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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Effect of Part Size, Displacement Rate, and Aging on Compressive Properties of Elastomeric Parts of Different Unit Cell Topologies Formed by Vat Photopolymerization Additive Manufacturing

Due to its ability to achieve geometric complexity at high resolution and low length scales, additive manufacturing (AM) has increasingly been used for fabricating cellular structures (e.g., foams and lattices) for a variety of applications. Specifically, elastomeric cellular structures offer tunability of compliance as well as energy absorption and dissipation characteristics. However, there are limited data available on compression properties for printed elastomeric cellular structures of different designs and testing parameters. In this work, the authors evaluate how unit cell topology, part size, the rate of compression, and aging affect the compressive response of polyurethane-based simple cubic, body-centered, and gyroid structures formed by vat photopolymerization AM. Finite element simulations incorporating hyperelastic and viscoelastic models were used to describe the data, and the simulated results compared well with the experimental data. Of the designs tested, only the parts with the body-centered unit cell exhibited differences in stress–strain responses at different part sizes. Of the compression rates tested, the highest displacement rate (1000 mm/min) often caused stiffer compressive behavior, indicating deviation from the quasi-static assumption and approaching the intermediate rate response. The cellular structures did not change in compression properties across five weeks of aging time, which is desirable for cushioning applications. This work advances knowledge on the structure–property relationships of printed elastomeric cellular materials, which will enable more predictable compressive properties that can be traced to specific unit cell designs.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Solving differential equations using deep neural networks

Recent work on solving partial differential equations (PDEs) with deep neural networks (DNNs) is presented. The paper reviews and extends some of these methods while carefully analyzing a fundamental feature in numerical PDEs and nonlinear analysis: irregular solutions. First, the Sod shock tube solution to the compressible Euler equations is discussed and analyzed. This analysis includes a comparison of a DNN-based approach with conventional finite element and finite volume methods, and demonstrates that the DNN is competitive in terms of degrees of freedom required for a given accuracy. Further, the DNN-based approach is extended to consider performance improvements and simultaneous parameter space exploration. Next, a shock solution to compressible magnetohydrodynamics (MHD) is solved for, and used in a scenario where experimental data is utilized to enhance a PDE system that is a priori insufficient to validate against the observed/experimental data. This is accomplished by enriching the model PDE system with source terms that are then inferred via supervised training with synthetic experimental data. The resulting DNN framework for PDEs enables straightforward system prototyping and natural integration of large data sets (be they synthetic or experimental), all while simultaneously enabling single-pass exploration of an entire parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1992

User's Manual for Aerofcn: a FORTRAN Program to Compute Aerodynamic Parameters

The computer program AeroFcn is discussed. AeroFcn is a utility program that computes the following aerodynamic parameters: geopotential altitude, Mach number, true velocity, dynamic pressure, calibrated airspeed, equivalent airspeed, impact pressure, total pressure, total temperature, Reynolds number, speed of sound, static density, static pressure, static temperature, coefficient of dynamic viscosity, kinematic viscosity, geometric altitude, and specific energy for a standard- or a modified standard-day atmosphere using compressible flow and normal shock relations. Any two parameters that define a unique flight condition are selected, and their values are entered interactively. The remaining parameters are computed, and the solutions are stored in an output file. Multiple cases can be run, and the multiple case solutions can be stored in another output file for plotting. Parameter units, the output format, and primary constants in the atmospheric and aerodynamic equations can also be changed.

Conley, Joseph L.↗
DOE OSTI2025

DESI 2024 V: Full-Shape galaxy clustering from galaxies and quasars

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range 0.1 < z < 2.1 divided into six redshift bins over a ∼ 7,500 square degree footprint, from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). By fitting the full power spectrum, we extend previous DESI DR1 baryon acoustic oscillation (BAO) measurements to include redshift-space distortions and signals from the matter-radiation equality scale. For the first time, this Full-Shape analysis is blinded at the catalogue-level to avoid confirmation bias and the systematic errors are accounted for at the two-point clustering level, which automatically propagates them into any cosmological parameter. When analyzing the data in terms of compressed model-agnostic variables, we obtain a combined precision of 4.7% on the amplitude of the redshift space distortion (RSD) signal reaching a similar precision with just one year of DESI data than with twenty years of observation from the previous generation survey. We also analyze the data to directly constrain the cosmological parameters within the ΛCDM model using perturbation theory and combine this information with the reconstructed DESI DR1 galaxy BAO. Using a Big Bang Nucleosynthesis Gaussian prior on the baryon density parameter, ω b , and a weak Gaussian prior on the spectral index, n s , we constrain the matter density is Ω m = 0.296±0.010 and the Hubble constant H 0 = (68.63 ± 0.79)[km s -1 Mpc -1 ]. Additionally, we measure the amplitude of clustering σ 8 = 0.841±0.034. The DESI DR1 galaxy clustering results are in agreement with the ΛCDM model based on general relativity with parameters consistent with those from Planck. The cosmological interpretation of these results in combination with DESI DR1 Ly-α forest data and external datasets are presented in the companion paper [1].

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Matter searches with photons at the LHC

We unveil blind spot regions in dark matter (DM) direct detection (DMDD), for weakly interacting massive particles with a mass around a few hundred GeV that may reveal interesting photon signals at the LHC. We explore a scenario where the DM primarily originates from the singlet sector within the Z 3 -symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). A novel DMDD spin-independent blind spot condition is revealed for singlino-dominated DM, in cases where the mass parameters of the higgsino and the singlino-dominated lightest supersymmetric particle (LSP) exhibit opposite relative signs (i.e., κ < 0), emphasizing the role of nearby bino and higgsino-like states in tempering the singlino-dominated LSP. Additionally, proximate bino and/or higgsino states can act as co-annihilation partner(s) for singlino-dominated DM, ensuring agreement with the observed relic abundance of DM. Remarkably, in scenarios involving singlino-higgsino co-annihilation, higgsino-like neutralinos can distinctly favor radiative decay modes into the singlino-dominated LSP and a photon, as opposed to decays into leptons/hadrons. In exploring this region of parameter space within the singlino-higgsino compressed scenario, we study the signal associated with at least one relatively soft photon alongside a lepton, accompanied by substantial missing transverse energy (E T ) and a hard initial state radiation jet at the LHC. In the context of singlino-bino co-annihilation, the bino state, as the next-to-LSP, exhibits significant radiative decay into a soft photon and the LSP, enabling the possible exploration at the LHC through the triggering of this soft photon alongside large E T and relatively hard leptons/jets resulting from the decay of heavier higgsino-like states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Two-temperature effects in Hall-MHD simulations of the HIT-SI experiment

A two-temperature Hall-magnetohydrodynamic (Hall-MHD) model, which evolves the electron and ion temperatures separately, is implemented in the PSI-Tet 3D MHD code and used to model plasma dynamics in the Helicity Injected Torus–Steady Inductive (HIT-SI) experiment. The two-temperature model is utilized for HIT-SI simulations in both the PSI-Tet and NIMROD codes at a number of different injector frequencies in the 14.5–68.5 kHz range. At all frequencies, the NIMROD two-temperature model results in increased toroidal current, lower chord-averaged density, higher average temperatures, outward radial shift of the current centroid, and axial symmetrization of the current centroid, relative to the single-temperature NIMROD simulations. The two-temperature PSI-Tet model illustrates similar trends, but at high frequency operation, it exhibits lower electron temperature, smaller toroidal current, and decreased axial symmetrization with respect to the single-temperature PSI-Tet model. With all models, average temperatures and toroidal currents increase with the injector frequency. Power balance and heat fluxes to the wall are calculated for the two-temperature PSI-Tet model and illustrate considerable viscous and compressive heating, particularly at high injector frequency. Parameter scans are also presented for artificial diffusivity, wall temperature, and density. Both artificial diffusivity and the density boundary condition significantly modify the plasma density profiles, leading to larger average temperatures, toroidal current, and relative density fluctuations at low densities. As a result, a low density simulation achieves sufficiently high current gain (G > 5) to generate significant volumes of closed flux lasting 1–2 injector periods.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Electronic structure and two-band superconductivity in unconventional high- T c cuprates Ba 2 CuO 3+δ

We report that the recently discovered cuprate superconductor Ba 2 CuO 3+δ exhibits a high T c ≃73 K at δ ≃ 0.2. The polycrystal grown under high pressure has a structure similar to La 2 CuO 4 but with dramatically different lattice parameters due to the CuO 6 octahedron compression. The crystal field in the compressed Ba 2 CuO 4 leads to an inverted Cu 3d e g complex with the d x 2 -y 2 orbital sitting below the d 3z 2 - r 2 and an electronic structure highly unusual compared to the conventional cuprates. We construct a two-orbital Hubbard model for the Cu d 9 state at hole doping x = 2δ and study the orbital-dependent strong correlation and superconductivity. For the undoped case at x = 0 , we found that strong correlation drives an orbital-polarized Mott-insulating state with the spin-1/2 moment of the localized d 3z 2 - r 2 orbital. In contrast to the single-band cuprates where superconductivity is suppressed in the overdoped regime, hole doping the two-orbital Mott insulator leads to orbital-dependent correlations and the robust spin and orbital exchange interactions produce a high-T c antiphase d-wave superconductor even in the heavily doped regime at x = 0.4 . We conjecture that Ba 2 CuO 3+δ realizes mixtures of such heavily hole-doped superconducting Ba 2 CuO 4 and disordered Ba 2 CuO 3 chains in a single-layer or predominately separated bilayer structure. Our findings suggest that unconventional cuprates with liberated orbitals as doped two-band Mott insulators can be a direction for realizing high-T c superconductivity with enhanced transition temperature T c .

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterization of Tri-lab β-Tin (Sn)

This report documents details of the microstructure and mechanical properties of -tin (Sn), that is used in the Tri-lab (Los Alamos National Laboratory (LANL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL)) collaboration project on Multi-phase Tin Strength. We report microstructural features detailing the crystallographic texture and grain morphology of as-received -tin from electron back scatter diffraction (EBSD). Temperature and strain rate dependent mechanical behavior was investigated by multiple compression tests at temperatures of 200K to 400K and strain rates of 0.0001 /s to 100 /s. Tri-lab tin showed significant temperature and strain rate dependent strength with no significant plastic anisotropy. A sample to sample material variation was observed from duplicate compression tests and texture measurements. Compression data was used to calibrate model parameters for temperature and rate dependent strength models, Johnson-Cook (JC), Zerilli-Armstrong (ZA) and Preston-Tonks-Wallace (PTW) strength models.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Role of physical properties of liquids in cavitation erosion

The dependence of erosion rates on the ambient temperature of water is discussed. The assumption that the gas inside the bubble is compressed adiabatically during collapse gives better agreement with experiments than the assumption that the gas is isothermally compressed. Acoustic impedance is an important liquid parameter that governs the erosion intensity in vibratory devices. The investigation reveals that the major physical properties of liquids governing the intensity of erosion include density, sound speed, surface tension, vapor pressure, gas content, and nuclei distribution.

Thiruvengadam, A.↗
NASA NTRS2002

Microgravity: Molecular Dynamics Simulations at the NCCS Probe the Behavior of Liquids in Low Gravity

The life of the very small, whether in something as complicated as a human cell or as simple as a drop of water, is of fundamental scientific interest: By knowing how a tiny amount of material reacts to changes in its environment, scientists maybe able to answer questions about how a bulk of material would react to comparable changes. NASA is in the forefront of computational research into a broad range of basic scientific questions about fluid dynamics and the nature of liquid boundary instability. For example, one important issue for the space program is how drops of water and other materials will behave in the low-gravity environment of space and how the low gravity will affect the transport and containment of these materials. Accurate prediction of this behavior is among the aims of a set of molecular dynamics experiments carried out on the NCCSs Cray supercomputers. In conventional computational studies of materials, matter is treated as continuous - a macroscopic whole without regard to its molecular parts - and the behavior patterns of the matter in various physical environments are studied using well-established differential equations and mathematical parameters based on physical properties such as compressibility density, heat capacity, and vapor pressure of the bulk material.

Source record↗
DOE OSTI2022

Calibration method for a spectral computerized tomography system

A calibration method for an x-ray computerized tomography system and a method of tomographic reconstruction are provided. The calibration method includes steps of measuring at least one point spread function (PSF) at each of a plurality of points, compressing each PSF, and in one or more storing operations, storing the compressed PSFs in a computer-accessible storage medium. The PSF measurements are made in a grid of calibration points in a field of view (FOV) of the system. In the measuring step, an absorber is positioned at each of the calibration points, and an x-ray projection is taken at least once at each of those absorber positions. In the method of tomographic image reconstruction, projection data from an x-ray tomographic projection system are input to an iterative image reconstruction algorithm. The algorithm retrieves and utilizes a priori system information (APSI) The APSI comprises comprising point spread functions (PSFs) of all voxels in a voxelization of the field of view that are compressed in the form of vectors of parameters. For utilization, each retrieved vector of parameters is decompressed so as to generate a discretized PSF.

Jimenez, Jr., Edward Steven↗
DOE OSTI2021

Consumable development to tailor residual stress in parts fabricated using directed energy deposition processes

Distortion and residual stresses are major challenges that limit the ability to fabricate large scale structures using Additive Manufacturing (AM). Researchers worldwide are evaluating techniques to induce compressive residual stress in the parts via intermittent rolling. While reasonable success has been documented, the idea of lowering the martensite start temperature to induce compressive stresses has not been evaluated in the context of AM, despite demonstrated success by the welding community. This study validates the hypothesis that, by a proper selection of materials and process parameters, one may effectively reduce distortion and induce a compressive residual stress in AM parts. Using neutron diffraction to measure residual stresses in parts, we demonstrate that, in addition to selection of the correct materials, the inter pass temperature plays a major role in controlling the residual stress evolution. The observations relating to the residual stresses are rationalized based on a microstructural evolution in these samples. Based on this preliminary study, a strategy to fabricate large structures with minimal distortion and residual stress is outlined.

36 MATERIALS SCIENCE↗
NASA NTRS1943

Description of stress-strain curves by three parameters

A simple formula is suggested for describing the stress-strain curve in terms of three parameters; namely, Young's modulus and two secant yield strengths. Dimensionless charts are derived from this formula for determining the stress-strain curve, the tangent modulus, and the reduced modulus of a material for which these three parameters are given. Comparison with the tensile and compressive data on aluminum-alloy, stainless-steel, and carbon-steel sheet in NACA Technical Note No. 840 indicates that the formula is adequate for most of these materials. The formula does not describe the behavior of alclad sheet, which shows a marked change in slope at low stress. It seems probable that more than three parameters will be necessary to represent such stress-strain curves adequately.

Ramberg, Walter↗
NASA NTRS2015

Simplified Methodology to Estimate the Maximum Liquid Helium (LHe) Cryostat Pressure from a Vacuum Jacket Failure

The aircraft-based Stratospheric Observatory for Infrared Astronomy (SOFIA) is a platform for multiple infrared astronomical observation experiments. These experiments carry sensors cooled to liquid helium temperatures. The liquid helium supply is contained in large (i.e., 10 liters or more) vacuum-insulated dewars. Should the dewar vacuum insulation fail, the inrushing air will condense and freeze on the dewar wall, resulting in a large heat flux on the dewar's contents. The heat flux results in a rise in pressure and the actuation of the dewar pressure relief system. A previous NASA Engineering and Safety Center (NESC) assessment provided recommendations for the wall heat flux that would be expected from a loss of vacuum and detailed an appropriate method to use in calculating the maximum pressure that would occur in a loss of vacuum event. This method involved building a detailed supercritical helium compressible flow thermal/fluid model of the vent stack and exercising the model over the appropriate range of parameters. The experimenters designing science instruments for SOFIA are not experts in compressible supercritical flows and do not generally have access to the thermal/fluid modeling packages that are required to build detailed models of the vent stacks. Therefore, the SOFIA Program engaged the NESC to develop a simplified methodology to estimate the maximum pressure in a liquid helium dewar after the loss of vacuum insulation. The method would allow the university-based science instrument development teams to conservatively determine the cryostat's vent neck sizing during preliminary design of new SOFIA Science Instruments. This report details the development of the simplified method, the method itself, and the limits of its applicability. The simplified methodology provides an estimate of the dewar pressure after a loss of vacuum insulation that can be used for the initial design of the liquid helium dewar vent stacks. However, since it is not an exact tool, final verification of the dewar pressure vessel design requires a complete, detailed real fluid compressible flow model of the vent stack. The wall heat flux resulting from a loss of vacuum insulation increases the dewar pressure, which actuates the pressure relief mechanism and results in high-speed flow through the dewar vent stack. At high pressures, the flow can be choked at the vent stack inlet, at the exit, or at an intermediate transition or restriction. During previous SOFIA analyses, it was observed that there was generally a readily identifiable section of the vent stack that would limit the flow – e.g., a small diameter entrance or an orifice. It was also found that when the supercritical helium was approximated as an ideal gas at the dewar condition, the calculated mass flow rate based on choking at the limiting entrance or transition was less than the mass flow rate calculated using the detailed real fluid model2. Using this lower mass flow rate would yield a conservative prediction of the dewar’s wall heat flux capability. The simplified method of the current work was developed by building on this observation.

Ungar, Eugene K.↗
NASA NTRS1993

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗
NASA NTRS1971

Factors affecting the thermal shock behavior of yttria stabilized hafnia based graphite and tungsten composites.

Hafnia-based composites containing either graphite or tungsten were investigated as rocket nozzle throat inserts in solid propellant rocket engines. The thermal shock resistance of these materials is considered in terms of macroscopic thermal conductivity, thermal expansion, modulus of elasticity, and compressive fracture stress. The effect of degree of hafnia stabilization, density, and graphite or tungsten content upon these parameters is discussed. The variation of the ratio of elastic modulus to compressive fracture stress with density and its effect upon thermal shock resistance of these materials are discussed in detail.

Lineback, L. D.↗
DOE OSTI2021

Utilizing computer vision and artificial intelligence algorithms to predict and design the mechanical compression response of direct ink write 3D printed foam replacement structures

Additive Manufacturing (AM) of porous polymeric materials, such as foams, recently became a topic of intensive research due their unique combination of low density, impressive mechanical properties, and stress dissipation capabilities. Conventional methods for fabricating foams rely on complex and stochastic processes, making it challenging to achieve precise architectural control of structured porosity. In contrast, AM provides access to a wide range of printable materials, where precise spatial control over structured porosity can be modulated during the fabrication process enabling the production of foam replacement structures (FRS). Current approaches for designing FRS are based on intuitive understanding of their properties or an extensive number of finite element method (FEM) simulations. These approaches, however, are computationally expensive and time consuming. As such, in this work, we present a novel methodology for determining the mechanical compression response of direct ink write (DIW) 3D printed FRS using a simple cross-sectional image. By obtaining measurement data for a relatively small number of samples, an artificial neural network (ANN) was trained, and a computer vision algorithm was used to make inferences about foam compression characteristics from a single cross-sectional image. Finally, a genetic algorithm (GA) was used to solve the inverse design problem, generating the AM printing parameters that an engineer should use to achieve a desired compression response from a DIW printed FRS. The methods developed herein present an avenue for entirely autonomous design and analysis of additively manufactured structures using artificial intelligence.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory

Metal oxide-based Resistive Random-Access Memory (RRAM) exhibits multiple resistance states, arising from the activation/deactivation of a conductive filament (CF) inside a switching layer. Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM. Here a phase-field model is developed, based on materials properties determined by ab initio calculations, to investigate the role of electrical bias, heat transport and defect-induced Vegard strain in the resistive switching behavior, using MO 2- x systems such as HfO 2- x as a prototypical model system. It successfully captures the CF formation and resultant bipolar resistive switching characteristics. High-throughput simulations are performed for RRAMs with different material parameters to establish a dataset, based on which a compressed-sensing machine learning is conducted to derive interpretable analytical models for device performance (current on/off ratio and switching time) metrics in terms of key material parameters (electrical and thermal conductivities, Vegard strain coefficients). These analytical models reveal that optimal performance (i.e., high current on/off ratio and low switching time) can be achieved in materials with a low Lorenz number, a fundamental material constant. This work provides a fundamental understanding to the resistive switching in RRAM and demonstrates a computational data-driven methodology of materials selection for improved RRAM performance, which can also be applied to other electro-thermo-mechanical systems.

36 MATERIALS SCIENCE↗