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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

In-situ EBSD study of deformation behavior of retained austenite in a low-carbon quenching and partitioning steel via uniaxial tensile tests

Through using in-situ electron back-scattered diffraction and uniaxial tensile tests, this work mainly focuses on the deformation behavior of retained austenite (RA) in a low-carbon quenching and partitioning (Q&P) steel. In this paper, three different types of RA can be distinguished from different locations, respectively, RA grains at the triple edges, twinned austenite and RA grains positioned between martensite. The results have shown that grains at the triple edges and twinned austenite could transform easily with increasing strain, i.e. are less stable when compared with RA grains distributed between martensite that could resist a larger plastic deformation. Meanwhile, the strain leads to rotations of RA grains distributed at the triple edges and between martensite. Moreover, RA grains with a similar orientation undergone similar rotations with the same true strain. These RA grains rotated along a specific slip plane and slip direction and the grain rotation is taken as a significant factor to improve the ductility of steel. In addition, grain sizes of RA decreased gradually with an increase of true strain and smaller (0–0.2 μm) grains were more capable of resisting the deformation. According to kernel average misorientation (KAM) analysis, it can be found that strain distribution is preferentially localized near martensite–austenite phase boundaries and in the interior of martensite. The average KAM values increased continuously with increasing true strain. - Highlights: •The in-situ and ex-situ tensile specimens differ to some extent in mechanical properties. •Retained austenite grains at the triple edges and twinned austenite transformed easily at the early stage of true strain. •Film-like retained austenite grains only rotated prior to the transformation during straining. •Retained austenite grains having with a similar orientation experienced similar rotations during the same true strain.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. Here, we anticipate this refined methodology will better leverage material correlations, improving design optimization results.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The total neutron cross section of liquid and solid ammonia

Ammonia is a material of interest for future neutron moderators at high-power sources due to its high hydrogen density, low melting point, and resistance to polymerization in an intense radiation field. Its performance in such applications cannot currently be calculated due to the absence of suitable computer models for the interaction of neutrons with ammonia under relevant conditions. In an effort to develop suitable scattering kernels for computer simulations of moderator performance, we have conducted a series of Density Functional Theory and Molecular Dynamics calculations of the molecular-level thermal properties of ammonia at various temperatures within both the solid and liquid phases. In this paper, we compare computer calculations for the energy-dependent total neutron cross section of ammonia, based on these models, to experimental measurements of those cross sections at temperatures of 221 K, 180 K, and 35 K. The experimental data were collected over an energy range from 0.1 meV to 10 eV using time-of-flight techniques at the Low Energy Neutron Source (LENS) facility at Indiana University. This comparison provides a first validation in the development of thermal scattering libraries for Monte Carlo source design simulations based on liquid and solid ammonia. In conclusion, we also provide some insights into where additional development of tools for creating such models may be needed.

Ammonia↗
DOE OSTI2023

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Automated Experiment in 4D-STEM: Exploring Emergent Physics and Structural Behaviors

Automated experiments in 4D scanning transmission electron microscopy (STEM) are implemented for rapid discovery of local structures, symmetry-breaking distortions, and internal electric and magnetic fields in complex materials. Deep kernel learning enables active learning of the relationship between local structure and 4D-STEM-based descriptors. With this, efficient and “intelligent” probing of dissimilar structural elements to discover desired physical functionality is made possible. This approach allows effective navigation of the sample in an automated fashion guided by either a predetermined physical phenomenon, such as strongest electric field magnitude, or in an exploratory fashion. Therefore, we verify the approach first on preacquired 4D-STEM data and further implement it experimentally on an operational STEM. The experimental discovery workflow is demonstrated using graphene and subsequently extended toward a lesser-known layered 2D van der Waals material, MnPS 3 . This approach establishes a pathway for physics-driven automated 4D-STEM experiments that enable probing the physics of strongly correlated systems and quantum materials and devices, as well as exploration of beam-sensitive materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Bayesian Active Learning for Scanning Probe Microscopy: From Gaussian Processes to Hypothesis Learning

Recent progress in machine learning methods and the emerging availability of programmable interfaces for scanning probe microscopes (SPMs) have propelled automated and autonomous microscopies to the forefront of attention of the scientific community. However, enabling automated microscopy requires the development of task-specific machine learning methods, understanding the interplay between physics discovery and machine learning, and fully defined discovery workflows. This, in turn, requires balancing the physical intuition and prior knowledge of the domain scientist with rewards that define experimental goals and machine learning algorithms that can translate these to specific experimental protocols. Here, we discuss the basic principles of Bayesian active learning and illustrate its applications for SPM. We progress from the Gaussian process as a simple data-driven method and Bayesian inference for physical models as an extension of physics-based functional fits to more complex deep kernel learning methods, structured Gaussian processes, and hypothesis learning. These frameworks allow for the use of prior data, the discovery of specific functionalities as encoded in spectral data, and exploration of physical laws manifesting during the experiment. Here, the discussed framework can be universally applied to all techniques combining imaging and spectroscopy, SPM methods, nanoindentation, electron microscopy and spectroscopy, and chemical imaging methods and can be particularly impactful for destructive or irreversible measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Synthesis of phase-pure U 2 N 3 microspheres and its decomposition into UN

Uranium mononitride (UN) is important as a nuclear fuel. Fabrication of UN in its microspherical form also has its own merits since the advent of the concept of accident-tolerant fuel, where UN is being considered as a potential fuel in the form of TRISO particles. But, not many processes have been well established to synthesize kernels of UN. Therefore, a process for synthesis of microspherical UN with a minimum amount of carbon is discussed herein. First, a series of single-phased microspheres of uranium sesquinitride (U 2 N 3 ) were synthesized by nitridation of UO 2 +C microspheres at a few different temperatures. Resulting microspheres were of low-density U 2 N 3 and decomposed into low-density UN. The variation of density of the synthesized sesquinitrides as a function of its chemical composition indicated the presence of extra (interstitial) nitrogen atoms corresponding to its hyperstoichiometry, which is normally indicated as α-U 2 N 3 . Average grain sizes of both U 2 N 3 and UN varied in a range of 1–2.5 μm. In addition, these had a considerably large amount of pore spacing, indicating the potential sinterability of UN toward its use as a nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Quantifying the Importance of Rapid Adjustments for Global Precipitation Changes

Different climate drivers influence precipitation in different ways. Here we use radiative kernels to understand the influence of rapid adjustment processes on precipitation in climate models. Rapid adjustments are generally triggered by the initial heating or cooling of the atmosphere from an external climate driver. For precipitation changes, rapid adjustments due to changes in temperature, water vapor, and clouds are most important. In this study we have investigated five climate drivers (CO 2 , CH 4 , solar irradiance, black carbon, and sulfate aerosols). The fast precipitation responses to a doubling of CO 2 and a 10-fold increase in black carbon are found to be similar, despite very different instantaneous changes in the radiative cooling, individual rapid adjustments, and sensible heating. The model diversity in rapid adjustments is smaller for the experiment involving an increase in the solar irradiance compared to the other climate driver perturbations, and this is also seen in the precipitation changes.

58 GEOSCIENCES↗
DOE OSTI2022

Deep transfer operator learning for partial differential equations under conditional shift

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. Here, we propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.

42 ENGINEERING↗
DOE OSTI2013

Spectral properties of the Dirichlet operator Σ{sub i=1}{sup d}(−∂{sub i}{sup 2}){sup s} on domains in d-dimensional Euclidean space

In this article, we investigate the distribution of eigenvalues of the Dirichlet pseudo-differential operator Σ{sub i=1}{sup d}(−∂{sub i}{sup 2}){sup s}, s∈(0,1] on an open and bounded subdomain Ω⊂R{sup d} and predict bounds on the sum of the first N eigenvalues, the counting function, the Riesz means, and the trace of the heat kernel. Moreover, utilizing the connection of coherent states to the semi-classical approach of quantum mechanics, we determine the sum for moments of eigenvalues of the associated Schrödinger operator.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Preparation and characterization of plasticized palm-based polyurethane solid polymer electrolyte

Palm-based polyurethane solid polymer electrolyte was prepared via prepolymerization method between palm kernel oil based polyols (PKO-p) and 2,4’-diphenylmethane diisocyanate (2,4’-MDI) in acetone at room temperature with the vary amount of lithium trifuoromethanesulfonate (LiCF{sub 3}SO{sub 3}) salt and polyethylene glycol (PEG). The film was analyzed using attenuated total reflection infrared (ATR-IR) spectroscopy, electrochemical impedance spectroscopy (EIS) and X-ray diffractometry (XRD). EIS result indicated ionic conductivity obtained with 30 wt% LiCF3SO3 increased to 6.55 × 10{sup −6} S cm{sup −1} when 10 wt.% of plasticizer was added into the system. FTIR analysis showed the interaction between lithium ions and amine (-N-H) at 3600–3100 cm{sup −1}, carbonyl (-C=O) at 1750–1650 cm{sup −1} and ether (-C-O-C-) at 1150–1000 cm{sup −1} of the polyurethane forming polymer-salt complexes. The XRD result confirmed that LiCF{sub 3}SO{sub 3} salt completely dissociated within the polyurethane film with the absence of crystalline peaks of LiCF{sub 3}SO{sub 3}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Software optimization for electrical conductivity imaging in polycrystalline diamond cutters

We previously reported on an electrical conductivity imaging instrument developed for measurements on polycrystalline diamond cutters. These cylindrical cutters for oil and gas drilling feature a thick polycrystalline diamond layer on a tungsten carbide substrate. The instrument uses electrical impedance tomography to profile the conductivity in the diamond table. Conductivity images must be acquired quickly, on the order of 5 sec per cutter, to be useful in the manufacturing process. This paper reports on successful efforts to optimize the conductivity reconstruction routine, porting major portions of it to NVIDIA GPUs, including a custom CUDA kernel for Jacobian computation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Hermite polynomials and quasi-classical asymptotics

We study an unorthodox variant of the Berezin-Toeplitz type of quantization scheme, on a reproducing kernel Hilbert space generated by the real Hermite polynomials and work out the associated quasi-classical asymptotics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Semi-classical properties of Berezin–Toeplitz operators with C{sup k}-symbol

We obtain the semi-classical expansion of the kernels and traces of Toeplitz operators with C{sup k}-symbol on a symplectic manifold. We also give a semi-classical estimate of the distance of a Toeplitz operator to the space of self-adjoint and multiplication operators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Linearized Boltzmann collision integral with the correct cutoff

In the calculation of the linearized Boltzmann collision operator for an inverse-square force law interaction (Coulomb interaction) F(r)=κ/r{sup 2}, we found the widely used scattering angle cutoff θ≥θ{sub min} is a wrong practise since the divergence still exists after the cutoff has been made. When the correct velocity change cutoff |v′−v|≥δ{sub min} is employed, the scattering angle can be integrated. A unified linearized Boltzmann collision operator for both inverse-square force law and rigid-sphere interactions is obtained. Like many other unified quantities such as transition moments, Fokker-Planck expansion coefficients and energy exchange rates obtained recently [Y. B. Chang and L. A. Viehland, AIP Adv. 1, 032128 (2011)], the difference between the two kinds of interactions is characterized by a parameter, γ, which is 1 for rigid-sphere interactions and −3 for inverse-square force law interactions. When the cutoff is removed by setting δ{sub min}=0, Hilbert's well known kernel for rigid-sphere interactions is recovered for γ = 1.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

The multifacet graphically contracted function method. I. Formulation and implementation

The basic formulation for the multifacet generalization of the graphically contracted function (MFGCF) electronic structure method is presented. The analysis includes the discussion of linear dependency and redundancy of the arc factor parameters, the computation of reduced density matrices, Hamiltonian matrix construction, spin-density matrix construction, the computation of optimization gradients for single-state and state-averaged calculations, graphical wave function analysis, and the efficient computation of configuration state function and Slater determinant expansion coefficients. Timings are given for Hamiltonian matrix element and analytic optimization gradient computations for a range of model problems for full-CI Shavitt graphs, and it is observed that both the energy and the gradient computation scale as O(N{sup 2}n{sup 4}) for N electrons and n orbitals. The important arithmetic operations are within dense matrix-matrix product computational kernels, resulting in a computationally efficient procedure. An initial implementation of the method is used to present applications to several challenging chemical systems, including N{sub 2} dissociation, cubic H{sub 8} dissociation, the symmetric dissociation of H{sub 2}O, and the insertion of Be into H{sub 2}. The results are compared to the exact full-CI values and also to those of the previous single-facet GCF expansion form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

First-order derivative couplings between excited states from adiabatic TDDFT response theory

We present a complete derivation of derivative couplings between excited states in the framework of adiabatic time-dependent density functional response theory. Explicit working equations are given and the resulting derivative couplings are compared with derivative couplings from a pseudo-wavefunction ansatz. For degenerate excited states, i.e., close to a conical intersection (CI), the two approaches are identical apart from an antisymmetric overlap term. However, if the difference between two excitation energies equals another excitation energy, the couplings from response theory exhibit an unphysical divergence. This spurious behavior is a result of the adiabatic or static kernel approximation of time-dependent density functional theory leading to an incorrect analytical structure of the quadratic response function. Numerical examples for couplings close to a CI and for well-separated electronic states are given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Numerical simulation of scattering of acoustic waves by inelastic bodies using hypersingular boundary integral equation

A numerical method for solving a problem of diffraction of acoustic waves by system of solid and thin objects based on the reduction the problem to a boundary integral equation in which the integral is understood in the sense of finite Hadamard value is proposed. To solve this equation we applied piecewise constant approximations and collocation methods numerical scheme. The difference between the constructed scheme and earlier known is in obtaining approximate analytical expressions to appearing system of linear equations coefficients by separating the main part of the kernel integral operator. The proposed numerical scheme is tested on the solution of the model problem of diffraction of an acoustic wave by inelastic sphere.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗