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NASA NTRS2008

Remote-Sensing Time Series Analysis, a Vegetation Monitoring Tool

The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify a variety of plant phenomena and improve monitoring capabilities.

McKellip, Rodney↗

DOE OSTI2020

Minimum Detectable Quantity Calculation for Radiation Portal Monitors

The minimum detectable quantity of a radiation portal monitor is the smallest amount of radioactive material that can be detected passing through the monitor (with a specified detection probability). The minimum detectable quantity is a function of many factors, including isotope, velocity, distance between pillars, height of portal and vehicle, source distribution and position, background radiation, background suppression, detector volume and positions, detector efficiency, decision metrics and algorithms, the presence of NORM (naturally occurring radioactive material), and alarm thresholds. Experimentally testing a radiation portal monitor to failure for all combinations of these factors is extremely time-consuming and often cost prohibitive. This document outlines a straightforward method for quickly estimating the minimum detectable quantity for moving sources over a large variety of conditions with a minimal number of static measurements. The document also describes a proof of concept software application that the International Atomic Energy Agency has designed based on the described method that can be used by Member States to estimate a monitor’s minimum detectable quantity.

Blessinger, Christopher S.↗

DOE OSTI2025

Optical Fiber H2 Sensor Operating in Harsh Environments of Subsurface H2 Storage Reservoirs

Monitoring hydrogen concentration in the subsurface storage reservoirs is vital to ensure the integrity and safety of the storage facilities. An optical fiber hydrogen sensor consisting of a palladium-based sensing layer and a protective polymer layer was developed and evaluated in simulated subsurface hydrogen storage conditions. The developed optical fiber hydrogen sensor has demonstrated successful sensing performance at ~80 °C, ~1,000 psi, and ~100% RH. In addition, the sensor was exposed to real subsurface microbial samples in the harsh environments to monitor microbially induced changes in hydrogen concentration. The sensor has shown stable H2 sensing responses in the replicated underground hydrogen storage conditions without deterioration or loss of H2 sensitivity in the presence of biological samples.

filter layer↗

NASA NTRS2013

Chemical Pollution from Combustion of Modern Spacecraft Materials

Fire is one of the most critical contingencies in spacecraft and any closed environment including submarines. Currently, NASA uses particle based technology to detect fires and hand-held combustion product monitors to track the clean-up and restoration of habitable cabin environment after the fire is extinguished. In the future, chemical detection could augment particle detection to eliminate frequent nuisance false alarms triggered by dust. In the interest of understanding combustion from both particulate and chemical generation, NASA Centers have been collaborating on combustion studies at White Sands Test Facility using modern spacecraft materials as fuels, and both old and new technology to measure the chemical and particulate products of combustion. The tests attempted to study smoldering pyrolysis at relatively low temperatures without ignition to flaming conditions. This paper will summarize the results of two 1-week long tests undertaken in 2012, focusing on the chemical products of combustion. The results confirm the key chemical products are carbon monoxide (CO), hydrogen cyanide (HCN), hydrogen fluoride (HF) and hydrogen chloride (HCl), whose concentrations depend on the particular material and test conditions. For example, modern aerospace wire insulation produces significant concentration of HF, which persists in the test chamber longer than anticipated. These compounds are the analytical targets identified for the development of new tunable diode laser based hand-held monitors, to replace the aging electrochemical sensor based devices currently in use on the International Space Station.

Mudgett, Paul D.↗

NASA NTRS2009

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Riggs, George A.↗

DOE OSTI2021

Comparison of temperature adaptive calibration methods for laser induced fluorescence based fuel-in-oil instrument

Fuel dilution of engine oil (or fuel-in-oil, FiO) is an important issue as multiple and late-cycle fuel injection, integral to many combustion efficiency and emissions improvements (e.g., downsized boosted gasoline engines and catalyst thermal management) increases FiO rate. In addition to causing general wear and corrosion in engine due to decreased oil viscosity and pH buffering, FiO is also believed to cause destructive low speed pre-ignition (or super knock) in boosted SI engines. To understand the effects of engine operating conditions on the FiO rate, an optical diagnostic capable of measuring transient FiO on minute timescales was recently developed and demonstrated on a modified GM Ecotech engine system (Neupane et al., Applied Spectroscopy 2021). The measurement is based on adding a dye to the fuel and monitoring for its presence in oil via laser-induced fluorescence (LIF). The measured LIF signal is related to FiO concentration via pre-determined calibration factors using a multivariate classical least square (CLS) method.Since fluorescence quantum yield is a function of temperature, measured LIF intensity not only depends on FiO concentration but also oil temperature. To expand the applicability of the FiO diagnostic to transient oil-temperature conditions (e.g., cold start in practical engines), this study develops a method to account for oil-temperature variations. The effect of oil temperature (20°C - 95°C) on the LIF spectra of eight FiO samples ranging from ~0.8-15% was investigated. LIF intensities of the FiO samples decreased linearly with increasing temperature; the reductions being more significant at dye peaks. We develop a new calibration model (T-adaptive CLS) incorporating the temperature (T) effects on LIF intensity that enables simultaneous calculation of FiO and oil temperature. The improved FiO diagnostic with T-adaptive calibration is more robust, and applicable to varying oil-temperature conditions. For example, when strategies such as multiple/late fuel injections are applied to overcome cold-start instability due to use of low vaporization bio-based fuels such as ethanol, FiO rate is expected to be very high; the improved T-adaptive FiO diagnostic is hence relevant for engine- and fuel-system calibration and optimization. The diagnostic could also provide validation data for flow-field and spray interaction CFD models, further broadening the diagnostic’s utility for advancing engine technology and efficiency.

Neupane, Sneha↗

DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning

Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive.

PIDcontrol↗

NASA NTRS1997

Estimating Trabecular Bone Mechanical Properties From Non-Invasive Imaging

An important component in developing countermeasures for maintaining musculoskeletal integrity during long-term space flight is an effective and meaningful method of monitoring skeletal condition. Magnetic resonance imaging (MRI) is an attractive non-invasive approach because it avoids the exposure to radiation associated with X-ray based imaging and also provides measures related to bone microstructure rather than just density. The purpose of the research for the 1996 Summer Faculty Fellowship period was to extend the usefulness of the MRI data to estimate the mechanical properties of trabecular bone. The main mechanical properties of interest are the elastic modulus and ultimate strength. Correlations are being investigated between these and fractal analysis parameters, MRI relaxation times, apparent densities, and bone mineral densities. Bone specimens from both human and equine donors have been studied initially to ensure high-quality MR images. Specimens were prepared and scanned from human proximal tibia bones as well as the equine distal radius. The quality of the images from the human bone appeared compromised due to freezing artifact, so only equine bone was included in subsequent procedures since these specimens could be acquired and imaged fresh before being frozen. MRI scans were made spanning a 3.6 cm length on each of 5 equine distal radius specimens. The images were then sent to Dr. Raj Acharya of the State University of New York at Buffalo for fractal analysis. Each piece was cut into 3 slabs approximately 1.2 cm thick and high-resolution contact radiographs were made to provide images for comparing fractal analysis with MR images. Dual energy X-ray absorptiometry (DEXA) scans were also made of each slab for subsequent bone mineral density determination. Slabs were cut into cubes for mechanical using a slow-speed diamond blade wafering saw (Buehler Isomet). The dimensions and wet weights of each cube specimen were measured and recorded. Wet weights were also recorded. Each specimen was labeled and marked to denote anatomic orientations, i.e. superior/inferior (S/I), media/lateral (M/L), and anterior/posterior (A/P). The actual locations of each cube cut were documented and images distributed to define ROI locations for other analyses (to Raj Acharya for fractal analysis, to Jon Richardson at Baylor College of Medicine for DEXA, and to Chen Lin at Baylor College of Medicine for T2* MRI analysis). Quasistatic mechanical testing consisted of compressive loading in all three mutually perpendicular anatomic directions. Cyclic loading was applied for 10 cycles to precondition the specimen and results calculated for the eleventh. For one of three directions tested on each specimen, the 10 cycles were followed with loading to failure. Testing is currently proceeding and once completed the results will be correlated with data from the other analyses. One of the main points of interest is the relationship between fractal dimension and mechanical properties. Throughout preparation and testing all specimens were maintained hydrated with physiological saline and stored frozen when not being used.

Hogan, Harry A.↗

DOE OSTI2022

Resonant acoustic monitoring of damage in plug‐screw feeders

Abstract Plug‐screw feeders are critical in many industrial processes for compressing slurry materials via a rotating plug‐screw feeder. Over time, increasing plug‐screw feeder wear will eventually lead to catastrophic mechanical failure. Early detection of the wear state can prevent unplanned catastrophic failures resulting in operational shut‐downs, costly repairs, and most importantly the health and safety of workers. We present a theoretical basis for a noninvasive, in operando acoustic resonant technique to monitor the wear state of plug‐screw feeders. The technique is based on tracking the resonant acoustic modes of the plug‐screw feeder, which are sensitive to the plug‐screw feeder geometry, material, and operating conditions. We implemented a multivariate polynomial model to estimate the plug‐screw feeder wear state using multiple acoustic resonances by simulating the acoustic resonant modes for two categories of wear (tip and thread damage) that are common in plug‐screw feeders. Fitting multiple resonances to the polynomial model, we demonstrate accurate estimation of the total mass loss, as well as characterization of the type of damage (i.e., tip vs. thread). Current approaches for monitoring plug‐screw feeder wear rely on shutting down the operation and visually inspecting the plug‐screw feeder. The presented acoustic technique offers a noninvasive, in operando measurement approach that mitigates unplanned catastrophic failures. The acoustic resonance technique presented in the paper has a broad range of industrial applications including the Pharmaceutical, Mining, Integrated Biorefineries (IBR), and Additive Manufacturing industries, to name a few.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Augmented Monitoring and Condition Assessment Program (AMCAP) Material Test Reactor (MTR) (Fuel Inspection Program Report)

The AMCAP MTR Fuel Inspection Program, a special inspection program comprised of four inspection campaigns, examined a total of ten (10) pre-selected aluminum-clad, aluminum-based fuel core spent nuclear fuel assemblies (ASNF) stored in the L Area Disassembly Basin (L Basin) at the Savannah River Site. A full description of the inspections and the results are reported. The fuel had been stored in bundled-tube storage (Vertical Tube Storage) for periods of 18 to 21 years. The prior service experience of the individual 10 assemblies varies, but all included irradiation followed by wet storage at international research reactor sites prior to shipment to the US and storage in L Basin. The 10 assemblies were expected to be among the “worst"’ in terms of prior corrosion damage of the entire inventory of the direct-bundle-stored ASNF in L Basin. The inventory of MTR ASNF in L Basin will continue to be stored in the bundled-tube configuration or in slug-storage buckets with inserts pending retrieval for ultimate disposition. The MTR fuel inspections focused on collecting information for characterization of the material condition of the ASNF considering various types of aluminum fuel corrosion degradation of its assembled materials in water storage. A custom-designed Fuel Inspection Table was used to stage the fuel for remote, enhanced visual examination (close-up video imaging & recording) with controlled lighting and positioning that enables reproducibility of imaging conditions. The inspections were conducted by fuel subject matter expert staff from Spent Fuel Project Engineering (SFPE) and the Savannah River National Laboratory (SRNL). Stills captured from the video records were used to compare the corrosion evolution from previous records, as available. This evaluation of the inspection results including the comparison to the previous inspection results demonstrate that the water quality and the storage configuration of ASNF in L Basin do not cause aggressive corrosion degradation of the fuel; mitigation of the prior corrosion degradation of the fuel also appears to have been achieved with the good water quality conditions of L Basin. Recommendations are made for future inspection of the fuel to trend corrosion degradation and demonstrate continued safe wet storage of the ASNF in L Basin. The next fuel examination is recommended to be performed in 5 years.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRSDate not supplied

Enabling more routine monitoring of volcanic CO2 degassing from space

“Volcanic aerosol impacts on satellite trace gas retrievals: enabling volcanic emission plume composition measurements to track evolving sub-surface magmatic conditions” ROSES 2019 IDS (Interdisciplinary Science), 2020-2023 (S. Broccardo, PI) Main hypothesis: Space-based observations of the CO2, SO2, and H2O emissions of passively degassing volcanoes, and the derived mass fluxes and their ratios, can be used to constrain a physical model of the pre-eruptive conditions of magmatic reservoirs and thereby improve our understanding of sub-surface processes.

Enabling↗

NASA NTRS1981

Engine condition monitoring: CF6 family 60's through the 80's

The on condition program is described in terms of its effectiveness as a maintenance tool both at the line station as well as at home base by the early detection of engine faults, erroneous instrumentation signals and by verification of engine health. The system encompasses all known methods from manual procedures to the fully automated airborne integrated data system.

Kent, H. J.↗

DOE OSTI2020

Review of on-line and near real-time spectroscopic monitoring of processes relevant to nuclear material management

Spectroscopic chemometric based on-line monitoring of used nuclear fuel (UNF) reprocessing solutions and characterization of legacy nuclear waste (LNW) stored at Hanford is discussed in this manuscript. Using on-line and near real-time monitoring with spectroscopy, as opposed to traditional off-line monitoring, can significantly reduce the cost, risk and improve the efficiency of characterizing UNF reprocessing streams and LNW since the data can be collected non-destructively and analyzed nearly instantly. It can accurately quantify and qualify nuclear material in near-real time which can help to know the immediate UNF reprocessing stream and LNW conditions and detect the diversion of materials. The ability to be applied in a real reprocessing plant and in an actual Hanford waste tank/transfer pipe has been demonstrated by applying this technique to accurately quantify analytes in real UNF streams and LNW samples. Furthermore, the future development of spectroscopy based on-line monitoring is also discussed in this manuscript.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

Vehicle Integrated Prognostic Reasoner (VIPR) 2010 Annual Final Report

Honeywell's Central Maintenance Computer Function (CMCF) and Aircraft Condition Monitoring Function (ACMF) represent the state-of-the art in integrated vehicle health management (IVHM). Underlying these technologies is a fault propagation modeling system that provides nose-to-tail coverage and root cause diagnostics. The Vehicle Integrated Prognostic Reasoner (VIPR) extends this technology to interpret evidence generated by advanced diagnostic and prognostic monitors provided by component suppliers to detect, isolate, and predict adverse events that affect flight safety. This report describes year one work that included defining the architecture and communication protocols and establishing the user requirements for such a system. Based on these and a set of ConOps scenarios, we designed and implemented a demonstration of communication pathways and associated three-tiered health management architecture. A series of scripted scenarios showed how VIPR would detect adverse events before they escalate as safety incidents through a combination of advanced reasoning and additional aircraft data collected from an aircraft condition monitoring system. Demonstrating VIPR capability for cases recorded in the ASIAS database and cross linking them with historical aircraft data is planned for year two.

Hadden, George D.↗

DOE OSTI2023

Deep Vadose Zone Monitoring Test Bed (FY23 Status Report)

A subsurface air injection at the Hanford Site’s Deep Vadose Zone Monitoring Test Bed was completed to realize a change in subsurface hydrologic conditions in accordance with a soil desiccation remedy. The injection mimicked a previous injection at the site that was performed in accordance with a vadose zone treatability study. Unlike the previous test which relied on electrical methods only, the change in hydrologic conditions during the recent test was also monitored using cross-hole seismic sensing methods to assess the ability of the seismic methods to evaluate changes in moisture conditions of desiccated sediment at the field scale. Data from in situ neutron probes indicates a reduction of soil moisture in the vicinity of the injection well due to the air injection. Similar changes were observed in the time-lapse electrical resistivity and seismic data, which indicates a loss of soil moisture over time. Tomographic inversions of the time-lapse geophysical data illustrate the 2D and 3D features of the soil moisture distribution over time. Time-lapse electrical resistivity tomography (ERT) results show a reduction in the electrical conductivity of the subsurface in the vicinity of the injection well, with most changes occurring within the screened interval. Similar patterns are observed in the seismic tomography results, with both methods illustrating two lobe-shaped features of reduced soil moisture. Use of seismic and ERT technologies in tandem takes advantage of two complementary geophysical monitoring technologies, providing increased sensitivity to specific hydrologic conditions. The multiphysics approach, therefore, has the potential to improve the ability to estimate subsurface moisture conditions from sensor-based and remotely sensed geophysical data that will ultimately improve the ability of remediation contractors to evaluate remedy performance.

58 GEOSCIENCES↗

NASA NTRS1986

Adhesive joint evaluation by ultrasonic interface and lamb waves

Some results on the application of interface and Lamb waves for the study of curing of thin adhesive layers were summarized. In the case of thick substrates (thickness much more than the wave length) the interface waves can be used. In this case the experimental data can be inverted and the shear modulus of the adhesive film may be explicitly found based on the measured interface wave velocity. It is shown that interface waves can be used for the study of curing of structural adhesives as a function of different temperatures and other experimental conditions. The kinetics of curing was studied. In the case of thin substrates the wave phenomena are much more complicated. It is shown that for successful measurements proper selection of experimental conditions is very important. This can be done based on theoretical estimations. For correctly selected experimental conditions the Lamb waves may be a sensitive probe of adhesive bond quality and may be used or cure monitoring.

Rokhlin, S. I.↗

NASA NTRS2015

Remote Sensing Technologies Mitigate Drought

Ames Research Center has partnered with the California Department of Water Resources to develop satellite-based technologies to mitigate drought conditions. One project aims to help water managers adjust their irrigation to match the biological needs of each crop, and another involves monitoring areas where land is fallow so emergency relief can more quickly aid affected communities.

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