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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
NASA NTRS2014

Strain Gage Load Calibration of the Wing Interface Fittings for the Adaptive Compliant Trailing Edge Flap Flight Test

This is the presentation to follow conference paper of the same name. The adaptive compliant trailing edge (ACTE) flap experiment safety of flight requires that the flap to wing interface loads be sensed and monitored in real time to ensure that the wing structural load limits are not exceeded. This paper discusses the strain gage load calibration testing and load equation derivation methodology for the ACTE interface fittings. Both the left and right wing flap interfaces will be monitored and each contains four uniquely designed and instrumented flap interface fittings. The interface hardware design and instrumentation layout are discussed. Twenty one applied test load cases were developed using the predicted in-flight loads for the ACTE experiment.

strain gage calibration↗
NASA NTRSDate not supplied

ASDC Distribution and Services of TEMPO Data

The Tropospheric Emissions: Monitoring of POllution (TEMPO) instrument represents a groundbreaking advancement in remote sensing technology, providing real-time and high-resolution measurements of atmospheric pollutants and air quality monitoring. This presentation will highlight the significance of TEMPO and its data distribution by NASA’s Atmospheric Science Data Center (ASDC) to facilitate the analyses by the research and end user communities. TEMPO's geostationary orbit allows for continuous and high-resolution measurements of key atmospheric pollutants, including nitrogen dioxide (NO 2 ), ozone (O 3 ), and formaldehyde (HCHO). As a result, researchers can investigate the distribution patterns of these pollutants on an hourly basis, providing valuable information for understanding regional and temporal variations in air quality. Furthermore, this presentation will highlight the usability of TEMPO data in complementing ground-based air quality monitoring networks. ASDC distribution services and tools facilitate efficient data handling and analysis to support research and application uses of TEMPO data. As part of NASA’s Earthdata ecosystem, TEMPO will be available through Earthdata Search and Worldview, as well as have variable and spatial subsetting capabilities. This presentation will provide an overview of data access and services available for TEMPO data.

Hazem Mahmoud↗
NASA NTRS1976

Onboard Experiment Data Support Facility (OEDSF): Conceptual design study

The Onboard Experimental Data Support Facility (OEDSF) is an inflight data processor based on a totally new architecture specifically developed to cost-effectively process the data of Shuttle payloads sensors. Processing data onboard fills the following needs: (1) reduction of data bulk by conversion to information (2)quick-look for evaluation, interactive operation, etc. (3) real-time computation of engineering representation of sensed phenomena. For example: Value of backscatter coefficient (sigma) of a scatterometer as a function of latitude and longitude (4) exploitation of the real-time availability of ancillary data, thereby obviating the need for time-tagging, recording, and recorrelation and (5) providing data or information immediately usable by the experimenter or user. The OEDSF is made up of modular and cascadable matrix processors. Each matrix has been sized to process the data of a full typical shuttle payload. Cost analyses indicate that significant savings are realized by processing data with the OEDSF compared with conventional ground facilities.

Source record↗
NASA NTRS1991

Real-time expert system and neural network for the classification of remotely sensed data

The paper examines software techniques for classifying remotely sensed data such that the number of computational steps and the amount of resources are bounded. The combination of both neural network and expert system methodology for classifying these data based on land use/land cover categories is examined. The method involves pipelining images through a neural net for initial classification and then through the expert system which resolves the ambiguous classifications. As with any pipeline, every component must have approximately equivalent run-times or otherwise a bottleneck will occur. If real-time is a requirement, each of the components must execute within a bounded number of steps. Attention is focused on the real-time system technique, which is argued to prevent a bottleneck for this data classification application.

Short, Nicholas, Jr.↗
NASA NTRS2004

Networking Technologies Enable Advances in Earth Science

This paper describes an experiment to prototype a new way of conducting science by applying networking and distributed computing technologies to an Earth Science application. A combination of satellite, wireless, and terrestrial networking provided geologists at a remote field site with interactive access to supercomputer facilities at two NASA centers, thus enabling them to validate and calibrate remotely sensed geological data in near-real time. This represents a fundamental shift in the way that Earth scientists analyze remotely sensed data. In this paper we describe the experiment and the network infrastructure that enabled it, analyze the data flow during the experiment, and discuss the scientific impact of the results.

Johnson, Marjory↗
NASA NTRS2002

Real-Time Integrity Monitoring of Stored Geo-Spatial Data Using Forward-Looking Remote Sensing Technology

Terrain Awareness and Warning Systems (TAWS) and Synthetic Vision Systems (SVS) provide pilots with displays of stored geo-spatial data (e.g. terrain, obstacles, and/or features). As comprehensive validation is impractical, these databases typically have no quantifiable level of integrity. This lack of a quantifiable integrity level is one of the constraints that has limited certification and operational approval of TAWS/SVS to "advisory-only" systems for civil aviation. Previous work demonstrated the feasibility of using a real-time monitor to bound database integrity by using downward-looking remote sensing technology (i.e. radar altimeters). This paper describes an extension of the integrity monitor concept to include a forward-looking sensor to cover additional classes of terrain database faults and to reduce the exposure time associated with integrity threats. An operational concept is presented that combines established feature extraction techniques with a statistical assessment of similarity measures between the sensed and stored features using principles from classical detection theory. Finally, an implementation is presented that uses existing commercial-off-the-shelf weather radar sensor technology.

Young, Steven D.↗
DOE OSTI2023

Arrays of Functionalized Graphene Chemiresistors for Selective Sensing of Volatile Organic Compounds

Real-time detection of volatile organic compounds (VOCs) is being spotlighted for its importance in environmental monitoring and disease detection from the breath. However, the selective detection of VOCs in a small form factor gas sensor remains a challenge. Here, we present the VOC sensing performance of phthalocyanine (Pc)-functionalized graphene chemiresistors supported by the theoretical prediction of Pc's VOC interactions. Pc functionalization, with and without metalation, was studied parametrically with varying deposition parameters to explore impacts on the gas sensing response toward VOCs as a function of substrate coverage. Sensing data were collected to establish fingerprint signatures for a variety of VOCs. In conclusion, improvement in selectivity is shown by successfully differentiating the VOCs from each other when combined into a gas sensor array tested with acetone, ethanol, formaldehyde, and toluene.

2D materials↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2020

PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now): A Near-Real-Time, Quasi-Global Satellite Precipitation Dataset

This study presents the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Dynamic Infrared Rain Rate (PDIR-Now) near-real-time precipitation dataset. This dataset provides hourly, quasi-global, infrared-based precipitation estimates at 0.04° × 0.04° spatial resolution with a short latency (15–60 min). It is intended to supersede the PERSIANN–Cloud Classification System (PERSIANN-CCS) dataset previously produced as the near-real-time product of the PERSIANN family. We first provide a brief description of the algorithm’s fundamentals and the input data used for deriving precipitation estimates. Second, we provide an extensive evaluation of the PDIR-Now dataset over annual, monthly, daily, and subdaily scales. Last, the article presents information on the dissemination of the dataset through the Center for Hydrometeorology and Remote Sensing (CHRS) web-based interfaces. The evaluation, conducted over the period 2017–18, demonstrates the utility of PDIR-Now and its improvement over PERSIANN-CCS at all temporal scales. Specifically, PDIR-Now improves the estimation of rain/no-rain days as demonstrated by a critical success index (CSI) of 0.53 compared to 0.47 of PERSIANN-CCS. In addition, PDIR-Now improves the estimation of seasonal and diurnal cycles of precipitation as well as regional precipitation patterns erroneously estimated by PERSIANN-CCS. Finally, an evaluation is carried out to examine the performance of PDIR-Now in capturing two extreme events, Hurricane Harvey and a cluster of summer thunderstorms that occurred over the Netherlands, where it is shown that PDIR-Now adequately represents spatial precipitation patterns as well as subdaily precipitation rates with a correlation coefficient (CORR) of 0.64 for Hurricane Harvey and 0.76 for the Netherlands thunderstorms.

Rainfall↗
DOE OSTI2025

Monitoring strain evolution in water-sand systems using distributed acoustic sensing for geohazard early warning

Rainfall-driven hazards such as landslides, debris flows, and earthen dam failures often arise when water changes the internal strain within sand. This study evaluates the ability of distributed acoustic sensing to monitor these strain changes in real time. We embed a fiber-optic cable in a sand-filled glass cylinder and run controlled dry- and wet-sand experiments to measure how strain develops as water infiltrates, saturates, and drains from the sand. The sensing system detects uneven water movement in dry sand and enables millimeter-scale estimates of infiltration rates, and in wet sand it tracks rising water levels, delayed strain peaks after saturation, and abrupt strain shifts during drainage. These results show that fiber-optic sensing captures subtle strain evolution throughout the full water-sand interaction cycle. The study demonstrates that fiber-optic sensing offers promising potential for real-time and cost-effective monitoring and early warning of rainfall-induced geohazards.

58 GEOSCIENCES↗
DOE OSTI2023

Automated piezoresponse force microscopy domain tracking during fast thermally stimulated phase transition in CuInP 2 S 6

Real-time tracking of dynamic nanoscale processes such as phase transitions by scanning probe microscopy is a challenging task, typically requiring extensive and laborious human supervision. Smart strategies to track specific regions of interest (ROI) in the system during such transformations in a fast and automated manner are necessary to study the evolution of the microscopic changes in such dynamic systems. In this work, we realize automated ROI tracking in piezoresponse force microscopy during a fast (≈0.8 °C s –1 ) thermally stimulated ferroelectric-to-paraelectric phase transition in CuInP 2 S 6 . We use a combination of fast (1 frame per second) sparse scanning with compressed sensing image reconstruction and real-time offset correction via phase cross correlation. The applied methodology enables in situ fast and automated functional nanoscale characterization of a certain ROI during external stimulation that generates sample drift and changes local functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Flexible organic integrated electronics for self-powered multiplexed ocular monitoring

Smart contact lens has drawn extensive research interests due to the noninvasive real-time detection of the human body to provide biomedical information for health management. However, it has been difficult to accurately measure the physiological signals in tears, and the use of external power source has also hindered the future applications. Here, we demonstrated an organic electrochemical transistor based multiplexed sensors self-powered by the organic solar cells (OSCs). The integrated device was fabricated via simple process including solution blade-coating and thermal evaporation. OSCs were optimized to provide optimal operation voltage for the sensors that exhibit semilog-linear response to the glucose and calcium ions in tear fluids without any peripheral circuits. The sensing signals can be transmitted to the laptop wirelessly through a near filed communication unit. This integrated self-powered multiplexed sensing device will provide real-time monitoring of the biomarkers in tears, prospected to be installed on the smart contact lens for the early detection and diagnosis of diabetes.

36 MATERIALS SCIENCE↗
NASA NTRS1984

Method to estimate drag coefficient at the air/ice interface over drifting open pack ice from remotely sensed data

A knowledge in near real time, of the surface drag coefficient for drifting pack ice is vital for predicting its motions. And since this is not routinely available from measurements it must be replaced by estimates. Hence, a method for estimating this variable, as well as the drag coefficient at the water/ice interface and the ice thickness, for drifting open pack ice was developed. These estimates were derived from three-day sequences of LANDSAT-1 MSS images and surface weather charts and from the observed minima and maxima of these variables. The method was tested with four data sets in the southeastern Beaufort sea. Acceptable results were obtained for three data sets. Routine application of the method depends on the availability of data from an all-weather air or spaceborne remote sensing system, producing images with high geometric fidelity and high resolution.

Feldman, U.↗
NASA NTRS2017

Prediction and Warning of Transported Turbulence in Long-Haul Aircraft Operations

An aviation flight planning system is used for predicting and warning for intersection of flight paths with transported meteorological disturbances, such as transported turbulence and related phenomena. Sensed data and transmitted data provide real time and forecast data related to meteorological conditions. Data modelling transported meteorological disturbances are applied to the received transmitted data and the sensed data to use the data modelling transported meteorological disturbances to correlate the sensed data and received transmitted data. The correlation is used to identify transported meteorological disturbances source characteristics, and identify predicted transported meteorological disturbances trajectories from source to intersection with flight path in space and time. The correlated data are provided to a visualization system that projects coordinates of a point of interest (POI) in a selected point of view (POV) to displays the flight track and the predicted transported meteorological disturbances warnings for the flight crew.

Shipley, Scott T.↗
DOE OSTI2021

Synergistic Integration of Chemo-Resistive and SERS Sensing for Label-Free Multiplex Gas Detection

Practical sensing applications such as real-time safety alerts and clinical diagnoses require sensor devices to differentiate between various target molecules with high sensitivity and selectivity, yet conventional devices such as oxide-based chemo-resistive sensors and metal-based surface-enhanced Raman spectroscopy (SERS) sensors usually do not satisfy such requirements. In this report a label-free, chemo-resistive/SERS multimodal sensor based on a systematically assembled 3D cross-point multifunctional nanoarchitecture (3D-CMA), which has unusually strong enhancements in both “chemo-resistive” and “SERS” sensing characteristics is introduced. 3D-CMA combines several sensing mechanisms and sensing elements via 3D integration of semiconducting SnO 2 nanowire frameworks and dual-functioning Au metallic nanoparticles. It is shown that the multimodal sensor can successfully estimate mixed-gas compositions selectively and quantitatively at the sub-100 ppm level, even for mixtures of gaseous aromatic compounds (nitrobenzene and toluene) with very similar molecular structures. This is enabled by combined chemo-resistive and SERS multimodal sensing providing complementary information.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Magnetic and Impedance Analysis of Fe 2 O 3 Nanoparticles for Chemical Warfare Agent Sensing Applications

A dire need for real-time detection of toxic chemical compounds exists in both civilian and military spheres. In this paper, we demonstrate that inexpensive, commercially available Fe 2 O 3 nanoparticles are capable of selective sensing of chemical warfare agents (CWAs) using frequency-dependent impedance spectroscopy, with additional potential as an orthogonal magnetic sensor. X-ray magnetic circular dichroism analysis shows that Fe 2 O 3 nanoparticles possess moderately lowered moment upon exposure to 2-chloroethyl ethyl sulfide (2-CEES) and diisopropyl methylphosphonate (DIMP) and significantly lowered moment upon exposure to dimethyl methylphosphonate (DMMP) and dimethyl chlorophosphate (DMCP). Associated X-ray absorption spectra confirm a redox reaction in the Fe 2 O 3 nanoparticles due to CWA structural analog exposure, with differentiable energy-dependent features that suggest selective sensing is possible, given the correct method. Impedance spectroscopy performed on samples dosed with DMMP, DMCP, and tabun (GA, chemical warfare nerve agent) showed strong, differentiable, frequency-dependent responses. The frequency profiles provide unique “shift fingerprints” with which high specificity can be determined, even amongst similar analytes. The results suggest that frequency-dependent impedance fingerprinting using commercially available Fe 2 O 3 nanoparticles as a sensor material is a feasible route to selective detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗