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At least 721 records · Page 40

Surface Monitoring of CFRP Structures for Adhesive Bonding

Adhesive bonding of composite materials requires reliable monitoring and detection of surface contaminants to assure robust and durable bonded structures. Surface treatment and effective monitoring prior to bonding is essential in order to obtain a surface free from contaminants that may degrade structural performance. Two techniques which monitor the effectiveness of the laser surface treatment of carbon fiber reinforced polymer (CFRP) materials are being investigated: laser induced breakdown spectroscopy (LIBS) and optically stimulated electron emission (OSEE). The applicability of LIBS to detect silicone contaminants on CFRP composites is studied using ~35 ns Nd:YAG laser pulses at 355 nm with a pulse energy of 45 mJ. The LIBS regime in which pulse energies are < 100 mJ is referred to as mLIBS. CFRP surfaces were contaminated with polydimethylsiloxane (PDMS), a major component of silicone based mold release agents. The presence of PDMS is found by inspecting the Si I emission line at 288.2 nm. Untreated CFRP samples and CFRP contaminated with PDMS were tested. The PDMS areal density ranged from 0.36 Â+/- 0.04 to 0.51 Â+/- 0.16 mg/cm2. The results demonstrate the successful detection of PDMS on CFRP using mLIBS. In addition, OSEE was used to measure CFRP surface cleanliness pre- and post-treatment by laser ablation on specimens contaminated with PDMS coatings from 8 nm to 1311 nm in thickness. The results showed a significant increase in the OSEE photocurrent after laser surface treatment.

Ledesma, Rodolfo↗

Improving the Monitoring of Crop Productivity Using Spaceborne Solar-Induced Fluorescence

Large-scale monitoring of crop growth and yield has important value for forecasting food production and prices and ensuring regional food security. A newly emerging satellite retrieval, solar-induced fluorescence (SIF) of chlorophyll, provides for the first time a direct measurement related to plant photosynthetic activity (i.e. electron transport rate). Here, we provide a framework to link SIF retrievals and crop yield, accounting for stoichiometry, photosynthetic pathways, and respiration losses. We apply this framework to estimate United States crop productivity for 2007-2012, where we use the spaceborne SIF retrievals from the Global Ozone Monitoring Experiment-2 satellite, benchmarked with county-level crop yield statistics, and compare it with various traditional crop monitoring approaches. We find that a SIF-based approach accounting for photosynthetic pathways (i.e. C3 and C4 crops) provides the best measure of crop productivity among these approaches, despite the fact that SIF sensors are not yet optimized for terrestrial applications. We further show that SIF provides the ability to infer the impacts of environmental stresses on autotrophic respiration and carbon-use-efficiency, with a substantial sensitivity of both to high temperatures. These results indicate new opportunities for improved mechanistic understanding of crop yield responses to climate variability and change.

Guan, Kaiyu↗

A MODIS-Based Automated Flood Monitoring System for Southeast Asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.

Mekong River↗

New Sensors and Techniques for Aircraft Engine Health Monitoring

The ability to monitor the health of the rotating components in aircraft engines is of major interest to aero community in improving safety and reliability. The use of instrumentation for these applications remains very challenging. It requires sensors and techniques that are highly accurate, able to operate in a high temperature environment, and sensitive enough to detect minute changes and hidden flaws before undesired events occur. The National Aeronautics and Space Administration (NASA), through several Aviation Safety Programs, has taken a leadership role in the development of new sensor technologies and techniques for the in-situ health monitoring of aircraft turbine engines. Along with the development of new instrumentation for aircraft engines, the Glenn Research Center has a diverse set of ground test facilities that are used to simulate the conditions that a vehicle would see in flight allowing new concepts to be safely developed and evaluated on the ground before progressing to actual flight. In this presentation Mark will give an overview of the NASA Glenn Research Center, discuss its past aviation safety initiatives, present some select sensors that have been developed for aircraft engine safety, and then conclude with some new measurement techniques that have been developed for use at the NASA Glenn test facilities, enabling better measurement and simulation of flight conditions. He will discuss current research and development efforts that he is working on at the NASA Glenn Research Center in the areas of new sensors and techniques for aircraft engine health monitoring. This work is being conducted in support of aviation safety.

Turbine↗

Using Reanalysis in Crop Monitoring and Forecasting Systems

Weather observations are essential for crop monitoring and forecasting but they are not always available and in some cases they have limited spatial representativeness. Thus, reanalyses represent an alternative source of information to be explored. In this study, we assess the feasibility of reanalysis-based crop monitoring and forecasting by using the system developed and maintained by the European Commission- Joint Research Centre, its gridded daily meteorological observations, the biased-corrected reanalysis AgMERRA and the ERA-Interim reanalysis. We focus on Europe and on two crops, wheat and maize, in the period 1980-2010 under potential and water-imited conditions. In terms of inter-annual yield correlation at the country scale, the reanalysis-driven systems show a very good performance for both wheat and maize (with correlation values higher than 0.6 in almost all EU28 countries) when compared to the observations-driven system. However, significant yield biases affect both crops. All simulations show similar correlations with respect to the FAO reported yield time series. These findings support the integration of reanalyses in current crop monitoring and forecasting systems and point to the emerging opportunities linked to the coming availability of higher-resolution reanalysis updated at near real time.

Reanalysis↗

Establishing and Monitoring an Aseptic Workspace

When are aseptic operations necessary? In order to meet certain bioburden requirements, some components must undergo dry heat microbial reduction (DHMR) or other sterilizing procedures. If sensitive surfaces must be re-exposed after DHMR, this could compromise the bioburden levels. Recontaminating sterilized surfaces could be costly both in time by requiring repeated DHMR and risk to the hardware, which may not be compatible with repeated high temperature bakes. In order to prevent recontamination of the sensitive surfaces, an aseptic environment and sterile technique must be employed. Aseptic environments mean working in a space with almost no detectable bioburden in the air or on surfaces. Ideally, DHMR happens as late as possible to avoid requiring aseptic operations, as it can be considered a high-risk operations. Preparing the cleanroom for aseptic operations Establishing an ISO (International Organization for Standardization) class 5 space to minimize airborne particles. Maintain low bioburden in the cleanroom by using biocidal cleaners. Using multiple biocidal techniques decreases the likelihood of selecting for resistant microorganisms. 70% Isopropyl Alcohol (IPA) denatures the proteins in a microorganism (note: 70% IPA is better at killing microorganisms than 100% IPA) 7% hydrogen peroxide: damages DNA and proteins through oxygen radical damage. Ultraviolet-C (UV-C) lamps: causes crosslinking in DNA which prevents replication. Monitor cleanroom regularly for bioburden trending: Standard bioassay: Swab or wipe samples of cleanroom surfaces processed for colony forming unity (viable or spore selected); Rapid bioassay: Adenosine triphosphate (ATP) or Limulus amebocyte lysate (LAL) for a bioburden snapshot. High levels can signal an immediate re-cleaning before standard bioassay samples are taken. Airborne monitoring: Active (pulling air through a filter) or passive (particle fallout) for bioburden. Verify bioburden levels just before aseptic operation. Test hardware and cleanroom surfaces and air 3 days before the planned aseptic operation. Rapid bioburden just before aseptic operation to ensure room was not re-contaminated. Preparing personnel and tools: Personnel training. Everyone in the cleanroom: Standard cleanroom certification Everyone on the team: 1 day Planetary Protection overview. Aseptic operators only: Half-day aseptic operations training. Covers sterile garmenting/gloves, Sterile handling with a focus on contact transfer risk, tool/GSE preparation, and two-operator system for opening sterilized tools/components. Tool sterilization: All tools to be used during an aseptic operation need to be identified. Compatible tools are sterilized by DHMR or Autoclave. Double wrapped so that the exterior bag can be handled by a non-sterile operator, and the sterile. Tools that are not compatible with high heat do not come in contact with sensitive surfaces: either substitutes are found, or tools are isolated by wrapping in sterile foil. During an aseptic operation. Pre-task to make sure everyone understands the operations, who is handling what, and when the most critical surfaces will be exposed. Monitoring during the operation. Bioburden: active and passive airborne bioburden sampling, glove-tip dabs onto a plate after completion of operation (3 days for results). Particles: real time particle counter constantly running, with alarm for exceeding ISO 5 conditions.

aseptic processing↗

How to Leverage the Power of SAR Observations for Forest Monitoring Systems

Earth observations from Synthetic Aperture Radar (SAR) can provide unique observations related to forest structure and condition. Furthermore, SAR has many potential applications in forest monitoring systems, particularly where clouds have impeded optical observations. Currently, there is a reliable, freely-available, provision of SAR datasets, such as Sentinel-1, and there are plans to have more observations in the near- future (NISAR, BIOMASS). Given SAR’s enhanced earth observation characteristics, there is broad interest in using SAR datasets for decision support systems, such as deforestation early warning systems. However, applications of SAR are still underutilized. What is preventing users from using SAR data in their decision support systems? This study documents the experiences and lessons learned from the SERVIR network on the main limitations of incorporating SAR datasets into existing forest monitoring systems. This research also focuses on the major technical and scientific barriers we experience and best practices to address them. The results of this study are part of the SERVIR- SilvaCarbon collaboration. The primary goal of this collaboration is to build capacity in the applied use of SAR for forest monitoring and biomass estimation. The products of this effort aim to start closing the gap between SAR-science and forest applications. We will also present results to generate applied-ready knowledge for SAR.

SAR↗

New York City Panel on Climate Change 2019 Report Chapter 8: Indicators and Monitoring

The Indicators and Monitoring chapter of the first New York City Panel on Climate Change Report began with the paradigm: What cannot be measured cannot be managed (Rosenzweig et al., 2010). This statement is as valid today as it was then.The NPCC1 (2010) Indicators and Monitoring chapter addressed the need for assembling a suite of indicators to monitor climate change and adaptation in order to inform climate change decision making. It outlined criteria for selection of indicators (policy relevance, analytic soundness, measurability), defined categories of indicators (physical climate change; risk exposure, vulnerability, and impacts; adaptation; new research), and provided examples of specific indicators. Table 8.1 is a summary table of indicator development contribution from the NPCC1 I&M chapter (Jacob et al., 2011). The chapter explored the institutional requirements for indicator data availability, continuity, archiving, and public accessibility.

Blake, Reginald↗

A Strain Gauge Monitor (SGM) for Continuous Valve Gape Measurements In Bivalve Molluscs in Response to Laboratory Induced Diel-Cycling Hypoxia and pH

An inexpensive, laboratory-based, strain gauge valve gape monitor (SGM) was developed to monitor the valve gape behavior of bivalve molluscs in response to diel-cycling hypoxia. A Wheatstone bridge was connected to strain gauges that were attached to the shells of oysters (Crassostrea virginica). The recorded signals allowed for the opening and closing of the bivalves to be recorded continuously over two-day periods of experimentally-induced diel-cycling hypoxia and diel-cycling changes in pH. Here, we describe a protocol for developing an inexpensive strain gauge monitor and describe, in an example laboratory experiment, how we used it to measure the valve gape behavior of Eastern oysters (C. virginica), in response to diel-cycling hypoxia and cyclical changes in pH. Valve gape was measured on oysters subjected to cyclical severe hypoxic (0.6 mg/L) dissolved oxygen conditions with and without cyclical changes in pH, cyclical mild hypoxic (1.7 mg/L) conditions and normoxic (7.3 mg/L) conditions. We demonstrate that when oysters encounter repeated diel cycles, they rapidly close their shells in response to severe hypoxia and close with a time lag to mild hypoxia. When normoxia is restored, they rapidly open again. Oysters did not respond to cyclical pH conditions superimposed on diel cycling severe hypoxia. At reduced oxygen conditions, more than one third of the oysters closed simultaneously. We demonstrate that oysters respond to diel-cycling hypoxia, which must be considered when assessing the behavior of bivalves to dissolved oxygen. The valve SGM can be used to assess responses of bivalve molluscs to changes in dissolved oxygen or contaminants. Sealing techniques to better seal the valve gape strain gauges from sea water need further improvement to increase the longevity of the sensors.

Porter, Elka T.↗

Development of a MEMS Preconcentrator (PC) - Gas Chromatograph (GC) for the Spacecraft Atmosphere Monitor for ISS and Orion

In this paper, we report a MEMS preconcentrator (PC) - gas chromatograph (GC) that is a crucial part of the Spacecraft Atmosphere Monitor (S.A.M.). The S.A.M. is a highly miniature gas chromatograph - mass spectrometer (GC-MS) for monitoring the atmosphere of crewed spacecraft for both trace organic compounds and the major constituents of the cabin air. The S.A.M. instrument is the next generation of GC-MS, based on JPL’s Vehicle Cabin Air Monitor (VCAM), which was launched to the International Space Station (ISS) in April 2010 and successfully operated for two years [1, 2]. The S.A.M. employs a unique MEMS PC-GC technology that replaces the macro PC-GC unit in the VCAM. We report herein the current progress of the MEMS PC-GC for the S.A.M. instrument.

Bae, Byunghoon↗

Mapping of Spacecraft Atmosphere Monitor Signal to Major Constituent Abundances

The Spacecraft Atmosphere Monitor (S.A.M.) follows the JPL’s commitment to introduce and develop next-generation instrumentation concepts for sensing the air quality on manned space flights via continuous sampling, measuring, and reporting in 2s intervals on all gaseous pollutants. The S.A.M. will have two modes of operation: the Major Constituent Analysis (MCA) mode and the Trace Gas Analysis (TGA) mode. The MCA mode will report on molecular analytes such as CH4, H2O, N2, O2, Ar, and CO2 while the TGA mode will acquire minute amounts of volatile organic compounds. Both modes assess the composition of the ambient air with twenty full mass spectra per second giving rise to a substantial amount of data to be processed by a set of small footprint software stacks hosted by an on-board computer. Mass spectra will be accumulated as the number of counts recorded in a given mass-to-charge channel and converted into the absolute abundances of detected species using an efficient algorithm. The decomposition algorithm contains four units: peak identification, mass calibration, background and dead time correction, and an abundance analysis unit. The abundance analysis module identifies target species through their characteristic fragmentation patterns in the presence of molecular isobars, such as CO and N2. For example, in order to identify N2 analyte, the code will simultaneously monitor abundance ratios of the 14, 28 and 29 Th signals and will adapt to any instability caused by a decrease in ambient pressure or changes in humidity. This requirement becomes critical for instruments designed to monitor the near real-time quality of cabin air and promptly provide accurate feedbacks.

Nikolic, D.↗

Real-Time UAV Trajectory Prediction for Safety Monitoring in Low-Altitude Airspace

The rising number of small unmanned aerial vehicles (UAVs) expected in the next decade will enable a new series of commercial, service, and military operations in low altitude airspace as well as above densely populated areas. These operations may include on-demand delivery, medical transportation services, law enforcement operations, traffic surveillance and many more. Such unprecedented scenarios create the need for robust, efficient ways to monitor the UAV state in time to guarantee safety and mitigate contingencies throughout the operations. This work proposes a generalized monitoring and prediction methodology that utilizes realtime measurements of an autonomous UAV following a series of way-points. Two different methods, based on sinusoidal acceleration profiles and high-order splines, are utilized to generate the predicted path. The monitoring approach includes dynamic trajectory re-planning in the event of unexpected detour or hovering of the UAV during flight. It can be further extended to different vehicle types, to quantify uncertainty affecting the state variables, e.g., aerodynamic and other environmental effects, and can also be implemented to prognosticate safety-critical metrics which depend on the estimated flight path and required thrust. The proposed framework is implemented on a simplified, scalable UAV modeling and control system traversing 3D trajectories. Results presented include examples of real-time predictions of the UAV trajectories during flight and a critical analysis of the proposed scenarios under uncertainty constraints.

UAV trajectory prognosis↗

Hydrocarbon Burn Facility SWMU 007 Interim Groundwater Monitoring Report Kennedy Space Center, Florida

This Interim Groundwater Monitoring (IGWM) Report presents the findings of IGWM completed in September 2019 for the Northern Volatile Organic Compound (VOC) Area of the Hydrocarbon Burn Facility (HBF) at Kennedy Space Center (KSC), Florida. The IGWM was implemented for the Northern VOC Area to provide additional data, to be evaluated along with previous long-term monitoring data, to determine future IGWM options and to evaluate the efficacy of monitored natural attenuation (MNA) as a passive remedial measure in comparison to active remedies for the Northern VOC Area. The primary contaminants of concern in the Northern VOC Area are trichloroethene (TCE), cis-1,2-dichloroethene (cDCE), vinyl chloride (VC), and benzene.

groundwater↗

Short Time Scale Stability Monitoring of GOES-16 Visible Channels by Utilizing Daily Inter-Calibration Events with both VIIRS and MODIS Sensors

The CERES project relies on MODIS, VIIRS and geostationary (GEO) imager retrieved cloud properties to compute surface fluxes and convert CERES and GEO radiances into TOA fluxes. The CERES TOA and surface fluxes need to be of climate quality and any anomaly or discontinuity in the calibration of the MODIS, VIIRS, GEO radiances need to be mitigated or avoided. Due to the nature of the CERES instrument calibration, there is a lag time of 3 months before the processing of the CERES product. This allows the CERES team to investigate any unforeseen problems in the input datasets or GEO calibration discontinuities or anomalies before processing. The CERES geostationary imager calibration team is developing a daily monitoring system of GEO, MODIS and VIIRS visible channel radiances in order to detect any day to day discontinuities as part of the CERES/GEO coincident ray-matching calibration efforts. a GEO discontinuity can be detected using two MODIS and 2 VIIRS sensors simultaneously, thus increasing the sampling greatly over a single GEO/LEO radiance pairs. This method is being automated and has already detected three erroneous radiometric calibration events, which were verified with the GOES-16 event log page. One advantage of daily monitoring is that the time period needed to verify calibration drifts is reduced and the confidence to detect even smaller magnitudes of the calibration drift is increased over monthly monitoring. By utilizing Terra and Aqua MODIS and NPP and NOAA20 VIIRS imagers to inter-calibrate GOES-16, the GOES-16 calibration can be referenced to anyone of these imagers. If the CERES project transitions between the Aqua-MODIS imager to the NOAA20 VIIRS imager, the NOAA-20 VIIRS imager can be radiometrically scale to Aqua-MODIS calibration, thus avoiding a GOES-16 calibration discontinuity.

David Robert Doelling↗

Groundwater Drought: Environmental Controls and Monitoring

Arising from a long-term deficit of precipitation, groundwater drought often lags meteorological drought by months to years, depending on climate and subsurface hydrogeological conditions. Due to the paucity of in situ groundwater data in many parts of the world, global monitoring of groundwater storage variations and drought may be best achieved using satellite observations and/or groundwater time series simulated by hydrological models. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions have greatly benefitted the modeling and monitoring of groundwater storage changes and drought at the global scale. In this chapter, we first review environmental controls on the temporal variability of groundwater using in situ data. We then describe an approach that infuses GRACE and GRACE-FO observations into a land surface model for assessing groundwater storage changes and drought globally. We also discuss characteristics of simulated groundwater drought and the limitations of current groundwater drought monitoring approaches.

Bailing Li↗

Monitoring Human Performance on Future Deep Space Missions Abstract

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring. Space travel itself is difficult, but astronauts on deep space missions will face obstacles and unknowns never before experienced. In addition to the altered gravity and hostile/closed environment of a spacecraft, deep space crews will face increased radiation, isolation, and distance from Earth. During Extravehicular Activity (EVA, or “spacewalk”) operations, crew will experience increased physical and cognitive workload due to extended types, frequencies and durations of tasks performed on exploration missions in partial gravity environments. All of these stressors will impact crew physical and mental health and performance in difficult-to-anticipate ways. Crew autonomy may be one of the biggest challenges faced. Communication delays and blackouts will occur, and in those situations, the crew may not have access to the Mission Control Center (MCC). They may be forced to be solely dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance -- preferably unobtrusively as they perform their duties. Technologies and techniques must be developed to aid the crew in these assessments. A deep space mission is expected to have relatively short periods of high cognitive demand, stress, and fatigue, alongside potentially long periods of cognitive underload during the transit, where boredom, loneliness, and depression can set in. Both ends of this spectrum are dangerous. Crew must be made aware when their task performance drops significantly, when their cognitive workload is too high, when they have lost situation awareness, or when they are too stressed or too fatigued to perform well. They must be able to identify these risks, and then mitigate them with countermeasures available onboard. A number of self-monitoring technologies are presently being explored by NASA to advance crew state determination capabilities. These range from real-time, physiological workload and situation awareness assessments, to crew health measurements determining physical and mental fitness for duty, to task performance metrics such as suit resource expenditures. For EVA tasks during surface exploration missions, biomedical information such as metabolic rate may be provided to crewmembers for situational awareness related to task performance efficiency. In addition, translation distances, hydration, nutrition, inspired CO2 exposure and other consumables usage rates may be useful input metrics for modeling individualized performance during tasks to inform crew or provide estimates of work efficiency. Oculomotor metrics such as gaze dwell time, pupilometry, and eye tracking collected in advanced helmet mounted displays could potentially be used to characterize crew situation awareness. This paper highlights some of these projects, and provides broader discussion about the need for advanced monitoring and smart technologies, as NASA takes the leap into the next generation of space exploration.

Kritina Holden↗

Wildfire monitoring using Unmanned Aerial Vehicles operating under UTM (STEReO)

STEReO (Scalable Traffic Management for Emergency Response Operations) project at NASA Ames is designed to provide UTM (UAS Traffic Management) services to unmanned aerial vehicles (UAVs) used for natural disaster response scenarios like wildfire and hurricanes. This will facilitate the use of unmanned aerial vehicles in regions where UAVs are currently prohibited to fly. In this paper we describe a complete architecture of using UAVs for wild fire monitoring in this STEReO environment. We simulate a complete fire monitoring scenario in an high fidelity simulation environment. The simulation consists of a fire drill in the vicinity of Redding airport, one of the test sites for CAL-FIRE. The autonomous vehicle connects to the STEReO systems and gathers information of other operation in the vicinity. The vehicle then uses on-board path planners and decision making algorithms for fire monitoring and mapping. In this paper the vehicle on-board architecture is described in details and the requirements to fly and interact with the STEReO system is discussed.

UAV UTM↗

Monitoring Human Performance on Future Deep SpaceMissions

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring – primary among them is crew autonomy. Deep space mission crews will face communication delays and blackouts, and in those situations, the crew may not have access to Mission Control Center (MCC) experts. They will be dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance. A number of technologies are presently being explored by NASA to enable crew self-monitoring. This paper highlights select projects, and provides broader discussion about the need for advanced monitoring technologies

Kritina Anderson↗