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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

On plasticity-enhanced interfacial toughness in bonded joints

Here, the performance and reliability of many structures and components depend on the integrity of interfaces between dissimilar materials. Interfacial toughness Γ is the key material parameter that characterizes resistance to interfacial crack growth, and Γ is known to depend on many factors including temperature. For example, previous work showed that the toughness of an epoxy/aluminum interface decreased 40 % as the test temperature was increased from -60 °C to room temperature (RT). Interfacial integrity at elevated temperatures is of considerable practical importance. Recent measurements show that instead of continuing to decrease with increasing temperature, Γ increases when test temperature is above RT. Cohesive zone finite element calculations of an adhesively bonded, asymmetric double cantilever beam specimen of the type used to measure Γ suggest that this increase in toughness may be a result of R-curve behavior generated by plasticity-enhanced toughening during stable subcritical crack growth with interfacial toughness defined as the critical steady-state limit value. In these calculations, which used an elastic-perfectly plastic epoxy model with a temperature-dependent yield strength, the plasticity-enhanced increase in Γ above its intrinsic value Γ o depended on the ratio of interfacial strength σ* to the yield strength σyb of the bond material. There is a nonlinear relationship between Γ/Γ o and σ*/σ yb with the value Γ/Γ o increasing rapidly above a threshold value of σ*/σ yb . The predicted increase in toughness can be significant. For example, there is nearly a factor of two predicted increase in Γ/Γ o during micrometer-scale crack-growth when σ*/σ yb = 2 (a reasonable choice for σ*/σ yb ). Furthermore, contrary to other reported results, plasticity-enhanced toughening can occur prior to crack advance as the cohesive zone forms and the peak stress at the tip of the original crack tip translates to the tip of the fully formed cohesive zone. These results suggest that plasticity-enhanced toughening should be considered when modeling interfaces at elevated temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Persistent compositions of non-stoichiometric compounds with low bulk diffusivity: A theory and application to Nb3Sn superconductors

Non-stoichiometric compounds may develop a composition gradient when they are formed by reactive diffusional processes. This paper reports an interesting phenomenon that in compounds with low bulk diffusivities, which rely mainly on grain boundary diffusion for their growth, the final bulk compositions may be far from equilibrium, with a very low bulk diffusivity leading to fixed (persistent) compositions – one such example is Nb 3 Sn, a superconductor. We investigated the microchemistry at the reactive interface using atom probe tomography to clarify the diffusion reaction mechanism for this low-bulk-diffusivity case and thus propose a theory for what determines the compound composition, using Nb 3 Sn as an example for concreteness. Using certain approximations, we derive an explicit analytical equation that illustrates what factors determine its composition profile. We compare our model with the known facts of Nb 3 Sn and see good agreement. In particular, this model predicts that internal oxidation may lead to higher Sn contents than conventional, non-oxidized Nb 3 Sn. Our measurements show that this is indeed true, and that the internally-oxidized Nb 3 Sn also has higher upper critical fields, achieving up to 28.2 T at 4.2 K. We discuss the general applicability of this model to non-stoichiometric compounds with low bulk diffusivity, and propose it as a tool to help in the design and processing of such materials for compositional control.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A generalized approximate control variate framework for multifidelity uncertainty quantification

We describe and analyze a variance reduction approach for Monte Carlo (MC) sampling that accelerates the estimation of statistics of computationally expensive simulation models using an ensemble of models with lower cost. These lower cost models — which are typically lower fidelity with unknown statistics — are used to reduce the variance in statistical estimators relative to a MC estimator with equivalent cost. We derive the conditions under which our proposed approximate control variate framework recovers existing multifidelity variance reduction schemes as special cases. We demonstrate that existing recursive/nested strategies are suboptimal because they use the additional low-fidelity models only to efficiently estimate the unknown mean of the first low-fidelity model. As a result, they cannot achieve variance reduction beyond that of a control variate estimator that uses a single low-fidelity model with known mean. However, there often exists about an order-of-magnitude gap between the maximum achievable variance reduction using all low-fidelity models and that achieved by a single low-fidelity model with known mean. We show that our proposed approach can exploit this gap to achieve greater variance reduction by using non-recursive sampling schemes. The proposed strategy reduces the total cost of accurately estimating statistics, especially in cases where only low-fidelity simulation models are accessible for additional evaluations. Several analytic examples and an example with a hyperbolic PDE describing elastic wave propagation in heterogeneous media are used to illustrate the main features of the methodology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling

Scale bridging is a critical need in computational sciences, where the modeling community has developed accurate physics models from first principles, of processes at lower length and time scales that influence the behavior at the higher scales of interest. However, it is not computationally feasible to incorporate all of the lower length scale physics directly into upscaled models. This is an area where machine learning has shown promise in building emulators of the lower length scale models, which incur a mere fraction of the computational cost of the original higher fidelity models. We demonstrate the use of machine learning using an example in materials science estimating continuum scale parameters by emulating, with uncertainties, complicated mesoscale physics. Additionally, we describe a new framework to emulate the fine scale physics, especially in the presence of microstructures, using machine learning, and showcase its usefulness by providing an example from modeling fracture propagation. Our approach can be thought of as a data-driven dimension reduction technique that yields probabilistic emulators. Our results show well-calibrated predictions for the quantities of interests in a low-strain simulation of fracture propagation at the mesoscale level. Furthermore, on average, we achieve ~10% relative errors on time-varying quantities like total damage and maximum stresses. Successfully replicating mesoscale scale physics within the continuum models is a crucial step towards predictive capability in multi-scale problems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sylvester-preconditioned adaptive-rank implicit time integrators for advection-diffusion equations with variable coefficients

Here, we consider the adaptive-rank integration of multi-dimensional time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with high-order diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), which we solve with a projection-based adaptive-rank algorithm structured around two key strategies: (i) constructing dimension-wise subspaces using a novel atypical extended Krylov strategy, and (ii) efficiently solving the basis coefficient matrix with a preconditioned GMRES solver. The low-rank decomposition is performed in 2D using SVD and with high-order SVD (HOSVD) in 3D to represent the tensor in a compressed Tucker format. For d-dimensional problems (here, d = 2 or 3), the computational complexity and memory storage of the approach are found numerically to scale as and $\mathscr{O}(Nr^2) + \mathscr{O} (r^{d+1})$ and $\mathscr{O}(Nr) + \mathscr{O} (r^{d})$, respectively, with the one-dimensional resolution and the maximal rank during the Krylov iteration (which we find to be largely independent of on our numerical examples). We present numerical examples that illustrate the advertised properties of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Microchannel reactive distillation for the conversion of aqueous ethanol to ethylene

Here we demonstrate the proof-of-concept for microchannel reactive distillation for alcohol-to-jet application: combining ethanol/water separation and ethanol dehydration in one unit operation. Ethanol is first distilled into the vapor phase, converted to ethylene and water, and then the water co-product is condensed to shift the reaction equilibrium. Process intensification is achieved through rapid mass transfer—ethanol stripping from thin wicks using novel microchannel architectures—leading to lower residence time and improved separation efficiency. Energy savings are realized with integration of unit operations. For example, heat of condensing water can offset vaporizing ethanol. Furthermore, the dehydration reaction equilibrium shifts towards completion by immediate removal of the water byproduct upon formation while maintaining aqueous feedstock in the condensed phase. For aqueous ethanol feedstock (40% w ), 71% ethanol conversion with 91% selectivity to ethylene was demonstrated at 220 °C, 600 psig, and 0.28 h -1 wt hour space velocity. 2.7 stages of separation were also demonstrated, under these conditions, using a device length of 8.3 cm. This provides a height equivalent of a theoretical plate (HETP), a measure of separation efficiency, of ~3.3 cm. By comparison, conventional distillation packing provides an HETP of ~30 cm. Thus, 9.1× reduction in HETP was demonstrated over conventional technology, providing a means for significant energy savings and an example of process intensification. Finally, preliminary process economic analysis indicates that by using microchannel reactive distillation technology, the operating and capital costs for the ethanol separation and dehydration portion of an envisioned alcohol-to-jet process could be reduced by at least 35% and 55%, respectively, relative to the incumbent technology, provided future improvements to microchannel reactive distillation design and operability are made.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Reactive flash sintering and characterization of bulk high entropy nitrides

Over the past decade, numerous high-entropy ceramics have been synthesized, often displaying attractive properties. However, the study on facile preparation of bulk high entropy nitrides (HEN) are limited, despite its broad potential applications. This research demonstrates for the first time rapid fabrication (within ∼6 min) of bulk high-entropy nitrides, especially (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N, from binary nitride powder mixtures using a highly efficient reactive flash sintering (RFS) technique. X-ray diffraction (XRD) shows the HENs from RFS are near single-phase solid solutions with a rock salt crystal structure, while in situ synchrotron study carried out during RFS captured in real time the formation of HEN, which was preserved upon cooling, suggesting thermodynamic stability of the HEN phase, even up to extreme pressure (∼35.6 GPa). Microscopic analyses using SEM, STEM, and EDS reveal decent uniformity for HEN with no obvious segregation of elements, even to submicron scale. Some properties of the obtained bulk HENs are consistent with expectations. For example, their hardness and bulk modulus are close to estimates based on rule-of-mixture (ROM) values from the constituent binary nitrides. Meanwhile, some other measured properties seem to show surprises. For example, the fracture toughness for the HENs (e.g., 7.81 ± 1.40 MPa•m 1/2 or higher) turns out to be more than double of the expected ROM estimates. The significantly improved fracture toughness is attributed to the observed nano-layered structure of the HENs, despite the HEN’s cubic crystal structure and high hardness. In addition, the oxidation resistance shows improvement up till ∼800°C, possibly due to Ta doping that suppress oxygen vacancy formation in the oxide shell, while the 5-metal HEN of (Al 0.17 Nb 0.17 Ta 0.17 Ti 0.32 Zr 0.17 )N displays superconductivity (T c of ∼5–7 K from magnetism and resistivity measurements, slightly lower than ROM estimate), despite insulating property of starting AlN. Furthermore, future study combining experimental investigation using larger samples to confirm the observed increase in fracture toughness and oxidation resistance, theoretical modeling at different length scale, and more detailed structural/chemical characterization, especially at the atomic scale, are all needed to fully understand the inter-relationships between composition, processing, structure, and novel properties for these HENs and the development of related new materials for different applications.

Flash sintering↗
DOE OSTI2023

Even spheres as joint spectra of matrix models

The Clifford spectrum is a form of joint spectrum for noncommuting matrices. This theory has been applied in photonics, condensed matter and string theory. In applications, the Clifford spectrum can be efficiently approximated using numerical methods, but this only is possible in low dimensional example. In this paper we examine the higher-dimensional spheres that can arise from theoretical examples. We also describe a constructive method to generate five real symmetric almost commuting matrices that have a K-theoretical obstruction to being close to commuting matrices. For this, we look to matrix models of topological electric circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Reassessing early-age strength development of high-volume fly ash concretes for precast buildings

Increasing beneficial use of fresh or landfilled fly ash as a replacement for Portland cement can be more challenging for the construction of precast buildings or similar applications requiring rapid strength development. Therefore, the framework presented in this paper aims to reassess high-volume fly ash concretes but in the context of facilitating more sustainable precast buildings. More specifically, the framework was used to characterize strength development of concrete mixes with a target minimum 24-hour compressive strength of 24.1 MPa (3500 psi), selected as an example strength development metric to demonstrate the framework, and comprised of 40% Class C, Class F, and landfilled (harvested) fly ash – as a high-volume replacement of Type III or Type IL cement. High-early strength was driven by optimized dosages of commercial grade gypsum and accelerating admixtures, in addition to optimal aggregate packing and mix proportioning strategies. Early-age mechanical properties including compressive strength, modulus of rupture, and modulus of elasticity were reevaluated within 24 hours of batching with respect to common precast production demands. Simple data analyses were then used to highlight cases where currently accepted design provisions for the aforementioned properties are either overly-conservative or unconservative with respect to test data. Furthermore, the framework and demonstration of example mixes presented herein aim to promote confidence for using larger fractions of fresh or landfilled fly ashes for precast buildings to further enhance environmental benefits without sacrificing pertinent early-age structural performance.

42 ENGINEERING↗
DOE OSTI2020

UQpy: A general purpose Python package and development environment for uncertainty quantification

In this paper, we present the UQpy software toolbox, an open-source Python package for general uncertainty quantification (UQ) in mathematical and physical systems. The software serves as both a user-ready toolbox that includes many of the latest methods for UQ in computational modeling and a convenient development environment for Python programmers advancing the field of UQ. The paper presents an introduction to the software's architecture and existing capabilities, divided in the code in a set of modules centered around different UQ tasks such as sampling methods, generation of random processes and random fields, probabilistic inverse modeling, reliability analysis, surrogate modeling, and active learning. The paper also highlights the importance of the RunModel module, which is used to drive simulations in the uncertainty analyses performed in UQpy. This module conveniently allows the user to define computational models directly in Python, or to run simulations from a third-party software in serial or in parallel. To illustrate the various capabilities, two examples are tracked throughout the paper and analyzed repeatedly for various UQ tasks. The first is a Python model solving a nonlinear structural dynamics problem, used to illustrate UQpy's capabilities in sampling and forward propagation of high dimensional random vectors (stochastic processes), and probabilistic inference. The second model is a third-party Abaqus finite element model solving the thermomechanical response of a beam structure. This example is used to illustrate UQpy's capabilities in variance reduction sampling techniques, reliability analysis, surrogate modeling and active learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements

Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li 0.375 Sr 0.4375 )(Ta 0.375 Nb 0.375 Zr 0.125 Hf 0.125 )O 3-δ compared with the (Li 0.375 Sr 0.4375 )(Ta 0.75 Zr 0.25 )O 3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A General Strategy for Batch Development of High-Performance and Cost-Effective Sodium Layered Cathodes

High-performance and low-cost transition metal (TM) layered oxides using earth abundant elements are promising cathodes for Na-ion batteries. However, it is challenging to obtain desired materials because the large Na size, different Na occupations and various layer stacking sequences multiply the complication in determining the structure of a given composition and exacerbate uncertainty to the structure-property correlation. In this work, we use the attainment of desired NaxMnyNiZTM1-y-zO2-based cathode materials as an example to demonstrate a general roadmap for batch development of sodium layered cathodes towards practical applications. A synthesis phase diagram of NaxMnyNi1-yO2 was created for pre-screening and rational selection of the platform material of P2/O3-structured Na0.85Mn0.6Ni0.4O2. Cationic potential was leveraged in elemental substitution to further promote the material structural stability and electrochemical performance. Several cost-effective O3 and P2/O3 hybrid cathode materials have been obtained, all of which demonstrate excellent performance. In particular, the Na0.85Mn0.5Ni0.4Ti0.1O2 delivers a high specific capacity of ~130 mAh/g between 2-4 V and 91% retention after 500 cycles. The work discovers multiple materials as high-performance and cost-effective Na-ion battery cathodes and offers critical guidance to the rational design of future layered cathode materials.

Xiao, Biwei↗
DOE OSTI2024

Lie–Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries

An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamiltonian system contains two essential ingredients: the Poisson bracket and the Hamiltonian. Hamiltonian systems with symmetries, whose paradigm examples are the Lie–Poisson systems, have been shown to describe a broad category of physical phenomena, from satellite motion to underwater vehicles, fluids, geophysical applications, complex fluids, and plasma physics. The Poisson bracket in these systems comes from the symmetries, while the Hamiltonian comes from the underlying physics. We view the symmetry of the system as primary, hence the Lie–Poisson bracket is known exactly, whereas the Hamiltonian is regarded as coming from physics and is considered not known, or known approximately. Using this approach, we develop a network based on transformations that exactly preserve the Poisson bracket and the special functions of the Lie–Poisson systems (Casimirs) to machine precision. We present two flavors of such systems: one, where the parameters of transformations are computed from data using a dense neural network (LPNets), and another, where the composition of transformations is used as building blocks (G-LPNets). We also show how to adapt these methods to a larger class of Poisson brackets. We apply the resulting methods to several examples, such as rigid body (satellite) motion, underwater vehicles, a particle in a magnetic field, and others. The methods developed in this paper are important for the construction of accurate data-based methods for simulating the long-term dynamics of physical systems.

Data-based modeling↗
DOE OSTI2023

G4CMP: Condensed matter physics simulation using the G$\scriptsize{\mathrm{EANT}}$4 toolkit

G4CMP simulates phonon and charge transport in cryogenic semiconductor crystals using the Geant4 toolkit. The transport code is capable of simulating the propagation of acoustic phonons as well as electron and hole charge carriers. Herein processes for anisotropic phonon propagation, oblique charge-carrier propagation, and phonon emission by accelerated charge carriers are included. The simulation reproduces theoretical predictions and experimental observations such as phonon caustics, heat-pulse propagation times, and mean charge-carrier drift velocities. In addition to presenting the physics and features supported by G4CMP, this report outlines example applications from the dark matter and quantum information science communities. These communities are applying G4CMP to model and design devices for which the energy transported by phonons and charge carriers is germane to the performance of superconducting instruments and circuits placed on silicon and germanium substrates. The G4CMP package is available to download from GitHub: github.com/kelseymh/G4CMP.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Rayleigh–Taylor and Richtmyer–Meshkov instabilities: A journey through scales

Hydrodynamic instabilities such as Rayleigh–Taylor (RT) and Richtmyer–Meshkov (RM) instabilities usually appear in conjunction with the Kelvin–Helmholtz (KH) instability and are found in many natural phenomena and engineering applications. They frequently result in turbulent mixing, which has a major impact on the overall flow development and other effective material properties. This can either be a desired outcome, an unwelcome side effect, or just an unavoidable consequence, but must in all cases be characterized in any model. The RT instability occurs at an interface between different fluids, when the light fluid is accelerated into the heavy. The RM instability may be considered a special case of the RT instability, when the acceleration provided is impulsive in nature such as that resulting from a shock wave. Here in this pedagogical review, we provide an extensive survey of the applications and examples where such instabilities play a central role. First, fundamental aspects of the instabilities are reviewed including the underlying flow physics at different stages of development, followed by an overview of analytical models describing the linear, nonlinear and fully turbulent stages. RT and RM instabilities pose special challenges to numerical modeling, due to the requirement that the sharp interface separating the fluids be captured with fidelity. These challenges are discussed at length here, followed by a summary of the significant progress in recent years in addressing them. Examples of the pivotal roles played by the instabilities in applications are given in the context of solar prominences, ionospheric flows in space, supernovae, inertial fusion and pulsed-power experiments, pulsed detonation engines and Scramjets. Progress in our understanding of special cases of RT/RM instabilities is reviewed, including the effects of material strength, chemical reactions, magnetic fields, as well as the roles the instabilities play in ejecta formation and transport, and explosively expanding flows. The article is addressed to a broad audience, but with particular attention to graduate students and researchers who are interested in the state-of-the-art in our understanding of the instabilities and the unique issues they present in the applications in which they are prominent.

42 ENGINEERING↗
DOE OSTI2020

Structure formation in turbulence as an instability of effective quantum plasma

Structure formation in turbulence can be understood as an instability of “plasma” formed by fluctuations serving as effective particles. These “particles” are quantumlike in the sense that their wavelengths are non-negligible compared to the sizes of background coherent structures. The corresponding “kinetic equation” describes the Wigner matrix of the turbulent field, and the coherent structures serve as collective fields. This formalism is usually applied to manifestly quantumlike or scalar waves. Here, we show how to systematically extend it to more complex systems using compressible Navier–Stokes turbulence as an example. In this case, the fluctuation Hamiltonian is a five-dimensional matrix operator and diverse modulational modes are present. As an illustration, we calculate these modes for a sinusoidal shear flow and find two modulational instabilities. One of them is specific to supersonic flows, and the other one is a Kelvin–Helmholtz-type instability that is a generalization of the known zonostrophic instability. Our calculations are readily extendable to other types of turbulence, for example, magnetohydrodynamic turbulence in plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

More is definitely different: the zebrafish as witness: Comment on ”Structure and function in artificial, zebrafish and human neural networks” by Peng Ji et al.

We report half a century ago, in his foundation paper More is different, Anderson stated the relevance of including multiple scales of interaction, instead of trying to reduce everything to fundamental principles. By doing so, one may witness phenomena that were not expected by looking at each individual components separately. This idea finds indeed numerous realizations such as the synchronization phenomenon, where a multitude of dynamical systems suddenly start to behave coherently together, or the spreading of diseases over entire populations, to name only two examples. But if one would have to give a single example of a system where the multiplicity of interaction scales is of utmost importance, it would probably be the brain. In their review, Ji et al. give a comprehensive overview of the multiple scales of science required in the investigation of structural and functional brain networks, as well as their interplay.

59 BASIC BIOLOGICAL SCIENCES↗