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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systems, devices, and methods for contact measurement and modulation of material properties

Materials and devices are provided for the sensing and manipulation of biomechanical and physiochemical properties of tissues or tissue surfaces. Examples of use include soft tissues such as skin or adipose tissues or more dense tissues such as muscle or heart or dense tissues such as bone. The materials and devices provide for in vivo measurements of biomechanical properties at the tissue surface, e.g. near surface regions of the epidermis or dermis or underlying structures. The devices can be non-invasive and/or non-destructive to the material and, especially for the biomaterials, can be biocompatible and/or biodegradable. The materials and devices can use ultrathin, stretchable networks of mechanical actuators and sensors constructed with nanoribbons of piezoelectric materials.

Dagdeviren, Canan↗
DOE OSTI2024

Spacecraft surface charging as a function of material properties

Spacecraft material behavior plays a very important role in space missions. Spacecraft immersed in plasma get charged by absorbing plasma particles and by emitting electrons from spacecraft surfaces via photoelectron and secondary electron emission. Spacecraft charging depends heavily on material properties such as work function, secondary electron yield, dielectric constant, and electric conductivity among other. Material properties are typically assumed to be static in charging models. However, it is well known that this is not the case in space. This makes spacecraft charging predictions very challenging. Material properties are well characterized before the spacecraft is put in orbit through characterization in the lab under clean conditions. However, once in space, material properties change due to the harsh and very dynamic space environment. We present a new capability to predict material behavior in space from first-principles modeling. The ongoing effort seeks to couple material models, density functional theory (DFT) and molecular dynamic (MD) codes, with environment models, plasma kinetic codes. This preliminary study will show results of surface charging as a function of material work function, dielectric constant, and conductivity.

36 MATERIALS SCIENCE↗
NASA NTRS1994

Prediction of natural frequency variability due to uncertainty in material properties

Composite materials are widely used in various types of modern engineering structures. Traditional studies on composite structures have been based on the assumption that the material properties of the composites are characterized by a priori known elastic moduli, and no uncertainties of these moduli have been considered. However, the composite materials are invariably subject to a certain amount of scatter in their measured elastic moduli. To a large extent, the properties of composite materials are dependent on the fabrication process. But even the composite materials manufactured by the same process demonstrate differences in their elastic properties. This paper proposes a new, non-probabilistic method to predict the variability in the natural frequencies of the composite cylindrical shell, resulting from the unavoidable scatter in elastic moduli. The available measurements of elastic moduli are fitted by the four-dimensional uncertainty ellipsoid. The upper and lower bounds of the natural frequency are derived. With these bounds, designers will have a better understanding of the real dynamic behavior of the structure.

Li, Y. W.↗
DOE OSTI2020

NSUF Rad-AFM: Nanoscale Material Property Measurements of Radioactive Materials

Many advances in our understanding of radiation induced damage mechanisms are due to imaging techniques including transmission electron microscopy and scanning electron microscopy. However, the effect of these radiation induced defects characterized on the atomic to micrometer scale are often interpreted by changes in bulk material properties measured in the hundreds of microns to cm scale. In order to bridge this gap, additional tools need to be developed for use with radioactive materials. To this end a new Asylum Infinity multimodal atomic force microscope (AFM) has been added to the library of Nuclear Science User Facility (NSUF) instruments for use on radioactive materials. This instrument is housed in the Radiochemical Processing Laboratory (RPL) which is a Department of Energy Hazard Category II Non-Reactor Nuclear Facility operated by Battelle at Pacific Northwest National Laboratory. This allows for the handling, sample preparation, and characterization of highly radioactive materials providing a unique capability to understand fundamental properties of nuclear materials and the effects of radiation induced damage.

Riechers, Shawn L.↗
DOE OSTI2023

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system’s Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models have been developed and implemented on high-performance computing (HPC) architectures. In this paper, we describe two ML methods (linear mixing model and HydraGNN) as surrogates for first principles density functional theory (DFT) calculations with classical MC simulations. These two surrogate models are used to learn the dependence of target physical properties from complex compositions and interactions of their constituents. We present the predictive performance of these two surrogate models with respect to their complexity while avoiding the danger of overfitting the model. An important aspect of our approach is the periodic retraining with newly generated first principles data based on the progressive exploration of the system’s phase space by the MC simulation. The numerical results show that HydraGNN model attains superior predictive performance compared to the linear mixing model for magnetic alloy materials.

Eisenbach, Markus↗
NASA NTRS1984

Materials properties data base computerization

Material property data plays a key role in the design of jet engine components. Consistency, accuracy and efficient use of material property data is of prime importance to the engineering community. The system conception, development, implementation, and future plans for computer software that captures the Material Properties Handbook into a scientific data base are described. The engineering community is given access to raw data and property curves, display of multiple curves for material evaluation and selection, direct access by design analysis computer programs, display of the material specification, and a historical repository for the material evolution. The impact of this activity includes significant productivity gains and cost reductions; all users have access to the same information nd provides consistent, rapid response to the needs of the engineering community. Future plans include incorporating the materials properties data base into a network environment to access information from other data bases and download information to engineering work stations.

Baur, R. G.↗
NASA NTRS2001

Upgrades to the TPSX Material Properties Database

The TPSX Material Properties Database is a web-based tool that serves as a database for properties of advanced thermal protection materials. TPSX provides an easy user interface for retrieving material property information in a variety of forms, both graphical and text. The primary purpose and advantage of TPSX is to maintain a high quality source of often used thermal protection material properties in a convenient, easily accessible form, for distribution to government and aerospace industry communities. Last year a major upgrade to the TPSX web site was completed. This year, through the efforts of researchers at several NASA centers, the Office of the Chief Engineer awarded funds to update and expand the databases in TPSX. The FY01 effort focuses on updating correcting the Ames and Johnson thermal protection materials databases. In this session we will summarize the improvements made to the web site last year, report on the status of the on-going database updates, describe the planned upgrades for FY02 and FY03, and provide a demonstration of TPSX.

Squire, T. H.↗
DOE OSTI2026

Coupling Microstructural Evolution Simulations to Material Property Degradation Predictions for Plasma-Facing Materials

Reliable material performance is required for plasma-facing material (PFM) candidates. Previous research has shown that plasma and neutron radiation exposure induces microstructural changes in PFMs; changes in thermal and electrical conductivities and in material hardening and embrittlement were also observed after neutron irradiation. These material property changes will negatively impact the performance of the PFMs in a fusion reactor. Despite the well-known connection between material microstructure, properties, and performance, there is a need for validated modeling capabilities connecting PFM property degradation with microstructural evolution under fusion-relevant conditions. We are developing a simulation capability to couple plasma-induced microstructural evolution to material property degradation. Our approach relies on deliberate mapping between individual simulation models and experimental characterization for validation. The open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) software was used for this simulation capability development. A MOOSE phase-field model was coupled with the cluster dynamics code, Xolotl, to predict microstructural evolution. Microstructure characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), and laser scanning confocal microscopy (LSCM) are used to validate these microstructural evolution simulations. Calculation of thermal and electrical conductivities with first principles simulations was performed for bulk material and for grain boundaries; these results are used within MOOSE models to calculate effective thermal and electrical conductivities as a function of grain characteristics. Thermoreflectance and four-probe techniques were employed to measure the thermal and electrical conductivities, respectively. A MOOSE crystal plasticity model was adapted to predict microstructure-sensitive deformation behavior, and X-ray diffraction (XRD) was used to collect bulk dislocation density data for validation. After individual simulation validation, these models are coupled to predict material property changes resulting from plasma exposure. We focused here on an experimental design to emphasize the separate effects of moderate thermal loads and plasma exposure using tungsten. Annealing of tungsten was performed under a protective environment for temperatures ranging from 500 C to 1500 C. The plasma exposure was completed in the Tritium Plasma Experiment at Idaho National Laboratory under a deuterium flux of 1e22 D/m^2-s. This incremental approach is employed to build confidence in the modeling capability: separate-effects tests ensure that the models capture key mechanisms from single environmental conditions before predicting PFM property degradation under combined loads. We will show our early results from coupling these simulation models to predict PFM property changes from microstructural evolution. Comparisons of the simulation results with preliminary validation data will be discussed.

36 - MATERIALS SCIENCE↗
DOE OSTI2019

A Brief Note About Scaling Material Properties

Often tables of material properties are computed for a specific chemical or isotopic composition. When we use those pre-computed tables to model real experiments, there can be differences between the as-built material compositions and the pre-computed tables. This document describes several ways that we can scale these tables to account for this. We typically scale every lookup we make into the table in an attempt to account for dynamic changes in material composition over the coarse of the calculation. Even if the compositions are not changing dynamically in the calculation, these same scalings can be used to adjust the material properties from a near by composition to the as-built composition.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1985

Design of test specimens and procedures for generating material properties of Douglas fir/epoxy laminated wood composite material: With the generation of baseline data at two environmental conditions

In support of the design of wind turbine generator airfoils/blades utilizing Douglas Fir/West System Epoxy laminated composite material, a program was undertaken to define pertinent material properties utilizing small scale test specimens. Task 1 was the development of suitable monotonic tension, compression, short beam shear and full reversed cyclic specimen designs and the companion grips and testing procedures. Task 2 was the generation of the material properties at two environmental conditions utilizing the specimens and procedures developed in Task 1. The monotonic specimens and procedures generated results which compare favorably with other investigators while the cyclic results appear somewhat conservative. Adding moisture and heat or scarf joints degraded the monotonic performance but had a more nebulus effect with cyclic loading.

Johnson, Paul E.↗
NASA NTRS1983

Effects of long duration exposure to simulated space environment on nonmetallic materials properties

Nonmetallic materials specimens from the Viking program were tested in situ invacuo after continuous thermal vacuum exposure from 1971/1972 to the present. Eleven tests were done on appropriate specimens of 30 materials; however, no single material received all the tests. Some specimens also were exposed to 1 or 2.5 MeV electrons at differing fluences before testing. Baseline exposure data is reported for graphite/epoxy specimens that were exposed to vacuum since 1974. These materials were transferred to the thermal vacuum storage facility for future in situ testing and irradiation. Thin G/E specimens were tensile tested after thermal-vacuum cycling exposure. Photomicrographic examinations and SEM analyses were done on the failed specimens.

Peacock, C. L., Jr.↗
DOE OSTI2025

Adaptive Interface-PINNs (AdaI-PINNs) for inverse problems: Determining material properties for heterogeneous systems

Here, we determine spatially varying discontinuous material properties using a domain-decomposition based physics-informed neural networks (PINNs) framework named the Adaptive Interface-PINNs or AdaI-PINNs (Roy et al., 2024). We propose the use of distinct neural networks for the field variables and material properties within each material, utilizing adaptive activation functions. While the neural networks across different materials share the same weights and biases, their activation functions are uniquely tailored using a hyperparameter that influences the slope of the activation function. The proposed framework is tested on several one-dimensional and two-dimensional benchmark examples, and its performance is compared with conventional PINNs and existing domain-decomposition PINNs frameworks, namely, the Multi-domain physics-informed neural network (M-PINN), and the eXtended physics-informed neural networks (XPINNs). The results demonstrate that the proposed approach can determine randomly distributed discontinuous material properties with an L 2 error of $\mathscr{O}$ (10 -3 ) for the material property and the root-mean-square error of $\mathscr{O}$ (10 -3 ) for the primary variable while the other approaches yield errors that are approximately two orders of magnitude larger (that is, $\mathscr{O}$ (10 -1 )). Moreover, the spatial distribution of material properties obtained using the proposed framework is in close agreement with the true distribution, whereas the other approaches fare much worse. Additionally, the proposed approach is approximately 40% faster than its competitors, indicating its potential as a robust alternative for solving inverse problems in heterogeneous materials.

36 MATERIALS SCIENCE↗