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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Review of Graphite Properties Relevant to Micro-Reactor Design

Microreactors are advanced reactors that are designed to be small, scalable, and portable with up to 20 MW power. These reactors could operate independently, or as part of a microgrid, without need of a frequent refueling. Micro-reactors are designed to be deployable to provide electric power in remote locations, therefore providing greater flexibility. Several micro-reactors are under development in the US. These reactors are based on advanced gas-cooled or heat-pipe concepts. While Graphite can be found in various applications, some high-temperature reactors plan to employ Graphite in their core components due to its role as a moderator and its ability to maintain its structural integrity when subjected to elevated temperatures and high neutron fluence. Although there is significant experience with use of Graphite in nuclear applications, there are still unknowns that should be addressed. Graphite properties depend on the graphite type and affected by the fabrication procedures. Material properties are functions of the temperature and irradiation dose. The fundamental properties also affected by the oxidation environment. Therefore, in-depth understanding of graphite properties, including the effect of irradiation, oxidation and other influencing parameters are required to evaluate the design accurately. This draft aims to provide a comprehensive review of material properties for Graphite relevant to micro reactor design. For this work, two distinct grades of graphite were considered, namely IG-110 and Mersen-2114. Physical, thermal, and mechanical properties for these grades were comparatively evaluated. The properties that are discussed in this draft aim to serve as a basis for design and safety analysis.

42 ENGINEERING↗
NASA NTRS1977

Assessment of relative flammability and thermochemical properties of some thermoplastic materials

Thermomechanical properties, flammability, oxygen index, relative toxicity of pyrolysis effluents, and char yields were studied for 12 advanced polymers which are candidates for use in aircraft interiors as decorative films, compression- and injection-molded parts and thermoplastic parts. Polymers sampled included polyphenylene sulfide, 9,9 bis (4-hydroxyphenol) fluorene polycarbonate-poly (dimethylsiloxane), polyether sulfone, polyvinyl fluoride and polyvinylidene fluoride. Availability of these samples, whether in commercial form or in test quantities, is specified. An estimate of relative fire resistance for the materials was obtained; the five polymers listed above were found to be the most fire resistant of the 12 sampled.

Kourtides, D. A.↗
NASA NTRSDate not supplied

Bridging the Gap Between Microscale Modeling and Additive Manufacturing for TPS

An overview of the now open-source NASA ARC software PuMA will be provided. On top of the well-documented ability to import and compute material properties from micro-CT images, PuMA has the ability to design microstructures and compute/predict material properties such as: porosity, permeability, tortuosity, thermal and electrical conductivity, tensile strength, etc. Therefore, a whole new range of capabilities is available, and provides users with the ability to build TPS materials (both fibrous and woven types) and optimize their properties based on missions and requirements. This talk will aim to provide a seed to start bridging the gap between microscale modeling of TPS materials and additive manufacturing.

Conductivity↗
NASA NTRS1994

Evaluation of seals and lubricants used on the Long Duration Exposure Facility

This report described results from testing and analysis of seals and lubricants subsequent to the 69-month low-earth-orbit (LEO) exposure on the Long Duration Exposure Facility (LDEF). Results show that if the materials were shielded from exposure to LDEF's external environment, the 69-month exposure to LEO resulted in minimal changes to material properties. However, if the materials were exposed to LDEF's exterior environments (atomic oxygen, solar radiation, meteoroids, and/or space debris), a variety of events occurred, ranging from no material change, to changes in properties, to significant erosion of the material.

Dursch, H. W.↗
NASA NTRS2021

Optimal Experimental Design With Fast Neural Network Surrogate Models

Designing optimal experiments minimizes the uncertainty of results and maximizes the efficient use of resources. Herein, machine learning surrogate models and the approximate coordinate exchange (ACE) algorithm are used to determine optimum experimental designs over large or arbitrarily restrictive design spaces. Optimal experimental design is particularly salient in materials science where experiments are expensive and material properties must often be inferred indirectly. The proposed framework is demonstrated by finding optimal experiments with which the hidden constituent properties of composite materials can be most efficiently inferred from observable experimental outcomes. The optimum experimental design is given by an information-theoretic criteria, which maximizes the conditional mutual information between the hidden properties and the expected experimental outcomes. To perform tractable optimization a neural network is trained as a surrogate model to mimic a physics based simulation, which can calculate the expected experimental outcome based on a candidate experimental design and sampled constituent properties. The ACE algorithm is used to optimize over large design spaces with many tests and controlled parameters where an exhaustive search would be intractable even with the surrogate model. Using this approach, optimal experimental designs that are consistent with those produced by heuristic knowledge and established best practices are found; then optimal designs in larger design spaces where heuristic knowledge is unavailable are examined.

machine learning↗
NASA NTRS1987

Performance analysis of advanced spacecraft TPS

For the Space Shuttle several types of ceramic heat shield materials were developed, both flexible and rigid. Improvements on the materials are continuously evolving. Material properties that have been studied and improved are high temperature limits, material strength, surface ruggedness, and surface catalycity. The results are presented of preliminary analyses concerning the thermal protection systems of future spacecraft and their sensitivity to the above material properties.

Pitts, William C.↗
NASA NTRS2011

Electromagnetic Launch Vehicle Fairing and Acoustic Blanket Model of Received Power Using FEKO

Evaluating the impact of radio frequency transmission in vehicle fairings is important to electromagnetically sensitive spacecraft. This study employs the multilevel fast multipole method (MLFMM) from a commercial electromagnetic tool, FEKO, to model the fairing electromagnetic environment in the presence of an internal transmitter with improved accuracy over industry applied techniques. This fairing model includes material properties representative of acoustic blanketing commonly used in vehicles. Equivalent surface material models within FEKO were successfully applied to simulate the test case. Finally, a simplified model is presented using Nicholson Ross Weir derived blanket material properties. These properties are implemented with the coated metal option to reduce the model to one layer within the accuracy of the original three layer simulation.

Trout, Dawn H.↗
NASA NTRS1991

Nonlinear optical properties of composite materials

The optical properties of a new class of composite nonlinear materials composed of coated grains, such as cadmium sulfide with a silver coating, are examined. These materials exhibit intrinsic optical bistability and resonantly enhanced conjugate reflectivity. The threshold for intrinsic optical bistability is low enough for practical applications in optical communications and optical computing. Some problems associated with the fabrication of these materials are addressed. Based on preliminary results, switching times are expected to be in the subpicosecond range.

Haus, Joseph W.↗
DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spacecraft Geometry Effects on Kinetic Impactor Missions

Abstract The DART (Double Asteroid Redirection Test) mission will impact a spacecraft on the secondary (Dimorphos) of the binary asteroid system Didymos in 2022 September, with the goal of altering the orbital period of Dimorphos about Didymos sufficiently to be observed from ground-based observations. Numerical impact modeling is a crucial component in understanding the outcome of the DART experiment, and while many have investigated the effects of target properties, such as material strength and porosity (which remain unknown), an often overlooked factor is the importance of accurately representing the spacecraft itself in such models. Most impact modeling to date has considered simple impactor geometries such as a solid uniform sphere, but in reality the spacecraft is a complex shape full of different components, open spaces, and thin walled structures. At a minimum, a simple solid representation underestimates the surface area of the impact: for a small body such as Dimorphos (approximately 160 m in diameter), the difference between a spacecraft spanning 20 m (including solar arrays) impacting and a sub-1 m idealized shape may be important. In this paper, we compare models impacting high-fidelity models of the spacecraft based on the CAD geometry with various simplified impactors, in order to assess the potential importance of this effect. We find that the difference between the simplest impactor geometries (such as a uniform sphere) and the real spacecraft is measurable, and has an interesting dependence on the material properties of the asteroid itself.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Properties of coated and modified surfaces

The ability to create surface and near-surface properties distinct from the bulk properties of a material by means of coating or surface modification techniques is an important new field of development for engineering materials, encompassing ion implantation, plasma processing, reactive deposition methods, ion beam deposition and modification, molecular beam epitaxy, and fast quench thermal processing. Attention is given to specific areas of process control which illustrate (1) the use of process development to control material properties, (2) the importance of surface modification and coating to the tribology and ion-surface interactions of semiconductor film growth, and (3) recent findings in surface science which exemplify potential applications of ion-surface interactions that generate novel material qualities.

Mattox, D. M.↗
DOE OSTI2023

Mechanical properties of Zircaloy cladding tubes and contributions to M.E.T.A. mechanical property database

To support a multi-laboratory Methodology, Evaluation, Testing, and Analysis (M.E.T.A.) cladding properties database, Oak Ridge National Laboratory’s (ORNL’s) cladding mechanical test geometries were manufactured from several nuclear-relevant cladding alloys and subsequently tested. These geometries were developed as mechanical test specimens to evaluate the properties of tube materials that may be used for irradiation testing at ORNL’s High Flux Isotope Reactor. They may also be used as test articles to be harvested—via in-cell machining—from commercially irradiated fuel rods and later tested. This report explores the differences among axial, hoop, and SSJ tensile geometries with partially recrystallized Zircaloy-2 to test ORNL correlation-based methods on a plate material that approximates, to the greatest extent possible, the characteristics of nuclear industry tubing. Furthermore, several tests were conducted with ORNL’s Zircaloy-4 tube inventory to (1) develop material properties as a standard for future tests, (2) determine the effect of the US Department of Energy’s Advanced Fuels Campaign coating processes on tube mechanical properties, and (3) evaluate the effect of specimen machining methods on the mechanical properties of tube geometries.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1982

Review of approaches to the investigation of the scattering properties of material media

An attempt is made to identify gaps in the current understanding of the backscattering properties of natural media, with attention both to fundamental questions which relate to the physics of scattering from such media, and applications-related considerations of the extraction of data of interest to the user from a given sensor image output. It is suggested that experiments be conducted, and models developed, for the areas of canopy constituents' dielectric and attenuation properties, volume geometry, and emission characteristics, as well as backscattering properties.

Ulaby, F. T.↗
NASA NTRS2025

Physics-Based Modeling and Simulation of Self-Reacting Friction Stir Welding Using Computational Fluid Dynamics

A physics-based model was developed to simulate the behavior of material in a self-reacting friction stir welding (SR-FSW) process for the joining of metals. This steady-state model builds upon fundamental computational fluid dynamic (CFD) principles within Ansys Fluent to solve the discretized equations. The effective viscosity is calculated using a viscoplastic model using a Sheppard-Wright formulation of flow stress. Numerous advancements have been made in the incorporated physics including (1) temperature-dependent material properties; (2) locally adaptable flow and thermal boundary conditions; and (3) adapting material properties in nugget in response to microstructural changes. Simulation strategies to accelerate computation and improve numerical stability include adapting the mesh refinement and solver relaxation factors during simulation. The result is a highly robust and computationally efficient model capable of providing the material flow and temperature history across the domain. As material history determines the local microstructure and ultimately weld strength, an accurate and detailed physics-based model has the potential to accelerate SR-FSW process development. The model is highly adaptable to changes in process parameters, tool design, or alloy.

Process Modeling↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Updates to the Predictive Materials Modeling Software Tools

Updates on NASA's efforts to build predictive material models from the micro-scale to the macro-scale are presented. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. The PuMA (microstructure analysis), PATO (macroscale material response), SPARTA (direct simulation Monte Carlo) and ARCHeS (arc heater modeling) codes are described. A range of applications, encompassing calculating effective material properties, study of high shear boundary layer flow over woven materials, new model of silicone-based coatings, new model of mechanical erosion, as well as approach to loose multi-physics coupling, are presented.

material modeling↗
DOE OSTI2023

Frontal Polymerization of Stereoisomers: Distinct Microstructures and Properties in Endo- and Exo- Dicyclopentadiene Thermosets

This study explores the relationship between monomer stereochemistry and the materials properties of thermosets generated by frontal ring-opening metathesis polymerization (FROMP). The exo- and endo-stereoisomers of dicyclopentadiene (DCPD) undergo rapid FROMP to fabricate robust thermosets within seconds. The exo-isomer provides significant benefits compared to the commonplace endo-DCPD resins; exo-DCPD supports faster fronts (≈3 times faster) than the endo-analogue and is an easier-to-handle liquid at room temperature. This work is among the first to explore the materials properties of p(exo-DCPD) thermosets. Here, we demonstrate a significant decrease in T g for products derived from exo-DCPD, despite having a similar rubbery plateau compared to p(endo-DCPD). Soluble oligomers were synthesized as models to probe the microstructural features affected by the monomer stereochemistry. Interestingly, hydrogenated oligomers of endo-DCPD exhibit slight differences in stereoregularity compared to their exo-counterparts. Finally, we incorporated a bisnorbornenyl cross-linker in exo-DCPD resins to boost the polymer T g , while also maintaining fronts that propagate faster than endo-DCPD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗