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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

NASA Tech Briefs, January 2012

Contents of this issue are: (1) Energy-Based Tetrahedron Sensor for High-Temperature, High-Pressure Environments (2) Handheld Universal Diagnostic Sensor (3) Large-Area Vacuum Ultraviolet Sensors (4) Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures (5) Health-Enabled Smart Sensor Fusion Technology (6) Extended-Range Passive RFID and Sensor Tags (7) Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks (8) Self-Healing, Inflatable, Rigidizable Shelter (9) Improvements in Cold-Plate Fabrication (10) Technique for Radiometer and Antenna Array Calibration - TRAAC (11) Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment (12) Programmable Digital Controller (13) Use of CCSDS Packets Over SpaceWire to Control Hardware (14) Key Decision Record Creation and Approval Module (15) Enhanced Graphics for Extended Scale Range (16) Debris Examination Using Ballistic and Radar Integrated Software (17) Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) (18) Integration Manager (19) Eclipse-Free-Time Assessment Tool for IRIS (20) Automated and Manual Rocket Crater Measurement Software (21) MATLAB Stability and Control Toolbox Trim and Static Stability Module (22) Patched Conic Trajectory Code (23) Ring Image Analyzer (24) SureTrak Probability of Impact Display (25) Implementation of a Non-Metallic Barrier in an Electric Motor (26) Multi-Mission Radioisotope Thermoelectric Generator Heat Exchangers for the Mars Science Laboratory Rover (27) Uniform Dust Distributor for Testing Radiative Emittance of Dust-Coated Surfaces (28) MicroProbe Small Unmanned Aerial System (29) Highly Stable and Active Catalyst for Sabatier Reactions (30) Better Proton-Conducting Polymers for Fuel-Cell Membranes (31) CCD Camera Lens Interface for Real-Time Theodolite Alignment (32) Peregrine 100-km Sounding Rocket Project (33) SOFIA Closed- and Open-Door Aerodynamic Analyses (34) Sonic Thermometer for High-Altitude Balloons (35) Near-Infrared Photon-Counting Camera for High-Sensitivity Observations (36) Integrated Optics Achromatic Nuller for Stellar Interferometry (37) High-Speed Digital Interferometry (38) Ultra-Miniature Lidar Scanner for Launch Range Data Collection (39) Shape and Color Features for Object Recognition Search (40) Explanation Capabilities for Behavior-Based Robot Control (41) A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks (42) Quality Control Method for a Micro-Nano-Channel Microfabricated Device (43) Corner-Cube Retroreflector Instrument for Advanced Lunar Laser Ranging (44) Electrospray Collection of Lunar Dust (45) Fabrication of a Kilopixel Array of Superconducting Microcalorimeters with Microstripline Wiring Spacecraft Attitude Tracking and Maneuver Using Combined Magnetic Actuators (46) Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

Source record↗
DOE OSTI2023

Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations

The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. Furthermore, the link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.

Anions↗
DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗
DOE OSTI2021

Airborne Infrasound Makes a Splash

Natural and anthropogenic events may create low frequency sound waves, or infrasound, that can travel for vast distances in planetary atmospheres. They permit the remote monitoring of geophysical activity over local to global scales. Most studies have utilized ground-based recorders, but it is possible to deploy acoustic sensors to altitudes of over 50 km. Such elevated platforms can capture sounds that their surface analogs cannot access. High altitude balloons and low altitude aerostats are filling this observation gap, but key environments remain out of reach of both of these. Furthermore, recent work by den Ouden, Smets et al. (2021) addressed this with a new instrumentation platform—a large seabird flying just above the ocean's surface. Their work demonstrates that, infrasound sensing using heavier-than-air platforms in windy environments is possible, which has implications both terrestrially (e.g., extending sensor networks over the oceans) and extraterrestrially (proposed or planned missions to Venus and Titan).

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

GENESPACE R Package (GENESPACE) v1.0

In short, the GENESPACE pipeline conducts analysis of orthology networks, constrained within syntenic regions. Since analyses are limited to local tests conducted within syntenic blocks, GENESPACE is agnostic to ploidy, duplicated regions, inversions or other whole-genome chromosomal complexities that are common across many evolutionary lineages. This advantage allows for evolutionary tests in polyploids (e.g. switchgrass, manuscript in review), species with ancient, but retained whole-genome duplications (e.g. pecan, manuscript in prep), high levels of tandem array proliferation (e.g. eukalypts, manuscript in review) and many other factors that can confound comparative genomic analyses. The major advances of GENESPACE are three-fold: First, this is the first R package to integrate visualization and analysis of large-scale comparative genomics. R, which offers a high-level environment for graphical and statistical exploration of data, is often speed- and memory-limited and not used for computationally intensive tasks such as comparative genomics. The highly efficient C++ scripts used in GENESPACE (via data.table) permit a much faster and computationally lightweight implementation of comparative genomics than is currently available. Second, the pipeline itself is novel. To the best of our knowledge, no other program accomplishes synteny-constrained and ploidy-agnostic comparative genomics. Since nearly all plants and many animals have a history of whole-genome duplications, this is a major and necessary advance to the field. Third, GENESPACE offers high-level and intuitive multi-genome graphical outputs. The dotplots and 'riparian' plots produced herein, which are produced entirely through original R code, are publication-ready and easily customizable.

Schmutz, Jeremy↗
NASA NTRS1994

Permanent GPS Geodetic Array in Southern California (PGGA) and GPS observations in Indonesia

The Permanent GPS Geodetic Array (PGGA) is a network of permanent monitoring GPS stations in southern California devoted to the continuous measurement of crustal deformation in near real-time. The PGGA plays a unique role in studies of the kinematics of crustal deformation and the earthquake cycle in southern California because it is also providing temporally dense geodetic measurements of crustal motion over periods of minutes to variations in regional crustal strain. As it expands and matures the PGGA will play an increasingly important role in the study of active tectonics of southern California by bridging the frequency range between seismology, observatory geodesy, paleoseismology, and geology. In Indonesia GPS data is used for measurement of a large scale crustal deformation, extending from north China to the Indonesian archipelago. Indonesia offers a tremendous laboratory to study some of the processes that build continents, and mountains are active there. We began GPS observations in August 1989 on mainland Sumatra and the Mentawai Islands to study the phenomena of oblique plate convergence. We have analyzed the Indonesian data in conjunction with data collected on Christmas and Cocos Islands and at Darwin, Australia, and with the triangulation data in Sumatra.

Bock, Yehuds↗
NASA NTRS2011

Technique for Configuring an Actively Cooled Thermal Shield in a Flight System

Broad area cooling shields are a mass-efficient alternative to conductively cooled thermal radiation shielding. The shield would actively intercept a large portion of incident thermal radiation and transport the heat away using cryogenic helium gas. The design concept consists of a conductive and conformable surface that maximizes heat transfer and formability. Broad Area Cooled (BAC) shields could potentially provide considerable mass savings for spaceflight applications by eliminating the need for a rigid thermal radiation shield for cryogen tanks. The BAC consists of a network of capillary tubes that are thermally connected to a conductive shield material. Chilled helium gas is circulated through the network and transports unwanted heat away from the cryogen tanks. The cryogenic helium gas is pumped and chilled simultaneously using a specialized pulse-tube cryocooler, which further improves the mass efficiency of the system. By reducing the thermal environment temperature from 300 to 100 K, the radiative heat load on a cryogen tank could be reduced by an order of magnitude. For a cryogenic liquid propellant scenario of oxygen and hydrogen, the boiloff of hydrogen would be significantly reduced and completely eliminated for oxygen. A major challenge in implementing this technology on large tanks is that the BAC system must be easily scalable from lab demonstrations to full-scale missions. Also, the BAC shield must be conformable to complex shapes like spheres without losing the ability to maintain constant temperature throughout. The initial design maximizes thermal conductivity between the capillary tube and the conductive radiation shielding by using thin, corrugated aluminum foil with the tube running transverse to the folds. This configuration has the added benefit of enabling the foil to stretch and contract longitudinally. This allows the BAC to conform to the complex curvature of a cryogen tank, which is key to its success. To demonstrate a BAC shield system with minimal impact to current cryogen tank designs, the shielding must be applied after the final assembly of the tank and supporting structure. One method is to pre-fabricate the shield in long strips. A spool of corrugated aluminum foil with a thermally sunk aluminum capillary running through the center could then be simply wound around the cryogen tanks and encapsulated within the multi-layer insulation (MLI) blanket. Then, on orbit, the BAC would intercept thermal radiation coming in through the MLI and transport it away from the cryogen tanks. An optimization of the design could be done to take into account mass savings from thinner MLI blankets, eliminating solid thermal shields, and ultimately, a reduction in the required cryogen tank size.

Barkfknecht, Peter↗
DOE OSTI2020

Relevance of the Unfolded Protein Response to Spaceflight-Induced Transcriptional Reprogramming in Arabidopsis

Plants are primary producers of food and oxygen on Earth and will likewise be indispensable to the establishment of large-scale sustainable ecosystems and human survival in space. To contribute to the understanding of how plants respond to spaceflight stress, we examined the significance of the unfolded protein response (UPR), a conserved signaling cascade that responds to a number of unfavorable environmental stresses, in the model plant Arabidopsis thaliana. To do so, we performed a large-scale comparative transcriptome profiling in wild type and various UPR-defective mutants during the SpaceX-CRS12 mission to the International Space Station. We established that orbital culture substantially alters the expression of hundreds of stress-related genes compared with ground control conditions. Although expression of those genes varied in the UPR mutants on the ground, it was largely similar across the genotypes in the spaceflight condition. Furthermore, our results have yielded new information on how plants respond to growth in orbit and support the hypothesis that spaceflight induces the activation of signaling pathways that compensate for the loss of UPR regulators in the control of downstream transcriptional regulatory networks.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Three-dimensional compressible convection at low Prandtl numbers

Numerical simulations are used to study fully compressible thermal convection at large Rayleigh numbers. Results are presented from a sequence of three-dimensional simulations that reveal a transition from gradually-evolving laminar convection to nearly turbulent convection as the Prandtl number is reduced from a value of unity to one-tenth. The convective flows form irregular cellular patterns near the upper surface, possesing a network of fast downflow at cell peripheries and gentler upflow at cell centers. At greater depths the curving sheets of downflow collapse into plumes which may twist and possess substantial vertical vorticity. For the lowest Prandtl number, the convection near the bottom of the layer appears to be turbulent, yet the rapidly varying small-scale flow structure there is accompanied by more ordered sites of wavering upflow, with the latter able to penetrate all the way to the upper boundary. Thus a significant component of the flow is able to extend over multiple density scale heights, in contrast to what is argued in formulating mixing-length models for stellar convection. Results are also shown from two-dimensional simulations carried out with very high spatial resolution, which reveal that supersonic convection with fluttering shock systems can be realized.

Toomre, Juri↗
NASA NTRS2010

Cross-Characterization of Aerosol Properties from Multiple Spaceborne Sensors Facilitated by Regional Ground-Based Observations

Aerosol observations from space have become a standard source for retrieval of aerosol properties on both regional and global scales. Indeed, the large number of currently operational spaceborne sensors provides for unprecedented access to the most complete set of complimentary aerosol measurements ever to be available. Nonetheless, this resource remains under-utilized, largely due to the discrepancies and differences existing between the sensors and their aerosol products. To characterize the inconsistencies and bridge the gap that exists between the sensors, we have designed and implemented an online Multi-sensor Aerosol Products Sampling System (MAPSS) that facilitates the joint sampling of aerosol data from multiple sensors. MAPSS consistently samples aerosol products from multiple spaceborne sensors using a unified spatial and temporal resolution, where each dataset is sampled over Aerosol Robotic Network (AERONET) locations together with coincident AERONET data samples. In this way, MAPSS enables a direct cross-characterization and data integration between aerosol products from multiple sensors. Moreover, the well-characterized co-located ground-based AERONET data provides the basis for the integrated validation of these products.

Petrenko, Maksym↗
DOE OSTI2020

Rewilding of Fukushima's human evacuation zone

There is substantial interest in understanding the ecological impacts of the nuclear accidents at the Chernobyl and Fukushima Daiichi nuclear power plants. However, population‐level data for large mammals have been limited, and there remains much speculation regarding the status of wildlife species in these areas. Using a network of remote cameras placed along a gradient of radiological contamination and human presence, we collected data on population‐level impacts to wildlife (that is, abundance and occupancy patterns) following the 2011 Fukushima Daiichi nuclear accident. We found no evidence of population‐level impacts in mid‐ to large‐sized mammals or gallinaceous birds, and show several species were most abundant in human‐evacuated areas, despite the presence of radiological contamination. These data provide unique evidence of the natural rewilding of the Fukushima landscape following human abandonment, and suggest that if any effects of radiological exposure in mid‐ to large‐sized mammals in the Fukushima Exclusion Zone exist, they occur at individual or molecular scales, and do not appear to manifest in population‐level responses.

Lyons, Phillip C.↗
DOE OSTI2025

A small core in Vesta inferred from Dawn’s observations

Vesta’s large-scale interior structure had previously been constrained primarily using the gravity and shape data from the Dawn mission. However, these data alone still allow a wide range of possibilities for the differentiation state of the body. The moment of inertia is arguably the most diagnostic parameter related to the radial density distribution of a planetary body, making it crucial for assessing the body’s state of internal differentiation. Determining the moment of inertia requires additional measurements of the amplitudes of small rotational motions, such as precession and nutation. Here we report an updated estimate of the moment of inertia of Vesta inferred from Dawn’s Doppler tracking via the Deep Space Network and onboard imaging data. The recovered value for Vesta’s normalized polar moment of inertia is $\overline{C}$/MR 2 = 0.4208 ± 0.0047 (where M is the mass of Vesta and R is the reference radius), which is only 6.6% lower than the homogeneous value of 0.4505. This value, combined with the gravity field and global shape, suggests that Vesta’s interior has limited density stratification beneath its howardite–eucrite–diogenite-dominated crust. We propose two possible origin scenarios that are consistent with the observed constraints. In the first scenario, Vesta’s interior did not undergo full differentiation due to late accretion. In the second scenario, Vesta originated as an impact remnant of a larger differentiated body re-accreted with non-chondritic bulk composition produced from a catastrophic impact. Vesta did not experience complete differentiation in either scenario, suggesting that its current state reflects a complex interplay between its accretion timing, thermal evolution, redistribution of 26 Al bearing melt and/or impact processes.

CNEOS 2014-01-08 bolide↗
DOE OSTI2025

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Machine Learning Solutions for a Stable Grid Recovery

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

DECOVALEX-2023: Task F1 Final Report

DECOVALEX-2023 Task F is a comparison of models and methods for post-closure performance assessment (PA) of a deep geologic repository for radioactive waste. The general aims of Task F are to build confidence in the models, methods, and software used for PA and to stimulate additional research and development in PA methodologies. The task objectives are to motivate development of PA modelling skills and capabilities, to examine the influence of model choices on calculated repository performance, and to compare the uncertainties introduced by model choices to other sources of uncertainty. Task F involves no actual experiment or site. It is a PA modelling exercise that requires the conceptual development of hypothetical repository designs and geologic settings. Because three of the teams were interested in salt and the rest of the teams were interested in crystalline rock, Task F was split into two branches: Task F1 for crystalline rock and Task F2 for salt. This report is for Task F1, crystalline rock. Teams from seven countries (Canada, Czech Republic, Germany, Korea, Sweden, Taiwan, and United States) participated in Task F1. The teams worked together to define the features, events, and processes of the reference case repository and established a set of performance measures. In addition, they defined a set of benchmark problems designed to test and compare modelling capabilities for fracture flow and transport at different scales. The repository design and benchmark problems are documented in a Task Specification that evolved over time as the group honed the specifications. The benchmark problems verified that each team can aptly model flow and transport in fractured media in 1-, 2-, and 3-dimensions. Two general approaches were used for the 3-dimensional benchmarks: discrete fracture network (DFN) and equivalent continuous porous medium (ECPM). DFN modelling involves explicit meshing of each fracture while ECPM modelling aims to capture the effective porosity and directional permeability of each cell in a space-filling mesh as affected by intersecting fractures. In some models, a combination of the two is used, i.e., DFN for large known fractures and ECPM for the rest of the domain. Transport is solved by using either the advection-dispersion equation or particle tracking. Although some variation is observed among model breakthrough curves in the benchmark problems, there is strong agreement in breakthrough behaviour up to at least the 75 th percentile for all benchmarks. At the 90 th percentile, breakthrough results show larger differences, suggesting several models retain substantially higher fractions of tracer in regions of slower moving water. In addition to the flow and transport benchmarks, several teams completed the source term benchmark, verifying capabilities for modelling radionuclide decay and ingrowth, waste package breach, instant release fractions, fuel matrix degradation rates, and radionuclide solubility limitations. The reference case is conceptualized as a generic spent fuel repository at a depth of 450 m in fractured crystalline rock. The repository has 50 parallel backfilled drifts, each with 50 deposition holes 6 m apart. Each deposition hole contains a 4-PWR waste package and bentonite buffer. The rock domain is 5 km in length, 2 km in width, and 1 km in depth. It has 6 deterministic fractured deformation zones and a multitude of stochastic fractures. Teams generally used the ECPM approach for the entire rock or a hybrid approach in which the deterministic fracture zones are modelled with a DFN and the rest of the rock is modelled by ECPM. Of the reference case problems specified, only the results of the initial reference case problem are compared in this report. The initial problem focuses on transport from the deposition holes to the surface, i.e., it neglects waste package performance. Tracers are released at all waste package locations at time zero and tracked for their releases to the near field and ground surface. The water fluxes calculated at the ground surface entry and exit regions of the domain are similar for all models except for two that have considerably lower fluxes. For tracer transport, large differences are observed among models in the magnitude of tracer transported. Much of the difference appears to be due to how the repository is implemented and hence the different degrees of repository simplification. Models that exclude the drifts, buffer, and backfill from the domain tend to show greater release of tracers and radionuclides from the repository. The initial study presented here indicates that major differences in modelling important processes within the repository (e.g., diffusion through buffer and backfill) can produce broadly different release and transport results, especially when those processes are excluded. Even for the models that included all specified features, events, and processes, the results show significant differences and demonstrate the importance of examining multiple modelling approaches in performance assessment. The differences in results observed in this study are expected to motivate teams to either increase complexity in future versions of the reference case models or to improve methods to account for the effects of simplified features and processes. Either way, future improvements in these models are expected to produce results that more closely agree.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2002

Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

Rajkumar, T.↗
NASA NTRS2003

The NASA/NOAA Electronic Theater

The NASA/NOAA Electronic Theater presents Earth science observations and visualizations from space in a historical perspective. Fly in from outer space to Cambridge and Harvard University. Zoom through the Cosmos to SLC and site of the 2002 Winter Olympics using 1 m IKONOS "Spy Satellite" data. Contrast the 1972 Apollo 17 "Blue Marble" image of the Earth with the latest US and International global satellite images that allow us to view our Planet from any vantage point. See the latest spectacular images from NASA/NOAA remote sensing missions like Terra, GOES, TRMM, SeaWiFS, & Landsat 7, of storms & fires like Hurricane Isabel and the LNSan Diego firestorms of 2003. See how High Definition Television (HDTV) is revolutionizing the way we do science communication. Take the pulse of the planet on a daily, annual and 30-year time scale. See daily thunderstorms, the annual blooming of the northern hemisphere landmasses and oceans, fires in Africa, dust storms in Iraq, and carbon monoxide exhaust from global burning. See visualizations featured on Newsweek, TIME, National Geographic, Popular Science covers & National & International Network TV. Spectacular new global visualizations of the observed and simulated atmosphere & oceans are shown. See the currents and vortexes in the oceans that bring up the nutrients to feed tiny plankton and draw the fish, whales and fishermen. See the how the ocean blooms in response to El Niiioh Niiia climate changes. The Etheater will be presented using the latest High Definition TV (HDTV) and video projection technology on a large screen. See the global city lights, and the great NE US blackout of August 2003 observed by the "night-vision" DMSP satellite.

Hasler, A. F.↗
DOE OSTI2023

Design and Development of a High Fidelity Cyber-Physical Testbed

In order to ensure that future critical infrastructure systems are resilient to various types of such advanced and persistent threats, it is important to develop and integrate tailored solutions that holistically address cyber-attack detection and mitigation in a timely manner such that adverse system impacts that impact a large population are avoided. Further, it is essential to create environments that allow control, protection and communication to exist within a realistic environment to analyze the effects of adverse conditions and system operating modes. This project aims to establish a high-fidelity testbed environment for modeling and simulating a single microgrid all the way up to a network of microgrids along with baseline controls, protection, and associated cyber communication. This is an important activity because accurately modeling and simulating the various power-electronics-based DERs and loads in a microgrid is critical to adequately capturing their behaviors over a wide range of off-normal conditions, as well as to evaluate the resilience of the system using the developed controls. The work presented in this report focuses on the process of building this high-fidelity testbed and the associated experimentation it enables. The model enables the creation of high-fidelity use cases and associated datasets that have been used extensively within the initiative to study resilience and support novel control development and prototyping. The work heavily leverages existing capability that is part of the high-fidelity cyber-physical system experimentation lab to create a power hardware-in-the-loop setup. The report also details the creation of an automated model building platform that can enable high-fidelity real-time models to be built without much effort allowing existing low-fidelity models to be analyzed in higher fidelity. Lastly, the report also discusses efforts center around scaling to large complex power system models to make the experimentation more effective.

97 MATHEMATICS AND COMPUTING↗