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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Phase I Closeout Report: Invoking Artificial Neural Networks to Measure Insider Threat Mitigation

Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Nonlinear Spectroscopy of Collective Modes in an Excitonic Insulator

The nonlinear optical response of an excitonic insulator coupled to lattice degrees of freedom is shown to depend in strong and characteristic ways on whether the insulating behavior originates primarily from electron-electron or electron-lattice interactions. Linear response optical signatures of the massive phase mode and the amplitude (Higgs) mode are identified. Upon nonlinear excitation resonant to the phase mode, a new in-gap mode at twice the phase mode frequency is induced, leading to a huge second harmonic response. Excitation of in-gap phonon modes leads to different and much smaller effects. A Landau-Ginzburg theory analysis explains these different behaviors and reveals that a parametric resonance of the strongly excited phase mode is the origin of the photoinduced mode in the electron-dominant case. Here, the difference in the nonlinear optical response serves as a measure of the dominant mechanism of the ordered phase.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Strongly Correlated Quantum Spin Liquids versus Heavy Fermion Metals: A Review

This review considers the topological fermion condensation quantum phase transition (FCQPT) that explains the complex behavior of strongly correlated Fermi systems, such as frustrated insulators with quantum spin liquid and heavy fermion metals. The review contrasts theoretical consideration with recent experimental data collected on both heavy fermion metals (HF) and frustrated insulators. Such a method allows to understand experimental data. We also consider experimental data collected on quantum spin liquid in Lu 3 Cu 2 Sb 3 O 14 and quasi-one dimensional (1D) quantum spin liquid in both YbAlO 3 and Cu(C 4 H 4 N 2 )(NO 3 ) 2 with the aim to establish a sound theoretical explanation for the observed scaling laws, Landau Fermi liquid (LFL) and non-Fermi-liquid (NFL) behavior exhibited by these frustrated insulators. The recent experimental data on the heavy-fermion metal α-YbAl 1-x Fe x B 4 , with x=0.014, and on its sister compounds β-YbAlB 4 and YbCo 2 Ge 4 , carried out under the application of magnetic field as a control parameter are analyzed. We show that the thermodynamic and transport properties as well as the empirical scaling laws follow from the fermion condensation theory. We explain how both the similarity and the difference in the thermodynamic and transport properties of α-YbAl 1-x Fe x B 4 and in its sister compounds β-YbAlB 4 and YbCo 2 Ge 4 emerge, as well as establish connection of these (HF) metals with insulators Lu 3 Cu 2 Sb 3 O 14 , Cu(C 4 H 4 N 2 )(NO 3 ) 2 and YbAlO 3 . We demonstrate that the universal LFL and NFL behavior emerge because the HF compounds and the frustrated insulators are located near the topological FCQPT or are driven by the application of magnetic fields.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

DER Cybersecurity Detection and Response Suite

SAND2024-08475O The Distributed Energy Resource (DER) Cybersecurity Detection and Response Suite is a solution for distributed energy resource (DER) systems. The DER Security Orchestration, Automation, and Response (SOAR) solution that uses alerts from signature- and behavior-based Intrusion Detection Systems are intended to be deployed as bump-in-the-wire (BITW) devices in front of DER equipment. The fielded application would use multiple intrusion detection systems that report data to SOAR to respond to cyberattacks. The suite consists of two software components: • The proactive intrusion detection and mitigation system (PIDMS) secures grid-edge photovoltaic smart inverters and other equipment in distributed energy resource systems. It is a distributed BITW solution; cyber and physical data are automatically processed using network inspection tools and custom machine learning algorithms to detect abnormal events and correlate cyber-physical events. • The Security Orchestration, Automation, and Response for Distributed Energy Resources (SOAR4DER) application ingests data from several intrusion detection systems to quickly block attacks and revert DER systems to good states. Using a collection of intrusion detection system technologies on a BITW device, it incorporates physical and cyber data to detect abnormal and potential malicious behaviors. Multiple SOAR playbooks then use the intrusion detection system data streams to automatically defend the system. SOAR4DER system testing showed detection and response times under 30 seconds for all adversary reconnaissance, denial-of-service attacks, malicious Modbus commands, brute-force logins, and machine-in-the-middle attacks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Johnson, Jay↗
DOE OSTI2021

Reduced Order Model of Transactive Bidding Loads

Transactive energy (TE) has been identified to provide better grid efficiency and reliability by market-based transactive exchanges between energy producers and energy consumers. Simulations of TE systems are crucial to evaluate the benefits and impacts of different transactive mechanisms. However, such simulations can be time consuming due to the information exchange between various participants and complex co-simulation environments. In this paper, we develop a reduced order model to speed up the simulation of transactive systems in TE simulation platform (TESP) while achieving very low error between the reduced order and full model results. Specifically, the developed reduced order model consists of an aggregate responsive load agent which utilizes two Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTMs) to enable transactive elements to collectively participate in the TE system. The proposed aggregate responsive load (ARL) agent is able to produce similar transactive behaviors to the full simulation model while achieving significant simulation time reduction. Finally, we also show that the developed model enables generalization of simulation results across different dates and across different number of loads included in the simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

2014 Southern Nevada Household Travel Survey

The 2014 Southern Nevada Household Travel Survey collected information from residents in the Las Vegas area to update the regional travel demand model and assess travel behavior. The Regional Transportation Commission of Southern Nevada contracted with Westat to conduct the survey. The survey was conducted in two phases—from March to May 2014 and from August to October 2014. Participants provided demographic and travel data (via a travel log). A 10% subsample (1,694 participants) was randomly selected to take part in a wearable global positioning system (GPS) technology-based component of the study, the purpose of which was to assess the level of trip under-reporting in the self-reported travel logs. Participants in the GPS portion of the study were instructed to record trips in their travel logs on the first day only, while passively recording their travel for three full days.

1Hz data↗
DOE OSTI2022

Thermoelastic properties of zircon: Implications for geothermobarometry

Abstract A thermal-pressure equation of state has been determined for zircon (ZrSiO4) that characterizes its thermoelastic behavior at metamorphic conditions. New pressure-volume (P-V) data from a “Mud Tank” zircon have been collected from 1 bar to 8.47(1) GPa using X-ray diffraction, and elastic moduli were measured from room temperature up to 1172 K by resonance ultrasound spectroscopy. These data were fitted simultaneously with temperature-volume (T-V) data from the literature in EosFit7c using a new scaling technique. The parameters of a third-order Birch-Murnaghan EoS with a Mie-Grüneisen-Debye model for thermal pressure have compressional EoS parameters K0T = 224.5(1.2) GPa, K0T' = 4.90(31) with a fixed initial molar volume V0 = 39.26 cm3/mol and thermal parameters γ0 = 0.868(15), q = 2.37(80), and ΘD = 848(38) K. EoS parameters that describe the variation of unit-cell parameters with pressure and temperature were determined using an isothermal-type EoS. This new EoS confirms that zircons are stiffer than garnets and exhibit a much lower thermal expansion. This results in steep isomekes between zircon and garnets, which makes zircon trapped as inclusions in garnets at metamorphic conditions a good piezothermometer.

Geochemistry & Geophysics↗
DOE OSTI2022

Safety Testing of AGR-5/6/7 Compacts 2-2-2 and 2-2-4

Compacts 2-2-2 and 2-2-4 from the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program’s final irradiation campaign (AGR-5/6/7) were each separately subjected to a thermal soak at 1600°C for 300 h under flowing helium to simulated conditions experienced during a postulated high-temperature gas-cooled reactor (HTGR) depressurization conduction cooldown event. The safety testing of the fuel compacts’ performance under accident conditions was conducted in the Oak Ridge National Laboratory (ORNL) Core Conduction Cooldown Test Facility (CCCTF), which utilizes a stand-alone hot cell at the Irradiated Fuels Examination Laboratory (IFEL). The CCCTF heats fuel to temperatures up to 1900°C in a non-oxidizing environment while continuously monitoring the sweep gas for radioactive 85 Kr to detect release levels associated with hermetic failure of the tristructural-isotropic (TRISO) coating surrounding each fuel kernel. In addition, certain metallic radionuclides that escape the fuel compact are collected on deposition cups that are periodically exchanged with a new cup to obtain information on the overall retention behavior of the TRISO coating layers. Because cesium can diffuse through intact pyrocarbon layers, abnormal degradation of the silicon carbide (SiC) layer in the absence of holistic TRISO coating failure is indicated by release of 134 Cs at levels equivalent to an individual particle inventory in the absence of significant 85 Kr release (Hunn et al. 2014). Additional description of the CCCTF system is provided in Appendix A.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Automated Cloud Based Long Short-Term Memory Neural Network Based SWE Prediction

Snow derived water is a critical component of the US water supply. Measurements of the Snow Water Equivalent (SWE) and associated predictions of peak SWE and snowmelt onset are essential inputs for water management efforts. This paper aims to develop an integrated framework for real-time data ingestion, estimation, prediction and visualization of SWE based on daily snow datasets. In particular, we develop a data-driven approach for estimating and predicting SWE dynamics using the Long Short-Term Memory neural network (LSTM) method. Our approach uses historical datasets (precipitation, air temperature, SWE, and snow thickness) collected at NRCS Snow Telemetry (SNOTEL) stations to train the LSTM network and current year data to predict SWE behavior. The performance of our prediction was compared for different prediction dates and prediction training datasets. Our results suggest that the proposed LSTM network can be an efficient tool for forecasting the SWE timeseries, as well as Peak SWE and snowmelt timing. Results showed that the window size impacts the model performance (where the Nash Sutcliffe efficiency (NSE) ranged from 0.96 to 0.85 and the Rooted Mean Square Error (RMSE) ranged from 0.038 to 0.07) with an optimum number that should be calibrated for different stations and climate conditions. In addition, by implementing the LSTM prediction capability in a cloud based site-monitoring platform, we automate model-data integration. By making the data accessible through a graphical web interface and an underlying API which exposes both training and prediction capabilities. The associated results can be made easily accessible to a broad range of stakeholders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

1997/1998 Regional Travel Household Interview Survey

The 1997/1998 Regional Travel—Household Interview Survey was conducted in the 28-county New York-New Jersey-Connecticut metropolitan area, including 12 counties in New York, 14 counties in New Jersey, and 2 counties in Connecticut. The study was jointly funded by the New York Metropolitan Transportation Council and the North Jersey Transportation Planning Authority (NJTPA). The purpose of the survey was to provide information suitable for gaining an in-depth understanding of the travel behavior of households and individuals, as well as the activities, demographics, and other factors that affect such behavior. The survey was a diary-type travel survey, in which detailed travel information and basic demographic and socioeconomic features were collected for each member of the 11,264 participating households during an entire travel day. The sample for analysis of resident-based weekday travel is 10,971 for the entire 28-county metro area. The weekend sample, comprised of 275 households, is restricted to the NJTPA counties of northern New Jersey and compliments the results of the 1995 Nationwide Personal Transportation Survey for the entire area.

1Hz data↗
DOE OSTI2024

Summary of Gas Generation Behavior Observed in 3013 Surveillance and Monitoring Program Shelf-Life Experiments

Gas generation experiments have been conducted in small- and full-scale test containers at Los Alamos National Laboratory on samples of plutonium oxide material collected from plutonium processes across the DOE complex and tested at the bounding conditions for the Department of Energy 3013 Standard. The gas composition and pressures in the sealed experimental containers were measured over periods of months to years. These experiments have provided results for the formation and consumption of hydrogen and other gases. The conditions supporting the formation of flammable gas mixtures of hydrogen and oxygen in flammable gas mixtures were also determined. Different behaviors were observed between the materials tested based on their compositions, the stabilization performed on the material, and the post stabilization handling of the material. Many of the experiments are still ongoing. This report summarizes the results obtained for the gas generation behavior for high-purity plutonium oxides and salt-bearing impure plutonium oxides in sealed containers.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A CALPHAD-informed approach to modeling constituent redistribution in Zr-based metallic fuels using BISON

Here, a CALPHAD-informed (Computer Coupling of Phase Diagrams and Thermochemistry) constituent redistribution model was developed for Zr-based metallic fuels and incorporated into the BISON fuel performance code. Three uncertain model parameters associated with β and γ phase kinetics were calibrated using integral test data from U-Zr fuel elements irradiated in Experimental Breeder Reactor II. The calibrated constituent redistribution model was shown to predict the behavior of U-Zr fuels with excellent accuracy. Model predictions for U-Pu-Zr fuels were physically reasonable but less accurate. Reduction of uncertainties in the ternary phase transition temperatures and collection of kinetic data for the ζ phase are expected to improve the model’s ternary predictions. Finally, the new model was coupled to existing thermomechanics models in BISON to simulate irradiation of an entire U-Zr fuel element, demonstrating its ability to accurately predict the behavior of U-Zr fuels with realistic geometries and mesh resolutions at the engineering scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

In-Situ Observation of FCC→HCP Transformation-Induced Plasticity Behavior During Dynamic Deformation of CoCrNi Multi-principal Element Alloys

CoCrNi multi-principal element alloys (MPEAs) have potential as high toughness, blast resistant materials, but limited studies of their high strain rate behavior have been performed. Their high toughness has been attributed to low stacking fault energies and the activation of toughness enhancing deformation mechanisms, such as twinning- and transformation-induced plasticity (TWIP and TRIP, respectively), that may be affected by increased strain rate. This work examines a series of CoCrNi MPEAs tested with a miniaturized pressure bar, while simultaneous synchrotron x-ray diffraction and imaging are performed, and compares these results to those collected during quasi-static deformation at elevated temperatures. Here, the collected diffraction indicates that TRIP was active at elevated strain rates for the most Co-rich alloys containing 50 or 55 at. pct. Co, while TRIP behavior was suppressed at elevated strain rates in a 40 at. pct. Co alloy anticipated to experience TRIP. The suppression of TRIP in the lower Co alloy at high strain rates is attributed to adiabatic heating, consistent with suppression of TRIP as a result of elevated temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Darshan for HEP applications

Modern HEP workflows must manage increasingly large and complex data collections. HPC facilities may be employed to help meet these workflows’ growing data processing needs. However, a better understanding of the I/O patterns and underlying bottlenecks of these workflows is necessary to meet the performance expectations of HPC systems.Darshan is a lightweight I/O characterization tool that captures concise views of HPC application I/O behavior. It intercepts application I/O calls at runtime, records file access statistics for each process, and generates log files detailing application I/O access patterns.Typical HEP workflows include event generation, detector simulation, event reconstruction, and subsequent analysis stages. A study of the I/O behavior of the ATLAS simulation and filtering stage, and the CMS simulation workflow using Darshan is presented, including insights into the I/O operations and data access size.

Wang, Rui↗
DOE OSTI2026

Movement Models to Predict Low‐Altitude Flight of Soaring Birds Using Look‐Ahead Environmental Factors

Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (≤ 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.

17 WIND ENERGY↗
DOE OSTI2022

Model calibration of the liquid mercury spallation target using evolutionary neural networks and sparse polynomial expansions

The mercury constitutive model predicting the strain and stress in the target vessel plays a central role in improving the lifetime prediction and future target designs of the mercury targets at the Spallation Neutron Source. We leverage the experiment strain data collected over multiple years to improve the mercury constitutive model through a combination of large scale simulations of the target behavior and the use of machine learning tools for parameter estimation. We present two interdisciplinary approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse polynomial expansions. The newly calibrated simulations achieve 7% average improvement on the prediction accuracy and 8% reduction in mean absolute error compared to previously reported reference parameters, with some individual sensors experiencing up to 30% improvement. The calibrated simulations can aid in fatigue analysis to estimate the mercury target lifetime, which reduces abrupt failure and saves tremendous amount of costs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Self-Assembly Driven Microlithography via Near-Infrared Light Activation

Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.

36 MATERIALS SCIENCE↗