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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 1,081 records · Page 60
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DOE OSTI2018

A unified framework for polycrystal plasticity with grain boundary evolution

Plastic deformation in polycrystals is governed by the interplay between intra-granular slip and grain boundary-mediated plasticity. However, while the role played by bulk dislocations is relatively well-understood, the contribution of grain boundaries (GBs) has only recently begun to be studied. GB plasticity is known to play a key role along with bulk plasticity under a wide range of conditions, such as dynamic recovery, superplasticity, severe plastic deformation, etc., and developing models capable of simultaneously capturing GB and bulk plasticity has become a topic of high relevance. In this paper we develop a thermodynamically-consistent polycrystal plasticity model capable of simulating a variety of grain boundary-mediated plastic processes in conjunction with bulk dislocation slip. The model starts from the description of a single crystal and creates lattice strain-free polycrystalline configurations by using a specially-designed multiplicative decomposition developed by the authors. This leads to the introduction of a particular class of geometrically necessary dislocations (GND) that define fundamental GB features such as misorientation and inclination. The evolution of the system is based on an energy functional that uses a non-standard function of the GND tensor to account for the grain boundary energy, as well as for the standard elastic energy. Our implementation builds on smooth descriptions of GBs inspired on diffuse-interface models of grain evolution for numerical convenience. Here we demonstrate the generality and potential of the methodology by simulating a wide variety of phenomena such as shear-induced GB sliding, coupled GB motion, curvature-induced grain rotation and shrinkage, and polygonization via dislocation sub-grain formation.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Diffuse galactic gamma rays at intermediate and high latitudes. I. Constraints on the ISM properties

We study the high latitude (|b| > 10°) diffuse γ-ray emission in the Galaxy in light of the recently published data from the Fermi collaboration at energies between 100 MeV and 100 GeV. The unprecedented accuracy in these measurements allows to probe and constrain the properties of sources and propagation of cosmic rays (CRs) in the Galaxy, as well as confirming conventional assumptions made on the interstellar medium (ISM). Using the publicly available DRAGON code, that has been shown to reproduce local measurements of CRs, we study assumptions made in the literature on atomic (HI) and molecular hydrogen (H2) gas distributions in the ISM, and non spatially uniform models of diffusion in the Galaxy. By performing a combined analysis of CR and γ-ray spectra, we derive constraints on the properties of the ISM gas distribution and the vertical scale height of galactic CR diffusion, which may have implications also on indirect Dark Matter detection. We also discuss some of the possible interpretations of the break at high rigidity in CR protons and helium spectra, recently observed by PAMELA and their impact on γ-rays.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

A Finite-Element Approach for the Partitioning of Carbon in Q&P Steel

This paper reports a Galerkin finite-element analysis of carbon partitioning from martensite into austenite during the quenching and partitioning (Q&P) processes in steels. In contrast to classical or sophisticated diffusion field models, an alternative nonlinear governing equation based on chemical potential and composition is considered. The model is applied to simulate the carbon partitioning of modified 22MnB5 alloys assuming an immobile austenite–martensite phase boundary and, in turn, is compared with experimental measurements of the volume fraction and carbon content of retained austenite. The simulations show outstanding results on the influence of Si and C content on the partitioning parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions

Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Higher-order compositional modeling of three-phase flow in 3D fractured porous media based on cross-flow equilibrium

Numerical simulation of multiphase compositional flow in fractured porous media, when all the species can transfer between the phases, is a real challenge. Despite the broad applications in hydrocarbon reservoir engineering and hydrology, a compositional numerical simulator for three-phase flow in fractured media has not appeared in the literature, to the best of our knowledge. In this work, we present a three-phase fully compositional simulator for fractured media, based on higher-order finite element methods. To achieve computational efficiency, we invoke the cross-flow equilibrium (CFE) concept between discrete fractures and a small neighborhood in the matrix blocks. We adopt the mixed hybrid finite element (MHFE) method to approximate convective Darcy fluxes and the pressure equation. This approach is the most natural choice for flow in fractured media. The mass balance equations are discretized by the discontinuous Galerkin (DG) method, which is perhaps the most efficient approach to capture physical discontinuities in phase properties at the matrix-fracture interfaces and at phase boundaries. In this work, we account for gravity and Fickian diffusion. The modeling of capillary effects is discussed in a separate paper. We present the mathematical framework, using the implicit-pressure-explicit-composition (IMPEC) scheme, which facilitates rigorous thermodynamic stability analyses and the computation of phase behavior effects to account for transfer of species between the phases. A deceptively simple CFL condition is implemented to improve numerical stability and accuracy. We provide six numerical examples at both small and larger scales and in two and three dimensions, to demonstrate powerful features of the formulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

ASSOCIATION OF SUPRATHERMAL PARTICLES WITH COHERENT STRUCTURES AND SHOCKS

Various mechanisms have been proposed to explain observed suprathermal particle populations in the solar wind, including direct acceleration at flares, stochastic acceleration, shock acceleration, and acceleration by random compression or reconnection sites. Using magnetic field and suprathermal particle data from the Advanced Composition Explorer (ACE), we identify coherent structures and interplanetary shocks, and analyze the temporal association of energetic particle fluxes with these coherent structures. Coherent structures having a range of intensities are identified using the magnetic Partial Variance of Increments statistic, essentially a normalized vector increment. A stronger association of energetic particle flux in the 0.047-4.75 MeV range is found with intense magnetic discontinuities than is found with shocks. Nevertheless, the average profile of suprathermals near shocks is quite consistent with standard models of diffusive shock acceleration, while a significant amount of the energetic particles measured and strong discontinuities are found by ACE within six hours of a shock. This evidence supports the view that multiple mechanisms contribute to the acceleration and transport of interplanetary suprathermal particles.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The role of geographical spreaders in infectious pattern formation and front propagation speeds

The pattern formation and spatial spread of infectious populations are investigated using a kernel-based Susceptible–Infectious–Recovered (SIR) model applicable across a wide range of basic reproduction numbers R O . The goal is to examine the role of geographical spreaders on transient spatial pattern formation of infectious populations and the associated maximum invasive front speeds c max . In the simulations conducted here, geographical spreaders are defined as a portion of the infected population Φ experiencing high mobility between identical communities. The spatial organization of the infected population and c max are determined when the infections are randomly initiated in space within multiple communities. For small but finite , scaling analysis and numerical simulations in 1-dimension suggest that when the spreading kernel is Gaussian-shaped, where is the inverse of the infectious duration. This finding for agrees with a diffusion-based representation of mobility in 1-D. Numerical simulations in 2-D across wide-ranging suggest that , the variance of the spatial kernel describing mobility of long-distance geographical spreaders across communities, determines the spatial organization of infections across communities. When (long-distance mobility, where is the minimum spatial extent defining adjacent communities), the infectious population will experience a transient but spatially coherent pattern with a wavelength that can be derived from the spreading kernel properties. Moreover, the 2-D simulations for the bounded kernel suggest that attainment of is also dictated by but the magnitude is not sensitive to unlike diffusion-based models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

RELATIVE COMPOSITION AND ENERGY SPECTRA OF LIGHT NUCLEI IN COSMIC RAYS: RESULTS FROM AMS-01

Measurement of the chemical and isotopic composition of cosmic rays is essential for the precise understanding of their propagation in the galaxy. While the model parameters are mainly determined using the B/C ratio, the study of extended sets of ratios can provide stronger constraints on the propagation models. In this paper, the relative abundances of light-nuclei lithium, beryllium, boron, and carbon are presented. The secondary-to-primary ratios Li/C, Be/C, and B/C have been measured in the kinetic energy range 0.35-45 GeV nucleon{sup -1}. The isotopic ratio {sup 7}Li/{sup 6}Li is also determined in the magnetic rigidity interval 2.5-6.3 GV. The secondary-to-secondary ratios Li/Be, Li/B, and Be/B are also reported. These measurements are based on the data collected by the Alpha Magnetic Spectrometer AMS-01 during the STS-91 space shuttle flight in 1998 June. Our experimental results are in substantial agreement with other measurements, where they exist. We describe our light-nuclei data with a diffusive-reacceleration model. A 10%-15% overproduction of Be is found in the model predictions and can be attributed to uncertainties in the production cross-section data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

A novel method for calculating the energy barriers for carbon diffusion in ferrite under heterogeneous stress

A novel method for accurate and efficient evaluation of the change in energy barriers for carbon diffusion in ferrite under heterogeneous stress is introduced. This method, called Linear Combination of Stress States, is based on the knowledge of the effects of simple stresses (uniaxial or shear) on these diffusion barriers. Then, it is assumed that the change in energy barriers under a complex stress can be expressed as a linear combination of these already known simple stress effects. The modifications of energy barriers by either uniaxial traction/compression and shear stress are determined by means of atomistic simulations with the Climbing Image-Nudge Elastic Band method and are stored as a set of functions. The results of this method are compared to the predictions of anisotropic elasticity theory. It is shown that, linear anisotropic elasticity fails to predict the correct energy barrier variation with stress (especially with shear stress) whereas the proposed method provides correct energy barrier variation for stresses up to ∼3 GPa. This study provides a basis for the development of multiscale models of diffusion under non-uniform stress.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Quasilinear diffusion coefficients in a finite Larmor radius expansion for ion cyclotron heated plasmas

In this study, a reduced model of quasilinear velocity diffusion by a small Larmor radius approximation is derived to couple the Maxwell’s equations and the Fokker Planck equation self-consistently for the ion cyclotron range of frequency waves in a tokamak. The reduced model ensures the important properties of the full model by Kennel-Engelmann diffusion, such as diffusion directions, wave polarizations, and H-theorem. The kinetic energy change (Wdot ) is used to derive the reduced model diffusion coefficients for the fundamental damping (n = 1) and the second harmonic damping (n = 2) to the lowest order of the finite Larmor radius expansion. The quasilinear diffusion coefficients are implemented in a coupled code (TORIC-CQL3D) with the equivalent reduced model of the dielectric tensor. We also present the simulations of the ITER minority heating scenario, in which the reduced model is verified within the allowable errors from the full model results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Connecting the dots: Semi-analytical and random walk numerical solutions of the diffusion–reaction equation with stochastic initial conditions

We study a system with bimolecular irreversible kinetic reaction A+B→∅ where the underlying transport of reactants is governed by diffusion, and the local reaction term is given by the law of mass action. We consider the case where the initial concentrations are given in terms of an average and a white noise perturbation. Our goal is to solve the diffusion–reaction equation which governs the system, and we tackle it with both analytical and numerical approaches. To obtain an analytical solution, we develop the equations of moments and solve them approximately. To obtain a numerical solution, we develop a grid-less Monte Carlo particle tracking approach, where diffusion is modeled by a random walk of the particles, and reaction is modeled by annihilation of particles. The probability of annihilation is derived analytically from the particles' co-location probability. We rigorously derive the relationship between the initial number of particles in the system and the amplitude of white noise represented by that number. This enables us to compare the particle simulations and the approximate analytical solution and offer an explanation of the late time discrepancies. - Graphical abstract:.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. Yet, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. Here, we present an application of an unsupervised ML method (called NTF k ) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which is important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A+B→C , where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of the velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product C. The introduced ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTF k approach is not limited to reactive-mixing. NTFk can be readily applied to any observed or simulated datasets that can be represented as tensors (multi-dimensional arrays) and have separable latent signatures or features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

The ortho:para ratio of H{sub 3}{sup +} in laboratory and astrophysical plasmas

The discovery of H{sub 3}{sup +} in the diffuse interstellar medium has dramatically changed our view of the cosmic-ray ionization rate in diffuse molecular clouds. However, another surprise has been that the ortho:para ratio of H{sub 3}{sup +} in these clouds is inconsistent with the temperature derived from the excitation of H{sub 2}, the dominant species in these clouds. In an effort to understand this discrepancy, we have embarked on an experimental program to measure the nuclear spin dependence of the dissociative electron recombination rate of H{sub 3}{sup +} using the CRYRING and TSR ion storage rings. We have also performed the first measurements of the reaction H{sub 3}{sup +}+H{sub 2}→H{sub 2}+H{sub 3}{sup +} below room temperature. This reaction is likely the most common bimolecular reaction in the universe, and plays an important role in interconverting ortho- and para-H{sub 3}{sup +}. Finally, we have constructed a steady-state chemical model for diffuse clouds, which takes into account the spin-dependence of the formation of H{sub 3}{sup +}, its electron recombination, and its reaction with H{sub 2}. We find that the ortho:para ratio of H{sub 3}{sup +} in diffuse clouds is likely governed by a competition between dissociative recombination and thermalization by reactive collisions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolution in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Long term oxidation of NiCoCrAlY coated Ni-based superalloys: A comparison of observed and simulated interdiffusion

Lifetimes of MCrAlY-type coatings can easily surpass 25 kh when the criteria of ß-depletion is used. To reduce experimental effort, it’s necessary to develop models capable of simulating interdiffusion and oxidation. In the present study, NiCoCrAlY coatings were high velocity oxyfuel deposited on PWA 1483, MARM247, and CMSX-4 substrates, and samples were exposed at 900 °C for 5–20 kh in air+10 vol%H 2 O to study interdiffusion rates. Here, Thermo-Calc/DICTRA was used to reproduce the observed differences, and microstructures were compared. Based on the agreement between calculated and observed rates, simulations were projected past the experimental exposures, utilizing ß-depletion as lifetime criteria.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of Concurrent Solubilization and Fines Migration on Fracture Aperture Growth in Shales during Acidized Brine Injection

Acidic hydraulic fracturing fluid chemically and physically alters shale rock fabric during injection and shut-in, creating a “reaction-altered zone” along the fracture faces. To better characterize the variable thickness and composition of this reaction-altered zone under advective flow, we take a coupled experimental and modeling approach. A fluidic cell, with six fiducial markers, is first fabricated to keep the rock sample in place during the core floods and to allow image alignment of acquired images. Then, we conduct a series of reactive core floods in a clay-rich siliceous Wolfcamp shale sample with 10 wt % carbonate, using a synthetic fracturing fluid under no confining stress and at room temperature. High-resolution computed tomography (CT) scans are periodically conducted to observe the spatial alteration of the fracture network. We then perform scanning electron microscopy (SEM-EDS) on the two orthogonal surfaces (fracture surface and freshly cut profile face) to generate high-resolution elemental maps that show the change in mineralogy, both with distance along a given flow path along a fracture surface and with depth from the fracture surface into the shale matrix. These results are contrasted against a two-dimensional (2D) advection-diffusion reaction model developed previously for batch reactions between shale and synthetic fracturing fluids. The model simulates the geochemical interaction occurring at the fracture/matrix interface and penetrating into the shale matrix during the reactive core flood. Both model and experimental results show that the acidic brine is neutralized during the core flood, corresponding to an increase in fracture aperture as a function of fluid volume injected with the greatest change near the inlet. SEM-EDS scans reveal significant dissolution of carbonates on the fracture surface without pyrite oxidation. The reactive transport model indicates that carbonate depletion into the shale interior should be observable, yet SEM-EDS shows no discernible loss of carbonate in the orthogonal profile face. The combination of these observations suggests an additional fracture evolution mechanism in the reactive system, i.e., fines migration. We show that fines migration enhances the access of fracturing fluid to the matrix resulting in a more pronounced fracture widening. Finally, we conclude that coupled mineral dissolution and fines migration govern fracture aperture growth during acidized brine injection. In this work, we effectively show the underlying risk of relying solely on models that do not include an important (transport) process that can alter the system significantly and propose a combined chemomechanical mechanism for fracture evolution appropriate for this shale mineralogy.

58 GEOSCIENCES↗
DOE OSTI2025

Enhancing Unknown Waveform Detection by Learning Intra and Inter-domain Dependencies with Advanced Attention Fusion Mechanisms

Detection of unknown waveforms in mission-critical communications is a crucial area of interest for the Department of Energy (DoE). Traditional methods and recent deep learning-based approaches often assume that the training set includes all possible classes, which is impractical for detecting new waveforms. This limitation gives rise to the problem of open-set recognition (OSR), which involves correctly identifying known classes while detecting and rejecting unknown or unseen classes. To address this limitation, we propose a novel dual-domain complex-valued neural architecture that jointly processes time-domain and frequency-domain signal representations using transformer mechanisms. A transformer model is a deep learning architecture that uses self-attention mechanisms to process and learn relationships in sequential data. Our model employs a cosine similarity loss to extract domain-specific features and incorporates a transformer architecture in the latent space to weigh the importance of different features from the time and frequency domains. The transformer layer includes stacked self-attention and cross-attention modules to learn intra-domain and inter-domain dependencies, creating a more holistic signal representation. An attention-based fusion module intelligently combines the time and frequency-domain features using multi-head attention, enabling the network to learn the optimal feature for each domain in each input signal. Quantitative results demonstrate the impact of these architectural choices on overall performance, showing significant improvement after incorporating self and cross-attention modules and using complex attention fusion over simple weighted fusion. Our ongoing work will focus on addressing the limitations of threshold-based OSR methods by developing a novel generative framework that integrates a conditional diffusion probabilistic model (DPM). DPM is a generative framework that learns to synthesize complex data by reversing a gradual noising process using a neural network trained to denoise step-by-step. Our goal is to leverage the inherent strengths of DPMs for identifying unknown signals more robustly. One primary advantage of using a DPM is its ability to provide a more reliable anomaly score based on the model's reconstruction error, rather than relying solely on classifier confidence. Additionally, the iterative denoising process of DPMs makes this approach naturally resilient to low Signal-to-Noise Ratio (SNR) conditions, where traditional methods often fail. By implementing this generative framework, we aim to enhance the model's capability to accurately detect unknown waveforms and maintain performance in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

The contact area dependent interfacial thermal conductance

The effects of the contact area on the interfacial thermal conductance σ are investigated using the atomic Green’s function method. Different from the prediction of the heat diffusion transport model, we obtain an interesting result that the interfacial thermal conductance per unit area Λ is positively dependent on the contact area as the area varies from a few atoms to several square nanometers. Through calculating the phonon transmission function, it is uncovered that the phonon transmission per unit area increases with the increased contact area. This is attributed to that each atom has more neighboring atoms in the counterpart of the interface with the increased contact area, which provides more channels for phonon transport.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗