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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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DOE OSTI2021

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

We report a recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

42 ENGINEERING↗

DOE OSTI2020

Experimental investigation on the effects of fabric architectures on mechanical and damage behaviors of carbon/epoxy woven composites

The mechanical behaviors and damage evolutions of carbon/epoxy woven fabric composites with three different geometries, i.e., one plain weave and two twill weave patterns with different areal densities, are studied under tensile loading. The effects of weave patterns on mechanical properties are investigated by monotonic and cyclic tension tests. Remarkable variations in stress–strain curve, Poisson’s ratio, residual strain and strain map exist in the three composites. Crimp ratio is found to be a critical factor to govern the mechanical properties. With smaller crimp ratio, a quasi-linear stress–strain curve with higher elastic modulus and strength is observed. The stress–strain curves of composites with higher crimp ratio contain transition stages with significant tangent modulus degradation. Elastic modulus, strength and damage initiation are all correlated with the crimp ratio linearly regardless of the fabric pattern. Dramatic nonlinear evolution in Poisson’s ratio occurs in the composite with higher crimp ratio. Cyclic tension results indicate that the residual strain is a more appropriate damage indicator than the unloading elastic modulus. Microstructure examination shows that damage developments are essentially related to the fabric geometry, and result in various mechanical behaviors. This work provides important insights into the geometry-deformation mechanism-mechanical property relationship of the woven composites.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A robust fourth-order finite-difference discretization for the strongly anisotropic transport equation in magnetized plasmas

We propose a second-order temporally implicit, fourth-order-accurate spatial discretization scheme for the strongly anisotropic heat transport equation characteristic of hot, fusion-grade plasmas. Following Du Toit et al. (2018), the scheme transforms mixed-derivative diffusion fluxes (which are responsible for the lack of a discrete maximum principle) into nonlinear advective fluxes, amenable to nonlinear-solver-friendly monotonicity-preserving limiters. The scheme enables accurate multi-dimensional heat transport simulations with up to seven orders of magnitude of heat-transport-coefficient anisotropies with low cross-field numerical error pollution and excellent algorithmic performance, with the number of linear iterations scaling very weakly with grid resolution and grid anisotropy, and scaling with the square-root of the implicit timestep. We propose a multigrid preconditioning strategy based on a lower-order approximation that renders the scheme efficient and scalable under grid refinement. Several numerical tests are presented that display the expected spatial convergence rates and strong algorithmic performance, including fully nonlinear magnetohydrodynamics simulations of kink instabilities in a Bennett pinch in 2D helical geometry and of ITER in 3D toroidal geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Adaptations for gas exchange enabled the elongation of lepidopteran proboscises

The extensive biodiversification of butterflies and moths (Lepidoptera) is partly attributed to their unique mouthparts (proboscis [Pr]) that can span in length from less than 1 mm to over 280mm in Darwin's sphinx moths. Lepi-doptera, similar to other insects, are believed to inhale and exhale respiratory gases only through valve-like spi-racles on their thorax and abdomen, making gas exchange through the narrow tracheae (Tr) challenging for the elongated Pr. How Lepidoptera overcome distance effects for gas transport to the Pr is an open question that is important to understanding how the Pr elongated over evolutionary time. Here, we show with scanning electron microscopy and X-ray imaging that distance effects on gas exchange are overcome by previously unreported micropores on the Pr surface and by superhydrophobic Tr that prevent water loss and entry. We find that the density of micropores decreases monotonically along the Pr length with the maxima proportional to the Pr length and that micropore diameters produce a Knudsen number at the boundary between the slip and transition flow regimes. By numerical estimation, we further show that the respiratory gas exchange for the Pr predominantly occurs via diffusion through the micropores. In conclusion, these adaptations are key innovations vital to Pr elongation, which likely facilitated lepidopteran biodiversification and the radiation of angiosperms by coevolutionary processes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Strategies for microgrid operation under real-world conditions

Microgrids are an increasingly relevant technology for integrating renewable energy sources into electricity systems. Based on a microgrid implementation in California, in this study we investigate microgrid operation under real-world conditions. These conditions have not yet been considered in combination and encompass energy charges, demand charges, export limits, as well as uncertainty about future electricity demand and generation in the microgrid. Under these conditions, we evaluate the performance of two frequently applied groups of strategies for microgrid operation. The first group is composed of proactive strategies that optimize decisions based on forecasts of future electricity generation and demand. The second group includes reactive strategies that make operational decisions based exclusively on the current state of the microgrid. We evaluate the performance of the strategies under varying operational parameters, forecast accuracies, and microgrid configurations—well beyond our Californian showcase. Our results confirm the expectation that proactive strategies outperform reactive ones in the majority of settings. Yet, reactive strategies can perform better under short control intervals or under moderate prediction errors of PV generation or demand. Furthermore, the interplay between real-world conditions and operational strategies reveals several additional insights for research on microgrid operation. First, we find that demand charges and export limits decisively affect microgrid performance. Second, the impact of forecast errors is highly non-linear and non-monotonous. Third, escalating negative interactions between forecast errors and demand charges make proactive strategies benefit from longer control intervals. This result is contrary to existing best practice, which promotes short control intervals to minimize the impact of uncertainty.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A FFT-based mesoscale continuum dislocation mechanics with defect energy: Applications to composites and polycrystals

A crystal plasticity elastoviscoplastic FFT (fast Fourier transform) formulation with a mesoscale continuum field dislocation mechanics model is presented, which incorporates a defect energy density that depends on GND densities and an associated material length scale. This allows to thermodynamically derive internal length scale dependent intra-crystalline backstress and Peach–Koehler force acting on GND densities. The model considers GND density evolution through a filtered numerical spectral approach, which is coupled with stress equilibrium through the elastoviscoplastic FFT algorithm. The discrete Fourier transform (DFT) method together with finite difference (FD) schemes is applied to solve both the backstress tensor and the Fourier–Green operator. Numerical results are first reported for two-phase laminate composites with plastic single crystal channels and elastic precipitates for shear loadings. Channel size effects are simulated and analyzed on the overall and local hardening behaviors during monotonous loadings. In addition, the evolutions of GND densities and the role of their associated backstress on size effects are examined during reversible shear loading. In a second part, the role of the defect energy internal length scale on polycrystal’s hardening during tension–compression is discussed. The results are compared to those obtained using FFT-based continuum field dislocation mechanics without defect energy.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Numerical analysis of soot emissions from gasoline-ethanol and gasoline-butanol blends under gasoline compression ignition conditions

In the present work, computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition (GCI) engine were performed to investigate the impact of blending two biofuels, ethanol and n-butanol, with gasoline on the trade-off between combustion phasing and soot emissions under low load conditions. Here, in order to represent market gasoline (RD5-87), a four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 20% ethanol by mole; E20) was formulated using a neural network based octane predictor such that the surrogate had the same ethanol content, Research Octane Number (RON) and Octane Sensitivity (S). In addition, a novel skeletal kinetic mechanism for ETPRF and TPRF + n-butanol (BTPRF) blends, incorporating polycyclic aromatic hydrocarbon (PAH) chemistry, was developed. A three-dimensional (3D) engine CFD formulation employing the skeletal mechanism, adaptive mesh refinement (AMR), finite-rate chemistry approach, and hybrid method of moments (HMOM) was adopted to capture the in-cylinder combustion phenomena and soot emissions. The engine CFD model was validated against RD5-87 experimental data for a broad range of start-of-injection (SOI) timings (-21/-27/-36/-45 crank angle degrees (CAD) after top-dead center (aTDC)), with respect to in-cylinder pressure, heat release rate, combustion phasing, and soot emissions. The closed-cycle simulation results were analyzed to elucidate the non-monotonic trend of soot emissions versus SOI timing: SOI-36 > SOI-45 > SOI-21 > SOI-27. Thereafter, the validated CFD model was employed to simulate the combustion of a gasoline-ethanol blend with 45% (by mole) ethanol (E45) and a gasoline-butanol blend with 45% (by mole) n-butanol (B45) under the same operating conditions to study the effects of fuel composition and SOI timing on combustion phasing and soot emissions. The sooting propensity followed the trend: B45 > E20 > E45 at all SOI timings. Overall, it was observed that the autoignition propensity was primarily related to fuel chemistry. On the other hand, sooting propensity showed strong coupling with both fuel chemistry and physical properties, with greater impact of fuel physical properties at advanced SOI timings.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Observation of two different cool flame regimes of diethyl ether in a counterflow burner

This short communication reports, for the first time, the existence of two different self-sustaining cool flame regimes of diethyl ether (DEE) in a diffusion counterflow burner: a weaker autoignition-assisted cool flame near the fuel burner and a normal diffusion cool flame near the stagnation plane. Here, the results show that the normal diffusion cool flame extinction limit increases monotonically with the fuel mole fraction, while the autoignition-assisted cool flame approaches a plateau and can exist at a fuel mole fraction below the normal diffusion cool flame. It is shown that both flame regimes are governed by the same low-temperature chain-branching reaction pathway of DEE. By using in situ laser diagnostics, entrainment of unburned fuel stream to oxygen stream at the outer edge of the fuel burner is identified as the governing physical mechanism causing a partially premixed self-sustained hollow cool flame structure. The results reveal that when a fuel with high low-temperature reactivity, two different cool flame regimes can be observed in a counterflow flame experiment. Future studies with high-reactivity fuels in a counterflow burner must ensure to distinguish between the two self-sustaining cool flame regimes. Moreover, the existence of these different cool flame regimes needs to be examined so that they would not trigger an uncontrolled combustion phasing in advanced engines fed with high low-temperature reactivity fuels.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Modeling low cycle fatigue (LCF) of additively manufactured Hastelloy X using An accelerated crystal plasticity fatigue damage model

This paper presents a microstructure-based model for low cycle fatigue (LCF) behavior and life of Nickel-based alloy Hastelloy X manufactured using laser-powder bed fusion (L-PBF) additive manufacturing (AM). AM Hastelloy X, a solution-strengthened alloy, is tested at elevated temperature under fully reversed LCF conditions at different strain levels. A generalized plane strain finite element model is generated from electron backscatter diffraction (EBSD) characterization. The constitutive behavior of the material under fatigue is modeled using crystal plasticity and calibrated with both monotonic tensile and cyclic stress–strain data. The fatigue micro-crack initiation and propagation in the microstructure is modeled using a modified Chaboche fatigue damage model. An embedded boundary condition with a homogenous medium is used to apply the cyclic deformation and prevent numerically introduced over-constraints during fatigue simulation. A ‘cycle-jump’ method is used to accelerate the fatigue simulation and reduce the computational cost. The simulation results are compared to LCF experiments, showing satisfactory matches in cyclic stress behavior and number of cycles to macro-crack initiation for all applied strain ranges. In addition, the model illustrates the potential for quantifying microscale fatigue life impacting factors such as microstructure and surface roughness, which is needed to accurately quantify the reliability of AM components in service.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Particle-wall heat transfer in narrow-channel bubbling fluidized beds for thermal energy storage

Robust metal oxide particles can provide low-cost and stable thermal energy storage (TES) to temperatures up to 1200 °C or higher. The transfer of heat into and out of the particles in cost-effective high-temperature particle heat exchangers remains a principal challenge to implementing particle-based TES. The present work expands on prior studies of particle-wall heat transfer in narrow-channel fluidized beds operated in the bubbling fluidization regime. Batch-mode experiments with various oxide particles over a range of temperatures and airflow rates indicate that particle-wall heat transfer increases with higher bed temperature and decreasing particle size. Measured particle-wall heat transfer coefficients in a 12 mm deep channel are fit to a Nusselt number correlation proportional to a non-monotonic function of excess fluidization velocity. Particle-wall heat transfer coefficients rise rapidly with excess fluidization velocities until reaching a maximum at intermediate air velocities due to a trade-off between enhanced transverse particle mixing and decreasing particle volume fraction with increased fluidization velocity. The heat transfer coefficient reaches a maximum of ≈ 400% of values without fluidization for oxide particles ranging in diameter d p from 159 to 408 μm. For the smallest particles tested, composed of olivine sand, particle-wall heat transfer coefficients peak above 1100 Wm –2 K –1 at 450 °C. 100-h tests at 500 °C and near-optimal heat transfer fluidization velocities indicated minimal wall wear or oxide scaling. Furthermore, high particle-wall heat transfer coefficients suggest narrow channel fluidized beds as a potential pathway for reducing the required surface area and cost of high-temperature particle heat exchangers, as needed in large-scale thermal energy storage applications.

14 SOLAR ENERGY↗

DOE OSTI2024

Effect of impurities on hydrogen defect stability and migration barrier in yttrium dihydride crystal

The impurity or alloying atoms in YH 2 can alter the local electronic structure and so the hydrogen defect stability, as well as the H migration barrier energy. Thus, DFT calculations were employed to determine the effect of foreign elements from alkali and alkaline earth metals to transition metals and one critical impurity element, O, on H vacancy stability and retention characteristics in YH2. Results revealed that alloying elements act as hydrogen vacancy sinks by reducing the vacancy formation energy at neighboring sites. The implantation of non-magnetic foreign elements (s1, s2, and d10 valence electrons) in hydrogen energy landscape was calculated to be minor; while the hydrogen vacancy formation energy was reduced from 1.37 eV to 1.00 eV, the migration energy barrier of hydrogen was increased from 0.87 eV to 1.15 eV for non-magnetic foreign elements. The migration energy barrier monotonically decreased with increasing d-shell occupancy, reaching as low as 0.4 eV for Cr, Mo(d4), and Fe (d4). Alloying with late transition metals (d8 and d9) moderately impacted the hydrogen vacancy formation. Finally, it was found to be O addition into the YH 2- lattice did not alter the energy landscape of hydrogen vacancies. Since alloyed YH 2 has not been studied extensively, this study provides an atomistic understanding how alloying elements and impurities trap vacancies and affects hydrogen mobility YH 2 . Meanwhile, the main findings of this study may serve as guidelines for introducing alloying elements in ZrH 2 as well.

08 HYDROGEN↗

DOE OSTI2020

Effect of nickel on the kinematic stability of retained austenite in carburized bearing steels – In-situ neutron diffraction and crystal plasticity modeling of uniaxial tension tests in AISI 8620, 4320 and 3310 steels

The presence of kinematically metastable retained austenite in the microstructure of bearing components can significantly affect the macro and micro-mechanical material response. In the present work, the influence of Ni on the stability of the retained austenite within three different grades of high carbon bearing steel using in-situ neutron diffraction is investigated. For the first time, the results show that presence of Ni increases the stability of the austenite in the elastic regime whereas the transformation rate remains unaffected. Crystal plasticity finite element (CPFE) modeling was used to study the deformation in these three steels and shows that the predominant factor causing the difference in mechanical behavior of these steels is the austenite stability. Finally, the elastic and plastic response of the matrix martensite was found to be identical among all specimens while the austenite demonstrates similar elastic behavior but remarkably different stabilities under monotonic loading.

36 MATERIALS SCIENCE↗

DOE OSTI2021

On the correlation between plastic strain and misorientation in polycrystalline body-centered-cubic microstructures with an emphasis on the grain size, loading history, and crystallographic orientation

In this work, the correlation between plastic strain and crystallographic misorientation, grain size, grain orientation, distance from grain boundary, and loading history were investigated experimentally and numerically for body-centered-cubic (BCC) polycrystalline microstructures based on a misorientation deviation (MD) approach. Nine monotonic tensile experiments were performed on two BCC titanium alloys inside a scanning electron microscope (SEM). The influence of reference orientation was explored both at the grain scale and at the mesoscale using electron backscattered diffraction (EBSD). The correlation between global plastic strain and the MD was quantified. The tendency for orientation change was quantified as a function of plastic strain and grain orientation for three crystallographic orientations (i.e., [100], [110], and [111]) with respect to tensile direction. The subpopulation of small grains exhibited a lower level of misorientation dispersion compared with larger grains, and this discrepancy became more pronounced at higher strains. An empirical equation was proposed to estimate the level of misorientation dispersion for individual grains as a function of grain size and global plastic strain level. Two interrupted in-situ SEM experiments were performed, and this resulted in a significantly increased misorientation compared with uninterrupted tests performed to similar plastic strain levels.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Crystal plasticity modeling of strain-induced martensitic transformations to predict strain rate and temperature sensitive behavior of 304 L steels: Applications to tension, compression, torsion, and impact

This paper advances crystallographically-based Olson-Cohen (direct γ → α’) and deformation mechanism (indirect γ→ε→α’) phase transformation models for predicting strain-induced austenite to martensite transformation. Here, the advanced transformation models enable predictions of not only strain-path sensitive, but also of strain-rate and temperature sensitive deformation of polycrystalline stainless steels (SSs). The deformation of constituent grains in SSs is modeled as a combination of anisotropic elasticity, crystallographic slip, and phase transformation, while the hardening is based on the evolution of dislocation density and explicit shifts in phase fractions. Such grain-scale deformation is implemented within the meso-scale elasto-plastic self-consistent (EPSC) homogenization model, which is coupled with the implicit finite element (FE) method to provide a constitutive response at each FE integration point for solving boundary value problems at the macro-scale. Parameters pertaining to the hardening and transformation models within FEEPSC are calibrated and validated on a suite of data including flow curves and phase fractions for monotonic compression, tension, and torsion as a function of strain-rate and temperature for wrought and additively manufactured (AM) SS304L. To illustrate the potential and accuracy of the integrated multi-level FE-EPSC simulation framework, geometry, mechanical response, phase fractions, and texture evolution are simulated during gas-gun impact deformation of a cylinder and quasi-static tension of a notched specimen made of AM SS304L. Details of the simulation framework, comparison between experimental and simulation results, and insights from the results are presented and discussed.

304L steels↗

DOE OSTI2023

Training material models using gradient descent algorithms

High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. Here, we demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Cubic to hexagonal tuning in Fe 2 Mn(Si 1– x Ge x ) Heusler alloys

Here, the competition between the stability of the cubic and hexagonal full Heusler alloys and the implications concerning their magnetic properties were systematically studied through the detailed structural and magnetic characterization of the Fe 2 Mn(Si 1– x Ge x ) system. This system was specifically chosen as the parent compositions are cubic ( x = 0) and hexagonal ( x = 1). It is found that the formation of hexagonal phases occurs for the x ≥ 0.6 samples, whereas its phase fraction monotonically increases with x until the pure hexagonal Fe 2 MnGe is formed. The change in structure results in high sensitiveness of both the saturation of magnetization ($M_S$) and Curie temperature ($T_C$) with x values, related to a strong magnetocrystalline anisotropy of the hexagonal phase. Both cubic and hexagonal magnetic features were qualitatively reproduced by Density Functional Theory (DFT) calculations. This work provides an experimental and theoretical foundation for further design of Heusler systems with controlled structures and magnetic properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Macro copper-graphene composites with enhanced electrical conductivity

Composites demonstrating simultaneously enhanced-electrical conductivity, current density and lowered-temperature coefficient of resistance (TCR) compared to copper have been highly sought after for their advantages in efficient energy transport behavior. While such conductors have been demonstrated in 1D (nanowires) and 2D (films) samples, achieving similar behavior in 3D has been challenging owing to the limitations of the synthesis techniques used. In this paper, novel macro-scale 3D copper conductors were demonstrated with simultaneously increased electrical conductivity and decreased temperature coefficient of resistance (TCR) through the addition of graphene. Hot-extrusion was used to manufacture over 1-m-long, 2-mm-diameter copper-graphene composites with varying graphene content and defect density. Results showed that the electrical conductivity and current density in composites with low defect density graphene increased monotonically as a function of graphene content. They also demonstrated a significant decrease of over 17% in TCR with the addition of only 15 ppm graphene along with over 52% improvement in current density. Comparatively composites with high defect density graphene demonstrated lower electrical conductivity and current density. This study provides first-of-its-kind evidence of 3D metal composites whose bulk electrical performance has been enhanced using graphene additive in minute quantities. Further developments in this area are essential to achieve high performance composite conductors that can improve energy transport efficiency and pave way for industrial adoption of such materials in the future.

36 MATERIALS SCIENCE↗