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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Impact of K-factor on Short Circuit Program Convergence for Inverter-Based Resources during Faults

Classical short-circuit programs that linearize the power network are no longer applicable for inverter based resources (IBRs), necessitating an iterative approach. Phasor domain programs can model the IBRs using an iterative approach considering nonlinear fault responses. In phasor domain models, the IBR can be modeled as a voltage controlled current source (VCCS) in tabular form with positive, negative, and zero sequence information for balanced and unbalance short-circuit faults. In the VCCS modeling of the IBR, positive and negative incremental reactive current, also known as the k-factor, plays an important role in short circuit program convergence. In this work, a few approaches: conventional VCCS modeling with a k-factor of 2, conventional VCCS modeling with a k-factor of 2 with modified pre-fault voltages, VCCS characteristics based on the power flow solution with a k-factor of 2, and VCCS characteristics based on the power flow solution with a k-factor of 1 are investigated for short circuit program convergence under higher IBR penetration. The IEEE 39 bus New England Test System is taken as the test system, and simulations are carried out in PSS®CAPE 15.0.26 simulation software. Simulation results demonstrate that IBR penetration is higher for the VCCS model, which corresponds to the power flow solution with k-factor 1, compared to other approaches.

42 ENGINEERING↗
DOE OSTI2021

Study of microgrid resilience through co-simulation of power system dynamics and communication systems

The interdependence of power and communication systems in smart grid technologies is acknowledged, but difficult to quantify. Communication systems can be essential to maintaining stability in microgrids that are islanded due to extreme events. Though many power system studies assume the presence of communication networks, detailed modeling of power and communication systems for dynamic studies of microgrids is rare. The work presented in this paper develops a framework for power and communication system co-simulation to study the impact of communications system on microgrid stability. An operational use case is examined where a battery energy storage system operates to offset the loss of generation in an islanded microgrid. The framework is evaluated for different communication technologies, network structures, and communication media.

Thekkumparambath Mana, Priya↗
DOE OSTI2025

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms

In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.

Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

The value of adding black carbon to community monitoring of particulate matter

Low-cost particulate matter (PM) sensors are increasingly used by researchers, public health agencies, and the public to measure spatial and temporal variations in air pollution, which can inform strategies for community air pollution reduction. While low-cost PM sensors provide a valuable measure of harmful fine particulate matter (PM 2.5 ), a significant portion of ambient PM 2.5 is typically the secondary product of air pollution emitted by varied sources outside of community boundaries. In contrast, concentrations of black carbon (BC), a component of PM 2.5 , are directly emitted by a few specific sources, such as diesel engines within communities. Motivated by community organizations seeking to understand persistent sources of local pollution, this study deployed a suite of custom-built BC sensors alongside a network of low-cost PM sensors for four weeks in two seasons at 50 stationary locations in the adjacent cities of Richmond, North Richmond, and San Pablo, California, east of the San Francisco Bay. Concentrations of BC varied more than PM 2.5 both temporally and spatially. Monthly network-average BC was 3×higher in winter than late spring, while PM 2.5 was only 10% lower. In both seasons, average PM 2.5 concentrations at two-thirds of sites were within ±10% of the network average, whereas two-thirds of sites had BC levels outside of ±10% of the network-average concentration. The most and least polluted locations were more persistent across seasons for BC than PM 2.5 , and the temporal dynamics of BC at these sites were similar, signifying that they are impacted by the same emission sources. Together, these spatiotemporal trends show that BC is a better indicator of the proximity and activity of local pollution sources than PM 2.5 . Thus, including BC in addition to PM 2.5 in community monitoring networks can provide additional insights about local sources of air pollution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Assessing the impact of Byzantine attacks on coupled phase oscillators

Abstract For many coupled dynamical systems, the interaction is the outcome of the measurement that each unit has of the others as e.g. in modern inverter-based power grids, autonomous vehicular platoons or swarms of drones, or it is the result of physical flows. Synchronization among all the components of these systems is of primal importance to avoid failures. The overall operational state of these systems therefore crucially depends on the correct and reliable functioning of the individual elements as well as the information they transmit through the network. Here, we investigate the effect of Byzantine attacks where one unit does not behave as expected, but is controlled by an external attacker. For such attacks, we assess the impact on the global collective behavior of nonlinearly coupled phase oscillators. We relate the synchronization error induced by the input signal to the properties of the attacked node. This allows to anticipate the potential of an attacker and identify which network components to secure.

Tyloo, Melvyn (ORCID:0000000317614095)↗
DOE OSTI2025

An ecological framework for microbial metabolites in the ocean ecosystem

The ocean microbe‐metabolite network involves thousands of individual metabolites that encompass a breadth of chemical diversity and biological functions. These microbial metabolites mediate biogeochemical cycles, facilitate ecological relationships, and impact ecosystem health. While analytical advancements have begun to illuminate such roles, a challenge in navigating the deluge of marine metabolomics information is to identify a subset of metabolites that have the greatest ecosystem impact. Here, we present an ecological framework to distill knowledge of fundamental metabolites that underpin marine ecosystems. We borrow terms from macroecology that describe important species, namely “dominant,” “keystone,” and “indicator” species, and apply these designations to metabolites within the ocean microbial metabolome. These selected metabolites may shape marine community structure, function, and health and provide focal points for enhanced study of microbe‐metabolite networks. Applying ecological concepts to marine metabolites provides a path to leverage metabolomics data to better describe and predict marine microbial ecosystems.

microbial metabolites↗
DOE OSTI2021

Effects of raster terrain representation on GIS shortest path analysis

Spatial analysis extracts meaning and insights from spatially referenced data, where the results are highly dependent on the quality of the data used and the manipulations on the data when preparing it for analysis. Users should understand the impacts that data representations may have on their results in order to prevent distortions in their outcomes. We study the consequences of two common data preparations when locating a linear feature performing shortest path analysis on raster terrain data: 1) the connectivity of the network generated by connecting raster cells to their neighbors, and 2) the range of the attribute scale for assigning costs. Such analysis is commonly used to locate transmission lines, where the results could have major implications on project cost and its environmental impact. Experiments in solving biobjective shortest paths show that results are highly dependent on the parameters of the data representations, with exceedingly variable results based on the choices made in reclassifying attributes and generating networks from the raster. Based on these outcomes, we outline recommendations for ensuring geographic information system (GIS) data representations maintain analysis results that are accurate and unbiased.

Medrano, F. Antonio (ORCID:000000025913632X)↗
DOE OSTI2023

A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Objectives COVID-19 survivors suffer from persistent mental distress and impaired quality of life (QOL) after recovery from the infection. However, the symptom-symptom interaction between these psychological variables remained unexplored. The present study aimed to determine the symptom network of mental distress (depression, anxiety, sleep disturbance, fatigue, and post-traumatic stress disorder) and their association with QOL among 535 COVID-19 survivors 1 year after hospital discharge. Methods 9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Chalder fatigue scale, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and 36-Item Short-Form Health Survey were applied to measure depression, anxiety, fatigue, PTSD, sleep disturbances, and QOL, respectively. Two networks were estimated using Gaussian graphical model. Network 1 consisted of mental symptoms to determine the central and bridge symptoms. Network 2 additionally included QOL to determine which mental symptoms were mostly related to QOL. Results 60% of the COVID-19 survivors experienced mental distress 1 year after hospital discharge. Uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction were the most central symptoms. Daytime dysfunction and fatigue (especially mental fatigue and loss of energy) served as the bridge symptoms across the mental distress network and exhibited the most substantial association with QOL. Conclusion Our study demonstrated several key symptoms that played a vital role in mental distress and QOL among COVID-19 survivors. Prompt screening and targeted interventions for these symptoms might hold great promise in preventing mental distress and improving QOL in COVID-19 survivors.

Peng, Pu↗
DOE OSTI2024

Networked Microgrid Ownership, Data, and Control Implications: Challenges and Open Questions

Microgrid deployments increasingly favor the potential to form networks for greater benefits to resilience, reliability, and energy sovereignty. Both independent and networked micro-grids predominantly have a single-entity-ownership and control, where the associations from ownership to data requirements to control functions to microgrid objectives is linear. The emerging model, however, is cyclical, with bidirectional causal impacts between each of the 4 pillars: there are more complex mixed ownership models across the physical, electrical, data, communications, protection, and control boundaries that impact the data requirements for meeting control functions that help realize the use-cases or objectives. This paper is the first to delineate the pillars for effective ownership and controllability of both independent as well as networked microgrids through the cyclical model, and present barriers to the adoption of such a model.

Sundararajan, Aditya↗
DOE OSTI2023

Creating an Advanced Sensor Network to calculate real-time, mass-weighted flue gas composition and air heater leakage of a coal-fired utility boiler under dynamic operating conditions

Utilization of renewable energy sources to minimize the environmental impact of energy production has changed the way utility boilers operate, requiring frequent load cycling between full load and partial loads as low as 30%. Dynamic operation of coal-fired utility boilers significantly reduces boiler efficiency when compared to steady state at full load. Data-driven plant optimization has shown success with coal-fired utility boilers under dynamic operating conditions. The purpose of this work was to create an Advanced Sensor Network (ASN) to provide more extensive real-time data to inform dynamic plant optimization of Net Unit Heat Rate (NUHR). The ASN consists of gas sampling grids in the convective pass of the boiler and downstream of the air heater. These sampling grids allow for quantification of spatial variation of flue gas within the boiler and calculation of mass-weighted composition of flue gas through the combination of composition, velocity, and temperature measurements. The comparison of O 2 between the inlet and outlet of the air heater is used to calculate air leakage in real time. Flue gas composition and air heater leakage are both important factors in boiler efficiency and NUHR. Further, the results of this work support the value of mass-weighted averages for determining flue gas composition accurately. The measurements from the ASN show increased composition stratification during dynamic operation, with an average standard deviation 38% higher than observed during steady-state operation. Air heater leakage was also observed to increase from 2.8% to 5.1% following a load change. Prior to the installation of the ASN, these data would not have been available for dynamic control. These real-time data will be leveraged to calculate and optimize for NUHR during dynamic operation in future work.

42 ENGINEERING↗
DOE OSTI2024

The Effect of Grain Boundary Facet Junctions on Segregation and Embrittlement

Junctions are discontinuities in flat grain boundaries that arise in all polycrystalline materials and are thought to play important roles in the response of a grain boundary network to thermal and mechanical loads. A key open question concerns the mechanisms by which solute segregation to junctions impacts properties of the grain boundary. Here, in this work, we investigate the influence of grain boundary facet junctions on solute embrittlement, and we present an analytical model that uses the hydrostatic stress field contributed by dislocations at multiple junctions to describe these effects. Specifically, we study junctions between {112} facets of various lengths in Au $\langle111\rangle$ Σ3 tilt grain boundaries. Copper and silver solutes are employed to determine if the effect of junctions on solute segregation and embrittlement is dependent on size relative to the host. Combined, atomistic simulation data and the analytical model show that Cu and Ag have opposite segregation responses to junctions due to the sign of the hydrostatic stress field induced by junctions. However, a positive shift in the embrittling potency is computed near junctions regardless of solute type or the stress state of the segregation site. Hence, for the conditions studied, junctions consistently shift the energetic landscape towards embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Exercise Therapy and Radiation Therapy for Cancer: A Systematic Review

Exercise therapy (ET) is shown to improve toxicity and surrogates of survival for patients receiving chemotherapy. Current National Comprehensive Cancer Network (NCCN) guidelines lack recommendations for concurrent radiation therapy (RT) and ET. The main objective was to determine the impact of concurrent ET + RT with respect to (1) acceptability, feasibility, safety; and (2) to demonstrate how incorporating ET in cancer treatment can enhance patient-reported outcomes (PROs) and physical function—defined as strength or exercise capacity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Total absorption spectroscopy of two isomers in 70 Cu influencing nucleosynthesis signatures

Isomers have long been known to be important for astrophysical nucleosynthesis processes, yet they are often neglected in network calculations due to computational limitations or lack of data. "Astromers" are astrophysically metastable nuclear states that can greatly impact nucleosynthesis pathways. In this work we show that astromers further impact the time-dependent electromagnetic signal during heavy element nucleosynthesis. In an experiment performed at the National Superconducting Cyclotron Laboratory, three 𝛽-decaying states of 70 Cu (6 − ground state, and two isomeric states: 3 − and 1 + ) were produced. 𝛽-feeding values were extracted from experimental spectra and compared to shell-model and QRPA+PVC calculations. Here, average 𝛾-ray energies from the 𝛽-decay events were incorporated into simulations of heavy element nucleosynthesis and were found to exhibit different energy release profiles over time, which may impact, in aggregate, time-dependent observable signals.

59 ≤ A ≤ 89↗
DOE OSTI2025

2024 Workshop - Remote Sensing and Fluxes Upscaling for Real-world Impact - Tutorial v1

The software-tutorial was developed within the 2024 Remote Sensing and Fluxes Upscaling for Real-world Impact workshop as part of the hands-on session. The workshop was supported by AmeriFlux, National Ecological Observatory Network (NEON) and CarbonDew. The software provides basic tools to perform the following tasks: - gather remote sensing images using Google Earth Engine API; - gather flux data; - perform basic functions, such as plotting time-series, perform QA of the data, compute vegetation indices; - perform correlation analysis between flux data and remote sensing data; - perform flux predictions based on remote sensing data integrated in different modalities.

Falco, Nicola [Lawrence Berkeley National Laborato↗
DOE OSTI2026

Phoenix Dust Storm (PHX-DUST) Scale: A Cooperatively Developed Dust Storm Scale for Phoenix, Arizona

Using extensive localized meteorological and air quality data for central Arizona from 2010 to 2023, we create a postevent dust storm scale for use by the primary stakeholders in central Arizona for the Phoenix metropolitan area called the Phoenix Dust Storm (PHX-DUST) scale. To ensure usability across a wide spectrum of users, a core concept was to “keep it simple.” The PHX-DUST scale is based on (i) maximum dust concentration for particulate matter (PM10) across the network in micrograms per cubic meter which determines category, e.g., “category 5 (the highest observed category)” and category 4; (ii) the number of network monitors achieving dust concentrations above a threshold of 500 μg m−3 (categorized as “widespread” or “isolated,” as measures of spatial extent); (iii) a duration parameter (which determines “long duration” or “short duration”); and (iv) a measure of wind speed over the affected area (maximum wind gust recorded over the network, which may not be the maximum wind of the storm). Using this index for the 189 dust storms impacting the Phoenix metropolitan area for the period 2010–23, the most severe dust storm occurred on 5 July 2011 and is classified as a “Category 5, Widespread, Long-Duration High-Gust” event. This initial attempt at defining the PHX-DUST scale holds valuable applications for research, education, and applied analyses. It demonstrates that a consortium of interested groups can effectively work to create products benefiting the weather community and general public. Here, the PHX-DUST scale can also serve as a foundational framework for application in other regions where dust storms pose a threat to the well-being of populations, infrastructure, and ecosystems.

Atmosphere↗
DOE OSTI2020

Automated Discovery for Emulytics

Sandia has an extensive background in cybersecurity research and is currently extending its state-of-the-art modeling via emulation capability. However, a key part of Sandia's modeling methodology is the discovery and specification of the information-system under study, and the ability to recreate that specification with the highest fidelity possible in order to extrapolate meaningful results. This work details a method to conduct information system discovery and develop tools to enable the creation of high-fidelity emulation models that can be used to enable assessment of our infrastructure information system security posture and potential system impacts that could result from cyber threats. The outcome are a set of tools and techniques to go from network discovery of operational systems to emulating complex systems. As a concrete usecase, we have applied these tools and techniques at Supercomputing 2016 to model SCinet, the world's largest research network. This model includes five routers and nearly 10,000 endpoints which we have launched in our emulation platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi‐Decadal Decarbonization Pathways for U.S. Freight Rail

A‐STEP is a first‐of‐its‐kind, integrated, open‐source software tool aimed at guiding freight rail decarbonization decision‐making. It has tools for studying energy use details for individual trains, networks of trains, battery and hydrogen charging stations, national energy sourcing and pricing, and overall decarbonization costs and environmental impacts. It gives analysts an ability to study the challenges of making such change happen. Completely amenable to analyst specified inputs and parameter values, it can be customized to provide outputs for a wide variety of assumptions about future energy conditions and technological advances. Written in Python, C++, and VB.Net, A‐STEP can be implemented on both Windows and Linux‐based platforms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗