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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
NASA NTRS2017

Airborne Hyperspectral Sensing of Monitoring Harmful Algal Blooms in the Great Lakes Region: System Calibration and Validation

Harmful algal blooms (HABs) in Lake Erie have been prominent in recent years. The bloom in 2014 reached a severe level causing the State of Ohio to declare a state of emergency. At that time NASA Glenn Research Center was requested by stakeholders to help monitor the blooms in Lake Erie. Glenn conducted flights twice a week in August and September and assembled and distributed the HAB information to the shoreline water resource managers using its hyperspectral imaging sensor (in development since 2006), the S􀂱3 Viking aircraft, and funding resources from the NASA Headquarters Earth Science Division. Since then, the State of Ohio, National Oceanic and Atmospheric Administration (NOAA), and U.S. Environmental Protection Agency (EPA) have elevated their funding and activities for observing, monitoring, and addressing the root cause of HABs. Also, the communities and stakeholders have persistently requested NASA Glenn􀂶s participation in HAB observation. Abundant field campaigns and sample analyses have been funded by Ohio and NOAA, which provided a great opportunity for NASA to advance science and airborne hyperspectral remote sensing economically. Capitalizing on this opportunity to advance the science of algal blooms and remote sensing, NASA Glenn conducted the Airborne Hyperspectral Observation of harmful algal blooms campaign in 2015 that was, in many respects, twice as large as the 2014 campaign. Focusing mostly on Lake Erie, but also including other small inland lakes and the Ohio River, the campaign was conducted in partnership with a large number of partners specializing in marine science and remote sensing. Airborne hyperspectral observation of HABs holds promise to distinguish potential HABs from nuisance blooms, determine their concentrations, and delineate their movement in an augmented spatial and temporal resolution and under clouds􀂲all of which are excellent complements to satellite observations. Working with collaborators at several Ohio and Michigan institutions as well as one in South Dakota and one in Alabama, this effort was able to provide next-day georeferenced estimates of cyanobacteria and scum concentrations. Very prompt processing and analysis of the hyperspectral imagery is necessary for the information to be acted upon. For example, a next-day report of an overflight over the Ohio River indicated that a bloom could be present as far downstream as the Cincinnati intake, but the Ohio EPA had not received visual reports of a bloom that far downstream. Water samples were obtained at the Cincinnati water intake, based on the flight data, and detected microcystins in the source water. The flight data helped State and municipal authorities realize the potential extent of that bloom, and triggered response sampling, before the visual river-wide scums started forming. The present document describes the process that was utilized to take raw remote sensing data and create information products; this includes system calibration and validation, efforts to correct atmospheric effects, and algorithms that produce the data products. Furthermore, successful research into improved algorithms for expanding the capability to delineate in water constituents is included. Finally, comparisons that show expected relationships between ground-based measurements and hyperspectral imager version 2 (HSI2) data results are presented, giving confidence in the remote sensing products.

Hyperspectral Imaging↗
NASA NTRSDate not supplied

Eighteen Years of Aqua MODIS On-orbit Operation, Calibration, and Performance

Aqua MODIS has successfully operated for more than 18 years since its launch in May 2002and has generated numerous science products in support of studies of the Earth’s system and its changes via a set of geophysical and environmental parameters. On-orbit calibration and characterization activities have played a vital role in maintaining the quality of MODIS data products. In addition to data collected from sensor on-board calibrators (OBC), near-monthly lunar observations and select earth view targets have been used to monitor and characterize on-orbit changes in sensor responses and to derive and update the calibration look-up tables. In this paper, we provide an overview of Aqua MODIS on-orbit operations, calibration activities and approaches, and algorithm improvements and also illustrate the sensor on-orbit performance using examples derived from various calibration sources and targets. We will focus on issues identified from instrument operations and calibrations, such as solar diffuser degradation, electronic crosstalk, variations in the cold focal plane temperatures, and changes in response versus scan-angle. Also discussed in this paper are remaining challenges and future improvements.

Aqua↗
NASA NTRSDate not supplied

Application of the Global Precipitation Forecast dataset for Global Landslide Forecasting System

Landslides are extremely damaging, pervasive and cause fatalities and economic impacts. Extreme rainfall events, coupled with inopportune surface conditions, are the primary triggers of landslides around the world. Forecasting landslide events represent an area of open research. A global Landslide Hazard Assessment for Situational Awareness (LHASA) model has been created that provides near real-time dynamic landslide characterization using Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG). However, it does not provide information on prediction of landslides into future. This study considers how global precipitation forecast data compares to satellite rainfall at different spatiotemporal scales and outlines the potential for its use in landslide hazard prediction/forecasting system. NASA Goddard Earth Observing System (GEOS)-Forecast model assimilates new observations every 6 hours, at 00, 06, 12, and 18 UTC. The GEOS-Forecast model is initialized at 00 UTC and is evaluated with IMERG Early, using Multi-Radar Multi-Sensor gauge corrected (MRMS-GC) precipitation product as a reference over contiguous United States (CONUS). Categorical and continuous statistics along with probability density functions and cumulative distribution functions are considered to assess the performance of the precipitation products, with a focus on landslide hotspots. Seasonality appears to influence the performance of both the GEOS-Forecast and IMERG Early product. Moreover, the global comparison between the GEOS-Forecast and IMERG Early is carried out in terms of percentile difference, correlation, and bias maps. For extreme rainfall events in regions such as Mekong, Colombia, and Tajikistan, GEOS-Forecast appears to resolve high rainfall relative to IMERG Early more frequently. Validation over landslide points reveal that for 24hr rainfall accumulations > 100mm, GEOS-Forecast and IMERG coincide. GEOS-Forecast and IMERG Early precipitation matches more closely for tropical cyclones than other types of storms. Overall, the performance varies with respect to location, rainfall intensities, and type of precipitation events.

Sana Khan↗
DOE OSTI2019

Validation of Numerical Models of the Offshore Wind Turbine from the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III: Preprint

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project, is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) Idling below the cut-in wind speed; (2) Rotornacelle assembly (RNA) rotation maneuver below the cut-in wind speed; (3) Power production below and above the rated wind speed; and (4) Shut-down. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) Strains and accelerations recorded on the support structure; (2) Pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools. Therefore, strains were calculated based on out-of-plane bending moments, axial forces, and geometrical properties of the structural members. Also, a number of issues arose during the validation: (1) The need for a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties, which were only approximated in the modeling approach; (3) The importance of estimating and applying an appropriate damping value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms (DFTs), power spectral densities (PSDs), probability density functions (PDFs) of strains and accelerometers. A good match was achieved between the measurements and models set up by OC5 Phase III participants.

17 WIND ENERGY↗
DOE OSTI2025

Image-Driven Hybrid Structural Analysis Based on Continuum Point Cloud Method with Boundary Capturing Technique

Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure. The captured edge profiles are transformed into essential boundary conditions. This allows the construction of a strongly formulated boundary value problem (BVP), classified as the Dirichlet problem. Capturing boundary conditions from the digital image is novel, although a similar approach was applied to the point cloud data. It was shown that the CPCM is more efficient in this hybrid simulation framework than the weak-form-based numerical schemes. Unlike the finite element method (FEM), it can avoid aligning boundary nodes with regression points. A three-point bending test of a rubber beam was simulated to validate the developed technique. The simulation results were benchmarked against numerical results by ANSYS and various relevant numerical schemes. The technique can effectively solve the Dirichlet-type BVP, yielding accurate deformation, stress, and strain values across the entire problem domain when employing a linear strain model and increasing the number of CPCM nodes. In addition, comparative analysis with conventional displacement tracking techniques verifies the developed technique’s robustness. The proposed technique effectively circumvents the inherent limitations of traditional monitoring methods resulting from the reliance on physical gauges or target markers so that a robust and non-contact solution for remote structural health monitoring in real-scale infrastructures can be provided, even in unfavorable experimental environments.

Chemistry↗
DOE OSTI2019

Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) Idling below the cut-in wind speed; (2) Rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed; (3) Power production below and above the rated wind speed; and (4) Shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) Strains and accelerations recorded on the support structure; (2) Pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools. Therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. Also, a number of issues arose during the validation: (1) The need for a thorough quality check of sensor measurements; (2) The sensitivity of the turbine loads to the controller and airfoil properties, which were only approximated in the modeling approach; (3) The importance of estimating and applying an appropriate damping value for the structure; and (4) The importance of wind characteristics beyond turbulence on the loads. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, probability density functions of strains and accelerometers. A good match was achieved between the measurements and models set up by OC5 Phase III participants.

17 WIND ENERGY↗
NASA NTRS2014

Snowfall Rate Retrieval using NPP ATMS Passive Microwave Measurements

Passive microwave measurements at certain high frequencies are sensitive to the scattering effect of snow particles and can be utilized to retrieve snowfall properties. Some of the microwave sensors with snowfall sensitive channels are Advanced Microwave Sounding Unit (AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the follow‐on sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has been developed recently. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. The model employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derive the probability of snowfall (Kongoli et al., 2014). In addition, a set of NWP model based filters is also employed to improve the accuracy of snowfall detection. Cloud properties are retrieved using an inversion method with an iteration algorithm and a two‐stream radiative transfer model (Yan et al., 2008). A method developed by Heymsfield and Westbrook (2010) is adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The ATMS SFR product is validated against radar and gauge snowfall data and shows that the ATMS algorithm outperforms the AMSU/MHS SFR.

Meng, Huan↗
NASA NTRS2019

Considerations for Femtosecond Laser Electronic Excitation Tagging in High-Speed Flows

Femtosecond laser electronic excitation tagging (FLEET) is an unseeded method for molecular tagging which offers valuable opportunities for measurement of high-speed (transonic, supersonic or hypersonic) flows. The unique nature of high-speed testing demands certain performance from FLEET such as satisfactory signal-to-noise ratio (SNR) at depressed static conditions (i.e., low temperatures, pressures and densities), wide dynamic range for velocity determination (especially single-shot), and measurements with acceptable accuracy and precision. This dissertation strives to evaluate FLEET in those regards and provide strategies to maximize the method's capabilities. A zero-dimensional kinetics model in nitrogen explains FLEET signal changes with pressure/density and/or temperature in terms of plasma-chemical reactions. Poorly known rate coefficients are tuned by comparing model output to measurements, with temporal agreement up to several hundred nanoseconds. Modeling reveals that initial signal peaks at reduced density because of slowed temporal evolution (and decay) of excited populations. Low temperatures enhance signal by enlarging cluster ion populations which contribute to excited species via electron-ion dissociative recombination. A purpose-built free jet facility provides experimental validation of the kinetics model and assesses FLEET velocimetry in low temperature and pressure/density conditions. Signal, lifetime, accuracy and precision results are obtained from unheated subsonic through Mach 4.0 operation of the facility, with best results noted. FLEET measurements of a sweeping jet (SWJ) actuator in compressible operation showcase its advantages in a highly unsteady jet containing subsonic through supersonic velocities. FLEET velocimetry is performed in the device's internal and external flow fields, with the latter compared to hot-wire anemometry. Internal measurements reveal the absence of shockwaves theorized to occur at high pressure ratios. Simultaneous qualitative measurements of compressible jet mixing are shown as a proof-of-concept. Overall, the work demonstrates that previous understanding of SWJ incompressible operation readily extends into the compressible realm. Practical aspects of performing FLEET velocimetry are detailed, along with strategies for improving measurement quality. Determination of a fundamental precision in nitrogen and air is attempted. Experiments show that increasing time delay and/or SNR improves velocimetry precision. A comparison of five camera systems indicates sensors with larger pixels capture higher SNR data and produce more precise results.

FLEET↗
NASA NTRS2019

CloudSat Bias on Falling Snow Estimates Over the Daylight Only Operational Period (2012-2019)

Falling snow is a key component for the global atmospheric, hydrological and energy cycles, and its retrieval from space-based observations represents the best current capability to evaluate it globally. The Global Precipitation Measurement (GPM) Mission Core Observatory, launched in 2014, together with its constellation sensors, can provide quasi-global precipitation estimates every 30 minutes (for level 3 products). Evaluation and validation efforts for such products are crucial, and for global evaluations, one of the most suitable instruments is the Cloud Profiling Radar (CPR) on board CloudSat, which is sensitive to light rain and falling snow. However, due to a battery anomaly in 2011, during its period of overlapping observations with GPM the CPR has operated in a Daylight Only Operations mode (DO-Op) in which it makes measurements primarily during only the daylit portion of its orbit. The goal of this work is to estimate biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat's snowfall measurements during its Full Operations (Full-Op) period from 2006 to 2010 to evaluate the impact DO-Op mode would have had during this period. Results indicate that omitting the nocturnal component of the diurnal cycle of snowfall has nonnegligible impact on snowfall amounts in some regions. The lack of nighttime data during DO-Op biases the latitudinally averaged mean snowfall rates as well as some regional values. Hemispheric differences in bias may be due to more pronounced diurnal variability in the northern hemisphere owing to more prevalent land surface versus the southern hemisphere. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

Milani, Lisa↗
NASA NTRS2011

Subsurface Emission Effects in AMSR-E Measurements: Implications for Land Surface Microwave Emissivity Retrieval

An analysis of land surface microwave emission time series shows that the characteristic diurnal signature associated with subsurface emission in sandy deserts carry over to arid and semi-arid region worldwide. Prior work found that diurnal variation of Special Sensor Microwave/Imager (SSM/I) brightness temperatures in deserts was small relative to International Satellite Cloud Climatology Project land surface temperature (LST) variation and that the difference varied with surface type and was largest in sand sea regions. Here we find more widespread subsurface emission effects in Advanced Microwave Scanning Radiometer-EOS (AMSR-E) measurements. The AMSR-E orbit has equator crossing times near 01:30 and 13 :30 local time, resulting in sampling when near-surface temperature gradients are likely to be large and amplifying the influence of emission depth on effective emitting temperature relative to other factors. AMSR-E measurements are also temporally coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements, eliminating time lag as a source of LST uncertainty and reducing LST errors due to undetected clouds. This paper presents monthly global emissivity and emission depth index retrievals for 2003 at 11, 19, 37, and 89 GHz from AMSR-E, MODIS, and SSM/I time series data. Retrieval model fit error, stability, self-consistency, and land surface modeling results provide evidence for the validity of the subsurface emission hypothesis and the retrieval approach. An analysis of emission depth index, emissivity, precipitation, and vegetation index seasonal trends in northern and southern Africa suggests that changes in the emission depth index may be tied to changes in land surface moisture and vegetation conditions

Galantowicz, John F.↗
NASA NTRS2004

The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for Global Mapping of Soil Moisture and Land Freeze/Thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39 with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Entekhabi, D.↗
NASA NTRS2009

Efficient Method for Analyzing Hyperspectral Remote Sensing Data

The current and next generation satellite infrared sensors such as AIRS, IASI, CrIS, and CLARREO all have one thing in common: large number of spectral channels. In order to fully utilize the information content of these sensors, a large number of Radiative Transfer (RT) calculations through the inhomogeneous atmosphere are needed. It is also computationally intensive to invert atmospheric and surface properties using all the channels. Usually, only subsets of channels are used to perform physical inversions for atmospheric profiles. We will explore ways to speed up RT calculations and inversions and test the algorithms using data from the Infrared Atmospheric Sounding Interferometer (IASI) satellite instrument. We will describe a retrieval algorithm using a Principal Component-based Radiative Transfer Model (PCRTM) for generating atmospheric temperature/moisture/ozone profiles and surface properties. The retrieval algorithm performs both the radiative transfer calculations and inversions in the Principal Component (PC) domain. The inversion algorithm is based on a non-linear Levenberg- Marquardt method with climatology covariance matrices and a priori information as constraints. One advantage of this approach is that it uses all information content from the ultraspectral data so that the retrieval is less sensitive to instrument noise and eliminates the need for selecting a sub-set of the channels. We will also use data collected during the Joint Airborne IASI Validation Experiment (JAIVEx) field campaign to validate the algorithm and IASI retrievals.

Liu, Xu↗
NASA NTRS2001

High Frequency, Long Time Series Measurements from the Bermuda Testbed Mooring in Support of SIMBIOS

It has been recognized that optical moorings are important platforms for the validation of Sea-Viewing Wide Field-of-view Sensor (SeaWiFS). It was recommended that optical moorings be maintained in order to: (1) provide long-term time series comparisons between in situ and SeaWIFS measurements of normalized water-leaving radiance; (2) develop and test algorithms for pigment biomass and phytoplankton primary productivity; and (3) provide long-term, virtually continuous in situ observations which can be used to determine and optimize the accuracy of derived satellite products. These applications require the use of in situ radiometers for long periods of time to evaluate and correct for inherent satellite undersampling (aliasing and biasing) and degradation of satellite color sensors (e.g., drifts as experienced by the Coastal Zone Color Scanner). The Bermuda Testbed Mooring (BTM) program was initiated in 1994 at a site located about 80km southeast of Bermuda in waters of about 4530 m depth. In August 1997, with NASA's support, we started to provide the Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) program with large volumes of high frequency, long-term time-series bio-optical data from the BTM for SeaWiFS satellite ocean color groundtruthing and algorithm development. This NASA supported portion of the BTM activity spanned three years and covered five BTM deployments. During these three years, the quality of radiometric data has improved dramatically. Excellent agreement between BTM moored data and both SeaWiFS and nearby ship profile radiometric data demonstrate that technical advances in the moored optical observations have reduced the major difficulties that moored platforms face: biofouling and less frequent calibration.

Dickey, Tommy↗
NASA NTRS1995

State Estimation of Main Rotor Flap and Lead-Lag Using Accelerometers and Laser Transducers on the RASCAL UH-60 Helicopter

Modern rotorcraft flight control system designs which promise to yield high vehicle response bandwidth and good gust rejection can benefit from the use of rotor-state feedbacks. The measurement of main rotor blade motions is also desirable to validate and improve rotorcraft simulation models, to identify high-order linear flight dynamics models, to provide rotor system health monitoring; during flight test, and to provide for correlation with acoustic measurements from wind tunnel and flight tests. However, few attempts have been made to instrument a flight vehicle in this manner, and no previous system has had the robustness and accuracy required for these diverse applications. A rotor blade motion measurement and estimation system has been developed by NASA and the U.S. Army for use on the Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) helicopter. RASCAL is a UH-60 Blackhawk which is being modified at Ames Research Center in a phased development program for use in flight dynamics and controls, navigation, airspace management, and rotorcraft human factors research. The aircraft will feature a full-authority, digital, fly-by-wire research flight control system; a coupled ring laser gyro, differential GPS based navigation system; a stereoscopic color wide field of view helmet, mounted display; programmable panel mounted displays; and advanced navigation sensors. The rotor blade motion system is currently installed for data acquisition only, but will be integrated with the research flight control system when it is installed later this year.

Fletcher, Jay W.↗
DOE OSTI2015

In-Situ Acoustic Measurements of Temperature Profile in Extreme Environments

A gasifier’s temperature is the primary characteristic that must be monitored to ensure its performance and the longevity of its refractory. One of the key technological challenges impacting the reliability and economics of coal and biomass gasification is the lack of temperature sensors that are capable of providing accurate, reliable, and long-life performance in an extreme gasification environment. This research has proposed, demonstrated, and validated a novel approach that uses a noninvasive ultrasound method that provides real-time temperature distribution monitoring across the refractory, especially the hot face temperature of the refractory. The essential idea of the ultrasound measurements of segmental temperature distribution is to use an ultrasound propagation waveguide across a refractory that has been engineered to contain multiple internal partial reflectors at known locations. When an ultrasound excitation pulse is introduced on the cold side of the refractory, it will be partially reflected from each scatterer in the US propagation path in the refractory wall and returned to the receiver as a train of partial echoes. The temperature in the corresponding segment can be determined based on recorded ultrasonic waveform and experimentally defined relationship between the speed of sound and temperature. The ultrasound measurement method offers a powerful solution to provide continuous real time temperature monitoring for the occasions that conventional thermal, optical and other sensors are infeasible, such as the impossibility of insertion of temperature sensor, harsh environment, unavailable optical path, and more. Our developed ultrasound system consists of an ultrasound engineered waveguide, ultrasound transducer/receiver, and data acquisition, logging, interpretation, and online display system, which is simple to install on the existing units with minimal modification on the gasifier or use with new units. This system has been successfully tested with a 100 kW pilot scale down flow oxyfuel combustor, capturing in real time temperature changes during all relevant combustion process changes. The ultrasound measurements have excellent agreement with thermo- couple measurements, and appear to be more sensitive to temperature changes before the thermocouples response, which is believed to be the first demonstration of ultrasound measurements segmental temperature distribution across refractories.

47 OTHER INSTRUMENTATION↗
NASA NTRS2008

Contributions of TRMM and GPM to GEOSS

Two international satellite missions have a major role in the development of global precipitation data under the auspices of the Global Earth Observation System of Systems (GEOSS) to meet the needs of several Societal Benefit Areas set forth by the Group on Earth Observations (GEO). One is the joint U.S.-Japan Tropical Rainfall Measuring Mission (TRMM), launched in 1997, which provides combined radar/radiometer measurements to serve as a benchmark for tropical precipitation measurements by passive microwave radiometers and has played a key role in the development of the current generation of multi-satellite global rainfall products. The other is the Global Precipitation Measurement (GPM) Mission, an international satellite mission to be launched in 2013 to deliver the next-generation global precipitation observations from a constellation of dedicated and operational microwave sensors. GPM is a science mission with integrated applications goals aimed at (1) advancing the knowledge of the global water/energy cycle variability and freshwater availability and (2) improving weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with additional partners in satellite constellation and ground validation under discussion. With NASA and JAXA science team activities providing the framework and infrastructures for international collaboration on satellite inter-calibration, retrieval algorithm development, and ground validation, GPM has been identified as the cornerstone for the development of the Precipitation Constellation for the Committee on Earth Observation Satellites (CEOS). An overview of current accomplishments by TRMM and anticipated contributions by GPM to GEOSS activities will be presented.

Hou, Arthur Y.↗
NASA NTRS2009

The 2nd Generation Real Time Mission Monitor (RTMM) Development

The NASA Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decisionmaking for airborne and ground validation experiments. Developed at the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery and orbit data, radar and other surface observations (e.g., lightning location network data), airborne navigation and instrument data sets, model output parameters, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. In order to improve the usefulness and efficiency of the RTMM system, capabilities are being developed to allow the end-user to easily configure RTMM applications based on their mission-specific requirements and objectives. This second generation RTMM is being redesigned to take advantage of the Google plug-in capabilities to run multiple applications in a web browser rather than the original single application Google Earth approach. Currently RTMM employs a limited Service Oriented Architecture approach to enable discovery of mission specific resources. We are expanding the RTMM architecture such that it will more effectively utilize the Open Geospatial Consortium Sensor Web Enablement services and other new technology software tools and components. These modifications and extensions will result in a robust, versatile RTMM system that will greatly increase flexibility of the user to choose which science data sets and support applications to view and/or use. The improvements brought about by RTMM 2nd generation system will provide mission planners and airborne scientists with enhanced decision-making tools and capabilities to more efficiently plan, prepare and execute missions, as well as to playback and review past mission data. To paraphrase the old television commercial RTMM doesn t make the airborne science, it makes the airborne science easier.

Blakeslee, Richard↗