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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Operational Techniques for Dealing with Long Eclipses During the MMS Extended Mission

Launch window design for the Magnetospheric Multiscale (MMS) mission ensured that no excessive eclipses would be encountered during the prime mission. However, no orbit solutions exist that satisfy the eclipse constraints indefinitely: most extended mission years contain 1-3 eclipses long enough to potentially damage either the spacecraft or its scientific instruments. Two steps were taken to improve the situation. Firstly, raising apogee radius from 25 to 29.34 Earth radii altered the Sun-Earth-MMS phasing, so efficiently achieving reductions in the long eclipse durations. These maneuvers were performed early this year, in preparation for the first pair of long eclipses in August 2019. Secondly, a set of operational steps were taken around the time of the eclipses to help maintain spacecraft and instrument temperatures while preventing power load shedding. These operational steps included raising key onboard temperatures through adjusting the spacecraft attitude to tilt the instrument deck towards the Sun, and engaging select heaters prior to going into eclipses. In addition, all scientific instruments were turned off, as well as high-power, non-critical spacecraft systems, to conserve energy.These steps each came with trade-offs which will be discussed in the paper. Finally, the results that were obtained when the spacecraft experienced the first extremely long eclipses will be discussed, as will lessons learned for future long eclipses.

Williams, Trevor↗
NASA NTRS2004

Environmental Remediation Technologies Derived from Space Industry Research

Beginning in the 1950s and 1960s, an abundance of effort and initiative was focused on propelling the space industry outward for planetary exploration and habitation. During these early years, the push to take space science to new levels indirectly contributed to the evolution of another science field that would not fully surface until the early 1980s, environmental remediation. This field is associated with the remediation or cleanup of environmental resources such as groundwater, soil, and sediment. Because the space-exploration initiative began prior to the establishment of the U.S. Environmental Protection Agency (EPA) in December of 1970, many NASA Centers as well as space-related support contractors allowed for the release of spent chemicals into the environment. Subsequently, these land owners have been directed by the EPA to responsibly initiate cleanup of their impacted sites. This paper will focus on the processes and lessons learned with the development, testing, and commercialization initiatives associated with four remediation technologies. The technologies include installation techniques for permeable reactive barriers (PRBs), the use of ultrasound to improve long-term performance of PRBs, emulsified zero-valent iron for product-level solvent degradation, and emulsion technologies for application to metal and polychlorinated biphenyl contaminated media. Details of the paper cover technology research, evaluation, and testing; contracts and grants; and technology transfer strategies including patenting, marketing, and licensing.

Quinn, Jacqueline↗
NASA NTRS2012

Mercury's Sodium Exosphere: Observations during the MESSENGER Orbital Phase

The MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft entered into orbit about Mercury on March 18,2011. We now have approximately five Mercury years of data from orbit. Prior to the MESSENGER mission, Mercury's surface-bounded exosphere was known to contain H, He, Na. K, and Ca. The Ultraviolet and Visible Spectrometer (UVVS) began routine orbital observations of both the dayside and nightside exosphere on March 29. 2011, measuring altitude profiles for all previously detected neutral species except for He and K. We focus here on what we have learned about the sodium exosphere: its spatial, seasonal, and sporadic variation. Observations to date permit delineation of the relative roles of photon-stimulated desorption (PSD) and impact vaporization (IV) from seasonal and spatial effects, as well as of the roles of ions both as sputtering agents and in their possible role to enhance the efficiency of PSD. Correlations of Mercury's neutral sodium exosphere with measurements from MESSENGER's Magnetometer (MAG) and Energetic Particle and Plasma Spectrometer (EPPS) provide insight into the roles of ions and electrons. Models incorporating MAG observations provide a basis for identifying the location and area of the surface exposed to solar wind plasma, and EPPS observations reveal episodic populations of energetic electrons in the magnetosphere and the presence of planetary He(+), 0(+), and Na(+),

Killen, Rosemary M.↗
NASA NTRS2016

What Was Learned in Predicting Slender Airframe Aerodynamics with the F-16XL Aircraft

The second Cranked-Arrow Wing Aerodynamics Project, International, coordinated project has been underway to improve high-fidelity computational-fluid-dynamics predictions of slender airframe aerodynamics. The work is focused on two flow conditions and leverages a unique flight data set obtained with the F-16XL aircraft for comparison and validation. These conditions, a low-speed high-angle-of-attack case and a transonic low-angle-of-attack case, were selected from a prior prediction campaign wherein the computational fluid dynamics failed to provide acceptable results. In revisiting these two cases, approaches for improved results include better, denser grids using more grid adaptation to local flow features as well as unsteady higher-fidelity physical modeling like hybrid Reynolds-averaged Navier-Stokes/unsteady Reynolds-averaged Navier-Stokes/large-eddy simulation methods. The work embodies predictions from multiple numerical formulations that are contributed from multiple organizations where some authors investigate other possible factors that could explain the discrepancies in agreement (e.g., effects due to deflected control surfaces during the flight tests as well as static aeroelastic deflection of the outer wing). This paper presents the synthesis of all the results and findings and draws some conclusions that lead to an improved understanding of the underlying flow physics, finally making the connections between the physics and aircraft features.

Rizzi, Arthur↗
DOE OSTI2023

Verification of Spent Fuel Inside Dry Storage Casks by Cask Top Fast Neutron Mapping (FY2023 Mid-Year Report)

This project is developing a prototype scanner array verification system for detection of missing fuel assemblies in spent-fuel storage casks. The prototype consists of six fast-neutron scintillator detectors mounted to a linear actuator frame that is placed on the top of a spent fuel cask to scan across all fuel assembly positions. The scanner array was assembled and tested at LLNL in FY2022. A field test schedule has been requested at the Idaho National Laboratory (INL) Cask Farm site for FY2023. Note that the Cask Farm contractor determines this scheduling and not INL directly. Further system automation will be designed and implemented with the goal of obtaining a level of system operation that meets IAEA needs. This includes integration of the scanner array and data-acquisition control software into a single interface for operator use. In addition, commercial operators and the IAEA may have special requirements for portability, shipping, lifting, and installation. Prior to the Field Test at INL, the system will be operated at LLNL to exercise lifting procedure and linear actuators, monitor stability of detector energy and pulse-shape discrimination calibration, and test system software integration efforts. Following the Field Test, we will present results and discuss the technology with the IAEA. We will incorporate additional improvements to the system based on lessons learned from the field test and feedback from the IAEA. If successful, the technology can be transferred to the IAEA or other stakeholders for assessment.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2013

Conduit Coating Abrasion Testing

During my summer internship at NASA I have been working alongside the team members of the RESTORE project. Engineers working on the RESTORE project are creating ·a device that can go into space and service satellites that no longer work due to gas shortage or other technical difficulties. In order to complete the task of refueling the satellite a hose needs to be used and covered with a material that can withstand effects of space. The conduit coating abrasion test will help the researchers figure out what type of thermal coating to use on the hose that will be refueling the satellites. The objective of the project is to determine whether or not the conduit coating will withstand the effects of space. For the RESTORE project I will help with various aspects of the testing that needed to be done in order to determine which type of conduit should be used for refueling the satellite. During my time on the project I will be assisting with wiring a relay board that connected to the test set up by soldering, configuring wires and testing for continuity. Prior to the testing I will work on creating the testing site and help write the procedure for the test. The testing will take place over a span of two weeks and lead to an informative conclusion. Working alongside various RESTORE team members I will assist with the project's documentation and records. All in all, throughout my internship at NASA I hope to learn a number of valuable skills and be a part of a hard working team of engineers.

Sullivan, Mary K.↗
DOE OSTI2024

ML-based Dimension Reduction Strategies

Deep learning (DL)--based surrogate models have achieved success in various applications in carbon capture and storage (CCS). However, the model training on high-dimensional spaces is computationally expensive and impractical for large-scale and complex geological models, because the models usually contain hundreds of thousands to millions of grid cells, each with a set of parameters. Furthermore, the high cost of generating training data with sufficient variation is another limitation of model training on high-dimensional spaces, which may result in overfitting and reduce the model efficiency and prediction performance. We proposed the workflow incorporating dimension reduction methods and deep learning models, which aim to extract the latent variables of input parameters and output state variables, and then build the mapping function at the latent spaces. The proposed workflow can significantly reduce the computational complexity in solving both forward and inverse problems compared to models trained on high-dimensional spaces. Dimensionality reduction models showed great potential in workflows for fast reservoir simulation, history matching, prior model generation, visualization, and more, ultimately enhancing DL model performance in related SMART Work Packages.

Hosseini, Seyyed↗
DOE OSTI2021

Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.

Edelen, Jonathan P. (ORCID:0000000215180652)↗
NASA NTRS2003

Long-term changes in amphetamine-induced reinforcement and aversion in rats following exposure to 56Fe particle

Exposing rats to heavy particles produces alterations in the functioning of dopaminergic neurons and in the behaviors that depend upon the integrity of the dopaminergic system. Two of these dopamine-dependent behaviors include amphetamine-induced reinforcement, measure using the conditioned place preference procedure, and amphetamine-induced reinforcement, measured using the conditioned place preference procedure, and amphetamine-induced aversion, measured using the conditioned taste aversion. Previous research has shown that exposing rats to 1.0 Gy of 1GeV/n 56Fe particles produced a disruption of an amphetamine-induced taste aversion 3 days following exposure, but produced an apparent enhancement of the aversion 112 days following exposure. The present experiments were designed to provide a further evaluation of these results by examining taste aversion learning 154 days following exposure to 1.0 Gy 56Fe particles and to establish the convergent validity of the taste aversion results by looking at the effects of exposure on the establishment of an amphetamine-induced conditioned place preference 3, 7, and 16 weeks following irradiation. The taste aversion results failed to confirm the apparent enhancement of the amphetamine-induced CTA observed in the prior experiment. However, exposure to 56Fe particles prevented the acquisition of amphetamine-induced place preference at all three-time intervals. The results are interpreted as indicating that exposure to heavy particles can produce long-term changes in behavioral functioning. c2002 COSPAR. Published by Elsevier Science Ltd. All rights reserved.

Non-NASA Center↗
NASA NTRS1977

Space Technology For Tuna Boats

Freshly-caught tuna is stored below decks in wells cooled to about zero degrees by brine circulated through a refrigerating system. The wells formerly were insulated by cork or fiberglass, but both materials were subject to deterioration; cork, for instance, needs replacement every three years. The Campbell Machine Division of Campbell Industries, San Diego, which manufactures and repairs large boats for the commercial fishing industry, was looking for a better way to insulate tuna storage wells. Learning of the Rockwell technique, Campbell contracted for a test installation on one boat, then bought its own equipment and adopted the spray-foam procedure for their boats. The foam hardens after application. It not only is a superior insulator, it also is considerably lighter and easier to apply. Fishing industry spokesmen say that foam insulation is far more reliable, efficient and economical than prior techniques. More than 40 foam-insulated tuna boats, ranging in cost from $1 million to $4 million, have been built and sold. Principal customers are Ralston Purina's Van Camp Seafood Division and Star-Kist Inc.

Source record↗
NASA NTRS2009

Lunar Surface Operations: Surface Duration - Part 2

The objectives of this slide presentation are to review the activities on the lunar surface during the stay. The objectives include (1) Summarize Lunar Module Basics emphasizing module layout and storage. (2) Identify the primary activities occurring during each of the lunar s urface timelines, (3) List the EVA Prep tasks, (4) Identify the EVA Objectives, (5) Identify the activities associated with Post EVA (6) Describe the lessons learned during both EVA and Non EVA activities. Included are overview drawings of the Lunar Roving Vehicle, pictures of the tools, and sample return containers. There are also time lines for the Apollo 11, and Apollo 12 through 14, Apollo 15, Apollo 16 and Apollo 17. Diagrams of the EVA suits are shown, including the Liquid Cooling Garment, and the Pressure Garment Assembly. The activity prior to the EVA are reviewed. The science mission assignments of each mission are viewed. The activities after the EVA are reviewed

Interbartolo, Michael↗
NASA NTRSDate not supplied

Testing a Run-Time Assurance Framework Coupled with Integrated Risk Mitigation Capabilities for Autonomous Urban UAS Flights

The In-Time Aviation Safety Management System (IASMS) Concept of Operations (ConOps) envisions new capabilities to monitor, assess, and mitigate flight safety risks. Systems will be tailored to mission type, vehicle/equipage type, operational environment, and safety risk tolerance. Within an IASMS framework, several capabilities may be implemented spanning three operational phases (pre-flight, in-flight, and post-flight/off-line); and consisting of lower level functions and information services which may reside onboard the aircraft, on third-party server(s), and/or on ground/operator station(s). Each capability will be designed to produce and disseminate safety-relevant information; perform detection, diagnosis, and prediction of unsafe situations; and/or execute mitigation actions when hazardous events warrant such changes. This paper focuses on recent testing of airborne capabilities that demonstrate inflight aspects of the overarching concept for autonomous unmanned aircraft systems (UAS) operations in urban environments. A flight test architecture is described that applies run-time assurance principles (e.g., executes independent of the unassured autopilot), real-time risk assessment, and a technique to execute contingencies if necessary either automatically or via pilot intervention. Several tests using small UAS were conducted to verify the assured in-flight risk mitigation capability. The paper draws significantly from a larger NASA technical report and recent prior conference papers, providing additional details. Data is analyzed for two representative flights to illustrate the performance for various sequential and simultaneous hazards used during testing. During each automated flight, several hazards are encountered at various points along the flight path. At each point, the hazard is mitigated by the system, with the vehicle then continuing to subsequent points. The paper concludes with lessons-learned regarding relevant aspects of the overarching IASMS concept and how it may be updated and further advanced in the future.

population activity↗
DOE OSTI2024

Explosively driven Richtmyer–Meshkov instability jet suppression and enhancement via coupling machine learning and additive manufacturing

The ability to control the behavior of fluid instabilities at material interfaces, such as the shock-driven Richtmyer–Meshkov instability, is a grand technological challenge with a broad number of applications ranging from inertial confinement fusion experiments to explosively driven shaped charges. In this work, we use a linear-geometry shaped charge as a means of studying methods for controlling material jetting that results from the Richtmyer–Meshkov instability. A shaped charge produces a high-velocity jet by focusing the energy from the detonation of high explosives. The interaction of the resulting detonation wave with a hollowed cavity lined with a thin metal layer produces the unstable jetting effect. By modifying the characteristics of the detonation wave prior to striking the lined cavity, the kinetic energy of the jet can be enhanced or reduced. Modifying the geometry of the liner material can also be used to alter jetting properties. We apply optimization methods to investigate several design parameterizations for both enhancing or suppressing the shaped-charge jet. This is accomplished using 2D and 3D hydrodynamic simulations to investigate the design space that we consider. We also apply new additive manufacturing methods for producing the shaped-charge assemblies, which allow for the experimental testing of complicated design geometries obtained through computational optimization. We present a direct comparison of our optimized designs with experimental results carried out at the High Explosives Application Facility at Lawrence Livermore National Laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probabilistic Evaluation of Geoscientific Hypotheses with Geophysical Data: Application to Electrical Resistivity Imaging of a Fractured Bedrock Zone

As climate changes and populations grow, groundwater sustainability is becoming increasingly important. Groundwater models, based on a conceptual understanding of the subsurface structure, are crucial tools for making sustainable management decisions. Conceptual models of the subsurface are based on knowledge of geological processes, and, frequently, observations from geophysical data. A frequent problem in groundwater model development occurs when multiple geological phenomena could explain a single subsurface observation. Uncertainty in geophysical data makes it even more difficult to discern which explanations are consistent with the geophysics. Here, we present a framework for testing geological when a geological feature is observed in geophysical data, but its physical characteristics are uncertain. The framework builds on Popper-Bayes methods developed in previous work, and is applied to study a fractured bedrock zone in a mountainous watershed in southwest Colorado. First, we outline six hypotheses based on the geological history of the watershed. Then, using the proposed Popper-Bayes approach, we demonstrate that three of the six hypotheses are inconsistent with measured electrical resistivity data, even after accounting for uncertainty. Finally, we discuss the importance of the prior model, and how this framework for handling geophysical uncertainty can be applied in other settings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Combining lattice QCD and phenomenological inputs on generalised parton distributions at moderate skewness

Abstract We present a systematic study demonstrating the impact of lattice QCD data on the extraction of generalised parton distributions (GPDs). For this purpose, we use a previously developed modelling of GPDs based on machine learning techniques fulfilling the theoretical requirements of polynomiality, a form of positivity constraint and known reduction limits. A special care is given to estimate the uncertainty stemming from the ill-posed character of the connection between GPDs and the experimental processes usually considered to constrain them, like deeply virtual Compton scattering (DVCS). Moke lattice QCD data inputs are included in a Bayesian framework to a prior model based on an Artificial Neural Network. This prior model is fitted to reproduce the most experimentally accessible information of a phenomenological extraction by Goloskokov and Kroll. We highlight the impact of the precision, correlation and kinematic coverage of lattice data on GPD extraction at moderate $$\xi $$ ξ which has only been brushed in the literature so far, paving the way for a joint extraction of GPDs.

Physics↗
DOE OSTI2025

Adversarial Binaries: AI-guided Instrumentation Methods for Malware Detection Evasion

Adversarial binaries are executable files that have been altered without loss of function by an AI agent in order to deceive malware detection systems. Progress in this emergent vein of research has been constrained by the complex and rigid structure of executable files. Although prior work has demonstrated that these binaries deceive a variety of malware classification models which rely on disparate feature sets, a consensus as to the best approach has not been reached, either in terms of the optimization algorithms or the instrumentation methods. Furthermore, although inconsistencies in the data sets, target classifiers, and functionality verification methods make head-to-head comparisons difficult, here we extract lessons learned and make recommendations for future research.

malware obfuscation↗
NASA NTRS2023

A Regional Map of Mangrove Extent for Myanmar, Thailand, and Cambodia Shows Losses of 44% By 1996

Southeast Asia is home to some of the planet’s most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia over the past two decades, data prior to the mid-1990s is limited due to the scarcity of Earth Observation data of sufficient quality and the historical limitations to publicly available EO. We present a classification of mangrove extent using Landsat 1-2 MSS Tier 2 data from 1972-1977 for three Southeast Asian countries: Myanmar, Thailand, and Cambodia. Mangrove extent land cover maps were generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2. Accuracy assessments indicated that the classification for the mangrove and non-mangrove class had a producer’s accuracy of 79.63 % and 98.24 %a user’s accuracy of 89.58 % and 96.21 %, and an overall accuracy of 95.26 % . We found a decline of 6,830 km2 between the 1970’s and 2020, showing that44.29% of the mangrove area in these countries has been lost in the past 48 years. Most of this loss occurred between the 1970’s and 1996; rates of deforestation declined dramatically after 1996. This study also urges that these results be considered within the context of the local need for these coastal resources that helped them endure the political and financial challenges of their coastal homes.

Priscilla Baltezar↗
DOE OSTI2025

A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels

Here, in this work, a Variational Autoencoder (VAE)-based data-driven modeling framework is developed with the overarching goal of enabling fuel design. The VAE model is trained on a large dataset with several chemical species to learn a compressed latent space molecular representation. Chemical structure in the form of Simplified Molecular Input Line Entry System (SMILES) string is fed as input, encoded into the VAE latent space, and decoded back to the SMILES string using Long Short-Term Memory (LSTM) networks. Complexities of the VAE training loss function are thoroughly examined by varying the weightage (beta (𝜷) parameter) of the latent space regularization term, thereby assessing the balance between reconstruction accuracy and validity, and focusing on both accurate molecular structure reconstruction and latent space consistency. Two different strategies for 𝜷 variation are evaluated: linear annealing and cyclic annealing. In addition, the impact of total correlation adjustment and hierarchical priors is also studied with regard to the balance between reconstruction fidelity and latent space regularization, and potential issues such as posterior collapse, over-regularization, and poor disentanglement of latent variables. Overall, the best performance of the model is achieved with hierarchical priors and incrementally increasing 𝜷 from 0 to a threshold value of 0.25 over 75 epochs. The generative VAE model can be readily coupled with Quantitative Structure–Property Relationship (QSPR) analysis to develop an integrated end-to-end framework for fuel-property prediction and molecular design of novel promising fuels.

fuel design↗