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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life

Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.

Demographic evolution↗
DOE OSTI2026

Physical properties and anisotropic magnetism of EuBi 2

We report the synthesis of EuBi 2 single crystals and their magnetic and electronic properties. Plate-like crystals were grown in excess bismuth, and X-ray diffraction data confirm the previously reported tetragonal space group, I 4 1 /amd (No. 141). A Néel temperature of T N = 18.6 K was determined from the specific heat data, and corresponding anomalies occur in the magnetization and resistivity. We only observe one magnetic transition in zero magnetic field. The magnetization data above 70 K are well described by a Curie-Weiss model with a Weiss temperature of Θ CW ≈ −35 K and an effective moment near that of the expected spin-only moment of Eu 2+ (S = 7/2). Isothermal magnetization measurements reveal field-induced transitions for H||[100], including a hysteretic spin-flop centered at 10.1 T and a change in slope at 12 T and 2 K. However, while the isothermal magnetization for H||[110] displays nonlinear behavior, discrete metamagnetic transitions are not observed for H||[110] or H||[001], indicating significant magnetic anisotropy. The magnetization does not saturate by 13.5 T for any orientation. EuBi 2 is metallic and our crystals possess an in-plane residual resistivity ratio of ⍴300 K/⍴2 K ≈ 40. Single crystal neutron diffraction data reveal a large magnetic unit cell and non-trivial antiferromagnetic ordering. These results demonstrate that strong antiferromagnetic coupling drives complex magnetism in EuBi 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2020

Summer Proceedings 2019

The Computer Science Research Institute (CSRI) brings university faculty and students to Sandia for focused collaborative research on Department of Energy (DOE) computer and computational science problems. The institute provides an opportunity for university researchers to learn about problems in computer and computational science at DOE laboratories. Participants conduct leading-edge research, interact with scientists and engineers at the laboratories, and help transfer results of their research to programs at the labs. Some specific CSRI research interest areas are: scalable solvers, optimization, adaptivity and mesh refinement, graph-based, discrete, and combinatorial algorithms, uncertainty estimation, mesh generation, dynamic load-balancing, virus and other malicious-code defense, visualization, scalable cluster computers and heterogeneous computers, data-intensive computing, environments for scalable computing, parallel input/output, advanced architectures, and theoretical computer science. The CSRI Summer Program includes the organization of a weekly seminar series and the publication of this summer proceedings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

PROTOCALC, a W -band Polarized Calibrator for Cosmic Microwave Background Telescopes: Application to Simons Observatory and CLASS

Current- and next-generation cosmic microwave background (CMB) experiments will measure polarization anisotropies with unprecedented sensitivities. The need for high precision in these measurements underscores the importance of gaining a comprehensive understanding of instrument properties, with a particular emphasis on the study of the beam properties, and especially their polarization characteristics and the measurement of the polarization angle. In this context, a major challenge lies in the scarcity of millimeter polarized astrophysical sources with sufficient brightness and calibration knowledge to meet the stringent accuracy requirements of future CMB missions. This led to the development of a drone-borne calibration source designed for the frequency band centered on approximately 90 GHz, matching a commonly used channel in ground-based CMB measurements. The Prototype Calibrator for Cosmology, PROTOCALC, has undergone thorough in-lab testing, and its properties have been subsequently modeled through simulation software integrated into the standard Simons Observatory analysis pipeline. Moreover, the PROTOCALC system has been tested in the field, having been deployed twice on calibration campaigns with CMB telescopes in the Atacama Desert. The data collected constrain the roll angle of the source with a statistical accuracy of 0$^°_•$045.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Grid-Interactive Building Solutions: Synapse from Kinetic Buildings

This fact sheet describes a grid-interactive efficient building technology that is undergoing field validation tests through DOE's National Labs. It summarizes the technology application (Synapse software from Kinetic Buildings) and the characteristics of the field validation site.

grid-interactive efficient buildings, GEB, technol↗
DOE OSTI2023

Modeling the CEBAF Injector at 200 kV: Investigating K-Long Beam Conditions with and without Wien Filter

The upcoming K-Long experiment [1] ain Hall D at Jefferson Lab presents unique beam requirements, featuring a significantly low bunch repetition rate and an unusually high bunch charge. This experiment, which utilizes the CEBAF accelerator in conjunction with the GlueX experimental setup, aims to study strange hadron spectroscopy by measuring the differential cross section and polarizations of produced hyperons such as Λ, Σ, Ξ, and Ω. By directing an intense K-Long beam towards the LD2/LH2 target, new and valuable data can be obtained. To optimize the CEBAF injector specifically for this experiment, we employed Multi-Objective Genetic Optimization (MGO) using General Particle Tracer (GPT) simulations. Through this approach, we determined the optimal magnetic elements and radiofrequency (RF) settings required to achieve a K-Long bunch charge of 0.64 pC at an energy of 200 kV. We conducted simulations with both the Wien Filter turned on and off to examine its impact on the beam. Furthermore, we investigated the transmission efficiency and beam characteristics of electron beams with varying charge per bunch through the injector, considering the simultaneous operation of all four CEBAF Halls. The results of our study offer valuable insights and guidance for optimizing the CEBAF injector not only for the Jefferson Lab K-Long experiment but also for other experiments that entail similar beam conditions.

Pokharel, Sunil↗
DOE OSTI2022

Facilitating Machine Learning Collaborations Between Labs, Universities, And Industry

It is clear from numerous recent community reports, papers, and proposals that machine learning is of tremendous interest for particle accelerator applications. The quickly evolving landscape continues to grow in both the breadth and depth of applications including physics modeling, anomaly detection, controls, diagnostics, and analysis. Consequently, laboratories, universities, and companies across the globe have established dedicated machine learning (ML) and data science efforts aiming to make use of these new state-of-the-art tools. The current funding environment in the U.S. is structured in a way that supports specific application spaces rather than larger collaboration on community software. Here, we discuss the existing collaboration bottlenecks and how a shift in the funding environment, and how we develop collaborative tools, can help fuel the next wave of ML advancements for particle accelerators.

Edelen, J.P.↗
DOE OSTI2024

Theoretical Studies in Hadronic Physics and Fundamental Symmetries

The nucleons (protons and neutrons) are at the center of every atom, the fundamental building blocks of matter in our Universe. Their structure and properties have been actively studied at DOE’s Thomas Jefferson Lab as well as the future US electron-ion collider at Brookhaven National Lab, and are believed to be described by a mathematical theory known as quantum chromodynamics (QCD). In this project, the PI and collaborators invented a theoretical approach called “Generalized parton distributions through Universal Moment Parametrization” (GUMP) to construct the 3D structure of the nucleon from experimental data on Deeply virtual compton scattering and Deep-Exclusive meson production, and semi-inclusive jet or meson productions. The PI and collaborators have also explored the full power of a theory called the large- momentum effective theory (LaMET) to calculate the 3D structure of the nucleon from the first principles known as lattice QCD. In particular, the work paved the way for unprecedented precision calculations which are coming in next decade.

3D Structure↗
DOE OSTI2024

Strange Mesons and Gluonic Excitations

In the standard model of particle physics, the interactions between the fundamental constituents of nuclear matter, quarks and gluons, are governed by the theory of Quantum Chromodynamics (QCD). A central goal of nuclear physics is to understand how hadrons, such as protons and neutrons, are formed from these underlying quark and gluon degrees of freedom. A hadron is primarily constructed from three quarks or a quark-antiquark pair; however, the theory of QCD allows for much more exotic configurations. One of the predicted exotic configurations is known as a hybrid meson, which contains an excited gluonic field in addition to the usual quark-antiquark pair. This project aims to search for and study these gluonic excitations using the Gluonic Excitation (GlueX) experiment at Jefferson Lab in Newport News, VA. The discovery potential of the experiment will be significantly extended by studying the quark flavor composition of the meson spectrum through the completion and use of an enhanced detector to identify mesons containing strange quarks. The unprecedented statistical precision of the data collected at GlueX will allow us to search for a pattern of light-quark hybrid mesons, providing new insight into the interactions that bind the fundamental quarks and gluons into the hadrons we observe in nature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Evaluation of a streaming readout solution for Jefferson Lab experiments

Jefferson Lab (JLab) operates the CEBAF accelerator that provides a high energy (up to 12 GeV) and high current (up to 150 µA) polarized electron beam for fixed target experiments. JLab's mission is to provide substantial progress in understanding the Quantum Chromo Dynamics (QCD), the current theory of the spectrum and the dynamics of hadronic matter. Given the broad scientific program a general-purpose detector, CLAS12, has been designed and constructed. Such a detector requires a sophisticated trigger and current experiments use an on-line FPGA-based system that relies upon custom firmware and electronics both of which are difficult reconfigure from one experiment to the next. To overcome these challenges transitioning to a streaming readout has been proposed since it would allow a more flexible, easier to debug, software trigger to be developed. To facilitate this JLab is gaining experience with streaming readout systems and is actively working to design and test a streaming data acquisition architecture. Significant work has already been done to make existing front-end electronics compatible with operation in streaming mode. Software-wise, much work remains to be done but an existing framework, TriDAS, which was developed for underwater neutrino telescopes can, in principle, be used with the existing JLab hardware. A test stand is already in place in the INDRA DAQ development lab at JLab. This paper will present the development of this TriDAS based test system and it?s operation in-beam using parts of the CLAS12 detector to evaluate streaming readout for a large general purpose detector.

Battaglieri, Marco↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Real-World Cyber Security Demonstration for Networked Electric Drives

In this article, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber-attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyber-physical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed's capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyber-attack detection algorithms, which are integral to our testbed's security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed's usability but also offers insightful, real-time data for researchers and practitioners, thereby facilitating a deeper understanding of cyber-physical security dynamics in networked electric drive systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Advance Distribution Management System (ADMS)

This presentation explores the transition from traditional distribution management systems to Advanced Distribution Management Systems (ADMS) as a foundation for smart grid development. It highlights the key benefits of ADMS, including enhanced reliability, improved operational efficiency, and increased situational awareness. The presentation also addresses common implementation challenges such as system integration, data management, and organizational readiness. It concludes with a forward-looking perspective on the evolving role of ADMS and essential takeaways for utilities and stakeholders.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Computer vision-based rock bolt detection in orthomosaic imagery obtained in the Waste Isolation Pilot Plant underground facility

Assessing structural integrity of large underground tunnel facilities is often a time consuming and human-labor intensive task. Thus, research using various modes of sensing and automated detection of key structural components in mines is posed to aid in safety assessments and establishing overall structural health. We propose an approach utilizing off-the-shelf camera and lidar technology fixed to a custom sensing platform, image stitching techniques, fine-tuned object detection models, and specialized model-inference methods to automatically detect, count, and map roof bolts for assessment of structural safety in man-made underground tunnels. Results show a novel workflow for effective object counting in orthomosaic tunnel ceiling images generated from collections in GPS-denied mining environments. Additionally, we demonstrate effective fine-tuning of EfficientDet object detectors utilizing state-of-the-art image augmentation techniques known as the mosaic and mixup transformations. Our work is demonstrated on sensed data and imagery collected from the Department of Energy (DOE) Waste Isolation Pilot Plant (WIPP) where miles of tunnel ceiling must be assessed for structural integrity.

42 ENGINEERING↗
DOE OSTI2025

BLADE: An Automated Framework for Classifying Light Curves from the Center for Near-Earth Object Studies Fireball Database

Fireballs (bolides) are high-energy luminous phenomena produced when meteoroids and small asteroids enter Earth’s atmosphere at hypersonic speeds, often resulting in fragmentation or complete disintegration accompanied by significant energy release. The resulting bolide light curves capture temporal brightness variations as these objects traverse increasingly dense atmospheric layers, providing essential information on meteoroid entry dynamics, fragmentation behavior, and atmospheric energy deposition processes. The Center for Near-Earth Object Studies’ (CNEOS) continuously expanding fireball database offers a globally comprehensive archive of bolide events, including light curves and associated metadata. Events associated with infrasound detections allow direct correlations between acoustic signatures and light curve features, therefore enabling detailed analyses of fragmentation dynamics and energy deposition. Here, we introduce Bolide Light-curve Analysis and Discrimination Explorer (BLADE), a robust and high-fidelity framework specifically designed to analyze bolide light curves for objects detected from space. BLADE incorporates a processing pipeline integrating Savitzky–Golay filtering, prominence-based peak detection, and gradient analysis, enabling systematic identification and classification of fragmentation events and their associated energy release characteristics. Preliminary results demonstrate that BLADE reliably distinguishes distinct bolide behaviors, providing an objective, scalable methodology for characterization and analysis of large bolide light curve data sets. This foundational work establishes a novel pathway for advanced bolide research, with promising applications in planetary defense and global atmospheric monitoring. Future research should adopt an integrative approach combining CNEOS optical data with complementary infrasound measurements, further clarifying relationships between bolide energy deposition and acoustic signatures, thus refining our understanding of meteoroid and asteroid atmospheric entry processes.

Asteroids↗
DOE OSTI2024

Phylogeny, morphology, virulence, ecology, and host range of Ordospora pajunii (Ordosporidae), a microsporidian symbiont of Daphnia spp.

ABSTRACT The impacts of microsporidia on host individuals are frequently subtle and can be context dependent. A key example of the latter comes from a recently discovered microsporidian symbiont of Daphnia , the net impact of which was found to shift from negative to positive based on environmental context. Given this, we hypothesized low baseline virulence of the microsporidian; here, we investigated the impact of infection on hosts in controlled conditions and the absence of other stressors. We also investigated its phylogenetic position, ecology, and host range. The genetic data indicate that the symbiont is Ordospora pajunii , a newly described microsporidian parasite of Daphnia . We show that O. pajunii infection damages the gut, causing infected epithelial cells to lose microvilli and then rupture. The prevalence of this microsporidian could be high (up to 100% in the lab and 77% of adults in the field). Its overall virulence was low in most cases, but some genotypes suffered reduced survival and/or reproduction. Susceptibility and virulence were strongly host-genotype dependent. We found that North American O. pajunii were able to infect multiple Daphnia species, including the European species Daphnia longispina , as well as Ceriodaphnia spp. Given the low, often undetectable virulence of this microsporidian and potentially far-reaching consequences of infections for the host when interacting with other pathogens or food, this Daphnia–O. pajunii symbiosis emerges as a valuable system for studying the mechanisms of context-dependent shifts between mutualism and parasitism, as well as for understanding how symbionts might alter host interactions with resources. IMPORTANCE The net outcome of symbiosis depends on the costs and benefits to each partner. Those can be context dependent, driving the potential for an interaction to change between parasitism and mutualism. Understanding the baseline fitness impact in an interaction can help us understand those shifts; for an organism that is generally parasitic, it should be easier for it to become a mutualist if its baseline virulence is relatively low. Recently, a microsporidian was found to become beneficial to its Daphnia hosts in certain ecological contexts, but little was known about the symbiont (including its species identity). Here, we identify it as the microsporidium Ordospora pajunii . Despite the parasitic nature of microsporidia, we found O. pajunii to be, at most, mildly virulent; this helps explain why it can shift toward mutualism in certain ecological contexts and helps establish O. pajunii is a valuable model for investigating shifts along the mutualism-parasitism continuum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗