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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Shear strain gradient in Cu/Nb nanolaminates: Strain accommodation and chemical mixing

Disentangling the intragranular and interfacial plasticity contribution to the overall strain accommodation is crucial to understanding the microstructural evolution and mass transport upon deformation in materials with the nanoscale feature size. In this study, we devise a new approach to tackle the issue by introducing shear strain gradients into Cu/Nb nanolaminates of different layer thicknesses with the shear perpendicular to the laminate interfaces. The measurement of the strain gradient and the resultant lattice disorientation enables a quantitative understanding of the intragranular and interfacial plasticity contribution. We found that intragranular slip entirely governs the deformation in the 300 nm-layer laminate and, unexpectedly, contributes ~80% of the total plasticity in the 30 nm-layer laminate. The high intragranular plasticity in the thin laminate is attributed to the large width of confined slip planes and their remnant potential for storing dislocations. In addition, substantial forced chemical mixing is observed in the top region of the 30 nm-layer laminate where the effective layer thickness is reduced below 8 nm. The transition of deformation mechanism from confined layer slip to dislocation transmission is largely responsible for the initiation of substantial mixing. Our method and findings shed light on the deformation mechanism and deformation-induced mass transport behavior in nanostructured materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Friction stir gradient alloying: A high-throughput method to explore the influence of V in enabling HCP to BCC transformation in a γ-FCC dominated high entropy alloy

The compositional possibilities in high entropy alloys (HEAs) is very vast and effective strategies are needed to establish potential alloy chemistries. In this study, friction stir gradient alloying (FSGA), a recently-introduced high-throughput (HT) technique incorporating compositional and microstructural gradients, was used to explore the possibility of introducing a bcc transformation domain in γ-fcc dominated TRIP HEA Fe 38.5 Mn 20 Cr 15 Co 20 Si 5 Cu 1.5 (at.%) by vanadium addition. FSGA has accelerated the process of attaining the composition-microstructure library and the results suggest that vanadium addition of ~1 at.% results in nucleation of α-bcc. Additionally, this is the first observation of ε-hcp to α-bcc transformation in a TRIP HEA and supports the Olson-Cohen model of martensitic transformation (γ-fcc → ε-hcp → α-bcc). ε-hcp is nucleated with a pyramidal orientation by the formation of planar faults on the {111} close-packed planes of γ-fcc, and α-bcc is nucleated in the vicinity of ε-hcp along the {111} γ-fcc trace. There are indications for the formation of basal orientated ε-hcp from intersecting ε-hcp planes orientated for the pyramidal slip. Thus, the study shows the nucleation and growth of the α-bcc phase attributed to the dual effects of chemistry and strain and potential co-existence of all the three phases.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Activity gradient mass transport accelerated corrosion of stainless steel in molten salt

Advanced energy systems based on molten salt technologies have gained increasing interest, and their design and construction are likely going to involve the use of materials of dissimilar chemistries. With dissimilar materials being used, galvanic coupling induced corrosion acceleration is one of the mechanisms believed to be an issue for the deployment of molten salt systems. However, non-galvanic interaction mechanisms such as activity gradient mass transport have also been theorized to represent a potential challenge. Unfortunately, the two effects have often been compounded in the literature, such that it is still unclear which degradation mechanisms are at play in these systems. In the following study, electrochemical measurements are used to predict the effect that galvanic coupling would have on dissimilar materials submerged in the same molten salt medium. Dissimilar material salt exposure tests are then performed to investigate whether galvanic coupling or activity gradient driven processes account for the levels of materials degradation observed. Finally, these separate effect electrochemical and corrosion experiments uniquely elucidate the degradation mechanisms in dissimilar materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A B-spline based gradient-enhanced micropolar implicit material point method for large localized inelastic deformations

The quasi-brittle response of cohesive-frictional materials in numerical simulations is commonly represented by softening plasticity or continuum damage models, either individually or in combination. However, classical models, particularly when coupled with non-associated plasticity, often suffer from ill-posedness and a lack of objectivity in numerical simulations. Moreover, the performance of the finite element method significantly degrades in simulations involving finite strains when mesh distortion reaches excessive levels. This represents a challenge for modeling cohesive-frictional materials, given their tendency to experience strongly localized deformations, such as those occurring during shear band dominated failure. Hence, accurate modeling of the response of cohesive-frictional solids is a demanding task. To address these challenges, we present an extension of the material point method (MPM) for the unified gradient-enhanced micropolar continuum, aiming at the analysis of finite localized inelastic deformations in cohesive-frictional materials. The generalized gradient-enhanced micropolar continuum formulation is employed to tackle challenges related to localization and softening material behavior, while the MPM addresses issues arising from excessive deformations. The method utilizes a B-spline formulation for the rigid background mesh to mitigate the well-known cell crossing errors of the MPM. To demonstrate the performance of the method, 2D and 3D numerical studies on localized failure in sandstone in plane strain compression and triaxial extension tests are presented. A comparison with finite element results confirms the suitability of the formulation. Moreover, an efficient numerical implementation of the formulation is presented, and it is demonstrated that the additional MPM specific overhead is negligible.

B-spline↗
DOE OSTI2022

The effect of stress on the migration of He gas bubbles under a thermal gradient in Fe by phase-field modeling

Here a phase-field model is parameterized to study the effect of elastic stresses on the migration of He gas bubbles in Fe under a temperature gradient. Stresses caused by the gas bubble pressure and residual stress in the Fe matrix are considered. The dependence of He bubble migration velocity on the magnitude of the residual stress, average temperature, temperature gradient, and bubble size is measured. In agreement with a theoretical model based on surface diffusion, simulation results demonstrate that He bubbles move towards the high temperature region with velocities in Fe that are orders of magnitude faster than previously reported in UO 2 . It is found that local stresses in the matrix caused by the He bubble have negligible effect on the bubble migration process; however, residual stresses in the Fe matrix, potentially caused by processing or irradiation, can modestly modify bubble kinetics through pressure dependence of the He diffusion coefficients. Compressive residual stress decreases diffusion coefficients for bulk and surface diffusion mechanisms, thus reducing the migration velocity of the gas bubble. In contrast, tensile residual stress increases the diffusion coefficients, resulting in an increase in the gas bubble migration velocity. This pressure dependence is also consistent with a theoretical model. This phase field model lays the foundation for analysis of bubble coalescence-induced fracture in He bubble-containing steels.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effects of input gradient regularization on neural networks time-series forecasting of thermal power systems

This study proposes using neural networks, specifically gated recurrent unit (GRU), long-short-term memory (LSTM), and transformer networks, to improve control strategies in a 450 MW coal-fired power plant. However, neural networks face issues of becoming overly dependent on just a few variables to make predictions, which negatively impacts control decisions that rely on the model to determine the value of all manipulated variables. The paper introduces regularization techniques, including noise injection and input gradient regularization, during the training phase. Here, the work presents novel contributions in adapting neural networks to control industrial systems and applying regularization techniques from computer vision to industrial process control. Results demonstrate the effectiveness of input gradient regularization in reducing model dependence on subsets of variables, emphasizing the balance between fidelity and controllability. Further exploration is recommended, including the development of recurrent transformers, closed-loop control testing, and a sensitivity analysis on computer models to provide further insight.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2020

Characterization of precipitation in gradient Inconel 718 superalloy

Inconel 718 (IN718) superalloy is a well-established material for the application of aircraft engine and power generator due to its excellent performance at high temperatures. The outstanding mechanical properties of IN718 are largely determined by the precipitates. Here, we investigated the precipitation behaviors and the corresponding mechanical properties of the heat-treated gradient IN718 alloy. The microscopy studies reveal that high-density hexagonal η phase with chemistry of Ni 3 (Al,Ti,Nb) formed in the nanograined IN718 alloy after annealing at 700 °C. Additionally, the compressive stress introduced during severe plastic deformation may have prohibited the formation of γ” phase while accelerated the precipitation of γ’ phase, consequently favoring the precipitation of η phase. The corresponding evolution of mechanical behavior of gradient IN718 alloy is investigated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Capturing the softening in T91 steel annealed with molten LBE through interfacial gradient plasticity

In order to study the effect of annealing with molten lead–bismuth eutectic alloy (mLBE) on the mechanical properties of T91 steel, micropillar compression tests were analyzed through the consideration of mechanical interface energy terms within gradient plasticity. The stress–strain curves of the micropillars showed significant stochastic effects, characterized by a different elastic and plastic behavior response for each pillar. Among the various pillars, some exhibited a similar trilinear behavior. Scanning electron microscopy (SEM) images of these specimens demonstrated localized slip deformation and slip planes after compression, while transmission electron microscopy (TEM) images showed the presence of a severe slip zone along the random grain boundaries (RGBs), indicating that the GBs played a dominant role in the deformation/slip. By employing interfacial gradient plasticity that can explicitly account for the presence of GBs, the trilinear response was captured, allowing for the determination of the mechanical interface parameter. As anticipated the pillars which underwent severe slip at the GBs had a lower value for the mechanical interface parameter. Finally, annealing with mLBE can, therefore, in some cases result in softening in the overall stress–strain, as it lowers the mechanical interface energy of GBs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carbon-sequestration gradient insulation composites

The massive use of carbon-sequestration building materials promises a potential global carbon sink in decarbonizing the building industry. Renewable biogenic materials from abundant agriculture waste for building practice have been around over thousands of years. However, in addition to their flammability and moisture problems, addressing their low thermal and structural performance is also becoming indispensable and urgent when it comes to environmentally sustainable and energy-efficient buildings. Here, we report a nature-inspired biogenic gradient insulation composite with an optimized silica concentration of 30 wt %, a density of 0.246 g/cm 3 , and a porosity of 86%. The gradient hybrid composite exhibits a thermal conductivity of 28.2 mW m -1 K -1 , which is the lowest achieved under optimal preparation conditions. Here, it also shows a flexural modulus of 590 MPa for the aerogel-rich layer without surface modification, and it demonstrates superior fire retardancy and superhydrophobicity after surface treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strain Gradient Elasticity in SrTiO 3 Membranes: Bending versus Stretching

Young’s modulus determines the mechanical loads required to elastically stretch a material and also the loads required to bend it, given that bending stretches one surface while compressing the opposite one. Flexoelectric materials have the additional property of becoming electrically polarized when bent. The associated energy cost can additionally contribute to elasticity via strain gradients, particularly at small length scales where they are geometrically enhanced. Here, we present nanomechanical measurements of freely suspended SrTiO 3 crystalline membrane drumheads. In this work, we observe an unexpected nonmonotonic thickness dependence of Young’s modulus upon small deflections. Furthermore, the modulus inferred from a predominantly bending deformation is three times larger than that of a predominantly stretching deformation for membranes thinner than 20 nm. In this regime we extract a strain gradient elastic coupling of ~2.2 μN, which could be used in new operational regimes of nanoelectro-mechanics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Carbon-Sequestration Straw Cellulose-Aerogel Gradient Thermal Insulation Material

Green superinsulation materials are essential for net-zero sustainable building envelopes. Realizing such potential is indispensable for simultaneously achieving carbon-sequestration and superinsulation performance. Here, we report the synthesis of a water glass-based silica aerogel exhibiting a thermal conductivity of 17.2 mW/m·K and a high porosity of 92%. Here, we used carbon-sequestration wheat straw fiber to create a gradient cellulose-aerogel composite to improve mechanical stability. The as-prepared gradient composite exhibits a thermal conductivity of 27.1 mW/m·K and a flexural modulus of 824 MPa, while exhibiting superhydrophobicity (water contact angle of 135.4°) for the development of green building insulation materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fluoride-Rich, Organic–Inorganic Gradient Interphase Enabled by Sacrificial Solvation Shells for Reversible Zinc Metal Batteries

Zinc metal batteries are strongly hindered by water corrosion, as solvated zinc ions would bring the active water molecules to the electrode/electrolyte interface constantly. Herein, we report a sacrificial solvation shell to repel active water molecules from the electrode/electrolyte interface and assist in forming a fluoride-rich, organic-inorganic gradient solid electrolyte interface (SEI) layer. Additionally, the simultaneous sacrificial process of methanol and Zn(CF 3 SO 3 ) 2 results in the gradient SEI layer with an organic-rich surface (CH 2 OC- and C 5 product) and an inorganic-rich (ZnF 2 ) bottom, which combines the merits of fast ion diffusion and high flexibility. As a result, the methanol additive enables corrosion-free zinc stripping/plating on copper foils for 300 cycles with an average coulombic efficiency of 99.5%, a record high cumulative plating capacity of 10 A h/cm 2 at 40 mA/cm 2 in Zn/Zn symmetrical batteries. More importantly, at an ultralow N/P ratio of 2, the practical VO 2 //20 mu m thick Zn plate full batteries with a high areal capacity of 4.7 mAh/cm 2 stably operate for over 250 cycles, establishing their promising application for grid-scale energy storage devices. Furthermore, directly utilizing the 20 μm thick Zn for the commercial-level areal capacity (4.7 mAh/cm 2 ) full zinc battery in our work would simplify the manufacturing process and boost the development of the commercial zinc battery for stationary storage.

25 ENERGY STORAGE↗
DOE OSTI2024

Co-free gradient lithium-rich cathode for high-energy batteries with optimized cyclability

Lithium-rich layered oxides (LLOs) hold the promise for high-energy battery cathodes. However, its application has been hindered by voltage decay associated with irreversible reactions at high voltages despite decades of intensive efforts. Here, we first theoretically studied the molecular orbitals of Mn-based Li-rich configurations. We found that the π-bond ring formed within the LiMn 6 structure could participate in stable redox reactions as one unit, but Co could disrupt its symmetry. We thus designed and synthesized Co-free concentration-gradient LLOs (CF-CG-LLOs) materials. The combination of concentration gradient and Co removal leads to exceptional capacity retention without any fading over 100 cycles of the pouch cell. More importantly, it exhibits an extraordinarily low voltage decay of 0.15 mV/cycle, accompanied by a high Coulombic efficiency of 99.86%. This concept and demonstration of CF-CG-LLO cathodes reveal a viable avenue toward low-cost, high-energy-density battery cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Analytic gradients for compressed multistate pair-density functional theory

Photochemical reactions often involve states that are closely coupled due to near degeneracies, for example by proximity to conical intersections. Therefore, a multistate method is used to accurately describe these states; for example, ordinary perturbation theory is replaced by quasidegenerate perturbation theory. Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient way to approximate the full dynamical correlation energy of strongly correlated systems, and we recently proposed compressed multistate pair-density functional theory (CMS-PDFT) to treat closely coupled states. In the present paper, we report the implementation of analytic gradients for CMS-PDFT in both OpenMolcas and PySCF, and here we illustrate the use of these gradients by applying the method to the excited states of formaldehyde and phenol.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Three-dimensional inhomogeneity of electron-temperature-gradient turbulence in the edge of tokamak plasmas

Nonlinear multiscale gyrokinetic simulations of a Joint European Torus edge pedestal are used to show that electron-temperature-gradient (ETG) turbulence has a rich three-dimensional structure, varying strongly according to the local magnetic-field configuration. In the plane normal to the magnetic field, the steep pedestal electron temperature gradient gives rise to anisotropic turbulence with a radial (normal) wavelength much shorter than in the binormal direction. In the parallel direction, the location and parallel extent of the turbulence are determined by the variation in the magnetic drifts and finite-Larmor-radius (FLR) effects. The magnetic drift and FLR topographies have a perpendicular-wavelength dependence, which permits turbulence intensity maxima near the flux-surface top and bottom at longer binormal scales, but constrains turbulence to the outboard midplane at shorter electron-gyroradius binormal scales. Here our simulations show that long-wavelength ETG turbulence does not transport heat efficiently, and significantly decreases overall ETG transport—in our case by ~40%—through multiscale interactions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Bi-fidelity Gradient-Based Approach for Nonlinear Well Logging Inverse Problems

Solving a non-linear inverse problem is challenging in computational science and engineering. Sampling based methods require a large number of model valuations; gradientbased methods require fewer model evaluations but only find the local minima. Multifidelity optimization combines the low fidelity model and the high-fidelity model to achieve both high accuracy and high efficiency. In this paper, we present a bi-fidelity approach to solve non-linear inverse problems. In the bi-fidelity inversion method, the low-fidelity model is used to acquire a good initial guess, and the high-fidelity model is used to locate the global minimum. Combined with a multi-start optimization scheme, the proposed approach significantly increases the possibility of finding the global minimum for nonlinear inverse problems with many local minima. The method is tested with two toy problems and then applied to an electromagnetic well logging inverse problem, which is difficult to solve using traditional gradient-based methods. The bi-fidelity method provides promising inversion results and can be easily applied to traditional gradient-based methods.

42 ENGINEERING↗