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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.

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At least 739 records · Page 41

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NASA NTRS2015

Overview of the Space Launch System Transonic Buffet Environment Test Program

Fluctuating aerodynamic loads are a significant concern for the structural design of a launch vehicle, particularly while traversing the transonic flight environment. At these trajectory conditions, unsteady aerodynamic pressures can excite the vehicle dynamic modes of vibration and result in high structural bending moments and vibratory environments. To ensure that vehicle structural components and subsystems possess adequate strength, stress, and fatigue margins in the presence of buffet and other environments, buffet forcing functions are required to conduct the coupled load analysis of the launch vehicle. The accepted method to obtain these buffet forcing functions is to perform wind-tunnel testing of a rigid model that is heavily instrumented with unsteady pressure transducers designed to measure the buffet environment within the desired frequency range. Two wind-tunnel tests of a 3 percent scale rigid buffet model have been conducted at the Langley Research Center Transonic Dynamics Tunnel (TDT) as part of the Space Launch System (SLS) buffet test program. The SLS buffet models have been instrumented with as many as 472 unsteady pressure transducers to resolve the buffet forcing functions of this multi-body configuration through integration of the individual pressure time histories. This paper will discuss test program development, instrumentation, data acquisition, test implementation, data analysis techniques, and several methods explored to mitigate high buffet environment encountered during the test program. Preliminary buffet environments will be presented and compared using normalized sectional buffet forcing function root-meansquared levels along the vehicle centerline.

Piatak, David J.↗

NASA NTRS2009

Efficient File Sharing by Multicast - P2P Protocol Using Network Coding and Rank Based Peer Selection

In this work, we consider information dissemination and sharing in a distributed peer-to-peer (P2P highly dynamic communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the space network environments.

military↗

NASA NTRS2010

Risk Identification and Visualization in a Concurrent Engineering Team Environment

Incorporating risk assessment into the dynamic environment of a concurrent engineering team requires rapid response and adaptation. Generating consistent risk lists with inputs from all the relevant subsystems and presenting the results clearly to the stakeholders in a concurrent engineering environment is difficult because of the speed with which decisions are made. In this paper we describe the various approaches and techniques that have been explored for the point designs of JPL's Team X and the Trade Space Studies of the Rapid Mission Architecture Team. The paper will also focus on the issues of the misuse of categorical and ordinal data that keep arising within current engineering risk approaches and also in the applied risk literature.

Risk Scoring↗

NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗

NASA NTRS2021

Enabling Mars Radio Occultation by Smallsats

We introduce a Mars Radio Occultation (RO) mission concept leveraging on small satellite (smallsat) technologies that will enable measurements that address both Mars science and exploration priorities. The RO technique measures the Doppler shift of radio signals of a spacecraft occulting behind a planet’s limb that contains information about the planet’s atmospheric density, temperature, and pressure. We design a smallsat constellation and simulate “crosslink” RO observations between the smallsats to determine the expected accuracy and spatiotemporal coverage of Mars RO atmospheric profiles. Such measurements are key to spacecraft Entry, Descent, and Landing (EDL) and Ascent from the Surface (AST) of Mars. Today, there are limited number of Mars atmospheric profiles with high vertical resolution, especially measurements near the surface that are difficult to obtain from satellite-based passive instruments due to atmospheric absorption. We find that crosslink ROs between a constellation of six smallsats provide global and diurnal cycle coverage with dozens of occultations per day, providing temperature information from near-surface up to ~45 km with altitude-dependent accuracy that ranges between <0.5 K at lower altitude to <5 K in the middle-to-upper atmosphere. Ionospheric ROs reveal an expected electron density of ~5 – 10% at the peak ionospheric height between 100 and 140 km. We conclude that observations obtained from a smallsat constellation can augment existing observing platforms by reducing observational gaps and yield high resolution measurements required for safe spacecraft operations during EDL and AST.

Martinez, German↗

DOE OSTI2020

Exploring Inside the LANSCE H- Ion Source with Laser Absorption Techniques

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Accelerator Design, Technology, and Operations↗

DOE OSTI2021

Robustness of deep learning algorithms in astronomy -- galaxy morphology studies

Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

A next-generation toolset for the life-cycle analysis of electricity use

This works presents an overview--including background, data flow, and case study--of a tool for analyzing impacts utilizing the electricity baseline provided by ElectricityLCI and the impact-modeling techniques of Grid Mix Explorer (along with air and water quality) as a part of a next-generation LCA analytical framework.

Davis, Tyler↗

DOE OSTI2016

An X-band waveguide measurement technique for the accurate characterization of materials with low dielectric loss permittivity

In this work, we present a new X-band waveguide (WR90) measurement method that permits the broadband characterization of the complex permittivity for low dielectric loss tangent material specimens with improved accuracy. An electrically long polypropylene specimen that partially fills the cross-section is inserted into the waveguide and the transmitted scattering parameter (S{sub 21}) is measured. The extraction method relies on computational electromagnetic simulations, coupled with a genetic algorithm, to match the experimental S{sub 21} measurement. The sensitivity of the technique to sample length was explored by simulating specimen lengths from 2.54 to 15.24 cm, in 2.54 cm increments. Analysis of our simulated data predicts the technique will have the sensitivity to measure loss tangent values on the order of 10{sup −3} for materials such as polymers with relatively low real permittivity values. The ability to accurately characterize low-loss dielectric material specimens of polypropylene is demonstrated experimentally. The method was validated by excellent agreement with a free-space focused-beam system measurement of a polypropylene sheet. This technique provides the material measurement community with the ability to accurately extract material properties of low-loss material specimen over the entire X-band range. This technique could easily be extended to other frequency bands.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1976

Advanced combustion techniques for controlling NO sub x emissions of high altitude cruise aircraft

An array of experiments designed to explore the potential of advanced combustion techniques for controlling the emissions of aircraft into the upper atmosphere was discussed. Of particular concern are the oxides of nitrogen (NOx) emissions into the stratosphere. The experiments utilize a wide variety of approaches varying from advanced combustor concepts to fundamental flame tube experiments. Results are presented which indicate that substantial reductions in cruise NOx emissions should be achievable in future aircraft engines. A major NASA program is described which focuses the many fundamental experiments into a planned evolution and demonstration of the prevaporized-premixed combustion technique in a full-scale engine.

Rudey, R. A.↗

NASA NTRS2003

Remote Radio Sounding Science for JIMO

Green, J. L.↗

NASA NTRS2004

Radio Sounding Techniques for the Galilean Icy Moons and their Jovian Magnetospheric Environment

Radio sounding of the Earth's topside ionosphere and magnetosphere is a proven technique from geospace missions such as the International Satellites for Ionospheric Studies (ISIS) and the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE). Application of this technique to Jupiter's icy moons and the surrounding Jovian magnetosphere will provide unique remote sensing observations of the plasma and magnetic field environments and the subsurface conductivities, of Europa, Ganymede, and Callisto. Spatial structures of ionospheric plasma above the surfaces of the moons vary in response to magnetic-field perturbations from (1) magnetospheric plasma flows, (2) ionospheric currents from ionization of sputtered surface material, and (3) induced electric currents in salty subsurface oceans and from the plasma flows and ionospheric currents themselves. Radio sounding from 3 kHz to 10 MHz can provide the global electron densities necessary for the extraction of the oceanic current signals and supplements in-situ plasma and magnetic field measurements. While radio sounding requires high transmitter power for subsurface sounding, little power is needed to probe the electron density and magnetic field intensity near the spacecraft. For subsurface sounding, reflections occur at changes in the dielectric index, e.g., at the interfaces between two different phases of water or between water and soil. Variations in sub-surface conductivity of the icy moons can be investigated by radio sounding in the frequency range from 10 MHz to 50 MHz, allowing the determination of the presence of density and solid-liquid phase boundaries associated with oceans and related structures in overlying ice crusts. The detection of subsurface oceans underneath the icy crusts of the Jovian moons is one of the primary objectives of the Jupiter Icy Moons Orbiter (JIMO) mission. Preliminary modeling results show that return signals are clearly distinguishable be&een an ice crust with a thickness of 7 km on 1) an ocean and 2) a layer of bedrock. Knowledge of the ionospheric contributions to the time delay of the low-frequency subsurface radar is shown to be important in obtaining accurate depth information.

Green, James L.↗

DOE OSTI2020

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) that fits high-level information about the response surface and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected for evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. Additionally, this methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface and, subsequently, to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to conventional optimization techniques, such as particle swarm and genetic algorithm-based optimization techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning

In this work, a novel design optimization technique based on active learning, which involves dynamic exploration and exploitation of the design space of interest using an ensemble of machine learning algorithms, is presented. In this approach, a hybrid methodology incorporating an explorative weak learner (regularized basis function model) which fits high-level information about the response surface, and an exploitative strong learner (based on committee machine) that fits finer details around promising regions identified by the weak learner, is employed. For each design iteration, an aristocratic approach is used to select a set of nominees, where points that meet a threshold merit value as predicted by the weak learner are selected to be evaluated using expensive function evaluation. In addition to these points, the global optimum as predicted by the strong learner is also evaluated to enable rapid convergence to the actual global optimum once the most promising region has been identified by the optimizer. This methodology is first tested by applying it to the optimization of a two-dimensional multi-modal surface. The performance of the new active learning approach is compared with traditional global optimization methods, namely micro-genetic algorithm (pGA) and particle swarm optimization (PSO). It is demonstrated that the new optimizer is able to reach the global optimum much faster with a significantly fewer number of function evaluations. Subsequently, the new optimizer is also applied to a complex internal combustion (IC) engine combustion optimization case with nine control parameters related to fuel injection, initial thermodynamic conditions, and in-cylinder flow. It is again found that the new approach significantly lowers the number of function evaluations that are needed to reach the optimum design configuration (by up to 80%) when compared to particle swarm and genetic algorithm-based optimization techniques.

Machine learning, active learning, design optimiza↗

DOE OSTI2021

Enabling Computation on Sensitive Data in International Safeguards with Privacy-Preserving Encryption Techniques

Privacy-preserving machine learning is a field of study that explores how to protect and preserve the privacy of sensitive data while allowing the data to be used by machine learning algorithms. This field has had substantial industry investment due to heightened concerns about privacy in the technology industry, with a focus in two broad application areas: financial services and healthcare. Numerous privacy-preserving methods have also been proposed for international safeguards, but they have been difficult to enact because the data they require is con- sidered sensitive or proprietary by the nuclear facility operator. This work examines how current privacy-preserving approaches might be used to enable the International Atomic Energy Agency (IAEA) to use that data to contribute to a safeguards conclusion about a state while giving nuclear operators confidence that their sensitive data is adequately protected. This paper begins by exploring several broad categories of privacy-preserving techniques including homomorphic encryption, secure multiparty computation, secure enclaves, and zero-knowledge proofs. Then we discuss some of the security considerations related to using these methods, potential use cases, and a conceptual system design for applying privacy-preserving methods in international safeguards.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1973

Redundancy management of inertial systems.

The paper reviews developments in failure detection and isolation techniques applicable to gimballed and strapdown systems. It examines basic redundancy management goals of improved reliability, performance and logistic costs, and explores mechanizations available for both input and output data handling. The meaning of redundant system reliability in terms of available coverage, system MTBF, and mission time is presented and the practical hardware performance limitations of failure detection and isolation techniques are explored. Simulation results are presented illustrating implementation coverages attainable considering IMU performance models and mission detection threshold requirements. The implications of a complete GN&C redundancy management method on inertial techniques are also explored.

Mckern, R. A.↗

DOE OSTI2017

A Comparative Observational Study of YSO Classification in Four Small Star-forming H ii Regions

We have developed a new young stellar object (YSO) identification and classification technique using mid-infrared Wide-field Infrared Survey Explorer (WISE) data. We compare this new technique with previous WISE YSO detection and classification methods that used either infrared colors or spectral energy distribution slopes. In this study, we also use the new technique to detect and examine the YSO population associated with four small H ii regions: KR 7, KR 81, KR 120, and KR 140. The relatively simple structure of these regions allows us to effectively use both spatial and temporal constraints to identify YSOs that are potential products of triggered star formation. We are also able to identify regions of active star formation around these H ii regions that are clearly not influenced by the H ii region expansion, and thus demonstrate that star formation is on-going on megayear timescales in some of these molecular clouds.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1988

A modified ATI technique for nowcasting convective rain volumes over areas

This paper explores the applicability of the area-time-integral (ATI) technique for the estimation of the growth portion only of a convective storm (while the rain volume is computed using the entire life history of the event) and for nowcasting the total rain volume of a convective system at the stage of its maximum development. For these purposes, the ATIs were computed from the digital radar data (for 1981-1982) from the North Dakota Cloud Modification Project, using the maximum echo area (ATIA) no less than 25 dBz, the maximum reflectivity, and the maximum echo height as the end of the growth portion of the convective event. Linear regression analysis demonstrated that correlations between total rain volume or the maximum rain volume versus ATIA were the strongest. The uncertainties obtained were comparable to the uncertainties which typically occur in rain volume estimates obtained from radar data employing Z-R conversion followed by space and time integration. This demonstrates that the total rain volume of a storm can be nowcasted at its maximum stage of development.

Makarau, Amos↗