures, thus leaving a knowledge gap of microstructural characteristics. A promising electrolyte system design has recently been developed with the title of a localized high-concentration electrolyte (LHCE). These electrolytes are a summation of three components – a charge-carrying salt, a solvent that solvates the salt, and a diluent that is sparingly interactive with the salt in terms of ion solvation. This creates the perception of a high-concentration electrolyte (HCE) with high anion-cation interactions that supports a salt-derived, stable solid-electrolyte interphase, while an LHCE improves bulk electrolyte properties, such as ionic conductivity and viscosity, when compared to an HCE. Much trial by error design of this LHCE concept has been supported by simulations to confirm ionic interactions, cycling data to confirm cell stability, and post-mortem analysis to provide evidence of mechanisms. However, a systematic design to extend the capabilities of LHCEs is still to be desired. Here, we analyzed a specific LHCE mixture to dictate the best viable mixture that extends the localization of solvation structures while maintaining viable cell-level capabilities. We seek to observe and understand solvent-to-salt interactions that impact electrolyte performance. These interactions range from single-ion solvation, multidentate interactions (i.e., solvation-driven clustering), to more widespread network-level interactions. With the support of both experimental and computational analysis of different electrolyte features, we have begun a methodology of formulating the most ideal LHCE mixtures for improving the cyclability of high-capacity battery chemistries.

25 ENERGY STORAGE↗
DOE OSTI2025

Improving Cyber Situational Understanding

Effective cybersecurity operations require the ability to analyze large amounts of information to assess security risks and formulate defensive strategies against adversaries. This has become more complex in recent years as the sprawl and interconnectivity of devices grows through implementation of virtualization, cloud computing, and Internet of Things (IoT). The amount of data and analysis required for effective cybersecurity command and control decisions far exceeds humans’ capacity to perform manually. We characterize the analysis problem as cyber situational understanding. The research presented to improve cyber situational understanding focuses on vulnerability analysis and threat intelligence. Regarding vulnerabilities, entities must analyze and plan work for between thousands and tens of thousands of software vulnerabilities annually. Entities heavily use network firewalls to limit vulnerability exposure. As a result, some of these vulnerabilities permit exposure to adversarial exploitation, whereas others are inaccessible and therefore present negligible risk of exploitation. Distinguishing between high and low risk software vulnerabilities requires a deep understanding of the vulnerability, network firewall protection, and characteristics of the targeted device. This problem is solved by extracting network service features from vulnerability data features using both machine-learning and natural language processing. Then, the network firewall topology is parsed to determine which vulnerabilities are reachable by adversaries. Ultimately, a state-based safety analysis ascertains which vulnerabilities are unsafe. A related vulnerability analysis problem occurs in cybersecurity operations when associating an entity’s hardware and software assets to public vulnerability databases. Assets often reveal hardware and software through installation artifacts and network service identification, and entities store these artifacts in inventory databases. However, software and hardware vendors apply a standard Common Platform Enumeration (CPE) naming convention when publicly reporting vulnerabilities. Associating these two datasets often requires many hours to days of manual inspection. The proposed solution automates the mapping approach of human analysts using fuzzy matching techniques, natural language processing, and, ultimately, machine learning to present a small set of recommendations for mapping the two datasets. The result significantly reduces human analysis time and reduces the occurrence of false positives in vulnerability notifications. Finally, cyber threat intelligence (CTI) requires associating cyber observable artifacts, such as IP addresses, URIs, and file hashes, with cyber threat tactics, techniques, and procedures. Unfortunately, most CTI data is compartmentalized across multiple organizations and cannot be shared due to the legal and reputational risk with cyber threat being associated with the entity. The approach to solving this problem inovlves using a distributed ledger with anonymous token spending and authentication. This allows a consortium of semi-trusted entities to share the workload of curating CTI for a threat sharing community’s cooperative benefit.

Huff, Philip↗
DOE OSTI2023

Integrating Survival Analysis with Bayesian Statistics to Forecast the Remaining Useful Life of a Centrifugal Pump Conditional to Multiple Fault Types

To improve the viability of nuclear power plants, there is a need to reduce their operational costs. Operational costs account for a significant portion of a plant’s yearly budget, due to their scheduled-based maintenance approach. In order to reduce these costs, proactive methods are required that estimate and forecast the state of a machine in real time to optimize maintenance schedules. In this research, we use Bayesian networks to develop a framework that can forecast the remaining useful life of a centrifugal pump. To do so, we integrate survival analysis with Bayesian statistics to forecast the health of the pump conditional to its current state. We complete our research by successfully using the Bayesian network on a case study. This solution provides an informed probabilistic viewpoint of the pumping system for the purpose of predictive maintenance.

42 ENGINEERING↗