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Results for “first-principles model”

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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NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗

NASA NTRSDate not supplied

Ab initio ground states of strongly-correlated materials on quantum computers

The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving accurate many-body Hamiltonians including strong correlations, representing a subspace of interest of a material, using density functional theory calculations as a starting point. However, the solution of these material-specific models can scale exponentially on classical computers, constituting a challenge. Here we propose that utilizing quantum computers for obtaining the properties of downfolded Hamiltonians yields an accurate description of the ground state properties of strongly-correlated systems, while circumventing the exponential scaling problem. We benchmark the solution of Hubbard-like models obtained through downfolding by utilizing a classical tensor network implementation of variational quantum eigensolvers (VQE), and we reveal a strategy for driving the optimization through a hybrid minimization of the energy and maximization of the overlap with an approximate solution obtained through low-cost computational methods. This results in a reduction of the energy error by orders of magnitude compared to conventional VQE approaches, and allows us to reproduce long-range correlations for the first time. We demonstrate our first-principles approach for diverse strongly-correlated materials, correctly predicting the antiferromagnetic state of one-dimensional cuprate Ca 2 CuO 3 , the excitonic ground state of monolayer WTe2, and the charge-ordered state of correlated metal SrVO 3 . Our efficient computational implementation allows us to simulate large systems with up to 54 qubits and encompassing up to four correlated bands, which is indicative of the complexity that our framework can address.

Antonios M Alvertis↗

NASA NTRS1983

Rotor-vortex interaction noise

A theoretical and experimental study is carried out to develop a first-principles analysis for predicting noise generated by helicopter main-rotor shed vortices interacting with the tail rotor. The generalized prediction procedure calls for a knowledge of the incident vortex velocity field, the rotor geometry, and the rotor operating conditions. The analysis encompasses compressibility effects and chordwise and spanwise noncompactness and treats oblique intersections with the blade planform. In assessing the theory, a model rotor experiment which isolates the blade-vortex interaction noise from other rotor noise mechanisms is conducted. An isolated tip vortex, generated by an upstream semispan airfoil, is convected into the model rotor, and acoustic spectra, pressure signatures, and directivity are measured. Ingestion of the vortex by the rotor is experimentally observed to generate harmonic noise and impulsive waveforms.

Schlinker, R. H.↗

NASA NTRS1994

Modelling of Surfaces. Part 1: Monatomic Metallic Surfaces Using Equivalent Crystal Theory

We present a detailed description of equivalent crystal theory focusing on its application to the study of surface structure. While the emphasis is in the structure of the algorithm and its computational aspects, we also present a comprehensive discussion on the calculation of surface energies of metallic systems with equivalent crystal theory and other approaches. Our results are compared to experiment and other semiempirical as well as first-principles calculations for a variety of fcc and bcc metals.

Bozzolo, Guillermo↗

NASA NTRS1989

Prediction and measurement of low-frequency harmonic noise of a hovering model helicopter rotor

Far-field acoustic data for a model helicopter rotor have been gathered in a large open-jet, acoustically treated wind tunnel with the rotor operating in hover and out of ground-effect. The four-bladed Boeing 360 model rotor with advanced airfoils, planform, and tip shape was run over a range of conditions typical of today's modern helicopter main rotor. Near in-plane acoustic measurements were compared with two independent implementations of classical linear theory. Measured steady thrust and torque were used together with a free-wake analysis (to predict the thrust and drag distributions along the rotor radius) as input to this first-principles theoretical approach. Good agreement between theory and experiment was shown for both amplitude and phase for measurements made in those positions that minimized distortion of the radiated acoustic signature at low-frequencies.

Aggarawal, H. R.↗

NASA NTRS2011

Resolution Assessment in Dynamic Image Formation

Remote sensing and astronomical image formation is often complicated by deficiencies in measurement quality, density, or diversity. Penalized likelihood methods can incorporate additional first-principles physical prior knowledge and improve the image reconstructions, but a systematic bias is unavoidable as a consequence. This work derives theory to understand the bias and develops a computational tool to probe its effect on the reconstructed image and bound resolution limits. Though the focus is on image formation, the contributions of this paper apply to any inference problem that can be expressed under the linear state-space signal model.

multidimensional signal processing↗

NASA NTRS2000

Noise Prediction for Maneuvering Rotorcraft

This paper presents the initial work toward first-principles noise prediction for maneuvering rotors. Both the aeromechanical and acoustics aspects of the maneuver noise problem are discussed. The comprehensive analysis code, CAMRAD 2. was utilized to predict the time-dependent aircraft position and attitude, along - with the rotor blade airloads and motion. The major focus of this effort was the enhancement of the acoustic code WOPWOP necessary to compute the noise from a maneuvering rotorcraft. Full aircraft motion, including arbitrary transient motion, is modeled together with arbitrary rotor blade motions. Noise from a rotorcraft in turning and descending flight is compared to level flight. A substantial increase in the rotor noise is found both for turning flight and during a transient maneuver. Additional enhancements to take advantage of parallel computers and clusters of workstations, in addition to a new compact-chordwise loading formulation, are also described.

Brentner, Kenneth S.↗

NASA NTRS2021

Spin and Accretion Rate Dependence of Black Hole X-Ray Spectra

We present a survey of how the spectral features of black hole X-ray binary systems depend on spin, accretion rate, viewing angle, and Fe abundance when predicted on the basis of first-principles physical calculations. The power-law component hardens with increasing spin. The thermal component strengthens with increasing accretion rate. The Compton bump is enhanced by higher accretion rate and lower spin. The Fe Kα equivalent width grows sublinearly with Fe abundance. Strikingly, the Kα profile is more sensitive to accretion rate than to spin because its radial surface brightness profile is relatively flat, and higher accretion rate extends the production region to smaller radii. The overall radiative efficiency is at least 30%–100% greater than as predicted by the Novikov–Thorne model.

Brooks E Kinch↗

NASA NTRS2015

The Importance of the Cathode Plume and Its Interactions with the Ion Beam in Numerical Simulations of Hall Thrusters

Hall2De is a first-principles, 2-D axisymmetric code that solves the equations of motion for ions, electrons, and neutrals on a magnetic-field-aligned grid. The computational domain downstream of the acceleration channel exit plane is large enough to include self-consistently the cathode boundary. In this paper, we present results from numerical simulations of the H6 laboratory thruster with an internally mounted cathode, with the aim of highlighting the importance of properly accounting for the interactions between the ion beam and cathode plume. The anomalous transport of electrons across magnetic field lines in Hall2De is modelled using an anomalous collision frequency, νanom, yielding νanom approximately equal to omega ce (i.e., the electron cyclotron frequency) in the plume. We first show that restricting the anomalous collision frequency to only regions where the current density of ions is large does not alter the plasma discharge in the Hall thruster as long as the interaction between the ion beam and the cathode plume is captured properly in the computational domain. This implies that the boundary conditions must be placed sufficiently far as to not interfere with the electron transport in this region. These simulation results suggest that electron transport across magnetic field lines occurs largely inside the beam and may be driven by the interactions between beam ions and electrons. A second finding that puts in relevance the importance of including the cathode plume in numerical simulations is on the significance of accounting for the ion acoustic turbulence (IAT), now known to occur in the vicinity of the cathode exit. We have included in the Hall2De simulations a model of the IAT-driven anomalous collision frequency based on Sagdeev's model for saturation of the ion-acoustic instability. This implementation has allowed us to achieve excellent agreement with experimental measurements in the near plume obtained during the operation of the H6 thruster at nominal conditions (300V, 20A) and chamber background pressure of approximately 1.5 x 10(exp -5) Torr. In addition, the numerical results obtained with the latter approach exhibit less sensitivity to background pressure than previous attempts at explaining the features of the plasma properties in the near plume.

Lopez Ortega, Alejandro↗

NASA NTRS2016

Empirical Modeling of the Storm Time Innermost Magnetosphere Using Van Allen Probes and THEMIS Data: Eastward and Banana Currents

The structure of storm time currents in the inner magnetosphere, including its innermost region inside 4R(sub E), is studied for the first time using a modification of the empirical geomagnetic field model TS07D and new data from Van Allen Probes and Time History of Events and Macroscale Interactions during Substorms missions. It is shown that the model, which uses basis-function expansions instead of ad hoc current modules to approximate the magnetic field, consistently improves its resolution and magnetic field reconstruction with the increase of the number of basis functions and resolves the spatial structure and evolution of the innermost eastward current. This includes a connection between the westward ring current flowing largely at R > or approx. 3R(sub E) and the eastward ring current concentrated at R < or approx. 3R(sub E) resulting in a vortex current pattern. A similar pattern coined 'banana current' was previously inferred from the pressure distributions based on the energetic neutral atom imaging and first-principles ring current simulations. The morphology of the equatorial currents is dependent on storm phase. During the main phase, it is complex, with several asymmetries forming banana currents. Near SYM-H minimum, the banana current is strongest, is localized in the evening-midnight sector, and is more structured compared to the main phase. It then weakens during the recovery phase resulting in the equatorial currents to become mostly azimuthally symmetric.

Stephens, G. K.↗

NASA NTRSDate not supplied

Machine Learning Approaches for Rare-Earth Silicate Environmental Barrier Coating Thermochemical and Thermomechanical Property Predictions

Environmental barrier coatings (EBCs) are a necessary enabling technology for the transition from superalloys to silicon carbide (SiC) ceramic matrix composites (CMCs) in gas turbine engines for increased efficiency and decreased fuel costs. SiC-based CMCs are prone to oxidation-based degradation in the engine hot section, and rare-earth (RE) silicates are promising candidates for EBCs due to their close thermal expansion match to the composite substrate and oxidation resistance. However, the design of EBCs is hindered by the large chemical space of candidate materials and the difficulty in obtaining material properties for engineering optimization. This is especially difficult as research continues into mixed-cation or “high-entropy” RE silicates. First-principles computational methods such as density functional theory (DFT) are highly effective at calculating material properties to guide coating design but are limited by their computational cost. Atomistic simulations have the potential to both accelerate property calculations and expand the properties able to be calculated due to their lower computational compared to DFT. However, they require interatomic potentials (IAPs) specific to the material system of interest, and, to our knowledge, there are no suitable IAPs for RE silicates. Machine learning (ML) is a promising technique to accelerate material property predictions indirectly by generating IAPs for atomistic simulations or via direct prediction. In this work, we present two ML approaches to accelerate the calculation of RE silicate properties relevant to EBC design: 1) a ML-derived interatomic potential (IAP) for atomistic simulations of yttrium disilicate (Y2Si2O7) from DFT training data, and 2) a neural network (NN) model to directly predict thermochemical properties of RE silicates and oxides directly from easily obtainable unit cell parameters. Classical MD simulations using the IAP yield lattice properties and bond lengths in good agreement with both DFT and experimental results from x-ray diffraction. Thermodynamic properties calculated using the finite-displacement phonon method and quasi-harmonic approximation were orders of magnitude faster than DFT with good agreement to the DFT results. The IAP was also used to calculate properties such as coefficient of thermal expansion (CTE) that require large simulation supercells and are therefore difficult with DFT. The IAP correctly predicted the anisotropic nature of the CTE in three different phases of Y2Si2O7. The NN model predicts constant pressure heat capacity, Cp, orders of magnitude faster than DFT calculations, which can enable its use as a surrogate model for multiscale simulations. The two methods presented in this work demonstrate the utility of ML for accelerating the prediction of RE silicate properties, which can in turn accelerate EBC design and optimization.

machine learning↗

NASA NTRSDate not supplied

Mixed-Domain Charge Transport in S-Se Alloys as a Li-S Battery Cathode Material

Lithium-sulfur batteries are emerging candidate systems for the next-generation long-range electric aircraft application owing to their potential to deliver high energy density per weight. Their cathodes are to be made primarily with sulfur, but sulfur by itself has much too low electron conductivity to serve as a useful cathode. An idea that has been suggested to overcome this bottleneck is to alloy sulfur with selenium in the hope that electron mobility, which is thought to improve charge transport at some expense of increased weight and reduced energy density. However, understanding of these alloy structures and their transport mechanism is insufficient, and electron mobility at varying degrees of selenium content is not well charted, which are critical for determining the optimum selenium content and testing whether this strategy is generally feasible. The difficulty of computationally characterizing these alloy systems is rooted in the structure. The structures of both sulfur and selenium exhibit eight-atom rings that pack together in various orientations to form their respective crystals. For one, because these crystals (and presumably their alloys as well) feature multiple polymorphs such that their structural coherence is rather unclear. Secondly, these are also relatively large systems (32 atoms per cell) with low-symmetry, which complicate computation both in terms of accuracy and efficiency. Thirdly, such semi-molecular, semi-crystalline structures lead to a combination of band-transport characteristics and hopping-transport characteristics, each of which is a domain with its own physics and set of computational, theoretical challenges. In addition, there is a general dearth of experimental transport data for sulfur-selenium alloys. In this study, we make a comprehensive attempt to tackle this problem using a wide array of first-principles methods. We generate special quasirandom structures to simulate alloy structures, and compute their first-principles Raman spectra for comparison with experimental data to ensure their structural soundness. We then proceed to use recently developed, state-of-the-art tool (AMSET) in order to efficiently compute electronic mobilities under band transport of pure sulfur, pure selenium, as well as their alloys of various compositions at a reasonable accuracy. We then sample numerous dimer configurations of nearest-neighbor eight-atom-ring-pairs and compute electronic hopping rates between them, which yields hopping mobility. A combination of these efforts lead to a general mapping of electron transport behaviors throughout the alloy range. Preliminary results show that introduction of selenium into sulfur initially damages electron mobility due to disorder but eventually improves it beyond that of pure sulfur with additional selenium content, ultimately peaking at the pure-selenium limit. Band transport dominates for holes in sulfur and electrons in selenium, but in all other cases, hopping transport is dominant. Ongoing efforts include determination of charge concentration and conductivity, as well as performing multiphysics modeling to determine the optimum selenium content for best tradeoff between conductivity and energy density for aircraft range.

Junsoo Park↗

NASA NTRS2002

Phase Transition in Dusty Plasmas: A Microphysical Description

Dust grains immersed in plasma discharges acquire a large negative charge and settle into a dust cloud at the edge of the sheath. In this region, the plasma ions stream toward the electrode at a velocity u approx. cs=(T(sub e)/m(sub i))(exp 1/2). Experimentally at sufficiently high gas pressure P, the random kinetic energy of the grains is damped by gas friction, and the grains are strongly coupled and self-organize into a crystalline configuration. For lower pressures despite the dissipation of grain kinetic energy to gas friction, the dust grains reach a steady-state kinetic temperature T(sub d) which is much larger than the temperature of any other component in the plasma. T(sub d) is so large that the dust acts like a fluid. We have used the dynamically shielded dust (DSD) model to simulate these physical processes. We find that the known experimental features are nicely reproduced in the simulations, and that additional features are revealed. In the figure we plot the variation of T(sub d) as P is continuously varied in a DSD code run. A marked difference is evident between the critical pressure P(sub m) for the melting transition as P is decreased, and the critical pressure P(sub c) for the condensation transition as P is increased. For P(sub m) is less than P is less than P(sub c), mixed phase states are seen. This hysteresis occurs because the instability which triggers melting is different from the instability that heats the dust in the fluid phase and inhibits freezing. At low pressure, the dust is subject to a two-stream instability with the ions. This instability is responsible for the high temperature of the dust at low pressure. The basic physics underlying the melting transition has been elucidated in a series of papers. We are developing a first-principles analytic approach to the melting transition, which embodies the same physics that is present in the DSD code.

Joyce, Glenn↗