Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data Distribution”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34

Application of new electro-optic technology to Space Station Freedom data management system

A low risk design methodology to permit the local bus structures to support increased data carrying capacities and to speed messages and data flow between nodes or stations on the Space Station Freedom Data Management System in anticipation of growing requirements was evaluated and recommended. The recommended design employs a collateral fiber optic technique that follows a NATO avionic standard that is developed, tested, and available. Application of this process will permit a potential 25 fold increase in data transfer performance on the local wire bus network with a fiber optic network, maintaining the functionality of the low-speed bus and supporting all of the redundant transmission and fault detection capabilities designed into the existing system. The application of wavelength division multiplexing (WDM) technology to both the local data bus and global data bus segments of the Data Management System to support anticipated additional highspeed data transmission requirements was also examined. Techniques were examined to provide a dual wavelength implementation of the fiber optic collateral networks. This dual wavelength implementation would permit each local bus to support two simultaneous high-speed transfers on the same fiber optic bus structure and operate within the limits of the existing protocol standard. A second WDM study examined the use of spectral sliced technology to provide a fourfold increase in the Fiber Distributed Data Interface (FDDI) global bus networks without requiring modifications to the existing installed cable plant. Computer simulations presented indicated that this data rate improvement can be achieved with commercially available optical components.

Husbands, C. R.↗

NASA Center for Climate Simulation (NCCS) Presentation

The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.

Webster, William P.↗

Large scale advanced propeller blade pressure distributions - Prediction and data

Two Euler analysis techniques, finite difference and finite volume, are employed to predict the blade surface pressure distributions of a large scale advanced propeller. The predicted pressure distributions are compared with wind tunnel data. Both techniques produced blade pressure distributions which are in fairly good agreement with the data over the range of test Mach numbers of 0.2 to 0.78. However, the numerical simulations fail to predict correctly the measured pressure distributions for the low Mach number, high power case which seem to have a leading edge vortex. A discussion of the compressibility effects is also presented.

Nallasamy, M.↗

QuakeSim and the Solid Earth Research Virtual Observatory

We are developing simulation and analysis tools in order to develop a solid Earth science framework for understanding and studying active tectonic and earthquake processes. The goal of QuakeSim and its extension, the Solid Earth Research Virtual Observatory (SERVO), is to study the physics of earthquakes using state-of-the-art modeling, data manipulation, and pattern recognition technologies. We are developing clearly defined accessible data formats and code protocols as inputs to simulations, which are adapted to high-performance computers. The solid Earth system is extremely complex and nonlinear resulting in computationally intensive problems with millions of unknowns. With these tools it will be possible to construct the more complex models and simulations necessary to develop hazard assessment systems critical for reducing future losses from major earthquakes. We are using Web (Grid) service technology to demonstrate the assimilation of multiple distributed data sources (a typical data grid problem) into a major parallel high-performance computing earthquake forecasting code. Such a linkage of Geoinformatics with Geocomplexity demonstrates the value of the Solid Earth Research Virtual Observatory (SERVO) Grid concept, and advances Grid technology by building the first real-time large-scale data assimilation grid.

virtual observatory↗

Summary of the First AIAA Stability and Control Prediction Workshop

Results from the First AIAA Stability and Control Prediction Workshop are summarized in this paper. The workshop series was developed in support of three primary objectives: (1) to establish best practices for the prediction of stability and control derivatives using industry-standard Computational Fluid Dynamics (CFD) solvers, (2) to provide an impartial forum for evaluating the effectiveness of Reynolds-averaged-Navier-Stokes- and Detached-Eddy-Simulation-based modeling techniques, and (3) to identify areas in need of additional research and development. To address these objectives, the inaugural workshop focused on generating computational aerodynamic predictions for the ONERA version of the NASA/Boeing Common Research Model. The configuration includes the wing, body, horizontal tail, and a vertical tail designed by ONERA. While longitudinal wind tunnel test data for the model had been previously documented, unpublished lateral test data at small sideslip angles provided a unique opportunity for participants to generate blind computational predictions for wind tunnel data comparisons. Additional test cases included assessments of the Mach number effect on static lateral/directional stability and the impact of the wind tunnel sting on longitudinal stability. Participants were invited to generate solutions using two workshop-provided series of structured overset and unstructured grids, in addition to any participant custom grids of interest. Total and component-level breakdowns for the force and moment coefficients for each test case are presented, as well as sectional pressure distribution data on the wing and tail components, to assess the agreement between several different Reynolds-averaged Navier-Stokes CFD solvers.

CFD↗

Progress in Validation of Wind-US for Ramjet/Scramjet Combustion

Validation of the Wind-US flow solver against two sets of experimental data involving high-speed combustion is attempted. First, the well-known Burrows- Kurkov supersonic hydrogen-air combustion test case is simulated, and the sensitively of ignition location and combustion performance to key parameters is explored. Second, a numerical model is developed for simulation of an X-43B candidate, full-scale, JP-7-fueled, internal flowpath operating in ramjet mode. Numerical results using an ethylene-air chemical kinetics model are directly compared against previously existing pressure-distribution data along the entire flowpath, obtained in direct-connect testing conducted at NASA Langley Research Center. Comparison to derived quantities such as burn efficiency and thermal throat location are also made. Reasonable to excellent agreement with experimental data is demonstrated for key parameters in both simulation efforts. Additional Wind-US feature needed to improve simulation efforts are described herein, including maintaining stagnation conditions at inflow boundaries for multi-species flow. An open issue regarding the sensitivity of isolator unstart to key model parameters is briefly discussed.

Engblom, William A.↗

Resume of Present Data on Load Distribution on Slots and Flaps, Special Report

This report covers a study of the generally available data on load distribution on slots and flaps. The study was made by the National Advisory Committee for Aeronautics at the request of the Material Division, Army Air Corps to furnish information applicable to design criteria for slots and flaps of various types. The data are presented in three main sections: slots (Handley page type), auxiliary airfoils (fixed), and flaps.

Wenzinger, Carl J.↗

Utah FORGE: Well 16B(78)-32 Distributed Temperature Sensing Data from April and May 2024

This dataset includes Neubrex Energy Services fiber optic distributed temperature sensing (DTS) data from well 16B(78)-32 during stimulation and circulation, including interaction with well 16A(78)-32, during April and May 2024. The DTS data are stored in HDF5 file format and are accompanied by a PowerPoint report on the study. All times in this dataset are in UTC. Depths are in MD relative to Kelly Bushing Height, and temperatures are in degrees Fahrenheit. All DTS measurements were made using a Yokogawa 3000DTSX Distributed Temperature Sensing Interrogator Unit, with a spatial sampling interval of 3.28 feet and a temporal sampling rate of 129 seconds. The third-party Pressure-Temperature Gauge data should be used with caution after April 20, 2024, as its performance is not considered reliable beyond this date.

15 GEOTHERMAL ENERGY↗

A Support Database System for Integrated System Health Management (ISHM)

The development, deployment, operation and maintenance of Integrated Systems Health Management (ISHM) applications require the storage and processing of tremendous amounts of low-level data. This data must be shared in a secure and cost-effective manner between developers, and processed within several heterogeneous architectures. Modern database technology allows this data to be organized efficiently, while ensuring the integrity and security of the data. The extensibility and interoperability of the current database technologies also allows for the creation of an associated support database system. A support database system provides additional capabilities by building applications on top of the database structure. These applications can then be used to support the various technologies in an ISHM architecture. This presentation and paper propose a detailed structure and application description for a support database system, called the Health Assessment Database System (HADS). The HADS provides a shared context for organizing and distributing data as well as a definition of the applications that provide the required data-driven support to ISHM. This approach provides another powerful tool for ISHM developers, while also enabling novel functionality. This functionality includes: automated firmware updating and deployment, algorithm development assistance and electronic datasheet generation. The architecture for the HADS has been developed as part of the ISHM toolset at Stennis Space Center for rocket engine testing. A detailed implementation has begun for the Methane Thruster Testbed Project (MTTP) in order to assist in developing health assessment and anomaly detection algorithms for ISHM. The structure of this implementation is shown in Figure 1. The database structure consists of three primary components: the system hierarchy model, the historical data archive and the firmware codebase. The system hierarchy model replicates the physical relationships between system elements to provide the logical context for the database. The historical data archive provides a common repository for sensor data that can be shared between developers and applications. The firmware codebase is used by the developer to organize the intelligent element firmware into atomic units which can be assembled into complete firmware for specific elements.

FROM↗

DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection

Abstract Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, DeepAstroUDA , as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation (DA) and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be present in any of the two datasets (the labeled source domain, or the unlabeled target domain), and the method can even be used in the presence of unknown classes. We apply our method to three examples of galaxy morphology classification tasks of different complexities (three-class and ten-class problems), with anomaly detection: (1) datasets created after different numbers of observing years from a single survey (Legacy Survey of Space and Time mock data of one and ten years of observations); (2) data from different surveys (Sloan Digital Sky Survey (SDSS) and DECaLS); and (3) data from observing fields with different depths within one survey (wide field and Stripe 82 deep field of SDSS). For the first time, we demonstrate the successful use of DA between very discrepant observational datasets. DeepAstroUDA is capable of bridging the gap between two astronomical surveys, increasing classification accuracy in both domains (up to 40 % on the unlabeled data), and making model performance consistent across datasets. Furthermore, our method also performs well as an anomaly detection algorithm and successfully clusters unknown class samples even in the unlabeled target dataset.

79 ASTRONOMY AND ASTROPHYSICS↗

A Physical Validation Program for the GPM Mission

The GPM mission is currently planned for start in the late 2007 - early 2008 time frame. Its main scientific goal is to help answer pressing scientific problems arising within the context of global and regional water cycling. These problems cut across a hierarchy of scales and include climate-water cycle interactions, techniques for improving weather and climate predictions, and better methods for combining observed precipitation with hydrometeorological prediction models for applications to hazardous flood-producing storms, seasonal flood draught conditions, and fresh water resource assessments. The GPM mission will expand the scope of precipitation measurement through the use of a constellation of some 9 satellites, one of which will be an advanced TRMM-like core satellite carrying a dual-frequency Ku-Ka band precipitation radar and an advanced, multifrequency passive microwave radiometer with vertical-horizontal polarization discrimination. The other constellation members will include new dedicated satellites and co-existing operational/research satellites carrying similar (but not identical) passive microwave radiometers. The goal of the constellation is to achieve approximately 3-hour sampling at any spot on the globe -- continuously. The constellation's orbit architecture will consist of a mix of sun-synchronous and non-sun-synchronous satellites with the core satellite providing measurements of cloud-precipitation microphysical processes plus calibration-quality rainrate retrievals to be used with the other retrieval information to ensure bias-free constellation coverage. A major requirement before the retrieved rainfall information generated by the GPM mission can be used effectively by prognostic models to improve weather forecasts, hydrometeorological forecasts, and climate model reanalysis simulations is a capability to quantify the error characteristics of the retrievals. A solution for this problem has been upheld in past precipitation missions because of the lack of suitable error modeling systems incorporated into the validation programs and data distribution systems. An overview of how NASA intends to overcome this problem for the GPM mission using a physically-based error modeling approach within a multi-faceted validation program is described. The solution is to first identify specific user requirements and then determine the most stringent of these requirements that embodies all essential error characterization information needed by the entire user community. In the context of NASA s scientific agenda for the GPM mission, the most stringent user requirement is found within the data assimilation community. The fundamental theory of data assimilation vis-a-vis ingesting satellite precipitation information into the pre-forecast initializations is based on quantifying the conditional bias and precision errors of individual rain retrievals, and the space-time structure of the precision error (i.e., the spatial-temporal error covariance). By generating the hardware and software capability to produce this information in a near real-time fashion, and to couple the derived quantitative error properties to the actual retrieved rainrates, all key validation users can be satisfied. The talk will describe the essential components of the hardware and software systems needed to generate such near real-time error properties, as well as the various paradigm shifts needed within the validation community to produce a validation program relevant to the precipitation user community.

Smith, Eric A.↗

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

Whistler Waves Driven by Anisotropic Strahl Velocity Distributions: Cluster Observations

Observed properties of the strahl using high resolution 3D electron velocity distribution data obtained from the Cluster/PEACE experiment are used to investigate its linear stability. An automated method to isolate the strahl is used to allow its moments to be computed independent of the solar wind core+halo. Results show that the strahl can have a high temperature anisotropy (T(perpindicular)/T(parallell) approximately > 2). This anisotropy is shown to be an important free energy source for the excitation of high frequency whistler waves. The analysis suggests that the resultant whistler waves are strong enough to regulate the electron velocity distributions in the solar wind through pitch-angle scattering

Vinas, A.F.↗

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗

Highlights of NASA’s Orbital Debris Program Office In Situ and Laboratory Measurements

NASA’s Orbital Debris Program Office (ODPO) maintains various returned spacecraft materials, capabilities, and facilities used for in situ and laboratory measurements that directly support orbital debris environmental models. In situ measurements include the analysis of exposed and returned hardware surfaces. These surfaces serve as passive sensors for the small-sized micrometeoroid and orbital debris (MMOD) flux below the sensitivity of ground-based radar and optical sensors. Various instruments and techniques are used to determine the size and depth of selected impact features, and – if feasible – the composition of the projectile material. Analysis of the impactor residues enables the differentiation of MM and OD for debris below 1 mm to support modeling the OD environment. In addition, projectiles identified as OD can be further differentiated in low-, medium-, and high- density impactors based on chemical analyses. In addition to in situ measurements, the ODPO has also worked in collaboration with the U.S. Space Force Space Systems Command (formerly the U.S. Air Force Space and Missile Systems Center), the Aerospace Corporation, and the University of Florida on a laboratory-based hypervelocity impact test, DebriSat, conducted at the Air Force Arnold Engineering Development Complex in 2014. The resulting data from this impact test series are being analyzed to assess the fragments’ sizes/masses, materials/densities, shapes, and other parameters of interest. The DebriSat project provides the data needed to update NASA’s breakup models and size estimation models using the simulated orbital breakup of a modern, low Earth orbit spacecraft. Ultimately, over 200,000 fragments from this impact test will be stored at NASA Johnson Space Center (JSC) and further analyzed by the ODPO. This project will also use machine learning techniques to infer physical parameters of fragments embedded in the soft-catch foam used in the impact experiment. Applied to X-ray imagery of the foam panels, these techniques promise to minimize human-in-the-loop processes for fragment extraction and physical characterization. A brief overview of this project and data collected will be presented. Lastly, the ODPO provides various capabilities hosted at NASA JSC for optical inspections and measurements using a variety of techniques and scientific instrumentation to support both in situ and laboratory measurements. The ODPO’s Optical Measurement Center (OMC) is an advanced facility for photometric and spectroscopic laboratory measurements of targets, including fragments from the DebriSat project. The OMC simulates telescopic observations by using space-like illumination conditions and source-target-sensor orientation techniques. Additionally, the OMC is uniquely equipped to acquire pseudo-bidirectional reflectance distribution data for broadband photometric measurements, thus removing aspect angle dependencies that can affect target size estimates using the optical size estimation model. Narrow-band surface material characterization using spectroscopic instrumentation gives insight into how the albedo parameter – also important in the optical size estimation model – may vary depending on the state of the material. The OMC also performs simulations of photometric measurements using optical ray-tracing software to model the OMC optical throughput. In addition to the OMC, the ODPO houses a start-of-the-art Fragment Analysis Facility that uses multiple microscopic inspection instruments to support in situ measurements and material characterization. An overview of both facilities will be highlighted in this paper.

Orbital Debris↗

Highlights of NASA’s Orbital Debris Program Office In Situ and Laboratory Measurements

NASA’s Orbital Debris Program Office (ODPO) maintains various returned spacecraft materials, capabilities, and facilities used for in situ and laboratory measurements that directly support orbital debris environmental models. In situ measurements include the analysis of exposed and returned hardware surfaces. These surfaces serve as passive sensors for the small-sized micrometeoroid and orbital debris (MMOD) flux below the sensitivity of ground-based radar and optical sensors. Various instruments and techniques are used to determine the size and depth of selected impact features, and – if feasible – the composition of the projectile material. Analysis of the impactor residues enables the differentiation of MM and OD for debris below 1 mm to support modeling the OD environment. In addition, projectiles identified as OD can be further differentiated in low-, medium-, and high- density impactors based on chemical analyses. In addition to in situ measurements, the ODPO has also worked in collaboration with the U.S. Space Force Space Systems Command (formerly the U.S. Air Force Space and Missile Systems Center), the Aerospace Corporation, and the University of Florida on a laboratory-based hypervelocity impact test, DebriSat, conducted at the Air Force Arnold Engineering Development Complex in 2014. The resulting data from this impact test series are being analyzed to assess the fragments’ sizes/masses, materials/densities, shapes, and other parameters of interest. The DebriSat project provides the data needed to update NASA’s breakup models and size estimation models using the simulated orbital breakup of a modern, low Earth orbit spacecraft. Ultimately, over 200,000 fragments from this impact test will be stored at NASA Johnson Space Center (JSC) and further analyzed by the ODPO. This project will also use machine learning techniques to infer physical parameters of fragments embedded in the soft-catch foam used in the impact experiment. Applied to X-ray imagery of the foam panels, these techniques promise to minimize human-in-the-loop processes for fragment extraction and physical characterization. A brief overview of this project and data collected will be presented. Lastly, the ODPO provides various capabilities hosted at NASA JSC for optical inspections and measurements using a variety of techniques and scientific instrumentation to support both in situ and laboratory measurements. The ODPO’s Optical Measurement Center (OMC) is an advanced facility for photometric and spectroscopic laboratory measurements of targets, including fragments from the DebriSat project. The OMC simulates telescopic observations by using space-like illumination conditions and source-target-sensor orientation techniques. Additionally, the OMC is uniquely equipped to acquire pseudo-bidirectional reflectance distribution data for broadband photometric measurements, thus removing aspect angle dependencies that can affect target size estimates using the optical size estimation model. Narrow-band surface material characterization using spectroscopic instrumentation gives insight into how the albedo parameter – also important in the optical size estimation model – may vary depending on the state of the material. The OMC also performs simulations of photometric measurements using optical ray-tracing software to model the OMC optical throughput. In addition to the OMC, the ODPO houses a start-of-the-art Fragment Analysis Facility that uses multiple microscopic inspection instruments to support in situ measurements and material characterization. An overview of both facilities will be highlighted in this paper.

Orbital Debris↗

Automated Mapping of Flood Events in the Mississippi River Basin Utilizing NASA Earth Observations

The Mississippi River Basin is the fourth largest drainage basin in the world, and is susceptible to multi-level flood events caused by heavy precipitation, snow melt, and changes in water table levels. Conducting flood analysis during periods of disaster is a challenging endeavor for NASA's Short-term Prediction Research and Transition Center (SPoRT), Federal Emergency Management Agency (FEMA), and the U.S. Geological Survey's Hazards Data Distribution Systems (USGS HDDS) due to heavily-involved research and lack of manpower. During this project, an automated script was generated that performs high-level flood analysis to relieve the workload for end-users. The script incorporated Landsat 8 Operational Land Imager (OLI) tiles and utilized computer-learning techniques to generate accurate water extent maps. The script referenced the Moderate Resolution Imaging Spectroradiometer (MODIS) land-water mask to isolate areas of flood induced waters. These areas were overlaid onto the National Land Cover Database's (NLCD) land cover data, the Oak Ridge National Laboratory's LandScan data, and Homeland Infrastructure Foundation-Level Data (HIFLD) to determine the classification of areas impacted and the population density affected by flooding. The automated algorithm was initially tested on the September 2016 flood event that occurred in Upper Mississippi River Basin, and was then further tested on multiple flood events within the Mississippi River Basin. This script allows end users to create their own flood probability and impact maps for disaster mitigation and recovery efforts.

Bartkovich, Mercedes↗

Very accurate upward continuation to low heights in a test of non-Newtonian theory

Recently, gravity measurements were made on a tall, very stable television transmitting tower in order to detect a non-Newtonian gravitational force. This experiment required the upward continuation of gravity from the Earth's surface to points as high as only 600 m above ground. The upward continuation was based on a set of gravity anomalies in the vicinity of the tower whose data distribution exhibits essential circular symmetry and appropriate radial attenuation. Two methods were applied to perform the upward continuation - least-squares solution of a local harmonic expansion and least-squares collocation. Both methods yield comparable results, and have estimated accuracies on the order of 50 microGal or better (1 microGal = 10(exp -8) m/sq s). This order of accuracy is commensurate with the tower gravity measurments (which have an estimated accuracy of 20 microGal), and enabled a definitive detection of non-Newtonian gravity. As expected, such precise upward continuations require very dense data near the tower. Less expected was the requirement of data (though sparse) up to 220 km away from the tower (in the case that only an ellipsoidal reference gravity is applied).

Romaides, Anestis J.↗