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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers

Membrane polymers are a promising technology for use in many challenging gas separation applications. Here, the techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R 2 scores exceeding 0.82.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Spectral Radiative Properties of Ceramic Particles for Concentrated Solar Thermal Energy Storage Applications

This work investigates the spectral radiative properties of ceramic particles using a monochromator and a Fourier-transform infrared spectrometer (FTIR) with integrating spheres at wavelengths from 0.38 μm to 15 μm. Particles are encased between two transparent windows to obtain the directional-hemispherical reflectance of the particle bed. Two types of commercially available particles with three different sizes are examined. Integration over the solar spectrum reveals that the solar absorptance of the particle beds is between 0.940 and 0.957. The total emittance at 1000 K is also estimated by assuming the spectral emittance is independent of temperature. The optical constants of particles are modeled with effective medium approaches, considering the optical properties of individual constituent materials. The absorptance of the particle is estimated using the effective optical constants and compared with that of the particle bed from the measurement. Finally, this work facilitates the characterization of radiative properties of particles with a windowed method and provides a modeling scheme for approximating the radiative properties of composite ceramic materials.

14 SOLAR ENERGY↗
DOE OSTI2020

Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning

Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this work, we seek to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a crystal plasticity finite element model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs in a probabilistic manner. Furthermore, to solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures, which combines techniques from measure theory and Bayes’ rule to define a unique and numerically stable solution. This approach requires making an initial prediction using an initial guess for the distribution on model inputs and solving a stochastic forward problem. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning Bayesian regression model based on Gaussian processes and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-fidelity Uncertainty Quantification for Homogenization Problems in Structure-Property Relationships from Crystal Plasticity Finite Elements

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. In conclusion, our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanism of drying-induced change in the physical properties of concrete: A mesoscale simulation study

Although many studies have found that drying alters the mechanical properties of concrete, the mechanism behind this change remains unclarified. The aim of this study is to elucidate the mechanism of change in properties of concrete after drying through the numerical calculation: a 3D mesoscale rigid-body-spring model (RBSM) with three phases, i.e. mortar, aggregate, and the interfacial transition zone while considering the properties changes of mortar due to drying. Based on the RBSM results, it is concluded that the change in compressive strength due to drying and heating is determined by a balance of the impact of drying-induced microcracking around coarse aggregates and the change in mechanical properties of the mortar due to drying. These mechanisms change the applied load required to reach the critical crack width and distribution, at which rim of the specimen begins to isolate from the core region and the load sustained by the rim decreases.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational and experimental analysis of structural and Thermophysical properties of LiX-KX (X = chloride, iodide, or bromide) molten salts

Molten salts containing lithium (Li) and potassium (K) halides, including chlorides (Cl), bromides (Br), and iodides (I), are pivotal in advanced technological applications including nuclear reactor technologies, thermal batteries, and industrial pyrochemical processes. Despite their significance, the availability of the temperature-dependent structural and thermophysical properties of these molten salts remain scarce. Here, this study addresses this knowledge gap by predicting and measuring the key properties such as coordination number, radial distribution function, density, heat capacity, and volumetric thermal expansion of molten LiI-KI, LiBr-KBr and LiCl-KCl eutectic mixtures. Utilizing ab initio molecular dynamics (AIMD) simulations, we predict these properties of LiX-KX (X=Br,I, Cl) across various temperatures, with chlorides as a benchmark. Experimental measurements using Archimedes methods and Differential Scanning Calorimetry validate the densities and heat capacities of these molten salts. Our comprehensive analysis of the structural and thermophysical properties provides critical insights into the behavior of these molten salts at various temperatures, enhancing the understanding necessary for their application in advanced technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Sizing of discontinuous natural fibers: Effect of sizing approach and sizing concentration on composite properties

Natural fiber reinforced composites (NFRCs) are gaining attention in automotive applications as an alternative to glass fiber composites due to their lightweight and renewable sourcing. However, the inherent hydrophilicity of natural fibers leads to poor compatibility with hydrophobic polymers which adversely affects the mechanical properties of the composites and can limit their application to non-structural parts. Sizing is a common approach used for synthetic fibers to improve the interface between fiber and matrix. However, there is limited study on the sizing of natural fibers, and hence the focus of this work. Here, in this study, two different approaches to sizing discontinuous coir fibers were investigated, namely; (1) ex-situ sizing and (2) in-situ sizing. A commercial polypropylene (PP) based sizing agent was used and the effects of varying sizing solution concentrations (1.5, 2.5, and 3.5 wt%) on the properties of the composites was studied. Results showed that composites prepared via the in-situ sizing process had better fiber–matrix adhesion and improved tensile properties compared to ex-situ sized composites. On studying the effect of different sizing concentrations on composite properties, we found that the tensile strength of the composites increased (by ∼ 42 %) up to 2.5 wt% sizing concentration (in solution) and then decreased. However, the impact strength decreased significantly on increasing the sizing content beyond 1.5 wt% (by ∼ 40 %). Additionally, the study was further extended to investigate the effect of sizing on different NFRCs (coir, banana, and cottonized hemp fiber) where effectiveness of sizing was found to be influenced by the fiber surface morphology.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites

Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.

Hybrid composites↗
DOE OSTI2020

Stimuli-responsive petroleum cement composite with giant expansion and enhanced mechanical properties

Shrinkage, as an inherent property of cement during the curing process, has been a concern in the oil & gas industry for centuries. Therefore, expansive cement has been studied for many decades, primarily through incorporation of expansive additives. Nevertheless, traditional expansive additives are not able to achieve the required expansive ratio or maintain a good mechanical property for the cement composite at extreme underground condition. Accordingly, a new generation of expansive additive, which not only has the required expansion, but also work at the downhole high temperature and high pressure environment, and maintain or even enhance the mechanical property, is highly desired. In this work, a high enthalpy storage shape memory polymer (SMP) as a new class of expansive additive is investigated. It is found that the cement composite achieves 1.4% circumferential expansion by only 6% by weight of SMP additives. The compressive strength and flexural strength are also enhanced at the same time, which is hardly achievable by other expansive additives with the same concertation. Good pumpability is also proved by rheological study. The mechanism controlling the enhanced properties is reveled through morphological study and element mapping analysis at the SMP/cement interface.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MechElastic: A Python library for analysis of mechanical and elastic properties of bulk and 2D materials

We report the MechElastic Python package evaluates the mechanical and elastic properties of bulk and 2D materials using the elastic coefficient matrix ( C ij ) obtained from any ab-initio density-functional theory (DFT) code. The current version of this package reads the output of VASP, ABINIT, and Quantum Espresso codes (but it can be easily generalized to any other DFT code) and performs the appropriate post-processing of elastic constants as per the requirement of the user. This program can also detect the input structure's crystal symmetry and test the mechanical stability of all crystal classes using the Born-Huang criteria. Various useful material-specific properties such as elastic moduli, longitudinal and transverse elastic wave velocities, Debye temperature, elastic anisotropy, 2D layer modulus, hardness, Pugh's ratio, Cauchy's pressure, Kleinman's parameter, and Lame's coefficients, can be estimated using this program. Another existing feature of this program is to employ the ELATE package (2016) [29] and plot the spatial variation of several elastic properties such as Poisson's ratio, linear compressibility, shear modulus, and Young's modulus in three dimensions. Further, the MechElastic package can plot the equation of state (EOS) curves for energy and pressure for a variety of EOS models such as Murnaghan, Birch, Birch-Murnaghan, and Vinet, by reading the inputted energy/pressure versus volume data obtained via numerical calculations or experiments. This package is particularly useful for the high-throughput analysis of elastic and mechanical properties of materials.

2D materials↗
DOE OSTI2020

A data-driven model for thermodynamic properties of a steam generator under cycling operation

The varying electricity demand from coal power plants due to the intermittent nature of renewable sources leads to load-follow and on/off operations referred to as cycling. Cycling causes transients of properties such as pressure and temperature within various components of the steam generation system.These transients cause increased damage because of fatigue and creep-fatigue interactions shortening the life of components. An algorithm is developed to identify cycling operations from the gross power data. The data-driven model based on artificial neural networks (ANN) is developed using 10 years data from Coal Creek Station power plant located in North Dakota, USA to estimate properties of the steam generator components during cycling operations. Furthermore, the uniqueness of this model is the ability to predict component properties for the cycling as well as base-load operations and is reported for the first time. The ANN model estimates the component properties, for a given gross power profile and initial conditions, as they vary during cycling operations. As a representative example, the ANN estimates are presented for the superheater outlet pressure, reheater inlet temperature, and flue gas temperature at the air heater inlet. The changes in these variables as a function of the gross power over the time duration are compared with measurements to assess the predictive capability of the model. Mean square errors of 4.49E-04 for superheater outlet pressure, 1.62E-03 for reheater inlet temperature, and 4.14E-04 for flue gas temperature at the air heater inlet were observed.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

A numerical study of the spectral radiative properties of packed bed with mixed bauxite and silica spheres

Bauxite and silica particles have gained increasing attention for applications in the field of concentrated solar power. In this work, a Monte Carlo ray-tracing simulation is performed to predict the radiative properties (absorptance, reflectance, and transmittance) of packed beds with mixed bauxite and silica spherical particles at wavelengths of 0.5, 2.6, 9.0, and 9.35 µm. These wavelengths are representative for the visible, near-infrared, and the mid-infrared regions that are important for solar and thermal radiation, respectively. A repeating unit-column approach is used to mathematically represent the particle bed. The effects of particle mixing ratio, volume fraction, and wavelength on the predicted radiative properties are examined. The obtained radiative properties are inputted to an inverse method to retrieve the effective absorption and scattering coefficients as well as the scattering albedo, which may be later used in a continuous-scale radiative heat transfer analysis. Furthermore, the independent scattering model is used to obtain the absorption coefficient and scattering albedo based on the absorption and scattering cross sections predicted by a Monte Carlo algorithm for a single particle. It is shown that the independent scattering model underpredicts the scattering coefficient for opaque particles but overpredicts the scattering coefficient for semitransparent particles for sufficiently high particle volume fractions. Here, the radiative properties calculated from the independent scattering model are compared to the full Monte Carlo simulation of the particle bed to examine the influence of particle mixing on dependent scattering.

42 ENGINEERING↗
DOE OSTI2024

Understanding the effect of refractory metal chemistry on the stacking fault energy and mechanical property of Cantor-based multi-principal element alloys

Multi-principal-element alloys (MPEAs) based on 3d-transition metals show remarkable mechanical properties. In this study, the stacking fault energy (SFE) in face-centered cubic (fcc) alloys is a critical property that controls underlying deformation mechanisms and mechanical response. Here, we present an exhaustive density-functional theory study on refractory- and copper-reinforced Cantor-based systems to ascertain the effects of refractory metal chemistry on SFE. We find that even a small percent change in refractory metal composition significantly changes SFEs, which correlates favorably with features like electronegativity variance, size effect, and heat of fusion. For fcc MPEAs, we also detail the changes in mechanical properties, such as bulk, Young's, and shear moduli, as well as yield strength. A Labusch-type solute-solution-strengthening model was used to evaluate the temperature-dependent yield strength, which, combined with SFE, provides a design guide for high-performance alloys. We also analyzed the electronic structures of two down-selected alloys to reveal the underlying origin of optimal SFE and strength range in refractory-reinforced fcc MPEAs. These new insights on tuning SFEs and modifying composition-structure-property correlation in refractory- and copper-reinforced MPEAs by chemical disorder, provide a chemical route to tune twinning- and transformation-induced plasticity behavior in fcc MPEAs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fuel property evaluation of unique fatty acid methyl esters containing β-hydroxy esters from engineered microorganisms

Unique fatty acid methyl esters (FAME) containing ..beta..-hydroxy esters were produced using an engineered microorganism by glucose fermentation. This study investigated the properties of the unique FAME mixture both neat and in blends with conventional diesel, as well as properties of ..beta..-hydroxy esters. The unique FAME blend contained relatively shorter-chain FAME (average fatty acid chain carbon number 14.6) with 58 % monounsaturated fatty acids and 9 % saturated and monounsaturated ..beta..-hydroxy acid chains. The unique FAME had significantly lower distillation T90 (321 °C versus 352 °C) and higher cetane number (56.7 versus 52) compared to soy biodiesel. Cloud points were within method repeatability. Unexpectedly (because of the lack of methylene-interrupted double bonds), the unique FAME had low oxidation stability (1.5 h) as determined by Rancimat induction period. Stability could be improved through addition of commonly used antioxidants. We speculate that monounsaturated ..beta..-hydroxy FAME may be the source of this instability. Blends with conventional diesel up to 50 vol% showed similar kinematic viscosity (within method repeatability) as blends of conventional FAME. The unique FAME had no effect on distillation T90 even at the 80% blend level. A 30 vol% blend into conventional diesel had a Rancimat induction period of only 2 h, very nearly the same as the neat unique FAME sample. The addition of antioxidants produced blends of acceptable stability. Based on an assessment of the properties of individual ..beta..-hydroxy FAME molecules, they have higher boiling point, higher cloud point, lower cetane number, and potentially lower storage stability than analogous FAME not having the ..beta..-hydroxy group. Removing them from the fuel product in the production process may result in a biodiesel product with superior properties to what is on the market today.

09 BIOMASS FUELS↗
DOE OSTI2020

A Perspective on Biomass-Derived Biofuels: From Catalyst Design Principles to Fuel Properties

The hazards to health and the environment associated with the transportation sector include smog, particulate matter, and greenhouse gas emissions. Conversion of lignocellulosic biomass into biofuels has the potential to provide significant amounts of infrastructure-compatible liquid transportation fuels that reduce those hazardous materials. However, the development of these technologies is inefficient, due to: (i) the lack of a priori fuel property consideration, (ii) poor shared vocabulary between process chemists and fuel engineers, and (iii) modern and future engines operating outside the range of traditional autoignition metrics such as octane or cetane numbers. In this perspective, we describe an approach where we follow a “fuel-property first” design methodology with a sequence of (i) identifying the desirable fuel properties for modern engines, (ii) defining molecules capable of delivering those properties, and (iii) designing catalysts and processes that can produce those molecules from a candidate feedstock in a specific conversion process. Computational techniques need to be leveraged to minimize expenses and experimental efforts on low-promise options. This concept is illustrated with current research information available for biomass conversion to fuels via catalytic fast pyrolysis and hydrotreating; outstanding challenges and research tools necessary for a successful outcome are presented.

BIOMASS FUELS↗
DOE OSTI2025

Correlating energy density induced residual stress, porosity, and mechanical property variations in directed energy deposition using neutron diffraction and imaging techniques

In a directed energy deposition (DED) process, the energy density deposited into the materials drives the structure and properties. The energy density is influenced by several printing parameters, such as laser power, hatch spacing, scanning speed, etc. In this study, 316L stainless steels samples were fabricated with varying hatch spacing aiming to induce variations in sample microstructure and properties. A combination of techniques including engineering neutron diffraction, neutron computed tomography, and tensile testing, were employed to correlate the structure and macroscopic properties. Porosity was predominantly observed at the bottom of the deposited materials and was effectively reduced by decreased hatch spacing. Correlatively, compressive residual stress was observed at the bottom of the specimens, while internal stress is largely determined by the hatch spacing. The residual stress in general decreases as the hatch spacing decreases. The maximum ultimate tensile strength (UTS) was found to increase from 517.4 to 528.6 MPa with a strain increased from 0.847 to 0.897 as the hatch spacing decreased from 0.45 to 0.42 mm. However, further increase in energy density by reducing the hatch spacing resulted in a significant decrease in UTS (as low as 452 MPa). This work provides new insights from bulk non-destructive techniques into correlating energy density induced residual stress, porosity, and mechanical property variations in directed energy deposition.

316L stainless steel↗
DOE OSTI2023

Process-structure-property considerations for wire-based directed energy deposition of Ti-6Al-4V

Directed energy deposition (DED) is an attractive additive manufacturing (AM) process for large structural components. The rapid solidification and layer-by-layer process associated with DED results in non-ideal microstructures, such as large grains with strong crystallographic textures. These non-ideal microstructures can lead to severe anisotropy in the mechanical properties. Despite these challenges, DED has been identified as a potential solution for the manufacturing of near net shape Ti-6Al-4V preforms, replacing lost casting and forging capabilities. Two popular wire-based directed energy deposition (W-DED) processes were considered for the manufacturing of Ti-6Al-4V with assessments on their respective metallurgical and mechanical properties, as compared to a conventionally processed material. The two W-DED processes explored were wire arc additive manufacturing (WAAM) and electron beam additive manufacturing (EBAM). High throughput inspection and tensile testing procedures were utilized to generate statistically relevant data sets related to each process and sample orientation. The 2 AM technologies produced material with remarkably different microstructures and mechanical properties. Importantly, results revealed key differences in strength and ductility for the two disparate processes which were found to be related to differences in the metallurgical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A synergistic experimental and theoretical study elucidating the electronic and thermal properties in spinel CuInSnS 4

Quaternary chalcogenides of different structure types continue to be of interest due to the novel physical properties they exhibit and for applications ranging from optoelectronics to energy-related technologies. Herein we report on the electronic and thermal properties of spinel CuInSnS 4 . UV–vis–NIR spectroscopy indicates strong absorption in the visible-light region with an indirect bandgap of 1.52 eV. First-principles computations corroborate our experimental data and reveal that the band-edge electronic properties are due to strong p – d ands – p hybridization and antibonding interactions. Analyses of temperature-dependent thermal properties together with first principles simulations reveal strong anharmonicity and low-lying optical branches that hybridize with the acoustic modes effectively suppressing thermal conductivity. This intrinsically very low thermal conductivity is much lower than that of related chalcogenides. Finally, our findings are discussed in the light of ongoing interest in quaternary metal chalcogenide materials and may aid in the development of this and similar chalcogenides for applications of interest.

36 MATERIALS SCIENCE↗