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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Space Research and Technology Program: Program and specific objectives, document approval

A detailed view of the Space Research and Technology program work breakdown structure is provided down to the specific objective level. Goals or objectives at each of these levels are set forth. The specific objective narratives are structured into several parts. First, a short paragraph statement of the specific objective is given. This is followed by a list of subobjectives. A list of targets is then provided for those areas of the specific objective that are amenable to a quantitative description of technical accomplishment and schedule. Fluid and thermal physics, materials and structures, computer science and electronics, space energy conversion, multidisciplinary research, controls and human factors, chemical propulsion, spacecraft systems, transportation systems, platform systems, and spacecraft systems technology comprise the principal research programs.

Source record↗
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

Abstract The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Digital Valve for High Pressure High Flow Applications

To address the challenges, which are involved with the development of flow control valves that can meet the requirements of deep oil wells such as high pressure, high flow rate, limited power and limited space, the authors have conceived a novel design configuration [1]. This design consists of a digitalized flow control valve with multipath and multistage pressure reduction structures. Specifically, the valve is configured as a set of parallel flow paths from the inlet to the outlet. A choke valve controls the total flow rate by digitally opening different paths or different combination of the paths. Each path is controlled by a poppet cap valve basically operated in on-off states. The number of flow states is 2N where N is the number of flow paths. To avoid erosion from sand in the oil and high speed flow, the seal area of the poppet cap valve is located at a distance from the flow inlet away from the high speed flow and the speed is controlled to stay below a predefined erosion safe limit. The path is a multistage structure composed of a set of serial nozzles-expansion chambers that equally distribute the total pressure drop to each stage. The pressure drop of each stage and, therefore, the flow speed at the nozzles and expansion chambers is controlled by the number of stages. The paths have relatively small cross section and could be relatively long for large number of stages and still fit in the strict annular space limit of a typical downhole region of an oil well. The paper will present the design configuration, analysis and preliminary test results.

downhole↗
NASA NTRS2011

AVIATR - Aerial Vehicle for In-situ and Airborne Titan Reconnaissance A Titan Airplane Mission Concept

We describe a mission concept for a stand-alone Titan airplane mission: Aerial Vehicle for In-situ and Airborne Titan Reconnaissance (AVIATR). With independent delivery and direct-to-Earth communications, AVIATR could contribute to Titan science either alone or as part of a sustained Titan Exploration Program. As a focused mission, AVIATR as we have envisioned it would concentrate on the science that an airplane can do best: exploration of Titan's global diversity. We focus on surface geology/hydrology and lower-atmospheric structure and dynamics. With a carefully chosen set of seven instruments-2 near-IR cameras, 1 near-IR spectrometer, a RADAR altimeter, an atmospheric structure suite, a haze sensor, and a raindrop detector-AVIATR could accomplish a significant subset of the scientific objectives of the aerial element of flagship studies. The AVIATR spacecraft stack is composed of a Space Vehicle (SV) for cruise, an Entry Vehicle (EV) for entry and descent, and the Air Vehicle (AV) to fly in Titan's atmosphere. Using an Earth-Jupiter gravity assist trajectory delivers the spacecraft to Titan in 7.5 years, after which the AVIATR AV would operate for a 1-Earth-year nominal mission. We propose a novel 'gravity battery' climb-then-glide strategy to store energy for optimal use during telecommunications sessions. We would optimize our science by using the flexibility of the airplane platform, generating context data and stereo pairs by flying and banking the AV instead of using gimbaled cameras. AVIATR would climb up to 14 km altitude and descend down to 3.5 km altitude once per Earth day, allowing for repeated atmospheric structure and wind measurements all over the globe. An initial Team-X run at JPL priced the AVIATR mission at FY10 $715M based on the rules stipulated in the recent Discovery announcement of opportunity. Hence we find that a standalone Titan airplane mission can achieve important science building on Cassini's discoveries and can likely do so within a New Frontiers budget.

UAV↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1986

Investigations of toughening mechanisms of epoxy resins

Composite material technology was applied to the solid rocket booster by the development of a carbon filament-epoxy resin case which yields a net increase of 4000 lbs. in payload in the shuttle. The question of reusability of the new composite tanks has not yet been answered and will depend on the toughness of the matrix resin. The present study was aimed at providing conditions whereby test specimens of the epoxy resin (EPON/85) and curing agents of systematically varied structures could be produced in a controlled manner. Three sets of conditions were found that might allow the isolation of the structural effects on toughness from the cure effects. The kinetic methods leading to the determination of these conditions are described.

Koenig, T.↗
NASA NTRS1997

Rendering Three-Dimensional Solar Coronal Structures

An X-ray or EUV image of the corona or chromosphere is a 2D representation of an extended 3D complex for which a general inversion process is impossible. A specific model must be incorporated in order to understand the full 3D structure. We approach this problem by modeling a set of optically-thin 3D plasma flux tubes which we render these as synthetic images. The resulting images allow the interpretation of the X-ray/EUV observations to obtain information on (1) the 3D structure of X-ray images, i.e., the geometric structure of the flux tubes, and on (2) the internal structure using specific plasma characteristics, i.e., the physical structure of the flux tubes. The data-analysis technique uses magnetograms to characterize photospheric magnetic fields and extrapolation techniques to form the field lines. Using a new set of software tools, we have generated 3D flux tube structures around these field lines and integrated the plasma emission along the line of sight to obtain a rendered image. A set of individual flux-tube images is selected by a non-negative least-squares technique to Provide a match with an observed X-ray image. The scheme minimizes the squares of the differences between the synthesized image and the observed image with a non-negative constraint on the coefficients of the brightness of the individual flux-tube loops. The derived images are used to determine the specific photospheric foot points and physical data, i.e., scaling laws for densities and loop lengths. The development has led to Computer efficient integration and display software that is compatible for comparison with observations (e.g., Yohkoh SXT data, NIXT, or EIT). This analysis is important in determining directly the magnetic field configuration, which provides the structure of coronal loops, and indirectly the electric currents or waves, which provide the energy for the heating of the plasma. We have used very simple assumptions (i.e., potential magnetic fields and isothermal corona) to provide an initial test of the techniques before complex models are introduced. We have separated the physical and geometric contributions of the emission for a set of flux tubes and concentrated, in this initial study, on the geometric contributions by making approximations to the physical contributions. The initial results are consistent with the scaling laws derived from Yohkoh SXT data.

Gary, G. Allen↗
DOE OSTI2021

The Rise of Neural Networks for Materials and Chemical Dynamics

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. ML-based force fields, trained on large data sets of high-quality electron structure calculations, are particularly attractive due their unique combination of computational efficiency and physical accuracy. This Perspective summarizes some recent advances in the development of neural network-based interatomic potentials. Designing high-quality training data sets is crucial to overall model accuracy. One strategy is active learning, in which new data are automatically collected for atomic configurations that produce large ML uncertainties. Another strategy is to use the highest levels of quantum theory possible. Transfer learning allows training to a data set of mixed fidelity. A model initially trained to a large data set of density functional theory calculations can be significantly improved by retraining to a relatively small data set of expensive coupled cluster theory calculations. These advances are exemplified by applications to molecules and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

The computational structural mechanics testbed architecture. Volume 2: Directives

This is the second of a set of five volumes which describe the software architecture for the Computational Structural Mechanics Testbed. Derived from NICE, an integrated software system developed at Lockheed Palo Alto Research Laboratory, the architecture is composed of the command language (CLAMP), the command language interpreter (CLIP), and the data manager (GAL). Volumes 1, 2, and 3 (NASA CR's 178384, 178385, and 178386, respectively) describe CLAMP and CLIP and the CLIP-processor interface. Volumes 4 and 5 (NASA CR's 178387 and 178388, respectively) describe GAL and its low-level I/O. CLAMP, an acronym for Command Language for Applied Mechanics Processors, is designed to control the flow of execution of processors written for NICE. Volume 2 describes the CLIP directives in detail. It is intended for intermediate and advanced users.

Felippa, Carlos A.↗
DOE OSTI2025

Exploring nuclear structure with multiparticle azimuthal correlations at the LHC

Details of the nuclear structure of Xe, such as the quadrupole deformation and the nuclear diffuseness, are studied by extensive measurements of anisotropic-flow-related observables in Xe–Xe collisions at a centre-of-mass energy per nucleon pair $\sqrt{s_{NN}}$ = 5.44 TeV with the ALICE detector at the LHC. The results are compared with those from Pb–Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV for a baseline, given that the 208 Pb nucleus exhibits a very weak deformation. Furthermore, comprehensive comparisons are performed with a state-of-the-art hybrid model using IP-Glasma+MUSIC+UrQMD. It is found that among various IP-Glasma+MUSIC+UrQMD calculations with different values of nuclear parameters, the one using a nuclear diffuseness parameter of α 0 = 0.492 and a nuclear quadrupole deformation parameter of β 2 = 0.207 provides a better description of the presented flow measurements. These studies represent the first systematic exploration of nuclear structure at TeV energies, utilizing a comprehensive set of anisotropic flow observables. The measurements serve as a critical experimental benchmark for rigorously testing the interplay between nuclear structure inputs and heavy-ion theoretical models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Stabilizing hierarchical compensation for locally controlled large flexible structures

A two-level hierarchical control strategy is proposed for large flexible space structures. The lower level consists of a set of local controllers. The higher level is a stabilizing compensator to account for any instabilities caused by controller-structure interaction with unmodeled dynamics. The advantage of this hierarchical strategy is that the lower level can be designed to meet the performance requirements, and the higher level can be designed independently to produce overall stability.

Das, B.↗
NASA NTRS1988

Thermoviscoplastic nonlinear constitutive relationships for structural analysis of high-temperature metal matrix composites

A set of thermoviscoplastic nonlinear constitutive relationships (TVP-NCR) developed for application to high-temperature metal matrix composites (HT-MMC) is described. The structural response of a turbine blade, made from fiber-reinforced superalloy HT-MMC and subject to representative loading conditions, is evaluated. Results indicate that this set of TVP-NCR is computationally effective.

Chamis, Christos C.↗
DOE OSTI2023

Aminobenzoic Acid Derivatives Obstruct Induced Fit in the Catalytic Center of the Ribosome

The Escherichia coli (E. coli) ribosome can incorporate a variety of non-l-α-amino acid monomers into polypeptide chains in vitro but with poor efficiency. Although these monomers span a diverse set of compounds, there exists no high-resolution structural information regarding their positioning within the catalytic center of the ribosome, the peptidyl transferase center (PTC). Thus, details regarding the mechanism of amide bond formation and the structural basis for differences and defects in incorporation efficiency remain unknown. Within a set of three aminobenzoic acid derivatives–3-aminopyridine-4-carboxylic acid (Apy), ortho-aminobenzoic acid (oABZ), and meta-aminobenzoic acid (mABZ)–the ribosome incorporates Apy into polypeptide chains with the highest efficiency, followed by oABZ and then mABZ, a trend that does not track with the nucleophilicity of the reactive amines. Here, we report high-resolution cryo-EM structures of the ribosome with each of these three aminobenzoic acid derivatives charged on tRNA bound in the aminoacyl-tRNA site (A-site). The structures reveal how the aromatic ring of each monomer sterically blocks the positioning of nucleotide U2506, thereby preventing rearrangement of nucleotide U2585 and the resulting induced fit in the PTC required for efficient amide bond formation. They also reveal disruptions to the bound water network that is believed to facilitate formation and breakdown of the tetrahedral intermediate. Together, the cryo-EM structures reported here provide a mechanistic rationale for differences in reactivity of aminobenzoic acid derivatives relative to l-α-amino acids and each other and identify stereochemical constraints on the size and geometry of non-monomers that can be accepted efficiently by wild-type ribosomes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effects of Structure and Filler on Mechanical Properties of Direct Ink Write Pads

Direct ink write (DIW) is an extrusion-based additive manufacturing (AM) technique that enables superior repeatability, property tuning, and material customization compared to conventional manufacturing techniques. In this research, DIW is used to print silicone foams that must accommodate the thermal expansion of other parts. This expansion requires the material to compress and subsequently recover. Thus, a key performance metric is reducing “compression set” or deformation retained after the material is compressed. For this study, a silicone resin was compounded with a getter material, 1,4 bis(phenylethynyl)benzene (DEB) mixed with carbon-supported palladium (Pd) and printed in a variety of lattice structures. The effects of the filler and print structure on compression set were evaluated. In these experiments, 0.5-inch diameter coupons were compressed to 75% their original height for 22 hours at 70 °C, and the change in height calculated at intervals between 30 minutes and 24 hours post-decompression. The unfilled pad with skin layers had the best compression set at 0.7% ± 0.1%, followed by the unfilled pad with no skins at 1.5% ± 0.1%. As the number of filled layers increased, compression set performance degraded. The pad with filled skin layers and all filled layers had a compression set of 7.0% ± 0.3%. Additionally, the reaction extent of DEB was found to increase compression set, possibly due to the diffusion of DEB to the surface at elevated temperatures. Imaging reveals extensive crystallization at the surface. These investigations show that a non-reinforcing filler will increase compression set but that print structure can mitigate this effect.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Development of molecular cluster models to probe pyrite surface reactivity

Abstract The recent discovery that anaerobic methanogens can reductively dissolve pyrite and utilize dissolution products as a source of iron and sulfur to meet their biosynthetic demands for these elements prompted the development of atomic‐scale nanoparticle models, as maquettes of reactive surface sites, for describing the fundamental redox steps that take place at the mineral surface during reduction. The given report describes our computational approach for modeling n (FeS 2 ) nanoparticles originated from mineral bulk structure. These maquettes contain a comprehensive set of coordinatively unsaturated Fe (II) sites that are connected via a range of persulfide (S 2 2− ) ligation. In addition to the specific maquettes with n = 8, 18, and 32 FeS 2 units, we established guidelines for obtaining low‐energy structures by considering the pattern of ionic, covalent, and magnetic interactions among the metal and ligand sites. The developed models serve as computational nano‐reactors that can be used to describe the reductive dissolution mechanism of pyrite to better understand the reactive sites on the mineral, where microbial extracellular electron‐transfer reactions can occur.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Moving closer to experimental level materials property prediction using AI

Abstract While experiments and DFT-computations have been the primary means for understanding the chemical and physical properties of crystalline materials, experiments are expensive and DFT-computations are time-consuming and have significant discrepancies against experiments. Currently, predictive modeling based on DFT-computations have provided a rapid screening method for materials candidates for further DFT-computations and experiments; however, such models inherit the large discrepancies from the DFT-based training data. Here, we demonstrate how AI can be leveraged together with DFT to compute materials properties more accurately than DFT itself by focusing on the critical materials science task of predicting “formation energy of a material given its structure and composition”. On an experimental hold-out test set containing 137 entries, AI can predict formation energy from materials structure and composition with a mean absolute error (MAE) of 0.064 eV/atom; comparing this against DFT-computations, we find that AI can significantly outperform DFT computations for the same task (discrepancies of $$>0.076$$ > 0.076 eV/atom) for the first time.

36 MATERIALS SCIENCE↗