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All sources NASA NTRS DOE OSTI

DOE OSTI2015

Synthesis and Engineering Materials Properties of Fluid Phase Chemical Hydrogen Storage Materials for Automotive Applications

Among candidates for chemical hydrogen storage in PEM fuel cell automotive applications, ammonia borane (AB, NH3BH3) is considered to be one of the most promising materials due to its high practical hydrogen content of 14-16 wt%. This material is selected as a surrogate chemical for a hydrogen storage system. For easier transition to the existing infrastructure, a fluid phase hydrogen storage material is very attractive and thus, we investigated the engineering materials properties of AB in liquid carriers for a chemical hydrogen storage slurry system. Slurries composed of AB and high temperature liquids were prepared by mechanical milling and sonication in order to obtain stable and fluidic properties. Volumetric gas burette system was adopted to observe the kinetics of the H2 release reactions of the AB slurry and neat AB. Viscometry and microscopy were employed to further characterize slurries engineering properties. Using a tip-sonication method we have produced AB/silicone fluid slurries at solid loadings up to 40wt% (6.5wt% H2) with viscosities less than 500cP at 25°C.

ammonia borane↗

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↗

NASA NTRS1990

Real-time measurement of materials properties at high temperatures by laser produced plasmas

Determination of elemental composition and thermophysical properties of materials at high temperatures, as visualized in the context of containerless materials processing in a microgravity environment, presents a variety of unusual requirements owing to the thermal hazards and interferences from electromagnetic control fields. In addition, such information is intended for process control applications and thus the measurements must be real time in nature. A new technique is described which was developed for real time, in-situ determination of the elemental composition of molten metallic alloys such as specialty steel. The technique is based on time-resolved spectroscopy of a laser produced plasma (LPP) plume resulting from the interaction of a giant laser pulse with a material target. The sensitivity and precision were demonstrated to be comparable to, or better than, the conventional methods of analysis which are applicable only to post-mortem specimens sampled from a molten metal pool. The LPP technique can be applied widely to other materials composition analysis applications. The LPP technique is extremely information rich and therefore provides opportunities for extracting other physical properties in addition to the materials composition. The case in point is that it is possible to determine thermophysical properties of the target materials at high temperatures by monitoring generation and transport of acoustic pulses as well as a number of other fluid-dynamic processes triggered by the LPP event. By manipulation of the scaling properties of the laser-matter interaction, many different kinds of flow events, ranging from shock waves to surface waves to flow induced instabilities, can be generated in a controllable manner. Time-resolved detection of these events can lead to such thermophysical quantities as volume and shear viscosities, thermal conductivity, specific heat, mass density, and others.

Kim, Yong W.↗

DOE OSTI2018

Assessment of Material Properties for TRISO Fuel Particles in PARFUME

The purpose of this study is to identify the material properties that have the largest impact on the failure probability of tristructural isotropic (TRISO)-coated fuel particles under irradiation. For the most part, these material properties were obtained from experimental data of historical TRISO fuel development programs in Europe and the United States. The adequacy of these historical constitutive material properties is being evaluated as part of the Advanced Gas Reactor Fuel Development and Qualification Program. The TRISO fuel performance modeling code PARFUME was used to assess the material properties that have the main influence on the probability of failure of the silicon carbide (SiC) layer of TRISO fuel particles. Although some of these material properties exhibit a large variability according to published data, only a few of them have a significant impact on the SiC failure probability, namely the irradiation-induced creep and dimensional change of the inner pyrolytic carbon layer.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2018

Estimating Material Properties Under Extreme Conditions by Using Bayesian Model Calibration with Functional Outputs

Summary Dynamic material properties experiments provide access to the most extreme temperatures and pressures attainable in a laboratory setting; the data from these experiments are often used to improve our understanding of material models at these extreme conditions. We apply Bayesian model calibration to dynamic material property applications where the experimental output is a function: velocity over time. This framework can accommodate more uncertainties and facilitate analysis of new types of experiments relative to techniques traditionally used to analyse dynamic material experiments. However, implementation of Bayesian model calibration requires more sophisticated statistical techniques, because of the functional nature of the output as well as parameter and model discrepancy identifiability. We propose a novel Bayesian model calibration process to simplify and improve the estimation of the material property calibration parameters. Specifically, we propose scaling the likelihood function by an effective sample size rather than modelling the auto-correlation function to accommodate the functional output. Additionally, we propose sensitivity analyses by using the notion of 'modularization' to assess the effect of experiment-specific nuisance input parameters on estimates of the physical parameters. The Bayesian model calibration framework proposed is applied to dynamic compression of tantalum to extreme pressures, and we conclude that the procedure results in simple, fast and valid inferences on the material properties for tantalum.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Material Properties of Non-Irradiated Zircaloy 4 in Support of ASME Code Acceptance for Pressure Vessel Design

A program underway at Oak Ridge National Laboratory (ORNL) to support private industry design of a system for production of 99Mo includes review of Zircaloy-4 (Zry-4) properties as a potential material for a primary vessel in the system. Because Zry-4 is not a material specified in the ASME Boiler and Pressure Vessel Code, a review has been performed to obtain the necessary data in the non-irradiated condition from the literature and from additional testing of Zry-4 to enable creation of tables of material properties required for ASME Code Section III Article ND-3000 design. A strategy for an in-vessel surveillance program is also recommended for early detection of potential corrosion and irradiation degradation issues during operational life of the vessel. Additionally, the ASME Code Section II, Part D was reviewed to ascertain the requirements for proposing a new material for Code approval. A plate of Zry-4 procured by ORNL was used to obtain tensile properties that were used as the basis for determining the maximum allowable stresses applicable to the vessel design. The materials properties of primary interest for ASME Code application are tensile properties, fracture toughness, fatigue, and five specific physical properties required by the Code for approval of a new material. The literature review revealed that all reported experimental data showed tensile strengths to substantially exceed the minimum specifications in ASTM B352. Sufficient fracture toughness and fatigue data are available to conclude that Zry-4 has sufficient fracture and fatigue resistance in the unirradiated condition for vessel design and operation. Of the physical properties reviewed, only thermal conductivity revealed a discrepancy that is recommended for further review. A surveillance program plan has been developed that is probably the maximum that would need to be implemented for the specific vessel design considered. Once adequate non-irradiated and irradiated data are available from preproduction testing of the Zry-4 and weld metal and HAZ materials, the program could be reduced to reflect that either initial properties are very good and/or the effects of embrittlement appear to be minimal. However, the overall design of a surveillance program is, of course, also dependent on the ASME Code design class for the pressure vessel.

Nanstad, Randy↗

DOE OSTI2018

Characterisation of the spatial variability of material properties of Gilsocarbon and NBG-18 using random fields

Graphite is a candidate material for Generation IV concepts and is used as a moderator in Advanced Gas-cooled Reactors (AGR) in the UK. Spatial material variability is present within billets causing different material property values between different components. Variations in material properties and irradiation effects can produce stress concentrations and diverse mechanical responses in a nuclear reactor graphite core. In order to characterise the material variability, geostatistical techniques called variography and random field theory were adapted for studying the density and Young's modulus of a billet of Gilsocarbon and NBG-18 graphite grades. Variography is a technique for estimating the distance over which material property values have significant spatial correlation, known as the scale of fluctuation or spatial correlation length. The paper uses random field theory to create models that mimic the original spatial and statistical distributions of the original data set. This study found different values of correlation length for density and Young's modulus around the edges of a Gilsocarbon billet, while in the case of NBG-18, similar correlation lengths where found across the billet. Furthermore examples of several random fields are given to reproduce the spatial patterns and values found in the original data.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Cost-Performance Trade-off in thermoelectric air conditioning system with graded and constant material properties

Thermoelectric (TE) air cooling is a solid-state technology that has the potential to replace conventional vapor compression-based air conditioning. In this paper, we present a detailed system-level modeling for thermoelectric air conditioning system with position-dependent (graded) and constant material properties. Strategies for design optimization of the system are provided in terms of cost-performance trade-off. Realistic convection heat transfer at both sides of the system are taken into account in our modeling. Here, the effects of convection heat transfer coefficients, air flowrate, and thermoelectric material properties are investigated with varying key parameters such as TE leg thickness, module fill factor, and input current. Both constant material properties and graded properties are considered for the TE materials, and they are compared in terms of the degree of cooling, coefficient of performance (COP), and power consumption. For graded materials, we employ one-dimensional finite element methods to solve the coupled electrical and thermal current equations with arbitrary profile of material properties varying with position along the TE legs. We find that graded materials can enhance the degree of cooling, but only at the expense of COP, compared to the case of constant property materials. With constant material properties of ZT = 1 and relatively low electric current, the power consumption of TE coolers can be lower than those of conventional air conditioners at an equivalent cooling capacity. Considering additional advantages such as demand-flexible operation, low noise, and high scalability, thermoelectric cooling could be a competitive technology for future air conditioning applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Universal behavior in fragmenting brittle, isotropic solids across material properties

A bonded particle model is used to explore how variations in the material properties of brittle, isotropic solids affect critical behavior in fragmentation. To control material properties, a model is proposed which includes breakable two- and three-body particle interactions to calibrate elastic moduli and mode I and mode II fracture toughnesses. In the quasistatic limit, fragmentation leads to a power-law distribution of grain sizes which is truncated at a maximum grain mass that grows as a nontrivial power of system size. In the high-rate limit, truncation occurs at a mass that decreases as a power of increasing rate. A scaling description is used to characterize this behavior by collapsing the mean-square grain mass across rates and system sizes. Consistent scaling persists across all material properties studied, although there are differences in the evolution of grain size distributions with strain as the initial number of grains at fracture and their subsequent rate of production depend on Poisson's ratio. Importantly, this evolving granular structure is found to induce a unique rheology where the ratio of the shear stress to pressure, an internal friction coefficient, decays approximately as the logarithm of increasing strain rate. The stress ratio also decreases at all rates with increasing strain as fragmentation progresses and depends on elastic properties of the solid.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Development of a Multi-Press Assembly Device for Planar Dynamic Material Property Targets

A class of dynamic material property (DMP) experiments on the Sandia National Laboratories (SNL) pulse power Z-Machine requires planar samples to be held in a panel assembly. A custom press device to fabricate the assemblies has the ability to assemble one sample, window, or stack at a time, resulting in a one week lead time for a typical three-pocket panel assembly. Fabrication of targets with more than three pockets can take longer. In late 2015, General Atomics (GA) conceptualized a new multi-press device to enable several samples, windows, or stacks to be assembled simultaneously, and a prototype was designed, procured, and outfitted in six months. Since June 2016, this multi-press design has successfully assembled 60 planar DMP targets. Furthermore, the development considerations for this new device and the resulting benefits for the fabrication of targets are discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Acquisition of material properties in production for sheet metal forming processes

In past work a measurement system for the in-line acquisition of material properties was developed at IVP. This system is based on the non-destructive eddy-current principle. Using this system, a 100% control of material properties of the processed material is possible. The system can be used for ferromagnetic materials like standard steels as well as paramagnetic materials like Aluminum and stainless steel. Used as an in-line measurement system, it can be configured as a stand-alone system to control material properties and sort out inapplicable material or as part of a control system of the forming process. In both cases, the acquired data can be used as input data for numerical simulations, e.g. stochastic simulations based on real world data.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: A Joint Study

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

42 ENGINEERING↗

NASA NTRS2015

Analytic Thermoelectric Couple Modeling: Variable Material Properties and Transient Operation

To gain a deeper understanding of the operation of a thermoelectric couple a set of analytic solutions have been derived for a variable material property couple and a transient couple. Using an analytic approach, as opposed to commonly used numerical techniques, results in a set of useful design guidelines. These guidelines can serve as useful starting conditions for further numerical studies, or can serve as design rules for lab built couples. The analytic modeling considers two cases and accounts for 1) material properties which vary with temperature and 2) transient operation of a couple. The variable material property case was handled by means of an asymptotic expansion, which allows for insight into the influence of temperature dependence on different material properties. The variable property work demonstrated the important fact that materials with identical average Figure of Merits can lead to different conversion efficiencies due to temperature dependence of the properties. The transient couple was investigated through a Greens function approach; several transient boundary conditions were investigated. The transient work introduces several new design considerations which are not captured by the classic steady state analysis. The work helps to assist in designing couples for optimal performance, and also helps assist in material selection.

Seebeck Effect↗

DOE OSTI2025

Extracting Material Property Measurements from Scientific Literature with Limited Annotations

Extracting material property data from scientific text is pivotal for advancing data-driven research in chemistry and materials science; however, the extensive annotation effort required to produce training data for named entity recognition (NER) models for this task often makes it a barrier to extracting specialized data sets. Here, in this work, we present a comparative study of the conventional, supervised NER methodology to alternative few-shot learning architectures and large language model (LLM)-based approaches that mitigate the need to label large training data sets. We find that the best-performing LLM (GPT-4o) not only excels in directly extracting relevant material properties based on limited examples but also enhances supervised learning through data augmentation. We supplement our findings with error and data quality assessments to provide a nuanced understanding of factors that impact property measurement extraction.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: Joint Study (FY2020 Final Report)

58 GEOSCIENCES↗

DOE OSTI2022

Improving Predictive Capability in REHEDS Simulations with Fast, Accurate, and Consistent Non-Equilibrium Material Properties

Predictive design of REHEDS experiments with radiation-hydrodynamic simulations requires knowledge of material properties (e.g. equations of state (EOS), transport coefficients, and radiation physics). Interpreting experimental results requires accurate models of diagnostic observables (e.g. detailed emission, absorption, and scattering spectra). In conditions of Local Thermodynamic Equilibrium (LTE), these material properties and observables can be pre-computed with relatively high accuracy and subsequently tabulated on simple temperature-density grids for fast look-up by simulations. When radiation and electron temperatures fall out of equilibrium, however, non-LTE effects can profoundly change material properties and diagnostic signatures. Accurately and efficiently incorporating these non-LTE effects has been a longstanding challenge for simulations. At present, most simulations include non-LTE effects by invoking highly simplified inline models. These inline non-LTE models are both much slower than table look-up and significantly less accurate than the detailed models used to populate LTE tables and diagnose experimental data through post-processing or inversion. Because inline non-LTE models are slow, designers avoid them whenever possible, which leads to known inaccuracies from using tabular LTE. Because inline models are simple, they are inconsistent with tabular data from detailed models, leading to ill-known inaccuracies, and they cannot generate detailed synthetic diagnostics suitable for direct comparisons with experimental data. This project addresses the challenge of generating and utilizing efficient, accurate, and consistent non-equilibrium material data along three complementary but relatively independent research lines. First, we have developed a relatively fast and accurate non-LTE average-atom model based on density functional theory (DFT) that provides a complete set of EOS, transport, and radiative data, and have rigorously tested it against more sophisticated first-principles multi-atom DFT models, including time-dependent DFT. Next, we have developed a tabular scheme and interpolation methods that compactly capture non-LTE effects for use in simulations and have implemented these tables in the GORGON magneto-hydrodynamic (MHD) code. Finally, we have developed post-processing tools that use detailed tabulated non-LTE data to directly predict experimental observables from simulation output.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1989

Materials properties numerical database system established and operational at CINDAS/Purdue University

A computerized comprehensive numerical database system on the mechanical, thermophysical, electronic, electrical, magnetic, optical, and other properties of various types of technologically important materials such as metals, alloys, composites, dielectrics, polymers, and ceramics has been established and operational at the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) of Purdue University. This is an on-line, interactive, menu-driven, user-friendly database system. Users can easily search, retrieve, and manipulate the data from the database system without learning special query language, special commands, standardized names of materials, properties, variables, etc. It enables both the direct mode of search/retrieval of data for specified materials, properties, independent variables, etc., and the inverted mode of search/retrieval of candidate materials that meet a set of specified requirements (which is the computer-aided materials selection). It enables also tabular and graphical displays and on-line data manipulations such as units conversion, variables transformation, statistical analysis, etc., of the retrieved data. The development, content, accessibility, etc., of the database system are presented and discussed.

Ho, C. Y.↗