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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2013

An Overview of NASA's SubsoniC Research Aircraft Testbed (SCRAT)

National Aeronautics and Space Administration Dryden Flight Research Center acquired a Gulfstream III (GIII) aircraft to serve as a testbed for aeronautics flight research experiments. The aircraft is referred to as SCRAT, which stands for SubsoniC Research Aircraft Testbed. The aircraft’s mission is to perform aeronautics research; more specifically raising the Technology Readiness Level (TRL) of advanced technologies through flight demonstrations and gathering high-quality research data suitable for verifying the technologies, and validating design and analysis tools. The SCRAT has the ability to conduct a range of flight research experiments throughout a transport class aircraft’s flight envelope. Experiments ranging from flight-testing of a new aircraft system or sensor to those requiring structural and aerodynamic modifications to the aircraft can be accomplished. The aircraft has been modified to include an instrumentation system and sensors necessary to conduct flight research experiments along with a telemetry capability. An instrumentation power distribution system was installed to accommodate the instrumentation system and future experiments. An engineering simulation of the SCRAT has been developed to aid in integrating research experiments. A series of baseline aircraft characterization flights has been flown that gathered flight data to aid in developing and integrating future research experiments. This paper describes the SCRAT’s research systems and capabilities

aeronautics↗
NASA NTRS2013

An Overview of NASA's Subsonic Research Aircraft Testbed (SCRAT)

National Aeronautics and Space Administration Dryden Flight Research Center acquired a Gulfstream III (GIII) aircraft to serve as a testbed for aeronautics flight research experiments. The aircraft is referred to as SCRAT, which stands for SubsoniC Research Aircraft Testbed. The aircraft's mission is to perform aeronautics research; more specifically raising the Technology Readiness Level (TRL) of advanced technologies through flight demonstrations and gathering high-quality research data suitable for verifying the technologies, and validating design and analysis tools. The SCRAT has the ability to conduct a range of flight research experiments throughout a transport class aircraft's flight envelope. Experiments ranging from flight-testing of a new aircraft system or sensor to those requiring structural and aerodynamic modifications to the aircraft can be accomplished. The aircraft has been modified to include an instrumentation system and sensors necessary to conduct flight research experiments along with a telemetry capability. An instrumentation power distribution system was installed to accommodate the instrumentation system and future experiments. An engineering simulation of the SCRAT has been developed to aid in integrating research experiments. A series of baseline aircraft characterization flights has been flown that gathered flight data to aid in developing and integrating future research experiments. This paper describes the SCRAT's research systems and capabilities.

testbed↗
NASA NTRS2019

Orbital Debris Radar Measurements from the Haystack Ultra-Wideband Satellite Imaging Radar (HUSIR): 2014-2017

For many years, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to collect data on the orbital debris environment using the Haystack radar. These measurements are used to characterize the small debris environment in low Earth orbit (LEO), down to a noise-limited size of approximately 5 mm—depending on altitude. The Haystack radar operated by MIT Lincoln Lab underwent upgrades starting in May 2010, with operations resuming in 2014 as the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR). Hence, the data collected beginning in 2014 represents the first dataset available from this upgraded sensor. HUSIR is the primary source of data used by the ODPO to statistically sample orbital debris in the 5-mm to 10-cm size regime in LEO and is a key source of data to build and validate the NASA Orbital Debris Engineering Model. In this paper, we will present recent results from measurements performed during the US Government fiscal years 2014 – 2017. Using the NASA Size Estimation Model, a method based on laboratory radar measurements of debris, we will compare the size distributions of selected orbital debris populations over this 4-year period and flux measurements of orbital debris greater than 1 cm.

Murray, James↗
NASA NTRS2020

Improving Quantitative Laboratory Analysis of Phycobiliproteins to Provide High Quality Validation Data for Ocean Color Remote Sensing Algorithm

Identification and characterization of phytoplankton communities and their physiology is a primary aim of NASA's PACE satellite mission. The concentration and composition of phytoplankton pigments modulate the spectral distribution of light emanating from the ocean, which is measured by ocean color satellites, and thus provide critical information on phytoplankton community composition and physiological parameters. One diagnostic class of pigments not routinely well-characterized is the phycobiliproteins (PBPs), and NASA has a requirement to collect and distribute high quality in situ data in support of data product validation activities for ocean color missions. Phycobiliproteins are light-harvesting proteins that are the predominant photosynthetic pigments in some classes of phytoplankton including cyanobacteria, such as Synechococcus, Trichodesmium, and Microcystis. With the advance of hyperspectral ocean color sensors such as on PACE (expected to launch in late 2022), it is essential that we implement routine analysis of PBPs that satisfies several considerations: reproducible, high extraction efficiency for a variety of environments, and Suitable for large scale analysis. Published techniques for PBP analysis vary in recommendations for: collection, extraction, disruption mode, and analysis; evidence suggests the variation in results may depend at least in part on the species and even strain(s) of interest. Experiments that tested variations in these parameters have drawn very different conclusions regarding extraction efficiency and reproducibility. Cyanobacteria are more difficult to extract than other PBP-containing algae such as cryptophytes, but can be important primary producers. We used a cryptophyte (Rhodomonas salina) and cyanobacterium (Synechococcus sp.) to compare extraction efficiencies of water samples concentrated via centrifugation to filtered samples using two different extraction buffers (phosphate and asolectin-CHAPS). Samples were analyzed on a fluorometer configured for PC and PE detection. The results have important implications for collection and storage of samples for routine analysis; some previous studies (although not all) have suggested that filtered samples have a much lower extraction efficiency than whole water samples.

Kenemer, Christopher↗
NASA NTRS2021

UAS Integration in the NAS Flight Test 6: Full Mission Results

Recent standards development efforts for the integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS) such as those in RTCA Inc. Special Committee 228 (SC-228) have focused on relatively large UAS transitioning to and from Class A airspace. In an effort to expand the range of vehicle classes that can access the NAS, the NASA UAS Integration in the NAS project has investigated Low Size, Weight, and Power (Low SWaP) technologies that would allow smaller UAS to detect-and-avoid (DAA) traffic. Through batch and human in the loop (HITL) simulation studies, the UAS Integration in the NAS DAA subproject have identified candidate performance standards that would contribute to enabling extended Low SWaP, UAS operations under 10,000 feet. These candidate performance standards include minimum field of regard (FOR) values for Low SWaP air surveillance sensors as well as a DAA well-clear (DWC) definition which can be applied to non-cooperative traffic to reduce the required maneuver initiation range. To test the assumptions of the project’s simulation studies and validate the candidate performance standards, a live flight research event was executed at NASA Armstrong Flight Research Center. The UAS Integration in the NAS Project Flight Test 6 Full Mission sought to characterize UAS pilot responses to traffic conflicts using a representative Low SWAP DAA system in an operational NAS environment. To achieve this, live, virtual and constructive distributed environment (LVC-DE) elements were combined to simulate a sector of Oakland center airspace and induce encounters with a live, manned aircraft. A Navmar Applied Sciences Tigershark XP was used as the UAS ownship and was integrated into the test architecture to enable it to be controlled from a Vigilant Spirit Control Station (VSCS) research ground control station. Qualified UAS pilots were recruited to act as subject pilots under test (SPUT) to control the Tigershark XP in a simulated mission while coordinating with a participating air traffic controller in simulated airspace. The intruder speed, intruder equipage and encounter geometry were varied between six scripted encounters per SPUT. Various metrics were collected including pilot reaction time from the onset of DAA alert, ATC coordination rate, probability and severity of losses of DAA well clear, and subjective ratings of system acceptability. The implications of these results on the development of standards for Low SWAP DAA systems will be discussed.

UAV↗
NASA NTRS2021

SNPP VIIRS Day Night Band: Ten Years of On-Orbit Calibration and Performance

Aboard the polar-orbiting SNPP satellite, the VIIRS instrument has been in operation since launch in October 2011. It is a visible and infrared radiometer with a unique panchromatic channel capability designated as a day-night band (DNB). This channel covers wavelengths from 0.5 to 0.9 µm and is designed with a near-constant spatial resolution for Earth observations 24 h a day. The DNB operates at 3 gain stages (low, middle, and high) to cover a large dynamic range. An onboard solar diffuser (SD) is used for calibration in the low gain stage, and to enable the derivation of gain ratios between the different stages. In this paper, we present the SNPP VIIRS DNB calibration performed by the NASA VIIRS characterization support team (VCST). The DNB calibration algorithms are described to generate the calibration coefficient look up tables (LUTs) for the latest NASA Level 1B Collection 2 products. We provide an evaluation of DNB on-orbit calibration performance. This activity supports the NASA Earth science community by delivering consistent VIIRS sensor data products via the Land Science Investigator-led Processing Systems, including the SD degradation applied for DNB calibrations in detector gain and gain ratio trending. The DNB stray light contamination and its correction are highlighted. Performance validations are presented using comparisons to the calibration methods employed by NOAA’s operational Interface Data Processing Segment. Further work on stray light corrections is also discussed.

VIIRS↗
NASA NTRSDate not supplied

Importance of Radiative Transfer Models in Atmospheric Remote Sensing

Radiative transfer models (RTMs) play a significant role in the development of satellite instruments for remote sensing applications. These models simulate electromagnetic radiation's propagation through the atmosphere, providing valuable insights into atmosphere-radiation interactions. RTMs facilitate the optimization of satellite instrument designs, ensuring their ability to measure targeted atmospheric and surface properties accurately. Moreover, they aid in simulating instrument’s measurements under various atmospheric conditions, enabling calibration and validation processes to enhance data quality and reliability. RTMs are extensively used in the Observing System Simulation Experiments (OSSE), to generate synthetic observations. By incorporating RTMs into OSSE, we can assess the potential impact of future satellite missions, sensor configurations, and data assimilation techniques. This approach allows for the optimization of satellite instruments and constellations and the evaluation of their influence on weather forecasting, climate monitoring, and other Earth science applications. Another crucial application area of RT models is data assimilation, where they play a fundamental role in combining satellite observations with numerical models to improve atmospheric and environmental predictions. RTMs provide the link between observed radiances and atmospheric parameters, enhancing the accuracy of numerical models and generating more reliable forecasts for weather events, air quality assessments, and climate projections. Moreover, adapting RT models to capture the intricate radiation interactions within the Planetary Boundary Layer will significantly contribute to improving weather forecasting and climate change projections. Current community radiative transfer (RT) models are primarily developed and optimized for operational data assimilation of satellite observations. These models excel at assimilating satellite data into numerical weather prediction models to improve forecast accuracy. However, their focus on data assimilation limits their suitability for other important applications, such as satellite instrument development, OSSE, and Planetary Boundary Layer (PBL) studies. Moreover, for PBL studies, RT models need to be adapted to capture the intricate radiation interactions within this crucial atmospheric layer. Developing RT models that can represent the PBL's unique characteristics, such as surface interactions, will contribute significantly to understanding and predicting weather phenomena, air quality, and climate dynamics. This abstract provides a comprehensive overview of the current status of RT models and highlights their limitations concerning satellite instrument development, OSSE, and PBL studies. Addressing these shortcomings requires concerted efforts to enhance RT models' capabilities and expand their applications beyond data assimilation. By investing in research and development to improve these

Isaac Moradi↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Helmet and Extravehicular Visor Assembly (EVVA) Chamber B Thermal Vacuum Testing Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test methodology, hardware setup, and results from the xEMU helmet and extravehicular visor assembly (EVVA). Two helmets/EVVAs were tested simultaneously in Chamber B, with different thermal environments and EVVA configurations. For the helmet/EVVA on the Short xEMU (SxEMU) test article, five thermal profiles were tested during five simulated EVAs, with four different visor and shade configurations. For the helmet/EVVA on the second xEMU, eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing, with a single visor and shade configuration. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and through two separate heater cages surrounding each respective test article. The thermal effects of the Exploration Informatics (xINFO) lights and camera on the helmet/EVVA was also tested. Twenty-two temperature sensors were used to collect data in critical locations in the xEMU helmet/EVVA assembly. This paper will document the testing results and compare the test data against the xEMU helmet/EVVA and system-level thermal models for model validation.

xEMU↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Hard Upper Torso (HUT) Chamber B Thermal Vacuum Testing Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test methodology, hardware setup, and results from the xEMU hard upper torso (HUT). Two HUTs, one composite HUT and one aluminum HUT, were tested simultaneously in Chamber B, with different thermal environments. For the aluminum HUT on the Short xEMU (SxEMU) test article, five thermal profiles were tested during five simulated EVAs. For the composite HUT on the second xEMU, eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and through two separate heater cages surrounding each respective test article. Seventy-two temperature sensors were used to collect data in critical locations in the xEMU HUT assembly. This paper will document the testing results and compare the test data against the xEMU HUT and system-level thermal models for model validation.

xEMU↗
NASA NTRS2018

In-Flight Verification and Validation of Colloid Microthruster Performance

Colloid Micronewton Thrusters (CMNTs) use an electrospray to provide precision spacecraft position and pointing control. They were demonstrated in space for the first time as part of NASA’s Space Technology 7 (ST7) payload hosted by the European Space Agency’s (ESA’s) LISA Pathfinder (LPF) technology demonstration mission in January, 2016. CMNTs were the actuator in the disturbance reduction system (DRS) that provided drag-free operation of the LPF spacecraft, which will be necessary for future gravity wave observatories such as ESA’s Laser Interferometer Space Antenna (LISA) mission, currently in Phase A and scheduled for launch in 2034. The CMNT technology met performance requirements operating at 5-30 µN of thrust with 0.1 µN resolution and ≤0.1 µN/ÖHz thrust noise to deliver the required nanometer-level precision spacecraft control measured by the gravitational reference sensor (GRS) in the ESA LISA Technology Package (LTP). The performance of seven of the eight CMNTs in flight was consistent with ground test results, and as a system, all eight thrusters met mission-level performance requirements. The colloid microthruster performance model of thrust and thrust noise as a function of operational parameters (i.e. beam current, voltage, temperature, etc.) was validated in flight over a wide range of conditions. A model and simulation of the thruster control algorithm was developed and validated with flight data to predict thrust noise. This capability is important for future missions because it relates directly to the acceleration noise on the test masses, which provide the gravity wave measurements. The CMNT thruster model data and validation with LISA Pathfinder/ST7-DRS flight experiments are presented in this paper.

"O'Donnell, James"↗
DOE OSTI2015

Fully depleted CMOS pixel sensor development and potential applications

CMOS pixel sensors are often opposed to hybrid pixel sensors due to their very different sensitive layer. In standard CMOS imaging processes, a thin (about 20 μm) low resistivity epitaxial layer acts as the sensitive volume and charge collection is mostly driven by thermal agitation. In contrast, the so-called hybrid pixel technology exploits a thick (typically 300 μm) silicon sensor with high resistivity allowing for the depletion of this volume, hence charges drift toward collecting electrodes. But this difference is fading away with the recent availability of some CMOS imaging processes based on a relatively thick (about 50 μm) high resistivity epitaxial layer which allows for full depletion. This evolution extents the range of applications for CMOS pixel sensors where their known assets, high sensitivity and granularity combined with embedded signal treatment, could potentially foster breakthrough in detection performances for specific scientific instruments. One such domain is the Xray detection for soft energies, typically below 10 keV, where the thin sensitive layer was previously severely impeding CMOS sensor usage. Another application becoming realistic for CMOS sensors, is the detection in environment with a high fluence of non-ionizing radiation, such as hadron colliders. However, when considering highly demanding applications, it is still to be proven that micro-circuits required to uniformly deplete the sensor at the pixel level, do not mitigate the sensitivity and efficiency required. Prototype sensors in two different technologies with resistivity higher than 1 kΩ, sensitive layer between 40 and 50 μm and featuring pixel pitch in the range 25 to 50 μm, have been designed and fabricated. Various biasing architectures were adopted to reach full depletion with only a few volts. Laboratory investigations with three types of sources (X-rays, β-rays and infrared light) demonstrated the validity of the approach with respect to depletion, keeping a low noise figure. Especially, an energy resolution of about 400 eV for 5 keV X-rays was obtained for single pixels. The prototypes have then been exposed to gradually increased fluences of neutrons, from 10{sup 13} to 5x10{sup 14} neq/cm{sup 2}. Again laboratory tests allowed to evaluate the signal over noise persistence on the different pixels implemented. Currently our development mostly targets the detection of soft X-rays, with the ambition to develop a pixel sensor matching counting rates as affordable with hybrid pixel sensors, but with an extended sensitivity to low energy and finer pixel about 25 x 25 μm{sup 2}. The original readout architecture proposed relies on a two tiers chip. The first tier consists of a sensor with a modest dynamic in order to insure low noise performances required by sensitivity. The interconnected second tier chip enhances the read-out speed by introducing massive parallelization. Performances reachable with this strategy combining counting and integration will be detailed. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2015

BioSentinel: Mission Development of a Radiation Biosensor to Gauge DNA Damage and Repair Beyond Low Earth Orbit on a 6U Nanosatellite

We are designing and developing a 6U (10 x 22 x 34 cm; 14 kg) nanosatellite as a secondary payload to fly aboard NASAs Space Launch System (SLS) Exploration Mission (EM) 1, scheduled for launch in late 2017. For the first time in over forty years, direct experimental data from biological studies beyond low Earth orbit (LEO) will be obtained during BioSentinels 12- to 18-month mission. BioSentinel will measure the damage and repair of DNA in a biological organism and allow us to compare that to information from onboard physical radiation sensors. In order to understand the relative contributions of the space environments two dominant biological perturbations, reduced gravity and ionizing radiation, results from deep space will be directly compared to data obtained in LEO (on ISS) and on Earth. These data points will be available for validation of existing biological radiation damage and repair models, and for extrapolation to humans, to assist in mitigating risks during future long-term exploration missions beyond LEO. The BioSentinel Payload occupies 4U of the spacecraft and will utilize the monocellular eukaryotic organism Saccharomyces cerevisiae (yeast) to report DNA double-strand-break (DSB) events that result from ambient space radiation. DSB repair exhibits striking conservation of repair proteins from yeast to humans. Yeast was selected because of 1) its similarity to cells in higher organisms, 2) the well-established history of strains engineered to measure DSB repair, 3) its spaceflight heritage, and 4) the wealth of available ground and flight reference data. The S. cerevisiae flight strain will include engineered genetic defects to prevent growth and division until a radiation-induced DSB activates the yeasts DNA repair mechanisms. The triggered culture growth and metabolic activity directly indicate a DSB and its successful repair. The yeast will be carried in the dry state within the 1-atm PL container in 18 separate fluidics cards with each card having 16 independent culture microwells, with integral microchannels and filters to supply nutrients and reagents, confine the yeast to the wells, and enable optical measurement. The measurement subsystem will monitor each subgroup of culture wells continuously for several weeks, optically tracking DSB-triggered cell growth and metabolism. BioSentinel will also include physical radiation sensors based on the TimePix sensor, as implemented by JSCs RadWorks group, which record individual radiation events including estimates of their linear-energy-transfer (LET) values. Radiation-dose and LET data will be compared directly to the rate of DSB-and-repair events measured by the S. cerevisiae biosentinels.

Radiation Biosensor↗
DOE OSTI2024

Development and Validation of Smart Building Technology Modules for Academic and Professional Education

Slipstream leads a team developing a semester-long smart building curriculum for college students and adapting the contents into 16 training videos for building professionals and the public. The topics cover smart building technologies related content including industry trends and benefits, building systems, sensors and IoT devices, advanced building monitoring and controls, smart building control platform, methods, and applications.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2011

Cryogenic Thermal Distortion Model Validation for the JWST ISIM Structure

The James Webb Space Telescope (JWST) is a large, infrared-optimized space telescope consisting of an Optical telescope element (OTE), Integrated science instrument module (ISIM), a Spacecraft, and a Sunshield. The Integrated Science Instrument Module (ISIM) consists of the JWST science instruments (NIRCam, MIRI, NIRSpec), a fine guidance sensor (FGS), the ISIM Structure, and thermal and electrical subsystems. JWST's instruments are designed to work primarily in the infrared range of the electromagnetic spectrum, and the instruments and telescope operate at cryogenic temperatures (approximately 35 K for the instruments).

Johnston, John↗
NASA NTRS2022

Validation of IMERG Oceanic Precipitation over Kwajalein

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellitebased retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR+morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.

KPOL↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Mixed-phase Convective Clouds in the Polar Marine Boundary Layer

This proposal, a collaboration between two universities, firstly aims to describe the high-latitude CAO cloud regime using the array of instruments at the NSA (Barrow, Alaska) and at two mobile ARM sites (MARCUS 2017-18, and COMBLE 2020). These ARM data, and the multi-sensor variables we propose to derive, allow detailed descriptions of mesoscale organization of precipitation, thermodynamic profiles, vertical velocity, cloud depth, and cloud and precipitation properties. These observations are key for our second goal, which is to use output from validated cloud- and eddy-resolving simulations to gain insights into the linkages between microphysical processes, the MBL vertical structure, and the MBL dynamics that control cloud macrostructure.

54 ENVIRONMENTAL SCIENCES↗