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All sources NASA NTRS DOE OSTI

NASA NTRS2011

Dynamic Shape Reconstruction of Three-Dimensional Frame Structures Using the Inverse Finite Element Method

A robust and efficient computational method for reconstructing the three-dimensional displacement field of truss, beam, and frame structures, using measured surface-strain data, is presented. Known as shape sensing , this inverse problem has important implications for real-time actuation and control of smart structures, and for monitoring of structural integrity. The present formulation, based on the inverse Finite Element Method (iFEM), uses a least-squares variational principle involving strain measures of Timoshenko theory for stretching, torsion, bending, and transverse shear. Two inverse-frame finite elements are derived using interdependent interpolations whose interior degrees-of-freedom are condensed out at the element level. In addition, relationships between the order of kinematic-element interpolations and the number of required strain gauges are established. As an example problem, a thin-walled, circular cross-section cantilevered beam subjected to harmonic excitations in the presence of structural damping is modeled using iFEM; where, to simulate strain-gauge values and to provide reference displacements, a high-fidelity MSC/NASTRAN shell finite element model is used. Examples of low and high-frequency dynamic motion are analyzed and the solution accuracy examined with respect to various levels of discretization and the number of strain gauges.

Gherlone, Marco↗

NASA NTRS2020

A Systematic Review of Local to Regional Yield Forecasting Approaches and Frequently Used Data Resources

Forecasting crop yields, or providing an expectation of ex-ante harvest amounts, is highly relevant to the whole agricultural production chain. Farmers can adapt their management, traders or insurers their pricing schemes, suppliers their stocks, logistic companies their routes, national authorities their food balance sheets to guide import or export and, finally, international aid organizations can mobilize reliefs. Evidence has grown in the literature that such forecasts with a meaningful lead time are possible on various geographic scales and for a broad range of crops. Here, we present a systematic review of the methods applied in end-of-season yield forecasting and three frequently used data sources: weather data, satellite data and crop masks. Our literature database comprises 362 studies (2004–2019) which were evaluated regarding methods, crops, regions, data sources, lead time and performance. Moreover, we present 24 sources of real-time and predictive weather data, 21 sources of remote sensing data and 16 crop masks. Yield forecasting in our literature sample has been performed for 44 crops in 71 countries, also including many non-staple crops, but with an apparent bias in regions and crops. Forecasting performance depends on various factors, including crop, region, method, lead time to harvest and input diversity. Our systematic review supports a broader application of locally successful approaches at larger scales by providing a comprehensive, accessible compendium of necessary information for yield forecasting. We discuss improvement potentials with respect to methodological approaches and available data sources. We additionally suggest standardization procedures for future forecasting studies and encourage studying additional crops and geographic regions. Implications of forecasts for different target groups on different scales and the adaptation towards climate change are also discussed.

Seasonal crop yield forecasting↗

NASA NTRS2011

Optical Navigation for Dawn at Vesta

The Dawn S/C, launched in September 2007, towards Vesta and Ceres, will enter into orbit about asteroid Vesta in July 2011 and will conduct science remote sensing operations for approximately one year at various orbital altitudes. Vesta navigation operations begin with early approach in May 2011 until departure to Ceres in July 2012. A key navigation aspect is optical navigation, which will be conducted at all mission phases. Here we review the optical navigation plan, imaging, methodology, data types, as well as expected performance in the context of the overall mission navigation. A key aspect of optical navigation at Dawn that will receive particular attention is the extensive use of landmark navigation during most of mission phases. In addition to supporting real-time navigation operations, optical navigation will be used to determine some key physical characteristics of Vesta, such as the asteroid's pole & shape, to assist mission design & science operations.

Vesta↗

DOE OSTI2024

FiberFlex: Real-time FPGA-based Intelligent and Distributed Fiber Sensor System for Pedestrian Recognition

In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.

Distributed↗

NASA NTRS2008

Triboluminescent Materials for Smart Optical Damage Sensors for Space Applications

There is a need to develop a new technique of damage detection for composites, which could detect cracking or delamination from any desired location within a material structure in real time. Recently, triboluminescent materials have been proposed as smart sensors of structural damage. To sense the damage, these materials can be epoxy bonded, coated in a polymer matrix, or embedded in a composite host structure. When the damage or fracture takes place in the host structure, the resultant fracture of triboluminescent crystals creates a light emission. This will warn in real time that structural damage has occurred. The triboluminescent emission of the candidate phosphor has to be bright enough that the light reaching from the point of fracture to the detector through a fiber optic cable is detectable. There are a large number of triboluminescent materials, but few satisfy the above criterion. The authors have synthesized an organic material known as Europium tetrakis (dibenzoylmethide) triethylammonium (EuD4TEA), which is a potential candidate for application as a damage sensor and could be made into a wireless sensor with the addition of microchip, antenna, and electronics. Preliminary results on the synthesis and characterization of this material are presented.

Aggarwal, M. D.↗

NASA NTRS2013

Advances in Digital Calibration Techniques Enabling Real-Time Beamforming SweepSAR Architectures

Real-time digital beamforming, combined with lightweight, large aperture reflectors, enable SweepSAR architectures, which promise significant increases in instrument capability for solid earth and biomass remote sensing. These new instrument concepts require new methods for calibrating the multiple channels, which are combined on-board, in real-time. The benefit of this effort is that it enables a new class of lightweight radar architecture, Digital Beamforming with SweepSAR, providing significantly larger swath coverage than conventional SAR architectures for reduced mass and cost. This paper will review the on-going development of the digital calibration architecture for digital beamforming radar instrument, such as the proposed Earth Radar Mission's DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) instrument. This proposed instrument's baseline design employs SweepSAR digital beamforming and requires digital calibration. We will review the overall concepts and status of the system architecture, algorithm development, and the digital calibration testbed currently being developed. We will present results from a preliminary hardware demonstration. We will also discuss the challenges and opportunities specific to this novel architecture.

TR (Transmit/Receive)↗

NASA NTRS2001

Remote, Noncontact Strain Sensing by Laser Diffraction Developed

A system was developed at the NASA Glenn Research Center for continually monitoring, in real time, the in-plane strain tensor in opaque solids during high-temperature, long-term mechanical testing. The simple, noncontacting, strain-sensing methodology should also be suitable for measurement in hostile environments. This procedure has obvious advantages over traditional, mechanical, contacting techniques, and it is easier to interpret than moir and speckle interferometric approaches. A two-dimensional metallic grid of micrometer dimensions is applied to a metallographically prepared gauge section on the surface of a tensile test specimen by a standard photolithographic process. The grid on the fixtured specimen is interrogated by an He-Ne laser, and the resulting diffraction pattern is projected backwards onto a translucent screen. A charge-coupled device (CCD) camera is used to image the first-order diffraction peaks from the translucent screen. A schematic representation of the system is shown in the figure.

Freedman, Marc R.↗

NASA NTRS2004

Rapid Chemometric Filtering of Spectral Data

A method of rapid, programmable filtering of spectral transmittance, reflectance, or fluorescence data to measure the concentrations of chemical species has been proposed. By programmable is meant that a variety of spectral analyses can readily be performed and modified in software, firmware, and/or electronic hardware, without need to change optical filters or other optical hardware of the associated spectrometers. The method is intended to enable real-time identification of single or multiple target chemical species in applications that involve high-throughput screening of multiple samples. Examples of such applications include (but are not limited to) combinatorial chemistry, flow cytometry, bead assays, testing drugs, remote sensing, and identification of targets. The basic concept of the proposed method is to perform real-time crosscorrelations of a measured spectrum with one or more analytical function(s) of wavelength that could be, for example, the known spectra of target species. Assuming that measured spectral intensities are proportional to concentrations of target species plus background spectral intensities, then after subtraction of background levels, it should be possible to determine target species concentrations from cross-correlation values. Of course, the problem of determining the concentrations is more complex when spectra of different species overlap, but the problem can be solved by use of multiple analytical functions in combination with computational techniques that have been developed previously for analyses of this type. The method is applicable to the design and operation of a spectrometer in which spectrally dispersed light is measured by means of an active-pixel sensor (APS) array. The row or column dimension of such an array is generally chosen to be aligned along the spectral-dispersion dimension, so that each pixel intercepts light in a narrow spectral band centered on a wavelength that is a known function of the pixel position. The proposed method admits of two hardware implementations for computing cross-correlations in real time.

Beaman, Gregory↗

NASA NTRS2006

Flood and Landslide Applications of High Time Resolution Satellite Rain Products

Experimental, potentially real-time systems to detect floods and landslides related to heavy rain events are described. A key basis for these applications is high time resolution satellite rainfall analyses. Rainfall is the primary cause for devastating floods across the world. However, in many countries, satellite-based precipitation estimation may be the best source of rainfall data due to insufficient ground networks and absence of data sharing along many trans-boundary river basins. Remotely sensed precipitation from the NASA's TRMM Multi-satellite Precipitation Analysis (TMPA) operational system (near real-time precipitation at a spatial-temporal resolution of 3 hours and 0.25deg x 0.25deg) is used to monitor extreme precipitation events. Then these data are ingested into a macro-scale hydrological model which is parameterized using spatially distributed elevation, soil and land cover datasets available globally from satellite remote sensing. Preliminary flood results appear reasonable in terms of location and frequency of events, with implementation on a quasi-global basis underway. With the availability of satellite rainfall analyses at fine time resolution, it has also become possible to assess landslide risk on a near-global basis. Early results show that landslide occurrence is closely associated with the spatial patterns and temporal distribution of TRMM rainfall characteristics. Particularly, the number of landslides triggered by rainfall is related to rainfall climatology, antecedent rainfall accumulation, and intensity-duration of rainstorms. For the purpose of prediction, an empirical TMPA-based rainfall intensity-duration threshold is developed and shown to have skill in determining potential areas of landslides. These experimental findings, in combination with landslide surface susceptibility information based on satellite-based land surface information, form a starting point towards a potential operational landslide monitoring/warning system around the globe.

Adler, Robert F.↗

DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗

DOE OSTI2021

Distributed fiber optic strain sensing of bending deformation of a well mockup in the laboratory

Well integrity is critical to the safety and success of subsurface energy exploration and management, as leakage of fluids from subsurface reservoirs is often induced by compromised wells. This study investigates bending deformation of a mockup of an oil/gas well that is subjected to loads expected in deviated wells under reservoir compaction and assesses the feasibility of utilizing distributed fiber optic strain sensing to monitor the deformation. Here, a well mockup, which consists of outer and inner steel pipes with a cemented annulus, is tested under three-point bending loading and its strain and curvature development is monitored by Brillouin optical time domain reflectometry/analysis (BOTDR/A) as well as optical frequency domain reflectometry (OFDR). The primary objective of this research is to assess the strain sensing performance of newly fabricated fiber optic cables and to identify key cable characteristics which could improve the quality of distributed strain measurements with BOTDR/A. Results show that the tight-buffered cable is best suited for strain sensing as its maximum errors in the strain measurement were -36% and -24% against conventional sensors at the maximum elastic and plastic bending loads, respectively, whereas those of the non-tight-buffered cable were -45% and -71%, respectively. Similar trends were obtained in the bending curvature measurement. The detailed design of the tight-buffered cable is presented to elucidate key characteristics of such a cable, which will facilitate accurate distributed strain sensing in oil and gas wells.

42 ENGINEERING↗

NASA NTRS1971

Mechanism of underthrusting in southwest Japan: A model of convergent plate interactions

An elastic rebound mechanism consistent with underthrusting at the time of the magnitude 8.2 Nankaido earthquake of 21 December, 1946 accounts for a reversal in sense between seismic and preseismic changes in elevation throughout a large portion of southwest Japan. It is shown that the seismic movements during the earthquake were generated by slip on a complex thrust fault that is inferred to intersect the surface near the base of the continental slope. The preseismic deformation is explained by strain accumulation equivalent to a virtual dislocation with the same orientation as the fault surface but a sense opposite to that of the real dislocation that occurred at the time of the earthquake. Adjustments by slip along the fault surface and extensions of that surface can account for postseismic movements.

Fitch, T. J.↗

DOE OSTI2024

Mercury Detection Utilizing an Aquatic Animal as a Remote-Sensing Platform

Mercury species in open water, especially the accumulated methylmercury ion, pose a threat to fish and environment. Therefore, it is important to develop a small sensor package that can be integrated into a biotelemetry sensor carried by an aquatic animal, enhancing the ability to detect mercury contamination in large water areas. A quartz crystal microbalance (QCM) sensor using metal-organic framework (MOF) as sensing material was developed to detect mercury and methylmercury ions in real time based on acoustic wave perturbation. Thiol groups were introduced into the MOF UiO-66 through the organic linker to prepare the UiO-66-SH which was confirmed by infrared spectroscopy results. Batch adsorption experiments were conducted for the Hg 2+ , CH 3 Hg + , and Ca 2+ ions adsorption in the UiO-66-SH. The adsorption capacities of the mercury ions were more than an order of magnitude higher than those of the competing Ca 2+ ions at the same concentration. The frequency changes of the QCM sensor with the UiO-66-SH sensing film were an order of magnitude higher than those of the controlled baseline QCM sensor without a sensing film. Additionally, the frequency change can be tailored by adjusting the thickness of the MOF film and the adsorption properties of the sensing material. The sensor frequency change correlates well with ion adsorption capacities.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Efficient Network Partitioning: Application for Decentralized State Estimation in Power Distribution Grids

Increase in the proliferation of distributed energy resources require real-time situational awareness for efficient grid operations. State estimation plays an important role for the real-time control and management of the power grid. As the sensing infrastructure grows, aggregating and handling high volumes of data at a centralized location is extremely difficult. To address this challenge, this paper first proposes a novel and efficient hier-archical spectral clustering-based network partitioning algorithm followed by a decentralized compressive sensing (DCS)-based state estimation. The applicability of the proposed network partitioning algorithm is tested on an IEEE 123-bus network, an IEEE 8,500-node system, and a 6,000+ node distribution network. The results shows that the proposed approach efficiently divides the network into multiple sub-networks with the minimum number of edge connections among the neighbors. Then, we perform DCS-based state estimation on the 6,000+ node distribution network after dividing the network into 18 optimal partitions. Simulation results show that the DCS-based state estimation recovers the system states with high accuracy and low complexity.

alternating direction method of multipliers↗

DOE OSTI2022

Feature Extraction for Pipeline Defects Inspection Based Upon Distributed Acoustic Fiber Optic Sensing Data

Fiber-optic distributed acoustic sensing (DAS) is becoming an increasingly important tool for real-time monitoring of energy and civil infrastructure structural health such as pipelines. We present a systematic theoretical study of the potential for DAS to be directly coupled with guided ultrasonic waves typically used in conventional acoustic non-destructive evaluation (NDE) methods for real-time pipeline health monitoring. We are referring to this innovative new NDE technique as ultrasonic guided wave and optical fiber sensor fusion. In the practical application of DAS coupled with guided ultrasonic waves, the structural design of (1) the specific guided waves excited, (2) the physical installation of the acoustic transducers and the fiber optic sensors, and (3) the functional performance specifications (gauge length, sensitivity, Etc.) of fiber optic DAS have an important influence on overall capabilities of the monitoring system. Meanwhile, physics-based analysis of acoustic waves is still a challenge due to the complex nature of the Lamb wave when it propagates, scatters, and disperses in the presence of structural defects. In this work, we simulate carbon steel pipes relevant for oil and gas pipeline applications with diameters of approximately 6-12” and wall thickness of 0.5” as the objects to be monitored. By establishing and implementing these capabilities, we seek to pursue an in-depth study on structural parameter optimization of DAS network, measurement range, and signal processing with an ultimate goal of increasing the sensitivity and efficacy of DAS to defect identification for various modes of corrosion expected in practice. To study the characteristics of scattered acoustic waves and performance of DAS for defect identification, we simulated the response of DAS for multiple pipe structures, defect types, and DAS sensor network configuration using finite element software Ansys, then the properties of signal response are extracted to construct defect-sensitive features. The raw data simulated, and the associated features extracted can ultimately be utilized as annotated training data to benchmark various designs for DAS applications, guided acoustic excitation sources, and learning model parameters to enhance early detection of potentially problematic defects.

Pipeline Defects Inspection, Fiber-optic sensors, ↗

DOE OSTI2025

Sensos Smart Label Performance Summary as Observed by Oak Ridge National Laboratory

The Oak Ridge National Laboratory (ORNL) team performed an evaluation of the Sensos Smart Label Gen 2.0, as shown in Figure 1, for package tracking. A long-distance round-trip shipment between Oak Ridge, Tennessee, and Seattle, Washington, was completed to assess the device’s performance in location tracking, environment sensing capabilities, alerting features, threshold options, battery life, and real-time and historical data retrieval from the “Sync” data dashboard provided by Sensos. The evaluation was conducted to gain a general understanding of the capabilities of the device. Furthermore, due to time and resource constraints, ORNL did not conduct exhaustive testing to confirm reliability, availability, or effectiveness of alerting and tracking features. On equipment arrangement, Sensos (sensos.ai) graciously agreed to provide a Sensos Smart Label Gen 2.0 device to ORNL, at no cost, for testing and evaluation purposes. ORNL conducted assessments along with other commercial off-the-shelf (COTS) tracking devices. As a courtesy, ORNL will provide Sensos with this report summarizing the observations and findings specific to the Sensos label based on the tests performed.

42 ENGINEERING↗

NASA NTRS1980

Remote sensing of particulate concentrations in water

Determination of residual radiance is essential in remote sensing measurements of suspended solids in the absence of real-time ground-truth data. Some typical background corrections are presented for Lake Superior and the spectral distribution of the residual radiance is determined from three major categories of turbidity in the lake. The results tested with Landsat 2 digital tape data indicate that for large bodies of water general information on atmospheric scattering, water clarity, and optical properties of suspended solids enables one to estimate the concentrations of particulates to within + or - 0.5 mg/liter in the absence of real-time ground-truth data.

Sydor, M.↗

NASA NTRS1989

Passive millimeter-wave imaging - A tool for remote sensing

This paper describes a program of airborne radiometric imaging at 90 GHz and 140 GHz. Using high sensitivity (below 1 K) and high angular resolution (0.5-1.0 degree), high quality images have been made. The following measurements are discussed: cloud and fog penetration at 90 GHz, discrimination between agricultural and urban areas, discrimination between different vegetation types, detection of vehicles on roads, detection and classification of airports and airplanes, ship detection and quantitative oil spill sensing. The application of information enhancement techniques with automatic and real time application aspects is also described, and results of applied techniques for contrast and contour enhancement are shown.

Suess, Helmut↗