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NASA NTRS2016

The Effects of Ambient Conditions on Helicopter Harmonic Noise Radiation: Theory and Experiment

The effects of ambient atmospheric conditions, air temperature and density, on rotor harmonic noise radiation are characterized using theoretical models and experimental measurements of helicopter noise collected at three different test sites at elevations ranging from sea level to 7000 ft above sea level. Significant changes in the thickness, loading, and blade-vortex interaction noise levels and radiation directions are observed across the different test sites for an AS350 helicopter flying at the same indicated airspeed and gross weight. However, the radiated noise is shown to scale with ambient pressure when the flight condition of the helicopter is defined in nondimensional terms. Although the effective tip Mach number is identified as the primary governing parameter for thickness noise, the nondimensional weight coefficient also impacts lower harmonic loading noise levels, which contribute strongly to low frequency harmonic noise radiation both in and out of the plane of the horizon. Strategies for maintaining the same nondimensional rotor operating condition under different ambient conditions are developed using an analytical model of single main rotor helicopter trim and confirmed using a CAMRAD II model of the AS350 helicopter. The ability of the Fundamental Rotorcraft Acoustics Modeling from Experiments (FRAME) technique to generalize noise measurements made under one set of ambient conditions to make accurate noise predictions under other ambient conditions is also validated.

Greenwood, Eric↗

NASA NTRS2016

A Natural Interaction Interface for UAVs Using Intuitive Gesture Recognition

The popularity of unmanned aerial vehicles (UAVs) is increasing as technological advancements boost their favorability for a broad range of applications. One application is science data collection. In fields like Earth and atmospheric science, researchers are seeking to use UAVs to augment their current portfolio of platforms and increase their accessibility to geographic areas of interest. By increasing the number of data collection platforms UAVs will significantly improve system robustness and allow for more sophisticated studies. Scientists would like be able to deploy an available fleet of UAVs to fly a desired flight path and collect sensor data without needing to understand the complex low-level controls required to describe and coordinate such a mission. A natural interaction interface for a Ground Control System (GCS) using gesture recognition is developed to allow non-expert users (e.g., scientists) to define a complex flight path for a UAV using intuitive hand gesture inputs from the constructed gesture library. The GCS calculates the combined trajectory on-line, verifies the trajectory with the user, and sends it to the UAV controller to be flown.

Chandarana, Meghan↗

NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗

NASA NTRSDate not supplied

Preliminary Steps in Developing Rapid Aero Modeling Technology

The Rapid Aero Modeling (RAM) approach is a method to efficiently and automatically obtain aerodynamic models during testing, significantly saving time and resources. Motivation for this technology results from demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities. These issues are typical in the responses presented by a class of vehicles categorized as Urban Air Mobility aircraft where many features from both airplane and rotorcraft are present. For UAM configurations, with typically many more factors than conventional aircraft, traditional test methods can lead to increased costs and missed interactions. RAM guides the test to obtain high-fidelity, statistically rigorous aircraft models, and the approach is applicable to computational, ground, or flight-test experiments. It combines concepts from Design of Experiment theory and Aircraft System Identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity typically, in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. An initial wind tunnel test to support development of RAM was conducted to assess potential metrics, algorithms, and procedures. This paper presents results from initial tests for the development of RAM technology and highlights some of the unique features of RAM applied toa conventional configuration during a ground-based, static, wind-tunnel test.

Patrick C Murphy↗

NASA NTRS2023

Effects of Ambient Conditions on Helicopter Harmonic Noise Radiation: Theory and Experiment

The effects of ambient atmospheric conditions, air temperature, and density on rotor harmonic noise radiation are characterized using theoretical models and experimental measurements of helicopter noise collected at three different test sites at elevations ranging from sea level to 7000 ft above sea level. Significant changes in the thickness, loading, and blade–vortex interaction noise levels and radiation directions are observed across the different test sites for an AS350 helicopter flying at the same indicated airspeed and gross weight. However, the radiated noise is shown to scale with ambient pressure when the flight condition of the helicopter is defined in nondimensional terms. Although the effective tip Mach number is identified as the primary governing parameter for thickness noise, the nondimensional weight coefficient also impacts lower harmonic loading noise levels, which contribute strongly to low-frequency harmonic noise radiation both in and out of the plane of the horizon. Strategies for maintaining the same nondimensional rotor operating condition under different ambient conditions are developed using an analytical model of single main rotor helicopter trim and confirmed using a CAMRAD II model of the AS350 helicopter. The ability of the Fundamental Rotorcraft Acoustics Modeling from Experiments (FRAME) technique to generalize noise measurements made under one set of ambient conditions to make accurate noise predictions under other ambient conditions is also validated.

rotorcraft↗

DOE OSTI2022

Performance Monitoring Program: Developing Comparative Metrics for Fitness-for-Duty Programs

To comply with U.S. Nuclear Regulatory Commission (NRC) regulations, licensees and entities authorized under Title 10 of the Code of Federal Regulations (CFR) Part 26 Section 26.3 (§ 26.3) are required to have fitness-for-duty (FFD) programs. Under § 26.3, the expectation of these FFD programs is to provide reasonable assurance that individuals who are granted unescorted access to nuclear power reactor protected areas and Category I fuel cycle facility material control areas are trustworthy, will perform their tasks in a reliable manner, are not under the influence of any substance, legal or illegal, that may impair their ability to perform their duties, and are not mentally or physically impaired from any cause that can adversely affect their ability to safely and competently perform their duties. Pacific Northwest National Laboratory (PNNL) was tasked with developing a performance monitoring program to risk inform NRC inspection and policy regarding quantitative FFD performance data. To meet this need, PNNL developed methodologies that could be implemented within a performance monitoring program. Throughout this report, these methodologies are referred to as comparative metrics. The NRC provided PNNL with 2016–2019 data from annual reporting forms and single positive test forms provided by licensees and other entities. For most of the comparative metrics, the analyses required customized processing, such as creating filtering fields, adding data fields and summaries, and joining datasets. The comparative metrics developed include: random testing rate, random policy violation rate, pre-access policy violation rate, subversion attempt rate, and number of policy violations by labor category. The comparative metrics can be used for parsing and visualizing FFD program data and monitoring FFD performance at the labor category, facility, licensee, and industry levels to risk inform NRC inspection and policy with regard to the FFD data currently collected from licensees and other entities that implement Part 26 requirements. Furthermore, these developed comparative metrics allow a more in-depth look at the FFD programs for the industry to discern trends and patterns that may warrant changes at an industry level and to inform policy decisions. These analyses should be refreshed as new FFD data become available.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The interactivity of sources and dietary levels of resistant starches – impact on growth performance, starch, and nutrient digestibility, digesta oligosaccharides profile, cecal microbial metabolites, and indicators of gut health in broiler chickens

In a 21-d study, 480 Cobb 500 (off-sex) male broiler chicks were used to investigate the effects of feeding different sources and levels of resistant starches (RS) on growth performance, nutrient and energy utilization, and intestinal health in broiler chickens. The birds were allocated to 10 dietary treatments in a 3 × 3 + 1 factorial arrangement. The factors were 3 RS-sources (RSS): banana starch (BS), raw potato starch (RPS), and high-amylose corn starch (HCS); each at 3 levels (RSL) 25, 50, or 100 g/kg plus a corn-soybean meal control diet. Birds and feed were weighed on d 0, 8, and 21. On d 21, samples of jejunal tissue and digesta were collected for chemical analysis. Data were analyzed using the mixed model procedure of JMP with factor levels nested with the control. In the 0 to 21 phase, the birds fed the RPS diets had higher (P = 0.011) FI than those fed HCS or control diets, and FCR was greater (P = 0.030) in birds that received BS diets than in other diets. RSS × RSL was significant (P < 0.05) for total tract nutrient retention, AME, and AMEn on d 21. The starch digestibility was higher (P < 0.001) in birds that received the control diet than in RS diets, and decreased as RS levels increased, except for HCS. The apparent metabolizable energy (AME) and nitrogen-corrected AME (AMEn) were higher (P < 0.001) in birds fed 100 g/kg HCS diet, with both decreasing with increasing levels of BS and RPS, except for HCS. Relative ileal oligosaccharides profile showed significant (P < 0.05) RSS × RSL with a higher relative abundance of Hex(3) (P = 0.01) and Pent(3) (P = 0.001) in HCS diets. In conclusion, RS may influence gut health and growth performance in broiler chickens through modulation of cecal SCFA and nutrient digestion, but these depend largely on the botanical origin and concentrations of individual RS.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1995

The Effect of Altitude Conditions on the Particle Emissions of a J85-GE-5L Turbojet Engine

Particles from a J85-GE-5L turbojet engine were measured over a range of engine speeds at simulated altitude conditions ranging from near sea level to 45,000 ft and at flight Mach numbers of 0.5 and 0.8. Samples were collected from the engine by using a specially designed probe positioned several inches behind the exhaust nozzle. A differential mobility particle sizing system was used to determine particle size. Particle data measured at near sea-level conditions were compared with Navy Aircraft Environmental Support Office (AESO) particle data taken from a GE-J85-4A engine at a sea-level static condition. Particle data from the J85 engine were also compared with particle data from a J85 combustor at three different simulated altitudes.

Rickey, June Elizabeth↗

NASA NTRS2025

Components Refurbishment and Chemical Analysis Facility, SWMU #041 - Per- and Polyfluoroalkyl Substances Site Assessment Report Kennedy Space Center, Florida

This PFAS Site Assessment (SA) Report presents the activities and results associated with PFAS investigations at the Components Refurbishment and Chemical Analysis (CRCA) facility located at Kennedy Space Center (KSC), Florida. In 2022, CRCA was identified as an Area of Potential Concern because the facility stores several potential PFAS-containing chemicals. A groundwater sample collected from an onsite monitoring well detected PFOA and PFOS at concentrations greater than State of Florida provisional Groundwater Cleanup Target Levels (pGCTLs). PFAS SA activities were conducted between March 2022 and February 2024. During this timeframe, a total of 16 direct-push technology (DPT) locations and 82 discrete samples were collected from these locations, along with 90 monitoring well samples. The analytical data screening process focused on State of Florida pGCTLs and United States Environmental Protection Agency (USEPA) Regional Screening Level (RSLs) from November 2023 to evaluate the data. Analytical results identified PFOS, PFOA, and PFBA at concentrations exceeding their respective RSLs at each depth interval (shallow, intermediate, and deep). PFOS and PFOA had the largest footprint of RSL exceedances in each depth interval, but pGCTL exceedances were only observed at limited locations in the shallow and intermediate intervals. PFBA had the highest detections of any PFAS compound, with concentrations exceeding 180,000 nanograms per liter (ng/L), which is 100-times the RSL of 1,800 ng/L in the shallow and intermediate intervals and 10-times the RSL in the deep interval. The maximum PFBA detection was 841,000 ng/L at shallow monitoring well, MW0006. The PFAS SA also included samples collected from the onsite hydraulic containment system (HCS) which was installed to control and treat the onsite chlorinated volatile organic compound (CVOC) plume. Influent and effluent aqueous samples were collected monthly from the system. PFAS concentrations were relatively the same for both influent and effluent samples, indicating that while the HCS has been effective for CVOC treatment, it does not provide any additional treatment for PFAS compounds. Since the HCS has achieved its objectives, the system was shut down in December 2024. PFAS data gaps still exist, to include surface water and soil, which were not sampled during this SA. Soil sampling near the Chemical Process Area is recommended. Surface water and soil sampling at select stormwater outfalls is also recommended. Additionally, further groundwater sampling is recommended (DPT and monitoring well) in all depth intervals to delineate the extent of PFAS impacts at CRCA.

K Alex Murphy↗

DOE OSTI2025

Scintillator based nuclear photovoltaic batteries for power generation at microwatts level

A nuclear photovoltaic battery uses scintillator to convert radiation into visible light, which is then collected by a photovoltaic (PV) cell to generate electricity. If the radiation is gamma-rays emitted from external sources, the battery may also be referred as gammavoltaic battery. In this study, a polycrystalline CdTe solar cell was optically coupled with a 2.0 cm × 2.0 cm × 1.0 cm Gadolinium Aluminum Gallium Garnet (GAGG) scintillator, and the resulting device was tested using intense gamma radiation fields from a Cs-137 (1.5 kRad/h) and a Co-60 (10 kRad/h) irradiator. Measurements with Cs-137 provided a maximum power output (P max ) of ~288 nW, with a short-circuit current density (J sc ) of ~1.22 μA/cm 2 and an open-circuit voltage (V oc ) of ~0.34 V. In contrast, Co-60 irradiator gave a P max of 1.5 μW, with a J sc of ~4.73 μA/cm 2 and a V oc of ~0.38 V. The CdTe was also paired with a Lutetium-Yttrium Oxyorthosilicate (LYSO) crystal and tested with the Cs-137 source. The experiment presents a scalable option to reach to higher power outputs by harvesting gamma radiation fields in many cases where high radiation field demands heavy shielding and is often regarded as unwanted waste.

25 ENERGY STORAGE↗

NASA NTRS1983

Organizing Space Shuttle parametric data for maintainability

A model of organization and management of Space Shuttle data is proposed. Shuttle avionics software is parametrically altered by a reconfiguration process for each flight. As the flight rate approaches an operational level, current methods of data management would become increasingly complex. An alternative method is introduced, using modularized standard data, and its implications for data collection, integration, validation, and reconfiguration processes are explored. Information modules are cataloged for later use, and may be combined in several levels for maintenance. For each flight, information modules can then be selected from the catalog at a high level. These concepts take advantage of the reusability of Space Shuttle information to reduce the cost of reconfiguration as flight experience increases.

Angier, R. C.↗

NASA NTRS2022

GPM Ground Validation at NASA Wallops Precipitation Research Facility

The Wallops Precipitation Research Facility (WPRF) at NASA Goddard Space Flight Center, Wallops Island, VA has been established as a semi-permanent super-site for the Global Precipitation Measurement (GPM) Ground Validation (GV) program. WPRF is home to research quality precipitation instruments, including NASA’s S-band dual-polarimetric radar (NPOL), and a network of profiling radars, disdrometers, and rain gauges. This study investigates the statistical agreement of the GPM Core Observatory Dual Frequency Precipitation Radar (DPR), combined DPR-GPM Microwave Imager (GMI) and GMI Level II precipitation retrievals compared to WPRF ground observations from a six-year collection of satellite overpasses. Multi-sensor observations are integrated using the System for Integrating Multiplatform Data to Build the Atmospheric Column (SIMBA) software package. SIMBA ensures measurements recorded in a variety of formats are synthesized into a common reference frame for ease in comparison and analysis. Given that instantaneous satellite measurements are observed above ground level, this study investigates the possibility of a time lag between satellite and surface mass-weighted mean diameter (D(sub m)), reflectivity (Z), and precipitation rate (R) observations. Results indicate that time lags vary up to 30 minutes after overpass time but are not consistent between cases. In addition, GPM Core D(sub m) retrievals are within Level I mission science requirements as compared to WPRF ground observations. Results also indicate GPM algorithms overestimate light rain (< 1.0 mm hr(exp -1)). Two very different stratiform rain vertical profiles show differing results when compared to ground reference data. A key finding of this study indicates multi-sensor DPR/GMI combined algorithms outperform single sensor DPR algorithm.

Charanjit S Pabla↗

NASA NTRS2000

Role of Collective Effects in Dominance of Scattering Off Thermal Ions Over Langmuir Wave Decay: Analysis, Simulations, and Space Applications

Langmuir waves driven to high levels by beam instabilities are subject to nonlinear processes, including the closely related processes of scattering off thermal ions (STI) and a decay process in which the ion response is organized into a product ion acoustic wave. Calculations of the nonlinear growth rates predict that the decay process should always dominate STI, creating two paradoxes. The first is that three independent computer simulation studies show STI proceeding, with no evidence for the decay at all. The second is that observations in space of type 3 solar radio bursts and Earth's foreshock, which the simulations were intended to model, show evidence for the decay proceeding but no evidence for STI. Resolutions to these paradoxes follow from the realization that a nonlinear process cannot proceed when its growth rate exceeds the minimum frequency of the participating waves, since the required collective response cannot be maintained and the waves cannot respond appropriately, and that a significant number of e-foldings and wave periods must be contained in the time available. It is shown that application of these 'collective' and 'time scale' constraints to the simulations explains why the decay does not proceed in them, as well as why STI proceeds in specific simulations. This appears to be the first demonstration that collective constraints are important in understanding nonlinear phenomena. Furthermore, applying these constraints to space observations, it is predicted that the decay should proceed (and dominate STI) in type 3 sources and the high beam speed regions of Earth's foreshock for a specific range of wave levels, with a possible role for STI alone at slightly higher wave levels. Deeper in the foreshock, for slower beams and weaker wave levels, the decay and STI are predicted to become ineffective. Suggestions are given for future testing of the collective constraint and an explanation for why waves in space are usually much weaker than in the simulations.

Cairns, Iver H.↗

DOE OSTI2021

Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017

Abstract. Data on irrigation patterns and trends at field-level detail across broad extents are vital for assessing and managing limited water resources. Until recently, there has been a scarcity of comprehensive, consistent, and frequent irrigation maps for the US. Here we present the new Landsat-based Irrigation Dataset (LANID), which is comprised of 30 m resolution annual irrigation maps covering the conterminous US (CONUS) for the period of 1997–2017. The main dataset identifies the annual extent of irrigated croplands, pastureland, and hay for each year in the study period. Derivative maps include layers on maximum irrigated extent, irrigation frequency and trends, and identification of formerly irrigated areas and intermittently irrigated lands. Temporal analysis reveals that 38.5×106 ha of croplands and pasture–hay has been irrigated, among which the yearly active area ranged from ∼22.6 to 24.7×106 ha. The LANID products provide several improvements over other irrigation data including field-level details on irrigation change and frequency, an annual time step, and a collection of ∼10 000 visually interpreted ground reference locations for the eastern US where such data have been lacking. Our maps demonstrated overall accuracy above 90 % across all years and regions, including in the more humid and challenging-to-map eastern US, marking a significant advancement over other products, whose accuracies ranged from 50 % to 80 %. In terms of change detection, our maps yield per-pixel transition accuracy of 81 % and show good agreement with US Department of Agriculture reports at both county and state levels. The described annual maps, derivative layers, and ground reference data provide users with unique opportunities to study local to nationwide trends, driving forces, and consequences of irrigation and encourage the further development and assessment of new approaches for improved mapping of irrigation, especially in challenging areas like the eastern US. The annual LANID maps, derivative products, and ground reference data are available through https://doi.org/10.5281/zenodo.5548555 (Xie and Lark, 2021a).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗

DOE OSTI2023

Utility of parentage‐based tagging for monitoring Coho salmon ( Oncorhynchus kisutch ) in the interior Columbia River basin

Abstract By the 1980s, after decades of declining numbers in the mid‐1900s, Coho salmon ( Oncorhynchus kisutch ) were considered extirpated from the interior Columbia River. In the mid‐1990s, the Confederated Tribes of the Umatilla Indian Reservation, the Confederated Tribes and Bands of the Yakama Nation, and the Nez Perce Tribe began successful reintroduction programs of Coho salmon upstream of Bonneville Dam, but which were initially sourced from lower Columbia River hatcheries. Here we present the first Coho salmon parentage‐based tagging (PBT) baseline from seven hatchery programs located in the interior Columbia River basin, and two sites at or downstream of Bonneville Dam, composed of over 32,000 broodstock samples. Analyses of baseline collections revealed that genetic structure followed a temporal pattern based on 3‐year broodlines rather than geographic location or stocking history. Across hatchery programs, similar levels of genetic diversity was present. The PBT baseline provided multiple direct applications such as identification of origin for Coho salmon collected in a mixed stock at Priest Rapids Dam and the detection of the proportion and distribution of hatchery‐origin fish on the spawning grounds in the Methow River basin. The PBT baseline for Coho salmon is freely available for use and can be downloaded from FishGen.net .

Horn, Rebekah L.↗

NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Chamber B Thermal Vacuum “Suit 2” Pressure Garment System Test Article 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 results from the “Suit 2” Pressure Garment System (PGS) test article. The primary objective of the “Suit 2” PGS test article was to evaluate system-level suit heat leak and Environmental Protection Garment (EPG) thermal performance. 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 a heater cage surrounding the test article. This paper will principally focus on system-level thermal results from the “Suit 2” PGS test article. This paper will examine data collected from one-hundred and seventy thermocouples located in critical locations inside and outside of the suit, as well as seven resistance thermometers (RTDs) for calorimetry to determine total heat flux in and out of the suit. The test data will be compared against the system-level PGS thermal models for model validation. To conclude, this paper will address knowledge gaps presented by unmanned xPGS thermal vacuum testing and the current state of lunar xPGS thermal modeling and testing.

xEMU↗