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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Ultra-Low SWaP CO 2 Sensing for Demand Control Ventilation (Final Report)

In this project PARC and Energy ETC aimed to develop an ultra-low cost, size, weight, and power (SWaP) printed CO 2 sensor system for occupancy detection to enable Demand Control Ventilation (DCV) on a per-room basis. The CO 2 sensor technology is based on the temperature variation when CO 2 reversibly physisorbs to a highly conductive and high surface area sorbent surface, and is compatible with integration with PARC innovations in printed sensors and flexible electronics, for which PARC is a globally leading research center. The printed CO 2 sensor itself is designed to be compatible with PARC’s “peel-and-stick” platform of ultra-low power, low-cost, distributed sensors, and to facilitate real-time DCV based on overall indoor air quality (IAQ). Previously, PARC has developed flexible hybrid electronics (FHE) compatible materials to measure humidity, temperature, light, strain, and gases such as carbon monoxide, methane, ammonia, and hydrogen sulfide. Through this project, PARC developed FHE-compatible materials to measure CO 2 . Thus, with one <$15 FHE “peel-and-stick” based sensor node, a building management system (BMS) will be able to capture a complete picture of the indoor environment. This includes IAQ, light, temperature, and other comfort factors that impact building operations. Combined with optimized DCV, this low-cost sensor capability can be a key enabler of annual primary energy savings of ~ 0.3-0.4 Quad in commercial buildings while ensuring healthy IAQ. Energy ETC is a leader in supplier-agnostic BMS deployments and will design the commissioning and deployment procedures to maximize system interoperability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Nanostructured Bulk Thermoelectric Generator for Efficient Power Harvesting for Self-powered Sensor Networks

The objective of this Nuclear Energy Enabling Technology research project is to develop high-efficiency and reliable thermoelectric generators for self-powered wireless sensors nodes utilizing thermal energy from nuclear plant or fuel cycle. The power harvesting technology has crosscutting significance to address critical technology gaps in monitoring nuclear plants and fuel cycle. The outcomes of the project will lead to significant advancement in sensors and instrumentation technology, reducing cost, improving monitoring reliability and therefore enhancing safety. The self-powered wireless sensor networks could support the long-term safe and economical operation of all the reactor designs and fuel cycle concepts, as well as spent fuel storage and many other nuclear science and engineering applications. The research is based on recent breakthroughs in high-performance nanostructured bulk (nanobulk) thermoelectric materials that enable high-efficiency direct heat-to-electricity conversion over a wide temperature range. The nanobulk thermoelectric materials that the research team at Boise State University and University of Houston has developed yield up to a 50% increase in the thermoelectric figure of merit, ZT, compared with state-of-the-art bulk counterparts. This report focuses on the selection of optimal thermoelectric materials for this project. The team has performed extensive study on two thermoelectric materials systems, i.e. the half-Heusler materials, and the Bismuth-Telluride materials. The report contains our recent research results on the fabrication, characterization and thermoelectric property measurements of these two materials.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Multiparameter optical fiber sensing for energy infrastructure through nanoscale light–matter interactions: From hardware to software, science to commercial opportunities

Monitoring of energy infrastructure through robust yet economical sensing platforms is becoming an area of increased importance, with ubiquitous applications including the electrical grid, natural gas and oil transportation pipelines, H2 infrastructure (storage and transportation), carbon storage, power generation, and subsurface environments. Plasmonic and functional nanomaterial enabled fiber optic sensors show excellent promise for a wide range of sensing applications due to their versatility to be engineered for specific analytes of interest while retaining inherent advantages of the optical fiber sensor platform. Through the design of novel sensing layers, the optical transduction mechanism and wavelength dependence can also be tailored for ease of integration with low-cost interrogation systems enabling an inexpensive yet highly functional optical fiber sensing platform. In addition, recent advances in artificial intelligence and machine learning theoretical methods have been leveraged to simultaneously extract multiple parameters through multi-wavelength interrogation such that unique wavelengths can also serve as unique sensing elements, analogous to electronic nose sensor technologies. The concept of an optical fiber based “photonic nose” via multiple interrogation wavelengths and/or sensor nodes offers a compelling platform technology to realize multiparameter speciation of chemical analytes within complex gas mixtures. In this Perspective, we further generalize the notion of multiparameter sensing through the novel “photonic nervous system” concept based upon low-cost, functionalized optical fiber sensor probes monitoring a variety of distinct analyte classes (physical, chemical, electromagnetic, etc.) simultaneously to provide broad situational awareness via integrated sensors.

Su, Yang-Duan (ORCID:0000000214820902)↗
NASA NTRS2011

Wireless Sensor Networks for Developmental and Flight Instrumentation

Wireless sensor networks (WSN) based on the IEEE 802.15.4 Personal Area Network and ZigBee Pro 2007 standards are finding increasing use in home automation and smart energy markets providing a framework for interoperable software. The Wireless Connections in Space Project, funded by the NASA Engineering and Safety Center, is developing technology, metrics and requirements for next-generation spacecraft avionics incorporating wireless data transport. The team from Stennis Space Center and Mobitrum Corporation, working under a NASA SBIR grant, has developed techniques for embedding plug-and-play software into ZigBee WSN prototypes implementing the IEEE 1451 Transducer Electronic Datasheet (TEDS) standard. The TEDS provides meta-information regarding sensors such as serial number, calibration curve and operational status. Incorporation of TEDS into wireless sensors leads directly to building application level software that can recognize sensors at run-time, dynamically instantiating sensors as they are added or removed. The Ames Research Center team has been experimenting with this technology building demonstration prototypes for on-board health monitoring. Innovations in technology, software and process can lead to dramatic improvements for managing sensor systems applied to Developmental and Flight Instrumentation (DFI) aboard aerospace vehicles. A brief overview of the plug-and-play ZigBee WSN technology is presented along with specific targets for application within the aerospace DFI market. The software architecture for the sensor nodes incorporating the TEDS information is described along with the functions of the Network Capable Gateway processor which bridges 802.15.4 PAN to the TCP/IP network. Client application software connects to the Gateway and is used to display TEDS information and real-time sensor data values updated every few seconds, incorporating error detection and logging to help measure performance and reliability in relevant target environments. Test results from our prototype WSN running the Mobitrum software system are summarized and the implications to the scalability and reliability for DFI applications are discussed. Our demonstration system, incorporating sensors for life support system and structural health monitoring is described along with test results obtained by running the demonstration prototype in relevant environments such as the Wireless Habitat Testbed at Johnson Space Center in Houston. An operations concept for improved sensor process flow from design to flight test is outlined specific to the areas of Environmental Control and Life Support System performance characterization and structural health monitoring of human-rated spacecraft. This operations concept will be used to highlight the areas where WSN technology, particularly plug-and-play software based on IEEE 1451, can improve the current process, resulting in significant reductions in the technical effort, overall cost and schedule for providing DFI capability for future spacecraft. RELEASED -

Alena, Richard↗
NASA NTRS2018

Prolonging Wireless Sensor Network Lifetime Using Routing Protocol

Prolonging network lifetime is one of the challenging issues of Wireless Sensor Networks (WSN). Many techniques have been proposed to achieve a longer battery life for the sensor nodes. In this paper, we focus on the routing technique to improve the battery life and extend the network lifetime. Our protocol is based upon the two existing protocols, namely, LEACH (Low-Energy Adaptive Clustering Hierarchy) and PEGASIS (Power Efficient GAthering in Sensor Information Systems). By combining these two basic routing techniques, we propose a new protocol which provides an increase in network lifetime compared to the existing basic protocols.

Salam, Mohammad Abdus↗
DOE OSTI2016

Experimental characterization of cantilever-type piezoelectric generator operating at resonance for vibration energy harvesting

A cantilever-type resonant piezoelectric generator (RPG) has been designed by gluing a PZT patch working in d{sub 31} mode onto a glass fibre reinforced composite cantilever beam with a discrete mass applied on its free end. The electrical and dynamic behaviour of the RPG prototype has been investigated by carrying out laboratory tests aimed to assess the effect of definite design parameters, specifically the electric resistance load and the excitation frequency. Results showed that an optimum resistance load exists, at which power generation is maximized. Moreover, it has been showed that power generation is strongly influenced by the vibration frequency highlighting that, at resonance, output power can be increased by more than one order of magnitude. Possible applications include inertial resonant harvester for energy recovery from vibrating machines, sea waves or wind flux and self-powering of wireless sensor nodes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A nonlinear MEMS electrostatic kinetic energy harvester for human-powered biomedical devices

This article proposes a silicon-based electrostatic kinetic energy harvester with an ultra-wide operating frequency bandwidth from 1 Hz to 160 Hz. This large bandwidth is obtained, thanks to a miniature tungsten ball impacting with a movable proof mass of silicon. The motion of the silicon proof mass is confined by nonlinear elastic stoppers on the fixed part standing against two protrusions of the proof mass. The electrostatic transducer is made of interdigited-combs with a gap-closing variable capacitance that includes vertical electrets obtained by corona discharge. Below 10 Hz, the e-KEH offers 30.6 nJ per mechanical oscillation at 2 g{sub rms}, which makes it suitable for powering biomedical devices from human motion. Above 10 Hz and up to 162 Hz, the harvested power is more than 0.5 μW with a maximum of 4.5 μW at 160 Hz. The highest power of 6.6 μW is obtained without the ball at 432 Hz, in accordance with a power density of 142 μW/cm{sup 3}. We also demonstrate the charging of a 47-μF capacitor to 3.5 V used to power a battery-less wireless temperature sensor node.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗
NASA NTRS2008

Simulating Operation of a Complex Sensor Network

Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.

Jennings, Esther↗
DOE OSTI2017

Methane trace-gas sensing enabled by silicon photonic integration

Due to the high radiative forcing of methane, fugitive natural gas leaks pose a significant challenge to the near-term environmental viability of oil and gas extraction. Reducing the subsequent environmental impact requires cost-effective sensor nodes for reliable, rapid, and continuous identification of extraneous methane emissions. The efficacy of laser spectroscopy has been widely demonstrated in both environmental and medical applications due to its sensitivity and specificity to the target analyte. However, the present cost and the lack of manufacturing scalability of free-space optical systems can limit their viability for economical wide-area sensor networks in localized leak detection applications. In this paper, we will review the development and performance of a cost-effective silicon photonic sensing platform. This platform uses silicon photonic waveguide and packaging integration technologies to enable on-chip evanescent field spectroscopy of methane.

03 NATURAL GAS↗
DOE OSTI2017

Methane Trace-Gas Sensing Enabled by Silicon Photonic Integration

Fugitive methane leaks occurring during extraction at typical natural gas wells have an adverse environmental impact due to the methane’s large radiative forcing, in addition to reducing the producer’s overall efficiency and cost. Mitigation of these concerns can benefit from cost-effective sensor nodes, performing reliable, rapid and continuous tracking of methane emissions. The efficacy of laser spectroscopy has been widely demonstrated in both environmental and medical applications due to its sensitivity and specificity to the target analyte. However, the present cost and lack of manufacturing scalability of traditional free-space optical systems can limit their viability for deployment in economical wide-area sensor networks. This presentation will review the development and performance of a cost-effective silicon photonic trace gas sensing platform that leverages silicon photonic waveguide and packaging technologies to perform on-chip evanescent field spectroscopy of methane.

03 NATURAL GAS↗
NASA NTRS2003

Aeroheating Thermal Model Correlation for Mars Global Surveyor (MGS) Solar Array

The Mars Global Surveyor (MGS) Spacecraft made use of aerobraking to gradually reduce its orbit period from a highly elliptical insertion orbit to its final science orbit. Aerobraking produces a high heat load on the solar arrays, which have a large surface area exposed to the airflow and relatively low mass. To accurately model the complex behavior during aerobraking, the thermal analysis needed to be tightly coupled to the spatially varying, time dependent aerodynamic heating. Also, the thermal model itself needed to accurately capture the behavior of the solar array and its response to changing heat load conditions. The correlation of the thermal model to flight data allowed a validation of the modeling process, as well as information on what processes dominate the thermal behavior. Correlation in this case primarily involved detailing the thermal sensor nodes, using as-built mass to modify material property estimates, refining solar cell assembly properties, and adding detail to radiation and heat flux boundary conditions. This paper describes the methods used to develop finite element thermal models of the MGS solar array and the correlation of the thermal model to flight data from the spacecraft drag passes. Correlation was made to data from four flight thermal sensors over three of the early drag passes. Good correlation of the model was achieved, with a maximum difference between the predicted model maximum and the observed flight maximum temperature of less than 5%. Lessons learned in the correlation of this model assisted in validating a similar model and method used for the Mars Odyssey solar array aeroheating analysis, which were used during onorbit operations.

Amundsen, Ruth M.↗
DOE OSTI2022

A Wireless Underground Sensor Network Field Pilot for Agriculture and Ecology: Soil Moisture Mapping Using Signal Attenuation

Wireless Underground Sensor Networks (WUSNs) that collect geospatial in situ sensor data are a backbone of internet-of-things (IoT) applications for agriculture and terrestrial ecology. In this paper, we first show how WUSNs can operate reliably under field conditions year-round and at the same time be used for determining and mapping soil conditions from the buried sensor nodes. We demonstrate the design and deployment of a 23-node WUSN installed at an agricultural field site that covers an area with a 530 m radius. The WUSN has continuously operated since September 2019, enabling real-time monitoring of soil volumetric water content (VWC), soil temperature (ST), and soil electrical conductivity. Secondly, we present data collected over a nine-month period across three seasons. We evaluate the performance of a deep learning algorithm in predicting soil VWC using various combinations of the received signal strength (RSSI) from each buried wireless node, above-ground pathloss, the distance between wireless node and receive antenna (D), ST, air temperature (AT), relative humidity (RH), and precipitation as input parameters to the model. The AT, RH, and precipitation were obtained from a nearby weather station. We find that a model with RSSI, D, AT, ST, and RH as inputs was able to predict soil VWC with an R 2 of 0.82 for test datasets, with a Root Mean Square Error of ±0.012 (m 3 /m 3 ). Hence, a combination of deep learning and other easily available soil and climatic parameters can be a viable candidate for replacing expensive soil VWC sensors in WUSNs.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Resilient Autonomous Wind Farms

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm level, taking into account individual wind turbine information to achieve wind-farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have experienced significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms that can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as from local meteorological stations, nearby radars, sodars, and lidars, and supervisory control and data acquisition (SCADA) data. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and ensuring more efficient overall control of farmwide optimizations through mechanisms such as wake steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach that distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks that seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft-Byzantine-Fault-Tolerant algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will...

fault tolerance↗
DOE OSTI2025

Thermomagnetic generators for ultra-low-grade marine thermal energy harvesting

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s -1 ). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2019

Wireless Sensor Networks for Improved Snow Water Equivalent and Runoff Estimates

We leverage the frontiers of the Internet of Things technology in a recently developed end-to-end wireless sensor network (WSN) system that samples, collects, stores, and displays mountain hydrology measurements in near real-time. At the core of the system lies an ultra-low power, radio channel-hoping, and self-organizing mesh that allows for remote autonomous sampling of snow. Such properties, combined with a rugged weather-sealed design of the devices and multi-level data replication, provides reliable real-time data at spatial and temporal scales previously impractical to achieve in mountain environments. The system was deployed at three 1 km 2 sites across the North Fork of the Feather River basin with a cluster of 12 sensor nodes for each location. Measurements show that existing operational autonomous systems are non-representative spatially, with biases that can reach up to 50%. A comparison between a wet and dry year showed that snow depths exhibit strong multi-scale inter-year spatial stationarity with major rank conservation. Temporally dense analysis using elastic net regression shows that dominant features at the sub-km 2 scale are site-dependent and differ from the watershed scale. Newly introduced explanatory variables, based on the nearest neighbor with a Landsat assimilated historical product, consistently explained up to 90% of the variance in the watershed-scale SWE for both years. At two WSN sites, lagged cross-correlation of snowmelt with stream flow measurements showed a significant improvement of up to 100% compared with existing systems, suggesting that WSNs can be instrumental in improving runoff forecasting and water management.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗