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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities: Preprint

Increased penetration of distributed variable re-newable generation may cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But due to the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network is needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69 node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring

Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Robust Chemiresistive Behavior in Conductive Polymer/MOF Composites

Metal-organic frameworks (MOFs) are promising materials for gas sensing but are often limited to single-use detection. A hybridization strategy is demonstrated synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. This work presents significant improvement in i) sensor recovery kinetics, ii) cycling stability, and iii) dynamic range at room temperature. The effect of hybridization across well-studied cMOFs is demonstrated based on 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaiminotriphenylene (HITP) ligands with varied metal nodes (Co, Cu, Ni). A comprehensive mechanistic study is conducted to relate energy band alignments at the heterojunctions between the MOFs and the polymer with sensing thermodynamics and binding kinetics. The findings reveal that hole enrichment of the cMOF component upon hybridization leads to selective enhancement in desorption kinetics, enabling significantly improved sensor recovery at room temperature, and thus long-term response retention. This mechanism is further supported by density functional theory calculations on sorbate–analyte interactions. It is also found that alloying cPs and cMOFs enables facile thin film co-processing and device integration, potentially unlocking the use of these hybrid conductors in diverse electronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks

Here, to provide complementary information on the hydrologically important rain–snow-transition elevation in mountain basins, this study provides two estimation methods using ground measurements from basin-scale wireless sensor networks: one based on wet-bulb temperature T wet and the other based on snow-depth measurements of accumulation and ablation. With data from 17 spatially distributed clusters (178 nodes) from two networks, in the American and Feather River basins of California’s Sierra Nevada, we analyzed transition elevation during 76 storm events in 2014–18. A T wet threshold of 0.5°C best matched the transition elevation defined by snow depth. Transition elevations using T wet in upper elevations of the basins generally agreed with atmospheric snow level from radars located at lower elevations, while radar snow level was ~100 m higher due to snow-level lowering on windward mountainsides during orographic lifting. Diurnal patterns of the difference between transition elevation and radar snow level were observed in the American basin, related to diurnal ground-temperature variations. However, these patterns were not found in the Feather basin due to complex terrain and higher uncertainties in transition-elevation estimates. The American basin tends to exhibit 100-m-higher transition elevations than does the Feather basin, consistent with the Feather basin being about 1° latitude farther north. Transition elevation averaged 155 m higher in intense atmospheric river events than in other events; meanwhile, snow-level lowering was enhanced with a 90-m-larger difference between radar snow level and transition elevation. On-the-ground continuous observations from distributed sensor networks can complement radar data and provide important ground truth and spatially resolved information on transition elevations in mountain basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Nickel-incorporated Oxide Composites for Fiber-Optic Based Gas Sensing

Numerous high-value applications within the energy sector involve environmental conditions that are incompatible with traditional sensor technology due to degradation of electrical interconnects, packaging, or the sensors themselves. These can include chemically harsh conditions, high temperature operation, or the presence of electromagnetic interference due to high voltage. The fiber optic platform, constructed of robust, electrically insulating glass or single crystal oxides, offers a versatile and often low cost per node solution to this problem, especially with the integration of distributed sensing techniques such as optical time-domain and frequency-domain reflectometry (OTDR, OFDR). A major challenge of gas and chemical sensing on the optical fiber platform is the fabrication of robust and stable sensing materials that interact quickly and reversibly to the presence of the target analytes. In this work, we discuss the utilization of nickel-incorporated oxides on evanescent-field optical fiber sensors for gas sensing under harsh conditions relevant to multiple energy infrastructure applications. This paper will discuss a Ni/GDC (Ni / Gd-doped ceria) based sensing layer targeting conditions relevant for high-temperature (up to at least 800 oC) hydrogen sensing applications (e.g., for operation within a solid oxide fuel cell or electrolyzer). The impact of hydrogen at elevated temperatures on the optical properties of Ni/GDC will be shown and the material mechanisms for the optical response will be discussed.

gas sensors↗
DOE OSTI2025

Nickel-incorporated Oxide Composites for Fiber-Optic Based Gas Sensing

Numerous high-value applications within the energy sector involve environmental conditions that are incompatible with traditional sensor technology due to degradation of electrical interconnects, packaging, or the sensors themselves. These can include chemically harsh conditions, high temperature operation, or the presence of electromagnetic interference due to high voltage. The fiber optic platform, constructed of robust, electrically insulating glass or single crystal oxides, offers a versatile and often low cost per node solution to this problem, especially with the integration of distributed sensing techniques such as optical time-domain and frequency-domain reflectometry (OTDR, OFDR). A major challenge of gas and chemical sensing on the optical fiber platform is the fabrication of robust and stable sensing materials that interact quickly and reversibly to the presence of the target analytes. In this work, we discuss the utilization of nickel-incorporated oxides on evanescent-field optical fiber sensors for gas sensing under harsh conditions relevant to multiple energy infrastructure applications. This paper will discuss a Ni/GDC (Ni / Gd-doped ceria) based sensing layer targeting conditions relevant for high-temperature (up to at least 800oC) hydrogen sensing applications (e.g., for operation within a solid oxide fuel cell or electrolyzer). The impact of hydrogen at elevated temperatures on the optical properties of Ni/GDC will be shown and the material mechanisms for the optical response will be discussed.

gas sensors↗
DOE OSTI2022

Self-Sensing, Stretchable, Active Circuit Arrays: Liquid Metal Paste as a Combination Interconnect and Strain Sensor

Stretchable electrical interconnects and substrate materials are increasingly used to create flexible and con-formable electronic systems for soft robotics, smart textiles, and wearable devices. Wires fabricated using composite or liquid conductive inks encapsulated in silicone enable high strain and are mechanically robust. The resistance of these inter-connects increases with strain, which can present challenges for power delivery and signaling among electronic components. However, this effect can be used directly to estimate strain within a stretchable circuit without the need for additional sensor components. In this work, we present an approach for fabricating fully-stretchable, multi-layer active circuit arrays in silicone using liquid metal paste interconnects, in which power and data wires are re-purposed as strain sensors to estimate substrate deformation. As a proof-of-principle, we demonstrate a 3×3 active circuit array that self-senses relative voltage supply at each node to estimate deformation and consumes less than 10 mA per node. Each node is digitally addressable and performs both analog measurement and digital conversion. All interconnects are fabricated using liquid metal paste, and power, data, and sensing are performed over a shared, four-wire interface in this multi-layer stretchable printed circuit board.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Thoreau: A fully-buried wireless underground sensor network in an urban environment

This paper describes the design and deployment of a functional Wireless Underground Sensor Network (WUSN) on a university campus. This is an unique system that is operational over a large area and over a long period of time, providing real time monitoring of the environment with the data being curated and displayed on an open website. The system details, including the architecture, node design, user interface, and deployment are provided. Initial measurement results are analyzed to show the functionality and performance of the system.

internet of things↗
DOE OSTI2021

Analysis of the EMCCD point-source response using x-rays

Electron Multiplying Charge Coupled Devices, EMCCD are used as x-ray detectors. The NSLS-II Soft Inelastic x-ray Scattering (SIX) beam line has two EMCCDs for x-ray detection in the spectrometer arm. The spectrometer with high resolving power disperses x-rays vertically. The x-ray vertical position on the sensor plane is related to its energy. This allows for very accurate x-ray energy measurements through x-ray coordinates. X-rays interact with silicon and create a number of electron-hole pairs proportional to the x-ray energy. Electrons drift and diffuse toward pixel gates and are collected there. The diffused electrons form a charge cloud distributed over several neighboring pixels. This charge sharing enables coordinate measurements with accuracy better than the pixel pitch. The charge distribution shape has to be taken into account to achieve ultimate accuracy in coordinate measurements. In this paper, we present a method of the charge distribution shape analysis and demonstrate its applications. The drift and diffusion of electrons ftom the point of generation to pixel gates results in the bell-shaped electron cloud usually approximated by Gaussian shape. The number of electrons collected under a pixel is proportional to the shape function integral. These electron packets get transferred to the sense node of the output amplifier. The transfer process could introduce distortions to the original charge distribution. For example, during transfers, electrons in the packet could be exposed to traps if they are present in the sensor. The trapping and later the release processes distort the apparent shape of the charge distribution. Therefore, deviations of the charge distribution shape ftom the originally symmetrical form can indicate the presence of trap centers in the sensor and can be used for sensor diagnostics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Wind turbine sensor array for monitoring avian and bat collisions

Assessment of avian and bat collisions with wind turbines is necessary to ensure that the benefits of renewable wind power generation are not outweighed by mortality of protected species. An onboard, integrated multisensor system capable of providing detection of turbine collision events, including taxonomic information, was developed. The conceptual design of a multisensor system including a vibration sensing node, an optics node, and an bioacoustic node with an event–driven trigger architecture was field–tested on utility–scale wind turbines. A pixel density computational model was built to estimate the spatial coverage and target resolution to the optimized configuration for camera placement. Field test results of the vibration node showed that nearly half of the recorded impact events were successfully identified by visual inspection and running short–time Fourier transform on recorded vibration signals. The remaining undetected impact events were masked under background noise due to low impact energy and high background noise of the operating turbine, which result in subsequent low signal–to–noise ratio. Furthermore, our results demonstrate the feasibility of triggering the system through single impact event sensed by vibration sensors.

17 WIND ENERGY↗
DOE OSTI2021

Chalcogenide Glass-Capped Fiber-Optic Sensor for Real-Time Temperature Monitoring in Extreme Environments

We demonstrate a novel chalcogenide glass (ChG)-capped optical fiber temperature sensor capable of operating within harsh environment. The sensor architecture utilizes the heat-induced phase change (amorphous-to-crystalline) property of ChGs, which rapidly (80–100 ns) changes the optical properties of the material. The sensor response to temperature variation around the phase change of the ChG cap at the tip of the fiber provides abrupt changes in the reflected power intensity. This temperature is indicative of the temperature at the sensing node. We present the sensing performance of six different compositions of ChGs and a method to interpret the temperature profile between 440 °C and 600 °C in real-time using an array structure. The unique radiation-hardness property of ChGs makes the devices compatible with high-temperature and high-radiation environments, such as monitoring the cladding temperature of Light Water (LWR) or Sodium-cooled Fast (SFR) reactors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

A Novel Energy-Aware Distributed Clustering Algorithm for Heterogeneous Wireless Sensor Networks in the Mobile Environment

In order to prolong the network lifetime, energy-efficient protocols adapted to the features of wireless sensor networks should be used. This paper explores in depth the nature of heterogeneous wireless sensor networks, and finally proposes an algorithm to address the problem of finding an effective pathway for heterogeneous clustering energy. The proposed algorithm implements cluster head selection according to the degree of energy attenuation during the network’s running and the degree of candidate nodes’ effective coverage on the whole network, so as to obtain an even energy consumption over the whole network for the situation with high degree of coverage. Simulation results show that the proposed clustering protocol has better adaptability to heterogeneous environments than existing clustering algorithms in prolonging the network lifetime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

2019 Wireless Test Bed Range Manual

Idaho National Laboratory (INL), Next Generation Wireless Test Bed (NGWTB) program, is part of the Critical Infrastructure Protection (CIP) initiative. Our nation’s reliance on wireless and Internet technology is rapidly evolving as more corporations and government agencies, employees, and handheld devices include mobile connectivity for both voice and data. The competitive advantage offered by a mobile workforce is leading many corporations to make wireless integration and convergence a top business priority. Similarly, military systems worldwide are rapidly embracing next-generation commercial technologies to accelerate network-centric capabilities and provide enhanced situational awareness. While handheld devices such as laptops, BlackBerrys, and cell phones once had limited mobility and range, they are rapidly incorporating new and multiple protocols such as Bluetooth, Wi-Fi, Satellite, and broadband cellular (3G/4G) on a single platform to increase their effectiveness and agility. Worldwide there is exponential growth in public Wi-Fi and/or Satellite connectivity access nodes, and 3G/4G cellular continues to evolve and expand the range and speed of these devices. Additionally, critical infrastructure networks, previously isolated or connected with dedicated wireline circuits, are incorporating Zigbee for wireless sensor networks, wireless LANs for maintenance functions, and cellular or Internet based backhaul to manage control centers. Yet with all the build-up surrounding wireless technology, few understand the complexities surrounding wireless protocols and security, the risks of converged network infrastructures, need for interoperability of communication systems, or mitigation measures to safely use and improve new technologies in both commercial and military environments. With no single vendor source for all the telecommunications equipment needed for wireless infrastructure upgrades, and no one entity providing end-to-end testing or independent validation, the INL's NGWTB program fulfills a niche for facilitating integrated testing of multiple technologies, across multiple wireless domains, of next generation wireless telecommunications systems. NGWTB engineers and technicians have constructed and operate domain infrastructure in the Wireless Personal Area Network (WPAN), Wireless Local Area Network (WLAN), Wireless Metropolitan Area Networks (WMAN), and Wireless Wide Area Networks (WWAN). Advantages that make the INL's NGWTB the place to conduct project and programmatic testing include: • NTIA Experimental Station authority • Geographically isolated and low-noise radio frequency (RF) test areas located on a 890 sm DOE reservation • Access to isolated high-speed LAN and FOT networks The rollout of next-generation networks will involve deploying new technologies at every level including new: handsets, frequencies, antenna arrays, cell configurations that are optimized for both data and voice, massive upgrade of cell controllers’ “land-line” phone links from low bandwidth connections to higher capacity fiber or wireless backhaul, and integration of voice switching and Internet/data network protocols (Voice-Over-IP, Web phones). These systems are expected to greatly transform our nation's communications infrastructure and the types of services it provides.

2G↗
DOE OSTI2023

Middleware for a Heterogeneous CAV Fleet

This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.

42 ENGINEERING↗