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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Module-OT: A Hardware Security Module for Operational Technology

Increased penetration levels of renewable energy and other types of distributed energy resources (DERs) on the modern electric grid-combined with technological advancements for electric system monitoring and control-introduce new cyberattack vectors and increase the cyberattack surface of energy systems. According to the IEEE Std. 1547-2018, DERs must use Modbus, Distributed Network Protocol 3 (DNP3), or Smart Energy Profile 2.0 (SEP2) as their communication protocol. Previous research identified several vulnerabilities and security breaches in each one of these communication protocols; despite this, existing standards for DERs do not recommend cybersecurity measures. In order to reduce vulnerabilities in power distribution systems, this paper presents a novel open-source hardware security module that improves both information and operational security to better protect data and communications on the distribution grid. The security hardware is called “module for operational technology,” or simply Module-OT, and it has been validated and tested in an emulated distribution system application. Module-OT is integrated within a communication system in the transport layer of the Open Systems Interconnection (OSI) model. It improves system security through encryption, authentication, authorization, certificate management, and user access control. The main advancement of Module-OT is the addition of hardware cryptographic acceleration that improves the overall communication performance in terms of end-to-end latency.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reducing the Parameter Dependency of Phase-Picking Neural Networks with Dice Loss

Training a neural network for picking seismic phase arrivals has been commonly posed as a segmentation problem. It is a highly imbalanced segmentation problem in the sense that the background vastly dominates the foreground because we are trying to pick the optimal single sample point that represents the arrival of a seismic phase in a many seconds long time window. Here, we test the Dice loss, which is a preferred loss function for highly imbalanced image segmentation problems. We show that phase-picking neural networks trained on the Dice loss behave in a binary fashion for which the prediction output is almost always either nearly 1 or nearly 0. This feature removes the strong dependence of data processing workflows on the prediction score threshold, which is an otherwise critical parameter to determine when using neural networks trained on the cross-entropy loss. When strategically used, models trained on the Dice loss can reduce the parameter dependency of machine learning-based seismic monitoring.

58 GEOSCIENCES↗
DOE OSTI2022

Trust But Verify

This documentary film was made in 1989 about the methods used by each country to monitor the others weapons nuclear processes and is narrated by Charlton Heston. The DC was reviewed by Darrin Wallace (3/22/22) and confirmed unclassified to move from the red network to the yellow, but it still requires LA-UR to be assigned.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Representing Complex Systems as Graphs for Debugging and Predictive Maintenance-Preliminary Thoughts

Representing complex systems as graphs enables use of mathematical tools to identify faults or predict failures. Graph nodes correspond to individual modules or subsystems, and edges link coupled system parts. ‘Probes’ measure the node outputs, monitoring the system health for unexpected behavior. Assuming one cannot probe every point, within a system, the fault correlates to a region—not necessarily the specific location. Bayesian networks trained to understand fault patterns can accurately identify the source. The diagnostic tool described aides debugging by pinpointing system failure causes. For predictive maintenance, probe data develop probability distribution functions describing subsystem mean time to failure. Unit lifetime can be estimated through these probability distributions. Two approaches include using Bayesian classifiers to infer the system failure source and developing maintenance schedules by treating systems as collections of random variables. When failure behavior does not follow a closed form function, use of similarity models is proposed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗
NASA NTRS1994

Multi-band automatic sun and sky scanning radiometer system for measurement of aerosols

A weather resistant automatic scanning Sun photometer system is assessed and demonstrated as practical for measurements of aerosol concentrations and properties at remote sites. Interfaced with a transmitter using the Geostationary Data Collection System (GDCS), the data are processed in near real time. The processing allows a time dependence of the aerosols and water vapor and an ongoing assessment of the health and calibration of the instruments. The system's automatic data acquisition, transmission, and processing offer immediate application to atmospheric monitoring and modeling on a regional to global scale and validation of satellite retrievals. It is estimated that under normal circumstances the retrieved aerosol optical thickness has a network wide accuracy of +/- 0.02 from 340 nm to 1020 nm, water vapor +/- 0.2 cm and size distribution from 0.1 to 3 micrometers.

Holben, B. N.↗
NASA NTRS1996

Automated Operations for Galileo Communications

Following the deployment failure of Galileo's high gain antenna, the downlink had to be redesigned so as to effectively use the low gain antenna. The downlink was redesigned to maximize the data return and increase the reliability which required the reconfiguration of the onboard software and the deep space network. The revised downlink features: data compression; antenna arraying; the recoding and reprocessing of telemetry; suppressed carrier tracking, and error-correction coding. The deep space network Galileo telemetry (DGT) subsystem was developed and deployed at three sites in Australia, Spain and the U.S. The DGT was designed as an automated system that continuously monitors and adjusts its parameters and environment in response to either pre-loaded sequences or changes in the internal status.

Statman, Joseph I.↗
NASA NTRS2002

3-D Flow Field Diagnostics and Validation Studies using Stereoscopic Tracking Velocimetry

The measurement of 3-D three-component velocity fields is of great importance in both ground and space experiments for understanding materials processing and fluid physics. Here, we present the investigation results of stereoscopic tracking velocimetry (STV) for measuring 3-D velocity fields. The effort includes diagnostic technology development, experimental velocity measurement, and comparison with analytical and numerical computation. The advantages of STV stems from the system simplicity for building compact hardware and in software efficiency for continual near-real-time process monitoring. It also has illumination flexibility for observing volumetric flow fields from arbitrary directions. STV is based on stereoscopic CCD observations of particles seeded in a flow. Neural networks are used for data analysis. The developed diagnostic tool is tested with a simple directional solidification apparatus using Succinonitrile. The 3-D velocity field in the liquid phase is measured and compared with results from detailed numerical computations. Our theoretical, numerical, and experimental effort has shown STV to be a viable candidate for reliably quantifying the 3-D flow field in materials processing and fluids experiments.

Cha, Soyoung Stephen↗
NASA NTRS2018

Comparisons of Spectral Aerosol Single Scattering Albedo in Seoul, South Korea

Quantifying aerosol absorption at ultraviolet (UV) wavelengths is important for monitoring air pollution and aerosol amounts using current (e.g., Aura/OMI (Ozone Monitoring Instrument)) and future (e.g., TROPOMI (TROPOspheric Monitoring Instrument), TEMPO (Tropospheric Emissions: Monitoring of POllution), GEMS (Geostationary Environment Monitoring Spectrometer) and Sentinel-4) satellite measurements. Measurements of column average atmospheric aerosol single scattering albedo (SSA) are performed on the ground by the NASA AERONET (AEROsol robotic NETwork) in the visible (VIS) and near-infrared (NIR) wavelengths and in the UV-VIS-NIR by the SKYNET (SKY radiometer NETwork) networks. Previous comparison studies have focused on VIS and NIR wavelengths due to the lack of co-incident measurements of aerosol and gaseous absorption properties in the UV. This study compares the SKYNET-retrieved SSA in the UV with the SSA derived from a combination of AERONET, MFRSR (MultiFilter Rotating Shadowband Radiometer), and Pandora (AMP) retrievals in Seoul, South Korea, in spring and summer 2016. The results show that the spectrally invariant surface albedo assumed in the SKYNET SSA retrievals leads to underestimated SSA compared to AMP values at near UV wavelengths. Re-processed SKYNET inversions using spectrally varying surface albedo, consistent with the AERONET retrieval improve agreement with AMP SSA. The combined AMP inversions allow for separating aerosol and gaseous (NO2 and O3) absorption and provide aerosol retrievals from the shortest UVB (305 nanometers) through VIS to NIR wavelengths (870 nanometers).

aerosol and gaseous absorption properties↗
NASA NTRS2018

Mobile Device Application for Monitoring Cyanobacteria Harmful Algal Blooms Using Sentinel-3 Satellite Ocean and Land Colour Instruments

Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near real-time to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.

Harmful Algal Bloom↗
NASA NTRS2020

Utilizing Small Telescopes Operated by Citizen Scientists for Transiting Exoplanet Follow-up

Due to the efforts by numerous ground-based surveys and NASA's Kepler and Transiting Exoplanet Survey Satellite (TESS), there will be hundreds, if not thousands, of transiting exoplanets ideal for atmospheric characterization via spectroscopy with large platforms such as James Webb Space Telescope and ARIEL. However their next predicted mid-transit time could become so increasingly uncertain over time that significant overhead would be required to ensure the detection of the entire transit. As a result, follow-up observations to characterize these exoplanetary atmospheres would require less-efficient use of an observatory's time—which is an issue for large platforms where minimizing observing overheads is a necessity. Here we demonstrate the power of citizen scientists operating smaller observatories (≤1 m) to keep ephemerides "fresh," defined here as when the 1σ uncertainty in the mid-transit time is less than half the transit duration. We advocate for the creation of a community-wide effort to perform ephemeris maintenance on transiting exoplanets by citizen scientists. Such observations can be conducted with even a 6 inch telescope, which has the potential to save up to ∼10,000 days for a 1000-planet survey. Based on a preliminary analysis of 14 transits from a single 6 inch MicroObservatory telescope, we empirically estimate the ability of small telescopes to benefit the community. Observations with a small-telescope network operated by citizen scientists are capable of resolving stellar blends to within 5''/pixel, can follow-up long period transits in short-baseline TESS fields, monitor epoch-to-epoch stellar variability at a precision 0.67% ± 0.12% for a 11.3 V-mag star, and search for new planets or constrain the masses of known planets with transit timing variations greater than two minutes.

Robert T. Zellem↗
DOE OSTI2025

Online thermal profile prediction for large format additive manufacturing: A hybrid CNN-LSTM based approach

Large format additive manufacturing (LFAM) is an advanced 3D printing technique that efficiently fabricates large-scale components through a layer-by-layer extrusion and deposition process. Accurate surface layer temperature monitoring is essential to prevent manufacturing failures and ensure final product quality. Traditional physics-based offline approaches for simulating thermal behavior are often inefficient and complex, posing challenges on real-time, in-situ monitoring. Here, to address this, we propose a data-driven hybrid CNN-LSTM model to predict sequential thermal images of arbitrary length using real-time infrared thermal imaging. In this approach, a Convolutional Neural Networks (CNN) is trained offline to capture spatial features, reduce dimensional complexity, and enhance time efficiency, while a stacked Long Short-Term Memory (LSTM) is applied online to capture temporal information for improved prediction of future thermal behavior in subsequent printing layers. Model performance is evaluated using MSE, SSIM, and PSNR metrics and is benchmarked against stacked LSTM and convolutional LSTM models, demonstrating superior accuracy and applicability. Additionally, to mitigate noise from moving extruders and gantry backgrounds in thermal images, a fine-tuned semantic segmentation model is implemented offline to extract printing geometry, enabling precise temperature tracking along the tool path for further thermal analysis. The frameworks developed in this study significantly advance temperature monitoring, thermal analysis, and in-situ manufacturing control for LFAM, bridging the gap between theoretical modeling and practical application.

Geometry extraction↗
NASA NTRS1996

Spectral imaging applications: Remote sensing, environmental monitoring, medicine, military operations, factory automation and manufacturing

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. The authors discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time target/decoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.

National Defense↗
NASA NTRS2010

Secure Autonomous Automated Scheduling (SAAS)

This report describes network-centric operations, where a virtual mission operations center autonomously receives sensor triggers, and schedules space and ground assets using Internet-based technologies and service-oriented architectures. For proof-of-concept purposes, sensor triggers are received from the United States Geological Survey (USGS) to determine targets for space-based sensors. The Surrey Satellite Technology Limited (SSTL) Disaster Monitoring Constellation satellite, the UK-DMC, is used as the space-based sensor. The UK-DMC's availability is determined via machine-to-machine communications using SSTL's mission planning system. Access to/from the UK-DMC for tasking and sensor data is via SSTL's and Universal Space Network's (USN) ground assets. The availability and scheduling of USN's assets can also be performed autonomously via machine-to-machine communications. All communication, both on the ground and between ground and space, uses open Internet standards

Walke, Jon G.↗
NASA NTRS2005

New Directions in Space Operations Services in Support of Interplanetary Exploration

To gain access to the necessary operational processes and data in support of NASA's Lunar/Mars Exploration Initiative, new services, adequate levels of computing cycles and access to myriad forms of data must be provided to onboard spacecraft and ground based personnel/systems (earth, lunar and Martian) to enable interplanetary exploration by humans. These systems, cycles and access to vast amounts of development, test and operational data will be required to provide a new level of services not currently available to existing spacecraft, on board crews and other operational personnel. Although current voice, video and data systems in support of current space based operations has been adequate, new highly reliable and autonomous processes and services will be necessary for future space exploration activities. These services will range from the more mundane voice in LEO to voice in interplanetary travel which because of the high latencies will require new voice processes and standards. New services, like component failure predictions based on data mining of significant quantities of data, located at disparate locations, will be required. 3D or holographic representation of onboard components, systems or family members will greatly improve maintenance, operations and service restoration not to mention crew morale. Current operational systems and standards, like the Internet Protocol, will not able to provide the level of service required end to end from an end point on the Martian surface like a scientific instrument to a researcher at a university. Ground operations whether earth, lunar or Martian and in flight operations to the moon and especially to Mars will require significant autonomy that will require access to highly reliable processing capabilities, data storage based on network storage technologies. Significant processing cycles will be needed onboard but could be borrowed from other locations either ground based or onboard other spacecraft. Reliability will be a key factor with onboard and distributed backup processing an absolutely necessary requirement. Current cluster processing/Grid technologies may provide the basis for providing these services. An overview of existing services, future services that will be required and the technologies and standards required to be developed will be presented. The purpose of this paper will be to initiate a technological roadmap, albeit at a high level, of current voice, video, data and network technologies and standards (which show promise for adaptation or evolution) to what technologies and standards need to be redefined, adjusted or areas where new ones require development. The roadmap should begin the differentiation between non manned and manned processes/services where applicable. The paper will be based in part on the activities of the CCSDS Monitor and Control working group which is beginning the process of standardization of the these processes. Another element of the paper will be based on an analysis of current technologies supporting space flight processes and services at JSC, MSFC, GSFC and to a lesser extent at KSC. Work being accomplished in areas such as Grid computing, data mining and network storage at ARC, IBM and the University of Alabama at Huntsville will be researched and analyzed.

Bradford, Robert N.↗
NASA NTRS2009

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Initial efforts to develop a deterministic model for predicting and simulating pollen release and downwind concentration to study dependencies of phenology on meteorology will be discussed. The development of a real-time, rapid response pollen release and transport system as a component of the New Mexico Environmental Public Health Tracking System (EPHTS), is based on meteorological models, NASA Earth science results (ESR), and an in-situ network of phenology cameras. The plan is to detect pollen release verified using ground based atmospheric pollen sampling within a few hours using daily MODIS daa in nearly real-time from Direct Broadcast, similar to the MODIS Rapid Response System for fire detection. As MODIS winds down, the NPOESS-VIIRS sensor will assume daily vegetation monitoring tasks. Also, advancements in geostationary satellites will allow 1km vegetation indices at 15-30 minute intervals. The pollen module in EPHTS will be used to: (1) support public health decisions for asthma and allergy alerts in New Mexico, Texas and Oklahoma; (2) augment the Centers for Disease Control and Prevention (CDC)'s Environmental Public Health Tracking Network (EPHTN); and (3) extend surveillance services to local healthcare providers subscribing to the Syndrome Reporting Information System (SYRIS). Previous studies in NASA's public health applications portfolios provide the infrastructure for this effort. The team is confident that NASA and NOAA ESR data, combined into a verified and validated dust model will yield groundbreaking results using the modified dust model to transport pollen. The growing ESR/health infrastructure is based on results from a rapid prototype scoping effort for pollen detection and simulation carried out by the principal investigators.

Luvall, Jeffrey↗
DOE OSTI2021

Achieving Runtime State Verification Assurance in Critical Cyber-Physical Infrastructures

Industrial Cyber-Physical Systems (ICPS) are an essential backbone of national critical infrastructures. They help monitor and control crucial cyber-enabled services such as energy generation. Commonly ICPS monitors the physical process through Supervisory Control and Data Acquisition (SCADA) systems. The SCADA ecosystem takes critical real-time and future system operational decisions based on the runtime state behavior of field sensors. Traditional SCADA systems use legacy and insecure communication protocols such as the Modbus protocol that lack adequate security mechanisms to provide robust runtime state behavior assurance of constrained field sensors. Therefore, constrained field sensors are commonly vulnerable to standard semantic attacks that gradually change the behavior state of infected devices. This paper discusses process integrity assurance techniques necessary to enhance the security of behavior-based protocols such as the Modbus protocol. The Runtime State Verification (RSV) protocol proposed in this paper aims to address semantic attacks in the SCADA ecosystem by integrating behavior-based Mandatory Results Automata (MRA) and a Hyperledger Fabric (HLF) network. The RSV protocol provides high process integrity assurance through enhanced behavior-based MRA suitable for the constrained field devices. A proof of concept of the RSV protocol has been evaluated in an emulated water-tube boiler. Preliminary evaluations of the RSV protocol aimed to measure the efficiency of the proposed protocol by monitoring an Combustion Efficiency (CE) process necessary to preserve optimal combustion, thus minimizing costs and future maintenance of water-tube boilers. We analyze the overall network overhead and latency of the proposed RSV protocol by evaluating the HLF network performance and comparing the proposed RSV protocol with the state-ofart BloSPAI protocol. Through the preliminary evaluations of the proposed RSV protocol, this paper demonstrates that the proposed RSV protocol overcomes the shortcomings and network overhead of the BloSPAI protocol by integrating behavior-based authentication through novel MRAs and HLF networks.

Rivera, Abel Gomez↗
DOE OSTI2025

Resilience of the Electric Grid Through Trustable IoT-Coordinated Assets

The electricity grid has evolved from a physical system to a cyberphysical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity cosimulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

distributed energy resources↗