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NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗
DOE OSTI2024

Survey of prospective techniques for molten salt reactor feed monitoring

Safeguards verification measurements of nuclear material content in fresh fuel salt for liquid-fueled molten salt reactors (MSRs) are likely to be required as part of nuclear material accountancy for International Atomic Energy Agency safeguards. Here, this paper presents a comprehensive review and evaluation of 18 potential candidate techniques to quantify total uranium and 235 U for input accountancy measurements for liquid-fueled MSRs. As part of an overall screening and down-selection effort to identify the most promising techniques for further development for an MSR feed monitoring system, this paper defines eight figures of merit (FOMs): reasonably achievable measurement uncertainty, measurement time required, capital cost, burden upon the facility operator, maintenance intensity, technological maturity, human capital requirements for operation, and whether the technique introduces a path for potential material removal. Each candidate technique is then evaluated across these FOMs to identify the techniques with the highest potential for future development for fresh fuel accountancy measurements in MSRs. Our findings indicate that no single technique or combination thereof currently has the requisite technological maturity for immediate implementation in nuclear material accountancy at a liquid-fueled MSR facility. While several promising techniques are identified, there is a critical lack of experimental data for most systems in the context of molten salt applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1998

Operation of the Prototype Holographic Atmospheric Scanner for Environmental Remote Sensing (PHASERS)

This study represents the first successful employment of the Holographic Optical Telescope and Scanner (HOTS) technology to make atmospheric backscattering measurements in a scanning mode. In several independent attempts the system successfully detected the return signal from atmospheric structures as they moved across the field-of-view (FOV) of the rotating Holographic Optical Element (HOE). Future plans include the addition of a narrow band filter and the necessary optics for day-time measurements, along with additional automation of the system and its data handling. In part, due to the success of this demonstration of the HOTS technology a new class of light weight scanning telescopes for lidar remote sensing from air and space craft platforms are under development and being tested for additional applications.

Guerra, David V.↗
NASA NTRS2022

One Year in the Extreme Isolation of Antarctica—Is This Enough to Modulate an “Allergic” Sensitization?

(1) Background: After spending a year wintering in Antarctica, individual expedition members have reported increased or even new allergic reactions to environmental allergens after their return. (2) Methods: Blood samples from five overwintering crews were analyzed using the chip based multiplex ALEX Allergy Explorer (MacroArray Diagnostics GmbH, Austria). (3) Results: About one third of the 39 participants displayed specific IgEs against pollen. In most individuals, kinetics showed a reduction in the specific IgE at the time about nine months after deployment to Antarctica. Five participants had the highest specific IgE levels after returning to the “normal” world. The examination of the specific IgE relative to house dust mites and storage mites showed different kinetics. Six out of 10 had the highest specific IgE concentrations at the inner Antarctic measurement time point. These data corresponded well to the general situation in the stations. At the stations themselves, there were almost no pollen particle load, especially at Concordia. (4) Conclusions: Antarctic long-term confinement can induce an altered immune function, which is in some individuals pronounced after return to the familiar allergen environment. Future prospective studies in larger cohorts are needed to further specify these first results.

Matthias Feuerecker↗
NASA NTRS2011

PING Gamma Ray and Neutron Measurements of a Meter-Sized Carbonaceous Asteroid Analog

Determining the elemental composition of carbonaceous (spectral type C) asteroids is still one of the basic problems when studying these objects. The only main source of elemental composition information for asteroids is from their optical, NIR and IR properties, which include their spectral reflectance characteristics, albedo, polarization, and the comparison of optical spectroscopy with meteorite groups corresponding to asteroids of every spectral type. Unfortunately, these sources reflect observations from widely contrasting spatial scales that presently yield a void in the continuum of microscopic and macroscopic evidence, a lack of in situ measurement confirmation, and require deeper sensing techniques to discern the nature of these asteroids. The Probing In situ with Neutrons and Gamma rays (PING) instrument is ideally suited to address this problem because it can be used to determine the bulk elemental composition, H and C content, the average atomic weight and density of the surface and subsurface layers of C-type asteroids, and can provide measurements used to determine the difference between and distinguish between different types of asteroids. We are currently developing the PING instrument that combines gamma ray and neutron detectors with a 14 Me V pulsed neutron generator to determine the in-situ bulk elemental abundances and geochemistry of C-type asteroids with a spatial resolution of 1 m down to depths of tens of cm to 1 m. One aspect of the current work includes experimentally testing and optimizing PING on a known meter-sized Columbia River basalt C-type asteroid analog sample that has a similar composition and the same neutron response as that of a C-type asteroid. An important part of this effort focuses on utilizing timing measurements to isolate gamma rays produced by neutron inelastic scattering, neutron capture and delayed activation processes. Separating the gamma ray spectra by nuclear processes results in higher precision and sensitivity elemental composition measurements. Using gated data acquisition techniques allows for the unambiguous identification of gamma ray lines from different isotopes and nuclear processes, especially in situations when limited detector resolution results in overlapping gamma ray lines that cannot be individually resolved. In this paper, we will present the PING basalt layering experimental data, taken at the test facility at NASA Goddard Space Flight Center using the time tagged event-by-event data analysis technique, compared to our MCNPX computer simulation results for the C-type asteroid and basalt layering simulant models. Comparison of these data will show the advantages, validity, and measurement sensitivity of PING's nuclear interrogation methods to obtain more precise and sensitive in situ bulk elemental composition and density measurements of the subsurface of asteroids.

Bodnarik, J.↗
NASA NTRS2023

Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products.

ROSAS↗
DOE OSTI2024

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. Here, this paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241 Am, 60 Co, 137 Cs, 192 Ir, 239 Pu, and 235 U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Comparison of measurement and simulation of ATLAS cavern radiation background

Sixteen Medipix2 pixel detector based (MPX) devices were operated at various positions within the ATLAS detector and cavern continuously from early 2008 up to 2013. In addition to photons, each MPX detector is capable to detect charged particles, and neutrons as it is covered with a mask of converter materials dividing its area into regions sensitive to thermal or fast neutrons. The MPX detector network was effectively used for real-time measurements of the spectral characteristics and composition of complex radiation fields in ATLAS. This article reports comparison of the results of measurements performed with MPX detectors during the LHC operation period in 2010 and 2011 with Monte Carlo simulations results from the FLUGG and GCALOR codes. For the purpose of this comparison, the MPX detectors were operated in tracking mode with low threshold (8–10 keV) allowing one to distinguish among particle categories based on the recognition of track patterns left by the particles in the MPX sensitive layer. The comparison of measurements with simulations shows that the agreement between measured and simulated data is satisfactory in most cases within a factor of two.

47 OTHER INSTRUMENTATION↗
NASA NTRS1996

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Ramirez, W. Fred↗
DOE OSTI2024

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

AEROICESTUDY Colorado State University Continuous Flow Diffusion Chamber (CFDC) version R0

This dataset comprises measurements of atmospheric ice nucleating particle (INP) number concentrations at ARM's Southern Great Plains (SGP) Site Guest Instrument Facility (GIF), in Lamont, Oklahoma, during the 2019 Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY). The results reported here are from the Colorado State University Continuous Flow Diffusion Chamber (CFDC-1H) and represent real-time measurements of ambient INPs.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Thin Film Sensors for Surface Measurements

Advanced thin film sensors that can provide accurate surface temperature, strain, and heat flux measurements have been developed at NASA Glenn Research Center. These sensors provide minimally intrusive characterization of advanced propulsion materials and components in hostile, high-temperature environments as well as validation of propulsion system design codes. The sensors are designed for applications on different material systems and engine components for testing in engine simulation facilities. Thin film thermocouples and strain gauges for the measurement of surface temperature and strain have been demonstrated on metals, ceramics and advanced ceramic-based composites of various component configurations. Test environments have included both air-breathing and space propulsion-based engine and burner rig environments at surface temperatures up to 1100 C and under high gas flow and pressure conditions. The technologies developed for these sensors as well as for a thin film heat flux gauge have been integrated into a single multifunctional gauge for the simultaneous real-time measurement of surface temperature, strain, and heat flux. This is the first step toward the development of smart sensors with integrated signal conditioning and high temperature electronics that would have the capability to provide feedback to the operating system in real-time. A description of the fabrication process for the thin film sensors and multifunctional gauge will be provided. In addition, the material systems on which the sensors have been demonstrated, the test facilities and the results of the tests to-date will be described. Finally, the results will be provided of the current effort to demonstrate the capabilities of the multifunctional gauge.

Martin, Lisa C.↗
DOE OSTI2020

Microbial potentiometric sensor array measurements in unsaturated soils

The overarching goal of this study is to demonstrate a novel technology for monitoring changes in electrical potential of unsaturated soils using biofilm-populated electrodes. The novelty of the study stems from the fact that it demonstrates a method for measuring open-circuit potentials (OCP) in environments without the presence of an electrolyte solution. This study also reveals that using a biofilm-populated electrode as a reference in stable environments could successfully be employed to assess and monitor the electrochemical potential generated by plants and microorganisms. The findings imply that long-term (months to years) and real-time measurements of the open-circuit potential in unsaturated soils are possible. Because MPS arrays can directly measure open-circuit potential from the biofilm, the challenges related to locally induced electrochemical changes caused by microorganisms in the soil to achieve optimum physiological levels are eliminated. The simplicity of the technology, which allows for multiple indicator electrodes to be referenced against an “internal” reference electrode, enables spatial-temporal monitoring of the changes in the soil and the generation of 2D- or 3D-signal patterns. Once a signal pattern, generated by an array of sensors, develops (usually after 30 to 90 days), it does not significantly change unless the soil is exposed to external stimuli. The observed OCP phenomena, however, suggests that the change in OCP signal is independent of changes in soil conductivity resulting from the addition of water. In brief, findings suggest that the proposed technology can enable multidimensional profiling and long-term monitoring of changes occurring in unsaturated soils without direct implications of presence of water. The changes in the 2D or 3-D signal patterns, however, can be correlated to other important parameters that characterize soil health.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Study of modeling and evaluation of remote manipulation tasks with force feedback

The use of time and motion study methods to evaluate force feedback in remote manipulation tasks are described. Several systems of time measurement derived for industrial workers were studied and adapted for manipulator use. A task board incorporating a set of basic motions was designed and built. Results obtained from two subjects in three manipulation situations for each are reported: a force-reflective manipulator, a unilateral manipulator, and the unaided human hand. The results indicate that: (1) a time-and-motion study techniques are applicable to manipulation; and that (2) force feedback facilitates some motions (notably fitting), but not others (such as positioning).

Hill, J. W.↗
DOE OSTI2022

Enhanced laser-energy coupling with small-spot distributed phase plates (SG5-650) in OMEGA DT cryogenic target implosions

Cryogenic deuterium–tritium ice target implosions on OMEGA with new small-spot (SG5-650) distributed phase plates (DPPs) achieved an (11 ± 4)% increase in energy coupling compared to implosions with larger-spot SG5-850 DPPs by decreasing the ratio of the laser spot diameter to the target diameter from 0.93 to 0.75. The SG5-650 DPPs provide a focus spot size of 674 μm, which is defined as the diameter that encircles 95% of the measured beam energy compared to 834 μm for the SG5-850, which are the standard DPPs in cryogenic target implosions on OMEGA. The hydrodynamic efficiency, defined as the ratio of the kinetic energy in the imploding shell to the laser energy, increased from 4.5% to 5.0% based on radiation-hydrodynamic calculations benchmarked to shell trajectory and bang-time measurements. Furthermore, the higher coupling came with a trade-off of an increased hot-electron production as well as increased hydrodynamic instabilities seeded by a larger mode-10 amplitude from the beam port geometry, both of which may have reduced the fusion neutron production and areal density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1994

A Phase Locked High Speed Real-Time Interferometry System for Large Amplitude Unsteady Flows

A high speed phase locked interferometry system has been designed and developed for real-time measurements of the dynamic stall flow over a pitching airfoil. Point diffraction interferograms of incipient flow separation over a sinusoidally oscillating airfoil have been obtained at rates of up to 20 KHz and for free stream Mach numbers of 0.3 and 0.45. The images were recorded on ASA 125 and ASA 400 film using a drum camera. Special electronic timing and synchronizing circuits were developed to trigger the laser light source from the camera, and to initiate acquisition of the interferogram sequence from any desired phase angle of oscillation. The airfoil instantaneous angle of attack data provided by an optical encoder was recorded via a FIFO and in EPROM into a microcomputer. The interferograms have been analyzed using software developed in-house to get quantitative flow density and pressure distributions.

Chandrasekhara, M. S.↗
DOE OSTI2024

Trustworthiness modeling and evaluation for a nearly autonomous management and control system

The Nearly Autonomous Management and Control (NAMAC) system supports the advanced reactor operation by recommending control actions to operators based on real-time measurements and digital twins (DTs) learning from the knowledge base. To enable the safe and reliable use of autonomous technologies, NAMAC and its recommendations should be trustworthy to operators and regulators at both the design and operation stages. This study proposes a NAMAC trustworthiness modeling and evaluation framework supported by trustworthiness ontologies and evidence-based approaches. The development-time and run-time ontologies are separately constructed and then converted to Bayesian networks to quantitatively evaluate the NAMAC trustworthiness. This evaluation is demonstrated by collecting and characterizing evidence from NAMAC practices, such as the development and assessment of the NAMAC system, data coverage assessment, and the training and optimizations of neural-network-based DTs. Our proposed approach can aggregate various trustworthiness attributes of complex artificial-intelligence-supported systems for safety-critical applications. It also considers the interaction between different DTs and extends beyond the trustworthiness evaluation of a single DT. In conclusion, the evidence-based method enhances the transparency of the trustworthiness modeling and evaluation processes and helps identify uncertainties and subjectivity involved in the processes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗