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DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

SU-E-T-373: Evaluation and Reduction of Contralateral Skin /subcutaneous Dose for Tangential Breast Irradiation

Purpose: Tangential breast irradiation is a standard treatment technique for breast cancer therapy. One aspect of dose delivery includes dose delivered to the skin caused by electron contamination. This effect is especially important for highly oblique beams used on the medical tangent where the electron contamination deposits dose on the contralateral breast side. This work aims to investigate and predict as well as define a method to reduce this dose during tangential breast radiotherapy. Methods: Analysis and calculation of breast skin and subcutaneous dose is performed using a Varian Eclipse planning system, AAA algorithm for 6MV x-ray treatments. Measurements were made using EBT3 Gafchromic film to verify the accuracy of planning data. Various materials were tested to assess their ability to remove electron contamination on the contralateral breast. Results: Results showed that the Varian Eclipse AAA algorithm could accurately estimate contralateral breast dose in the build-up region at depths of 2mm or deeper. Surface dose was underestimated by the AAA algorithm. Doses up to 12% of applied dose were seen on the contralateral breast surface and up to 9 % at 2mm depth. Due to the nature of this radiation, being mainly low energy electron contamination, a bolus material could be used to reduce this dose to less than 3%. This is accomplished by 10 mm of superflab bolus or by 1 mm of lead. Conclusion: Contralateral breast skin and subcutaneous dose is present for tangential breast treatment and has been measured to be up to 12% of applied dose from the medial tangent beam. This dose is deposited at shallow depths and is accurately calculated by the Eclipse AAA algorithm at depths of 2mm or greater. Bolus material placed over the contralateral can be used to effectively reduce this skin dose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Integrated Multi-Scale Data Analytics and Machine Learning for the Distribution Grid

We consider the field of machine learning and where it is both useful, and not useful, for the distribution grid and buildings interface. While analytics, in general, is a growing field of interest, and often seen as the golden goose in the burgeoning distribution grid industry, its application is often limited by communications infrastructure, or lack of a focused technical application. Overall, the linkage of analytics to purposeful application in the grid space has been limited. In this paper we consider the field of machine learning as a subset of analytical techniques, and discuss its ability and limitations to enable the future distribution grid. To that end, we also consider the potential for mixing distributed and centralized analytics and the pros and cons of these approaches. There is an exponentially expanding volume of measured data being generated on the distribution grid, which, with appropriate application of analytics, may be transformed into intelligible, actionable information that can be provided to the right actors - such as grid and building operators, at the appropriate time to enhance grid or building resilience, efficiency, and operations against various metrics or goals - such as total carbon reduction or other economic benefit to customers. While some basic analysis into these data streams can provide a wealth of information, computational and human boundaries on performing the analysis are becoming significant, with more data and multi-objective concerns. Efficient applications of analysis and the machine learning field are being considered in the loop. This paper describes benefits and limits of present machine-learning applications for use on the grid and presents a series of case studies that illustrate the potential benefits of developing advanced local multi-variate analytics machine-learning-based applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1975

LIBRA: An inexpensive geodetic network densification system

A description is given of the Libra (Locations Interposed by Ranging Aircraft) system, by which geodesy and earth strain measurements can be performed rapidly and inexpensively to several hundred auxiliary points with respect to a few fundamental control points established by any other technique, such as radio interferometry or satellite ranging. This low-cost means of extending the accuracy of space age geodesy to local surveys provides speed and spatial resolution useful, for example, for earthquake hazards estimation. Libra may be combined with an existing system, Aries (Astronomical Radio Interferometric Earth Surveying) to provide a balanced system adequate to meet the geophysical needs, and applicable to conventional surveying. The basic hardware design was outlined and specifications were defined. Then need for network densification was described. The following activities required to implement the proposed Libra system are also described: hardware development, data reduction, tropospheric calibrations, schedule of development and estimated costs.

Fliegel, H. F.↗

NASA NTRS1986

Preliminary estimates of radiosonde thermistor errors

It has been long realized that radiosonde temperature measurements are subject to errors, not the least of which is the effect of long- and short-wave radiation. Methods of adjusting the daytime temperatures to a nighttime equivalent temperature were developed a number of years ago, and are used by some analysis centers. Other than providing consistent observations for analysis this procedure does not provide a true correction. The literature discusses the problem of radiosonde temperature errors but it is not apparent what effort, if any, has been taken to quantify these errors. In order to quantify this radiation error, radiosondes containing multiple thermistors with different coatings were flown at Goddard Space Flight Center/Wallops Flight Facility. The coatings employed had different spectral characteristics and, therefore, different absorption and emissivity properties. Discrimination of the recorded temperatures enabled day and night correction values to be determined for the US standard white-coated rod thermistor. The correction magnitudes are given and a comparison of US measured temperatures before and after correction are compared with temperatures measured with the Vaisala radiosonde. The US and Vaisala radiosonde data are from the recently completed WMO International Radiosonde Intercomparison held at Wallops. The corrections are in the proper direction, day and night, and reduce day-night temperature differences to less than 0,5°C between surface and 30 hPa. The present uncorrected temperatures used with the Viz radiosonde have day-night differences that exceed 1°C at levels below 90 hPa. Additional measurements are planned to confirm these preliminary results and determine the solar elevation angle effect on the corrections. The technique used to obtain the corrections may also be used to recover a true 'absolute' value and might be considered a valuable contribution to the meteorological community for use as a reference instrument.

Temperature measurement↗

NASA NTRS1999

First International Symposium on Strain Gauge Balances

The first International Symposium on Strain Gauge Balances was sponsored and held at NASA Langley Research Center during October 22-25, 1996. The symposium provided an open international forum for presentation, discussion, and exchange of technical information among wind tunnel test technique specialists and strain gauge balance designers. The Symposium also served to initiate organized professional activities among the participating and relevant international technical communities. Over 130 delegates from 15 countries were in attendance. The program opened with a panel discussion, followed by technical paper sessions, and guided tours of the National Transonic Facility (NTF) wind tunnel, a local commercial balance fabrication facility, and the LaRC balance calibration laboratory. The opening panel discussion addressed "Future Trends in Balance Development and Applications." Forty-six technical papers were presented in 11 technical sessions covering the following areas: calibration, automatic calibration, data reduction, facility reports, design, accuracy and uncertainty analysis, strain gauges, instrumentation, balance design, thermal effects, finite element analysis, applications, and special balances. At the conclusion of the Symposium, a steering committee representing most of the nations and several U.S. organizations attending the Symposium was established to initiate planning for a second international balance symposium, to be held in 1999 in the UK.

John S Tripp↗

NASA NTRS1999

First International Symposium on Strain Gauge Balances

Tripp, John S↗

NASA NTRS2012

Forced Oscillation Wind Tunnel Testing for FASER Flight Research Aircraft

As unmanned air vehicles (UAVs) continue to expand their flight envelopes into areas of high angular rate and high angle of attack, modeling the complex unsteady aerodynamics for simulation in these regimes has become more difficult using traditional methods. The goal of this experiment was to improve the current six degree-of-freedom aerodynamic model of a small UAV by replacing the analytically derived damping derivatives with experimentally derived values. The UAV is named the Free-flying Aircraft for Sub-scale Experimental Research, FASER, and was tested in the NASA Langley Research Center 12- Foot Low-Speed Tunnel. The forced oscillation wind tunnel test technique was used to measure damping in the roll and yaw axes. By imparting a variety of sinusoidal motions, the effects of non-dimensional angular rate and reduced frequency were examined over a large range of angle of attack and side-slip combinations. Tests were performed at angles of attack from -5 to 40 degrees, sideslip angles of -30 to 30 degrees, oscillation amplitudes from 5 to 30 degrees, and reduced frequencies from 0.010 to 0.133. Additionally, the effect of aileron or elevator deflection on the damping coefficients was examined. Comparisons are made of two different data reduction methods used to obtain the damping derivatives. The results show that the damping derivatives are mainly a function of angle of attack and have dependence on the non-dimensional rate and reduced frequency only in the stall/post-stall regime

Hoe, Garrison↗

DOE OSTI2019

Joint Chance Constraints Reduction Through Learning in Active Distribution Networks

Due to an increase in distributed generation and controllable loads, distribution networks are frequently operating under high levels of uncertainty. Joint chance constraints, which seek to satisfy multiple constraints simultaneously with a prescribed probability, are one way to incorporate uncertainty across sets of constraints for optimization and control of these networks. Due to the complexity of evaluating these constraints directly, sampling approaches or approximations can be used to transform the joint chance constraint into deterministic constraints. However, sampling techniques may be extremely computationally expensive and not suitable for physical networks operating on fast timescales, and conservative approximations may needlessly result in a much higher cost of system operation. The proposed framework aims to provide a scalable, data-driven approach which learns operational trends in a power network, eliminates zero-probability events (e.g., inactive constraints), and uses this additional information to accurately and efficiently approximate the joint chance constraint directly.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1958

A Flight Evaluation of the Factors which Influence the Selection of Landing Approach Speeds

The factors which influence the selection of landing approach speeds are discussed from the pilot's point of view. Concepts were developed and data were obtained during a landing approach flight investigation of a large number of jet airplane configurations which included straight-wing, swept-wing, and delta-wing airplanes as well as several applications of boundary-layer control. Since the fundamental limitation to further reductions in approach speed on most configurations appeared to be associated with the reduction in the pilot's ability to control flight path angle and airspeed, this problem forms the basis of the report. A simplified equation is presented showing the basic parameters which govern the flight path angle and airspeed changes, and pilot control techniques are discussed in relation to this equation. Attention is given to several independent aerodynamic characteristics which do not affect the flight path angle or airspeed directly but which determine to a large extent the effort and attention required of the pilot in controlling these factors during the approach. These include stall characteristics, stability about all axes, and changes in trim due to thrust adjustments. The report considers the relationship between piloting technique and all of the factors previously mentioned. A piloting technique which was found to be highly desirable for control of high-performance airplanes is described and the pilot's attitudes toward low-speed flight which bear heavily on the selection of landing approach speeds under operational conditions are discussed.

Drinkwater, Fred J., III↗

NASA NTRS2010

Bird Migration Under Climate Change - A Mechanistic Approach Using Remote Sensing

The broad-scale reductions and shifts that may be expected under climate change in the availability and quality of stopover habitat for long-distance migrants is an area of increasing concern for conservation biologists. Researchers generally have taken two broad approaches to the modeling of migration behaviour to understand the impact of these changes on migratory bird populations. These include models based on causal processes and their response to environmental stimulation, "mechanistic models", or models that primarily are based on observed animal distribution patterns and the correlation of these patterns with environmental variables, i.e. "data driven" models. Investigators have applied the latter technique to forecast changes in migration patterns with changes in the environment, for example, as might be expected under climate change, by forecasting how the underlying environmental data layers upon which the relationships are built will change over time. The learned geostatstical correlations are then applied to the modified data layers.. However, this is problematic. Even if the projections of how the underlying data layers will change are correct, it is not evident that the statistical relationships will remain the same, i.e. that the animal organism may not adapt its' behaviour to the changing conditions. Mechanistic models that explicitly take into account the physical, biological, and behaviour responses of an organism as well as the underlying changes in the landscape offer an alternative to address these shortcomings. The availability of satellite remote sensing observations at multiple spatial and temporal scales, coupled with advances in climate modeling and information technologies enable the application of the mechanistic models to predict how continental bird migration patterns may change in response to environmental change. In earlier work, we simulated the impact of effects of wetland loss and inter-annual variability on the fitness of migratory shorebirds in the central fly ways of North America. We demonstrated the phenotypic plasticity of a migratory population of Pectoral sandpipers consisting of an ensemble of 10,000 individual birds in response to changes in stopover locations using an individual based migration model driven by remotely sensed land surface data, climate data and biological field data. With the advent of new computing capabilities enabled hy recent GPU-GP computing paradigms and commodity hardware, it now is possible to simulate both larger ensemble populations and to incorporate more realistic mechanistic factors into migration models. Here, we take our first steps use these tools to study the impact of long-term drought variability on shorebird survival.

Smith, James A.↗

NASA NTRS2021

Breakdown in Tantalum Capacitors

Failures in tantalum capacitors can be considered as a time-dependent-breakdown and reliability of all types of capacitors, including wet, MnO2 and polymer cathode parts, depends on the efficiency of self-healing that can mend parts after breakdown. A mechanism of self-healing in MnO2 capacitors is associated with oxygen reduction in the cathode layer and isolation of the breakdown site with high-resistive Mn2O3/Mn3O4 compositions. Although this mechanism is commonly accepted, details of self-healing have not been analyzed sufficiently yet, and the effects of scintillations on behavior of the parts has not been fully understood. There is a lack of data on the self-healing efficiency in different lots of capacitors. Even less is known about self-healing in polymer tantalum capacitors. In this work, different types of polymer and MnO2 capacitors have been tested for scintillation breakdown using a constant current stress (CCS) technique modified to allow detection of the amplitudes and durations of current spikes during breakdown. Characteristics of the parts and in particular leakage currents were measured to assess the efficiency of self-healing and improve screening processes for tantalum capacitors. Damaged sites were localized using infrared camera and their appearance analyzed after deprocessing and crosssectioning. Thermal effects during scintillations have been modeled, self-healing processes in polymer and MnO2 cathode capacitors discussed, and mechanisms of breakdown based on the growth of conductive filaments in the dielectric suggested.

tantalum polymer capacitors↗

DOE OSTI2026

Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition

Elevated turbidity levels pose significant public health risks by facilitating the transport of harmful pollutants, including metals, organic compounds, and pathogenic microorganisms into the surface water. These conditions create serious challenges for public recreational water use and drinking water treatment, leading to economic losses and health risks. This study utilizes water monitoring data in Pine Island Bayou, Texas, and develops a Sequence-to-Sequence (S2S) model to predict turbidity using Attention-based Gated Recurrent Units with Encoder-Decoder (AT-GRU-ED) and Long Short-Term Memory (LSTM), coupled with Variational Mode Decomposition (VMD). Compared to the model without VMD, the model demonstrates satisfactory 72-hour turbidity prediction performance, achieving MAEs of 2.60 and 3.29 NTU (reductions of 53% and 58%), RMSEs of 21.08 and 31.49 NTU (reductions of 82% and 80%), and R² values of 0.96 and 0.84 on the validation and test sets, respectively. Feature importance analysis reveals that water temperature is the dominant factor influencing seasonal turbidity patterns, while real-time hourly rainfall significantly contributes to short-term variability. Turbidity typically peaks within 48 hours after rainfall events due to lagged effects from surface runoff and upstream flow. Findings suggest suspending recreational water use and water supply pumping for three days after heavy rainfall can benefit public health and improve water treatment processes. Discharges above 100 m3/s are found to accelerate sediment dilution and transport, reducing turbidity levels more quickly after the peak. In conclusion, the proposed model demonstrates reliable 72-hour turbidity prediction, supporting decision-making for water treatment plant operations and providing early warning for public recreational water use.

Deep learning↗

DOE OSTI2022

Fast and flexible analysis of direct dark matter search data with machine learning

We present the results from combining machine learning with the profile likelihood fit procedure, using data from the Large Underground Xenon (LUX) dark matter experiment. This approach demonstrates reduction in computation time by a factor of 30 when compared with the previous approach, without loss of performance on real data. We establish its flexibility to capture non-linear correlations between variables (such as smearing in light and charge signals due to position variation) by achieving equal performance using pulse areas with and without position-corrections applied. Its efficiency and scalability furthermore enables searching for dark matter using additional variables without significant computational burden. We demonstrate this by including a light signal pulse shape variable alongside more traditional inputs such as light and charge signal strengths. Furthermore, this technique can be exploited by future dark matter experiments to make use of additional information, reduce computational resources needed for signal searches and simulations, and make inclusion of physical nuisance parameters in fits tractable.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

NASA NTRS1975

The effect of ring distortions on buckling of blunt conical shells

A rigorous analytical study of cones stiffened by many thin-gage, open-section rings is presented. The results are compared with data previously obtained from uniform pressure tests of the Viking mission flight aeroshell and of the Viking structural prototype aeroshells. A conventional analysis, in which the rings are modeled as discrete rigid cross sections, is shown to lead to large, unconservative strength predictions. A more sophisticated technique of modeling the rings as shell branches leads to much more realistic strength predictions and more accurately predicts the failure modes. It is also shown that if a small initial imperfection proportional to the shape of the buckling mode is assumed, the critical buckling modes from analysis and test are in agreement. However, the reduction in buckling strength from the perfect-shell predictions is small.

Heard, W. L., Jr.↗

NASA NTRS2000

Examples of Unsteady Combustion in Non-Metallized Propellants

For over thirty years the T-Burner has been the de facto standard for determining the unstable response of solid propellants. This paper reviews some of the different approaches that have been used, and briefly summarizes advantages and disadvantages of the different techniques. Data from a variety of sources have been reviewed and summarized showing the effect of various test conditions and propellant formulation variations. Much of the data taken in the 60's and early 70's were with so-called research propellants, with relatively low solids loading and usually monomodal AP as the oxidizer. Later data were obtained with propellants containing solids loading more typical and practical propellants. Several distinct differences were observed in the data for the different types of propellants. Data varying AP particle size distributions indicate that either additional fine AP or finer AP usually increases the propellant response. A significant reduction in the response can be achieved by reducing the amount of fine AP, while holding burning rate constant either by adding a catalyst or by varying the overall AP size distribution. A significant amount of data has been obtained for reduced smoke propellants containing stability additives. The data indicate that the additives provide significant particle damping, and most often, also reduce the propellant response. Data from double base propellants indicate a much broader response over a wide frequency range. This implies that double base propellants should be more unstable in tangential modes than composite propellants. This trend seems to be consistent with observed motor data. Measuring the response of metallized propellants has been a challenge due to the large damping due to metal oxide particles in the gas. Data from metallized propellants are not presented here, but will be summarized in a future paper.

Beckstead, M. W.↗

NASA NTRS2000

KDD Services at the Goddard Earth Sciences Distributed Active Archive Center

NASA's Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) processes, stores and distributes earth science data from a variety of remote sensing satellites. End users of the data range from instrument scientists to global change and climate researchers to federal agencies and foreign governments. Many of these users apply data mining techniques to large volumes of data (up to 1 TB) received from the GES DAAC. However, rapid advances in processing power are enabling increases in data processing that are outpacing tape drive performance and network capacity. As a result, the proportion of data that can be distributed to users continues to decrease. As mitigation, we are migrating more data mining and mining preparation activities into the data center in order to reduce the data volume that needs to be distributed and to offer the users a more useful and manageable product. This migration of activities faces a number of technical and human-factor challenges. As data reduction and mining algorithms are normally quite specific to the user's research needs, the user's algorithm must be integrated virtually unchanged into the archive environment. Also, the archive itself is busy with everyday data archive and distribution activities and cannot be dedicated to, or even impacted by, the mining activities. Therefore, we schedule KDD 'campaigns' (similar to reprocessing campaigns), during which we schedule a wholesale retrieval of specific data products, offering users the opportunity to extract information from the data being retrieved during the campaign.

Lynnes, Christopher↗

NASA NTRS1992

The role and future of space technology in disaster reduction

Disaster mitigation consists of many activities, including vulnerability assessment, disaster warning and prediction and disaster relief. Various types of satellites can be applied to these endeavors: communications, geophysical, meteorological and Earth resources. The latter two are considered 'remote sensing' satellites. There are many limitations in the design and development of remote sensing satellites; limitations in cost and the acceptable data rate and limitations in our technology. Nevertheless, there are a large number of satellites, both currently in orbit and planned, with capabilities pertinent to disaster mitigation. Some of these are operational and can be relied upon to provide continued data sources. Others are experimental and provide the disaster management community and opportunity to assess the potential usefulness of the techniques and to impact the design of future operational systems. A table lists the operational parameters and potential application in disaster mitigation of 44 current and planned remote sensing satellites and instruments.

Walter, Louis S.↗