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DOE OSTI2024

The persistence of memory in ionic conduction probed by nonlinear optics

Predicting practical rates of transport in condensed phases enables the rational design of materials, devices and processes. This is especially critical to developing low-carbon energy technologies such as rechargeable batteries. For ionic conduction, the collective mechanisms, variation of conductivity with timescales and confinement, and ambiguity in the phononic origin of translation, call for a direct probe of the fundamental steps of ionic diffusion: ion hops. However, such hops are rare-event large-amplitude translations, and are challenging to excite and detect. Here we use single-cycle terahertz pumps to impulsively trigger ionic hopping in battery solid electrolytes. This is visualized by an induced transient birefringence, enabling direct probing of anisotropy in ionic hopping on the picosecond timescale. The relaxation of the transient signal measures the decay of orientational memory, and the production of entropy in diffusion. We extend experimental results using in silico transient birefringence to identify vibrational attempt frequencies for ion hopping. Using nonlinear optical methods, we probe ion transport at its fastest limit, distinguish correlated conduction mechanisms from a true random walk at the atomic scale, and demonstrate the connection between activated transport and the thermodynamics of information.

25 ENERGY STORAGE↗

DOE OSTI2026

The 2025 Evaluation of Experimental Thermonuclear Reaction Rates (ETR25)

This work describes the formalism for estimating thermonuclear reaction rates for astrophysical applications, emphasizing modern statistical approaches such as Monte Carlo sampling and Bayesian models. We discuss related topics including the calculation of resonance energies from nuclear Q values, indirect estimates of particle partial widths, and matching of reaction rates at elevated temperatures to statistical model results. We have evaluated available experimental data on cross sections, resonance energies and strengths, partial widths, lifetimes, spin-parities, and spectroscopic factors. Based on these results, we have estimated numerical values of 78 experimental charged-particle thermonuclear reaction rates for target nuclei in the A = 2–40 mass region, for temperatures ranging from 1 MK to 10 GK. For each reaction, three rate values are provided: low, median, and high, corresponding to the 16th, 50th, and 84th percentiles, respectively, of the cumulative reaction rate probability density distribution. Additionally, we present the factor uncertainty of each rate at each temperature grid point. These results enable users to sample the reaction rate probability density in nucleosynthesis calculations, facilitating uncertainty estimates of nuclidic abundances. The rates presented here refer to their laboratory values. For use in stellar model simulations, these values need to be corrected for the effects of thermal excitations of the interacting nuclei. For each reaction, we include graphs that illustrate the fractional contributions to the overall reaction rate along with the associated uncertainty. These visuals are designed to assist both stellar modelers and nuclear experimentalists by identifying the primary sources of rate uncertain=^texttx);ty at specific stellar temperatures. A graphical comparison with earlier Monte Carlo rates is also provided.

Nuclear astrophysics↗

NASA NTRS2007

GEO Population Estimates using Optical Survey Data

Optical survey data taken using the NASA Michigan Orbital Debris Survey Telescope (MODEST) gives us an opportunity to statistically sample faint object population in the Geosynchronous (GEO) and near-GEO environment. This paper will summarize the MODEST survey work that has been conducted by NASA since 2002, and will outline the techniques employed to arrive at the current population estimates in the GEO environment for dim objects difficult to detect and track using current systems in the Space Surveillance Network (SSN). Some types of orbits have a higher detection rate based on what parts of the GEO belt is being observed, a straightforward statistical technique is used to debias these observations to arrive at an estimate of the total population potentially visible to the telescope. The size and magnitude distributions of these fainter debris objects are markedly different from the catalogued population. GEO debris consists of at least two different populations, one which follows the standard breakup power law and one which has anomalously high Area-to-Mass Ratios (1 to approx. 30 square meters per kilogram; a sheet of paper = approx. 13 square meters per kilogram). The Inter-Agency Space Debris Coordination Committee (IADC) is investigating objects in GEO orbits with anomalously high Area-to-Mass Ratios (AMRs). The ESA Space Debris Telescope discovered this population and has and its properties of inclinations (0 to 30 degrees), changing eccentricities (0 and 0.6), and mean motions (approx. 1 rev), will be presented. The accepted interpretation of this orbital behavior is that solar radiation pressure drives the perturbations causing time varying inclinations and eccentricities. The orbital parameters are unstable for this population and thus difficult to predict. Their dim visual magnitudes and photometric variability make observations a challenge. The IADC has enlisted a series of observatories (participating institutions: University of Michigan/CTIO, Astronomical Institute University of Bern, Boeing LTS / AMOS, Keldysh Institute of Applied Mathematics) at different longitudes. Complete observational coverage over periods of days to months will provide a better understanding of the properties, such as solar radiation pressure effects on orbital elements, size, shape, attitude, color variations, and spectral characteristics. Results from recent observational programs will be summarized, and includes a description of the orbit elements prediction processes, a summary of the metric tracking performance, and some photometric characteristics of this class of debris.

Barker, Edwin S.↗

NASA NTRS2019

Shockwave/Boundary-Layer Interaction Studies Performed in the NASA Langley 20-Inch Mach 6 Air Tunnel

This paper highlights results from a collaborative study performed by The University of Tennessee Space Institute (UTSI) and NASA Langley Research Center on the Shockwave/Boundary-Layer Interaction (SWBLI) generated by a cylindrical protuberance on a flat plate in a Mach 6 flow. The study was performed in the 20-Inch Mach 6 Air Tunnel at NASA Langley Research Center and consisted of two separate entries. In the first entry, simultaneous high-speed schlieren and high-speed pressure-sensitive paint (PSP) imaging – which was performed for the first time in the 20-Inch Mach 6 facility at NASA Langley – were performed as well as simultaneous high-speed schlieren and oil-flow imaging. In the second entry, the model configuration was modified to increase the size of the interaction region. High-speed schlieren and infrared thermography (IR) surface imaging were performed in this second entry. The goal of these tests was to characterize the SBLI in the presence of a laminar, transitional, and turbulent boundary layer using high-speed optical imaging techniques. AoA = sting angle-of-attack (°) dcylinder = cylinder diameter (mm) dtrip = cylindrical tripping element diameter (mm) Δshock = shock stand-off distance (mm) hcylinder = cylinder height (mm) htrip = cylindrical tripping element height (mm) HSS = high-speed schlieren M∞ = freestream Mach number PSP = pressure-sensitive paint Re∞ = freestream unit Reynolds number (m-1) SWBLI = shockwave/boundary-layer interaction θplate = model plate angle (°) Introduction his paper highlights two experimental entries performed in the 20-Inch Mach 6 Air Blowdown Tunnel at NASA Langley Research Center in collaboration with The University of Tennessee Space Institute (UTSI). The purpose of these entries was to characterize the dynamic shockwave/boundary-layer interaction (SWBLI) between a vertical cylinder on a flat plate and laminar, transitional (XSWBLI), and turbulent (SWTBLI) boundary layers with a freestream Mach number of 6 using non-intrusive optical diagnostics. Experiments performed by Murphree et al.1,2 were among the first to specifically characterize XSWBLI induced by a vertical cylinder on a flat plate geometry using several optical measurement techniques. Recent optical studies of XSWBLI phenomenon have been performed by UTSI at Mach 2 in their low-enthalpy blow wind tunnel3-8 and by Texas A&M University and UTSI at Mach numbers of 6 and 7 in their Adjustable Contour Expansion wind tunnel.9 The experiments described in this paper were intended to complement previous studies by expanding the freestream unit Reynolds number range, Re∞, over which the XSWBLI phenomena has been observed. Additionally these experiments, made possible under NASA’s new facility funding model under the Aeronautics Evaluation and Test Capabilities (AETC) project, promoted collaboration between university and NASA researchers. The initial entry in the 20-Inch Mach 6 Air Tunnel at NASA Langley occurred in December of 2016. Originally, testing was to occur in November of 2016 in the 31-Inch Mach 10 Air Tunnel at NASA Langley. This facility was chosen so that the XSWBLI phenomenon could be observed at much higher Mach numbers than had previously been attempted in ground test experiments. The model selected for this experiment, a 10° half-angle wedge with a sharp leading edge (described in detail in section II.B), had previously been used by Danehy et al. [10] for boundary layer transition studies using the nitric oxide planar laser-induced fluorescence (NO PLIF) flow visualization technique. In that work, it was determined that transition could be induced downstream of a single htrip = 1-mm tall, dtrip = 4-mm diameter cylindrical tripping element and that the streamwise location of the transition could be changed for a single Re∞ by changing the model angle-of-attack (AoA) (see Fig. A3 in Ref. [10] for more details). Based on the findings of that work, a decision was made to use the wedge model with the cylindrical tripping element to trip the boundary layer flow ahead of a cylindrical protuberance in order to achieve a XSWBLI. Unfortunately, the 31-Inch Mach 10 facility had been taken offline for repairs in October of 2016 and a decision was made to move the test to the 20-Inch Mach 6 facility. Since the behavior of the boundary layer with the chosen model configuration had not been studied before in that facility and the available test time was limited, the entry was considered to be exploratory and was used to collect spatially-resolved and time-resolved flow and surface visualization data that would be used to inform a second entry. Test techniques included simultaneous high-speed schlieren (HSS) captured at 160 kHz and high-speed pressure sensitive paint captured at 10 kHz as well as oil flow visualization, captured at 750 Hz. The second entry in the 20-Inch Mach 6 facility occurred in June and July of 2017. In this follow-on test, modifications to the wind tunnel model were made based on observations made during the first entry and included removing the cylindrical tripping element, increasing the size of the cylinder used to induce the SWBLI to increase the size of the interaction while simultaneously improving spatial resolution, and using a swept ramp array, similar to that described in Ref. [11], to trip the flow to turbulence. Simultaneous HSS (captured at 140 kHz, 100 kHz, and 40 kHz) and conventional IR thermography (captured at 30 Hz) imaging were performed simultaneously in this follow-on entry. This paper is intended to serve as a summary of the work performed during these two entries, to detail lessons learned from each entry, and to highlight some of the datasets acquired. Details on the experimental setup, model configuration, and techniques used are provided. Papers providing a more rigorous analysis of data acquired during the second entry, including statistical, spectral, and modal decomposition methods, can be found in Refs. [12,13]. An entry examining XSWBLI in the 31-Inch Mach 10 Blowdown Wind Tunnel facility is currently planned for mid-to-late calendar year 2019, pending the success of facility repairs. The work performed and described in this paper and the upcoming entry in the 31-Inch Mach 10 facility at NASA Langley have been made possible by NASA’s new facility funding model under the Aeronautics Evaluation and Test Capabilities (AETC) project. Wind Tunnel Facility All experiments discussed in this paper were performed in the 20-Inch Mach 6 Air Tunnel at NASA Langley Research Center. Specific details pertaining to this facility can be found in Refs. [14,15], with only a brief description of the facility provided here. For both entries, the nominal freestream unit Reynolds number was varied between 1.8×106 m-1 (0.5×106 ft-1) and 26.3×106 m-1 (8×106 ft-1). The nominal stagnation pressure was varied between 0.21 MPa and 3.33 MPa and the nominal stagnation temperature was varied between 480 K and 520 K to achieve the desired Re∞ condition. For all runs, the nominal freestream Mach number was 6. The nearly square test section is 520.7-mm (20.5-inches) wide by 508-mm (20-inches) high. Two 431.8-mm (17-inch) diameter windows made of Corning 7940, Grade 5F schlieren-quality glass serve as the side walls of the tunnel and provide optical access for the high-speed schlieren measurements. A rectangular window made of the same material as the side windows served as the top wall of the test section and provided optical access for the high-speed PSP and oil flow measurements. For the second entry, this top window was replaced with a Zinc Selenide (ZnSe) window with an anti-reflection coating capable of passing IR wavelengths between 8μm and 12μm with greater than 98% transmittance. The model was sting supported by a strut attached to a hydraulic system that allows for the model pitch angle to be adjusted between -5° to +55°. For the first entry, an initial pitch/pause sweep of the model AoA was performed to observe the resulting SWBLI. Ultimately, however, the sting pitch angle for this entry was fixed at +10.0° so that the angle of the top surface of the wedge relative to the streamwise axis of the tunnel (referred to herein as the plate angle, θplate), was θplate = 0°. For the second entry, θplate = 0° and θplate = -13.25° were initially tested with the swept ramp array (discussed in the following section) to determine which orientation produced conditions most favorable for XSWBLI to occur based on the heating signatures observed over the top surface of the model in the IR thermography images. Based on these initial tests, θplate = -13.25° was set for the remainder of the runs in the second entry. For both entries, any model changes were performed in a housing located beneath the closed test section. Prior to performing a run of the tunnel, the housing was sealed and the tunnel started. Once the appropriate freestream conditions were achieved, the model was injected into the test section using a hydraulic injection system. B. Model Geometry For all runs, a 10° half-angle (20° full-angle) wedge model with a sharp leading edge was used. The model is described in detail in Refs. [10,16]. The top surface of the sharp leading edge of the model extended 47.8 mm from its upstream-most edge to a junction with the upstream edge of a stainless steel top plate that then extended an (a) (c) (b) Fig. 1 (a) Schematic of top surface of wedge model with gas seeding insert, (b) perspective view of the model in the 20-Inch Mach 6 tunnel with centerline pressure orifices on sharp leading edge, and (c) a perspective view of the model with stainless steel (top) and SLA middle insert (bottom) during the first entry. Flow occurs from left to right.

Bathel, Brett F.↗

NASA NTRS2016

SC-228 Inclusion of DAA Warning Alert for TCAS Interoperability

This white paper summarizes NASA research results that have informed Special Committee 228 (SC-228) discussions and decisions regarding the inclusion of a warning-level alert within the detect and avoid (DAA) alerting structure for unmanned aircraft systems (UAS). For UAS, the removal of the pilot from onboard the aircraft has eliminated the ability of the ground-based pilot in command (PIC) to use out-the-window visual information to make judgments about a potential threat of a loss of well clear with another aircraft. As a result, the DAA traffic display will be the primary source of information that the PIC can use to execute the three primary well clear functions: 1) detect a potential loss of well clear, 2) determine a resolution maneuver, and 3) upload that maneuver to the aircraft via the ground control station (GCS). In addition, pilots are required to coordinate with air traffic control (ATC) prior to maneuvering off of their approved flight plan. In determining an appropriate resolution maneuver to avoid a loss of well clear, the PIC must decide both when and how to maneuver, and both the timeliness and the accuracy (i.e., correctness) of the maneuver are critical to reducing the likelihood and/or severity of a loss of well clear. Alerting information is one of three critical components of the DAA display, along with traffic information elements (e.g., relative heading, speed and altitude) and maneuver guidance. Alerting information and maneuver guidance, in particular, have been found to have a significant impact, both statistically and practically, on pilots' ability to avoid and minimize the severity of losses of well clear While all three display components are key to pilots performing the traffic avoidance task of remaining well clear, in general, alerting information provides crucial information about when a resolution maneuver is required while maneuver guidance assists the pilot in determining how best to maneuver. A fundamental task of the DAA alerting system is to provide critical timing information to the pilot about the potential for a loss of well clear with another aircraft. This is done by employing both temporal and spatial thresholds that indicate to the pilot the likelihood and imminence of a loss of well clear. The design of the DAA alerting thresholds is a balancing act between eliciting the desired pilot response in real loss of well clear threat events and reducing excessive, unnecessary, and/or uncoordinated UAS maneuvering within the air traffic environment; larger thresholds, both spatially and temporally, may increase the likelihood of a pilot avoiding a loss of well clear, but it can also increase the frequency of maneuvering - especially in cases where a maneuver is not actually needed to maintain well clear. A series of human in the loop (HITL) simulations have been conducted as part of NASA's Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) project. The purpose of these HITLs has been to provide empirical results in order to inform development of the minimum human-machine interface requirements for the DAA system. This white paper will present those results which provide evidence of a human performance benefit (in terms of response times and ability to remain well clear of other aircraft) of the DAA warning alert both with and without a collision avoidance system on board the aircraft.

TCAS interoperability↗

DOE OSTI2022

Demonstrating the viability of Lagrangian in situ reduction on supercomputers

Performing exploratory analysis and visualization of large-scale time-varying computational science applications is challenging due to inaccuracies that arise from under-resolved data. In recent years, Lagrangian representations of the vector field computed using in situ processing are being increasingly researched and have emerged as a potential solution to enable exploration. However, prior works have offered limited estimates of the encumbrance on the simulation code as they consider “theoretical” in situ environments. Further, the effectiveness of this approach varies based on the nature of the vector field, benefitting from an in-depth investigation for each application area. With this study, an extended version of Sane et al. (2021), we contribute an evaluation of Lagrangian analysis viability and efficacy for simulation codes executing at scale on a supercomputer. We investigated previously unexplored cosmology and seismology applications as well as conducted a performance benchmarking study by using a hydrodynamics mini-application targeting exascale computing. Here, to inform encumbrance, we integrated in situ infrastructure with simulation codes, and evaluated Lagrangian in situ reduction in representative homogeneous and heterogeneous HPC environments. To inform post hoc accuracy, we conducted a statistical analysis across a range of spatiotemporal configurations as well as a qualitative evaluation. Additionally, our study contributes cost estimates for distributed-memory post hoc reconstruction. In all, we demonstrate viability for each application — data reduction to less than 1% of the total data via Lagrangian representations, while maintaining accurate reconstruction and requiring under 10% of total execution time in over 90% of our experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Machine learning to discover mineral trapping signatures due to CO 2 injection

Mineral trapping is pursued as a geological CO 2 sequestration (GCS) mechanism because it permanently stores CO 2 in solid phases or minerals. However, CO 2 mineral-trapping mechanisms are poorly understood due to (1) lack of sufficient field and laboratory data characterizing these complex processes, and (2) challenges to develop site-specific reactive-transport models coupling fluid flow and geochemical reactions occurring at various temporal (from milliseconds to years) and spatial (from pore (millimeters) to field (kilometers)) scales. Reactive transport with additional complexities such as heterogeneity can make the simulation outputs even more difficult to interpret because of complex nonlinearity and multi-scale interdependencies. Furthermore, the values of model outputs such as concentrations can vary by several orders of magnitude, making it harder to correlate and characterize the impact of the variables via traditional data interpretation techniques such as exploratory data analyses. Recently, machine learning (ML) has shown promise in feature discovery and in highlighting hidden mechanisms that cannot be obtained by existing data-analytics and statistical methods. In this study, we applied an unsupervised ML approach, non-negative matrix factorization with custom -means clustering (NMF) to the data generated by reactive-transport simulations of GCS. The reactive-transport data consisted of 19 attributes, including four physio-chemical variables (pH, porosity, aqueous CO 2 , and sequestered CO 2 ), six chemical species (K + , Na + , HCO, Ca 2+ , Mg 2+ , Fe 2+ ), and four carbonate minerals (calcite, dolomite, siderite, and ankerite), a feldspar mineral (albite), and four clay minerals (illite, clinochlore, kaolinite, and smectite) over a period of 200 years of simulation time. Furthermore, the simulation data used was for Morrow B sandstone at the Farnsworth hydrocarbon unit in Texas. Data are sampled at two locations within the model domain: (1) at the injection well and (2) 200 m west of the injection well. The injection was performed for a period of 10 years. Using NMF, we estimated the temporal interdependencies among the 19 attributes over a span of 200 years. We found that NMF was able to identify four reaction stages and their dominant attributes; these cannot be directly discerned through traditional visualization (e.g., line plots, Pareto analysis, Glyph-based visualization methods) or exploratory data analysis tools of the simulation data. The four stages were: reactions in the injection phase followed by short-, mid-, and long-term reactions. The NMF analysis also revealed that 10 among the 19 attributes are dominant. These dominant attributes for mineral trapping include calcite, dolomite at injection well, siderite at 200 m away from the injection well, clinochlore, kaolinite, Na + , K + , Ca 2+ , Mg 2+ , pH, and aqeuous CO 2 . Finally, at late times (65–200 years), our results showed that calcite plays a major role in mineral trapping with insignificant contribution from siderite, ankerite, and clay minerals. These findings make the proposed unsupervised ML-model attractive for reactive-transport sensing towards real-time GCS monitoring.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deep learning for in situ data compression of large turbulent flow simulations

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

AEflow (Autoencoder fluid flow compression network) [SWR-22-29]

King, Ryan↗

DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Ultrasonic Bat Deterrent Technology

The project objective was to advance the development and testing of a near commercial bat-deterrent system with a goal to increase the current GE deterrent system effectiveness to over 50% with broad species applicability. Additionally, the research supported by this program has provided insights into bat behavior and ultrasonic deterrent design that had not previously been explored. Prior research and development had demonstrated the effectiveness of a commercial-grade, air-powered, ultrasonic bat deterrent to be between 30-50% depending upon the species of bat. However, the previous research provided limited insight into the behavioral responses of bats in the presence of ultrasonic deterrent sound fields that could be utilized to improve effectiveness. A unique bat flight room was utilized to observe the behavioral characteristics of bats in the presence of ultrasonic sound fields. Behavioral testing in the bat flight facility demonstrated that ultrasonic sounds similar to those produced by the GE deterrent influenced the activities and behaviors, primarily those associated with foraging, of the species exposed. The study also indicated that continuous and pulsing ultrasonic signals had a similar effect on the bats, and confirmed that as ultrasonic sounds attenuate, their influence on the bats’ activities and behavior decreases. Ground testing at Wolf Ridge Wind, LLC and Shawnee National Forest assessed both continuous and pulsing deterrent signals emitted from the GE deterrent system and further enhanced the behavioral understanding of bats in the presence of the deterrent. With these data and observations, the existing 4-nozzle continuous, or steady, emission ultrasonic system was redesigned to a 6-nozzle system that could emit a pulsing signal covering a larger air space around a turbine. Twelve GE 1.6-100 turbines were outfitted with the deterrent system and a formal three-month field study was performed using daily carcass searches beneath the 12 turbines. Additionally, a unique 3D bat flight path visualization system was utilized to monitor for and identify any changes in bat activity caused by the operation of the deterrent system. Both the carcass search and flight path visualization data indicated that the pulsed deterrent system was effective, but not more effective, than the steady system tested in prior years. However, an unanticipated byproduct of the pulsing system was the emission of intermittent water vapor from the deterrent devices due to the air compression process that powered the devices. This water vapor may have altered the ultrasonic signal and obscured the results in an unknown way. While a qualitative analysis of the effect of the water vapor on the deterrent signal had indicated there was not dramatic change in the expected ultrasonic signal, it was not possible to conclusively determine if the pulse signal would have been more effective in the absence of the water vapor. A mid-season installation of a desiccant system was performed and the dataset divided into two groups to account for the change. Prior to the system change, the deterrent was not effective (-23%, P = 0.8617, α = 0.05) at reducing eastern red bat fatalities and effective (38%, P = 0.0169, α = 0.05) for the non-eastern red bat species, combined. After the installation of the desiccant system, the pulsed deterrent system showed similar results, although not statistically significant, with a point estimates of 23% (P = 0.2924, α = 0.05) observed for eastern red bats and 54% (P = 0.0926, α = 0.05) for the other combined bat species.

17 WIND ENERGY↗

DOE OSTI2017

Peering through the flames: imaging techniques for reacting aluminum powders

Combusting metals burn at high temperatures and emit high-intensity radiation in the visible spectrum which can over-saturate regular imaging sensors and obscure the field of view. Filtering the luminescence can result in limited information and hinder thorough combustion characterization. A method for “seeing through the flames” of a highly luminescent aluminum powder reaction is presented using copper vapor laser (CVL) illumination synchronized with a high-speed camera. A statistical comparison of combusting aluminum particle agglomerate between filtered halogen and CVL illumination shows the effectiveness of this diagnostic approach. When ignited by an electrically induced plasma, aluminum particles are entrained as solid agglomerates that rotate about their centers of mass and are surrounded by emitted, burning gases. Furthermore, the average agglomerate diameter appears to be 160 micrometers when viewed with standard illumination and a high-speed camera. But, a significantly lower diameter of 50 micrometers is recorded when imaged with CVL illumination. Our results advocate that alternative imaging techniques are required to resolve the complexities of metal particle combustion.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. Herein we introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2004

Software and Algorithms for Biomedical Image Data Processing and Visualization

A new software equipped with novel image processing algorithms and graphical-user-interface (GUI) tools has been designed for automated analysis and processing of large amounts of biomedical image data. The software, called PlaqTrak, has been specifically used for analysis of plaque on teeth of patients. New algorithms have been developed and implemented to segment teeth of interest from surrounding gum, and a real-time image-based morphing procedure is used to automatically overlay a grid onto each segmented tooth. Pattern recognition methods are used to classify plaque from surrounding gum and enamel, while ignoring glare effects due to the reflection of camera light and ambient light from enamel regions. The PlaqTrak system integrates these components into a single software suite with an easy-to-use GUI (see Figure 1) that allows users to do an end-to-end run of a patient s record, including tooth segmentation of all teeth, grid morphing of each segmented tooth, and plaque classification of each tooth image. The automated and accurate processing of the captured images to segment each tooth [see Figure 2(a)] and then detect plaque on a tooth-by-tooth basis is a critical component of the PlaqTrak system to do clinical trials and analysis with minimal human intervention. These features offer distinct advantages over other competing systems that analyze groups of teeth or synthetic teeth. PlaqTrak divides each segmented tooth into eight regions using an advanced graphics morphing procedure [see results on a chipped tooth in Figure 2(b)], and a pattern recognition classifier is then used to locate plaque [red regions in Figure 2(d)] and enamel regions. The morphing allows analysis within regions of teeth, thereby facilitating detailed statistical analysis such as the amount of plaque present on the biting surfaces on teeth. This software system is applicable to a host of biomedical applications, such as cell analysis and life detection, or robotic applications, such as product inspection or assembly of parts in space and industry.

Talukder, Ashit↗

DOE OSTI2018

Imaging in Diagnostic Nuclear Medicine

Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are modern methods for visualization in diagnostic nuclear medicine. SPECT is known as a workhorse in cardiology, and PET is the gold standard in oncology. The development of nuclear medicine is provided by cooperation of physicists, mathematicians, biologists, medical doctors, and radio-chemists. In spite of extensive clinical applications, several problems may lead to false diagnoses. In particular, the correction of attenuation of gamma radiation in human organs must be taken into account. For interpretation and evaluation of such an effect on the clinical results, we perform physico-mathematical simulation of the SPECT diagnostics in cardiology in the absence and presence of the correction of attenuation. A brief review of the state-of-the art is presented. The simulation employs the first Russian anthropomorphic mathematical phantom that describes the distribution of radio-pharmacological agent ({sup 99m }Tc-methoxyisobutylisonitrile) in chest organs of a typical male patient. A model for calculation of raw images is developed with allowance for attenuation of radiation in biological tissues and the effect of collimator and detector. The results of the proposed models and calculated images are compared with clinical images obtained at the Meshalkin Institute of Circulation Pathology (Novosibirsk) and Myasnikov Institute of Clinical Cardiology (Moscow). Statistical algorithms are developed for the solution of the inverse problem of image reconstruction based on the entropy principle. The clinical and physico-mathematical approaches are compared in the evaluation of the effect of correction on the quality of reconstructed images of the left ventricle of myocardium.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2009

An Analysis of Rocket Propulsion Testing Costs

The primary mission at NASA Stennis Space Center (SSC) is rocket propulsion testing. Such testing is generally performed within two arenas: (1) Production testing for certification and acceptance, and (2) Developmental testing for prototype or experimental purposes. The customer base consists of NASA programs, DOD programs, and commercial programs. Resources in place to perform on-site testing include both civil servants and contractor personnel, hardware and software including data acquisition and control, and 6 test stands with a total of 14 test positions/cells. For several business reasons there is the need to augment understanding of the test costs for all the various types of test campaigns. Historical propulsion test data was evaluated and analyzed in many different ways with the intent to find any correlation or statistics that could help produce more reliable and accurate cost estimates and projections. The analytical efforts included timeline trends, statistical curve fitting, average cost per test, cost per test second, test cost timeline, and test cost envelopes. Further, the analytical effort includes examining the test cost from the perspective of thrust level and test article characteristics. Some of the analytical approaches did not produce evidence strong enough for further analysis. Some other analytical approaches yield promising results and are candidates for further development and focused study. Information was organized for into its elements: a Project Profile, Test Cost Timeline, and Cost Envelope. The Project Profile is a snap shot of the project life cycle on a timeline fashion, which includes various statistical analyses. The Test Cost Timeline shows the cumulative average test cost, for each project, at each month where there was test activity. The Test Cost Envelope shows a range of cost for a given number of test(s). The supporting information upon which this study was performed came from diverse sources and thus it was necessary to build several intermediate databases in order to understand, validate, and manipulate data. These intermediate databases (validated historical account of schedule, test activity, and cost) by themselves are of great value and utility. For example, for the Project Profile, we were able to merged schedule, cost, and test activity. This kind of historical account conveys important information about sequence of events, lead time, and opportunities for improvement in future propulsion test projects. The Product Requirement Document (PRD) file is a collection of data extracted from each project PRD (technical characteristics, test requirements, and projection of cost, schedule, and test activity). This information could help expedite the development of future PRD (or equivalent document) on similar projects, and could also, when compared to the actual results, help improve projections around cost and schedule. Also, this file can be sorted by the parameter of interest to perform a visual review of potential common themes or trends. The process of searching, collecting, and validating propulsion test data encountered a lot of difficulties which then led to a set of recommendations for improvement in order to facilitate future data gathering and analysis.

Ramirez-Pagan, Carmen P.↗