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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Monte-Carlo modeling of phonon thermal transport using DFT-based anisotropic dispersion relations over the full Brillouin zone

In this work, we present a Monte Carlo (MC) approach to solve the phonon Boltzmann transport equation (BTE) in which the anisotropic phonon dispersion relations over the full Brillouin zone (BZ) are used. In this approach, the discretization of the BZ used to compute the phonon relaxation time places constraints on the direction of scattered phonons in the real-space simulation domain. The phonon dispersion and phonon relaxation times are calculated using the density functional theory (DFT) approach. The modified MC approach is validated by a close examination of its ability to simulate phonon transport in both the ballistic and diffusive regimes for multiple materials including GaAs, InAs, ThO 2 , and α-U. In doing so, the phonon thermal conductivities from 100 K to 1000 K are calculated and compared with traditional non-transport solution of the phonon BTE. It is found that the phonon thermal conductivities of α-U and ThO 2 obtained from MC simulations using isotropic dispersion are larger than the values obtained using anisotropic phonon dispersion relations over the full BZ. The effect of phonon-defect scattering on the thermal conductivity of ThO 2 is also studied as an application of the current MC approach and found to agree with previously computed values in the literature. The MC solver developed here has been parallelized as a step to demonstrate its potential to solving computationally intensive phonon thermal transport problems at the mesoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Observation of Anisotropic Dispersive Dark-Exciton Dynamics in CrSBr

Many-body excitons in CrSBr are attracting intense interest in view of their highly anisotropic magneto-optical coupling and their potential for novel optical interfaces within spintronic and magnonic devices. Characterizing the orbital character and propagation of these electronic excitations is crucial for understanding and controlling their behavior; however, this information is challenging to access. High resolution resonant inelastic x-ray scattering is a momentum-resolved technique that can address these crucial questions. Here, we present measurements collected at the Cr 𝐿 3 -edge which show a rich spectrum of excitations with a variety of spin-orbital characters. While most of these excitations appear to be localized, the dispersion of the lowest energy dark exciton indicates that it is able to propagate along both the 𝑎 and 𝑏 directions within the planes of the crystal. This two-dimensional character is surprising as it contrasts with electrical conductivity and the behavior of the bright exciton, both of which are strongly one dimensional. The discovery of this propagating dark exciton highlights an unusual coexistence of one- and two-dimensional electronic behaviors in CrSBr.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simu-lations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction-di?usion equations using a non-negative finite element formulation for di?erent input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion strength/contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion strength/contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R2-score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction-diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O(107) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.

Mudunuru, Maruti K.↗
DOE OSTI2021

The ultrafast Kerr effect in anisotropic and dispersive media

The ultrafast optical Kerr effect (OKE) is widely used to investigate the structural dynamics and interactions of liquids, solutions, and solids by observing their intrinsic nonlinear temporal responses through nearly collinear four-wave mixing. Non-degenerate mixing schemes allow for background free detection and can provide information on the interplay between a material’s internal degrees of freedom. Here, we show a source of temporal dynamics in the OKE signal that is not reflective of the internal degrees of freedom but arises from a group index and momentum mismatch. It is observed in two-color experiments on condensed media with sizable spectral dispersion, a common property near an optical resonance. In particular, birefringence in crystalline solids is able to entirely change the character of the OKE signal via the off-diagonal tensor elements of the nonlinear susceptibility. We develop a detailed description of the phase-mismatched ultrafast OKE and show how to extract quantitative information on the spectrally resolved birefringence and group index from time-resolved experiments in one and two dimensions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1987

Microstrip dispersion including anisotropic substrates

A perturbation-iteration solution based on potential theory is develoepd for determining the effective dielectric constant, characteristic impedance, and current-charge distribution on a microstrip transmission line with isotropic and anisotropic substrates. The numerical implementation of the theory is described and is suitable for use on a personal computer. Computed data for several common substrate materials are included.

Kretch, Brian E.↗
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 OSTI2025

Magnon thermal conductivity in multiferroics with spin cycloids

Multiferroic materials, characterized by the occurrence of two or more ferroic properties, hold potential in future technological applications and also exhibit intriguing phenomena caused by the interplay of multiple orders. One such example is the formation of spin-cycloid structures within multiferroic materials, which we investigate in this work by focusing on their magnon excitations and transport based on a general multiferroic Hamiltonian with an antiferromagnetic order. More specifically, we identify the ground state and explore the dynamics of magnon modes, revealing distinct in-plane and out-of-plane modes with anisotropic dispersion relations. The magnon modes include a massless excitation, known as the Goldstone boson, originating from the spontaneous breaking of the translational symmetry by the formation of the cycloid structures. By employing the Boltzmann transport formalism, the magnonic thermal conductivity with spin cycloids and low-temperature anisotropic behaviors is discussed. This work provides pathways to envision the spin-textured multiferroics, which may serve as a fertile ground to look for novel thermal and spin transport with the rich interplay of quasiparticles such as magnons and phonons.

Park, Hyeon Woo↗
DOE OSTI2021

A comparative study of machine learning models for predicting the state of reactive mixing

Mixing phenomena are important mechanisms controlling flow, species transport, and reaction processes in fluids and porous media. Accurate predictions of reactive mixing are critical for many Earth and environmental science problems such as contaminant fate and remediation, macroalgae growth, and plankton biomass evolution. Here, to investigate the evolution of mixing dynamics under different scenarios (e.g., anisotropy, fluctuating velocity fields), a finite-element-based numerical model was built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations were performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast (i.e., ratio of longitudinal-to-transverse dispersion), and molecular diffusion. The outputs comprised concentration profiles of reactants and products. The inputs to and outputs from these simulations were concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) models intended to emulate system behavior. These 20 ML emulators, based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptrons (MLPs), were trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay (i.e., average concentration, square of average concentration), and degree of mixing (i.e., variances of species concentration). Unsurprisingly, linear classifiers and regressors failed to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP model accurately classified the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predicted the QoIs. At run time, trained ML emulators produced results times faster than the finite-element simulations. Due to their low computational expense and high accuracy, ensemble and MLP models are excellent emulators for these numerical simulations and great utilities in uncertainty quantification exercises, which can require 1,000s of forward model runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Connection between f -electron correlations and magnetic excitations in UTe 2

The detailed anisotropic dispersion of the low-temperature, low-energy magnetic excitations of the candidate spin-triplet superconductor UTe 2 is revealed using inelastic neutron scattering. The magnetic excitations emerge from the Brillouin zone boundary at the high symmetry Y and T points and disperse along the crystallographic $\hat{b}$ -axis. In applied magnetic fields to at least μ 0 H = 11 T along the $\hat{c}-{\rm{axis}}$ , the magnetism is found to be field-independent in the (hk0) plane. The scattering intensity is consistent with that expected from U 3+ /U 4+ f-electron spins with preferential orientation along the crystallographic $\hat{a}$ -axis, and a fluctuating magnetic moment of μ eff =1.7(5) μ B . We propose interband spin excitons arising from f-electron hybridization as a possible origin of the magnetic excitations in UTe 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Nematic ordering of hard rods under strong confinement in a dense array of nanoposts

The effect of confinement on the behavior of liquid crystals is interesting from a fundamental and practical standpoint. In this work, we report Monte Carlo simulations of hard rods in an array of hard nanoposts, where the surface-to-surface separations between nanoposts are comparable to or less than the length of hard rods. This particular system shows promise as a means of generating large-scale organization of the nematic liquid by introducing an entropic external field set by the alignment of nanoposts. The simulations show that nematic ordering of hard rods is enhanced in the nanopost arrays compared with that in bulk, in the sense that the nematic order is significant even at low concentrations at which hard rods remain isotropic in bulk, and the enhancement becomes more significant as the passage width between two nearest nanoposts decreases. An analysis of local distribution of hard-rod orientations at low concentrations with weak nematic ordering reveals that hard rods are preferentially aligned along nanoposts in the narrowing regions between two curved surfaces of nearest nanoposts; hard rods are less ordered in the passages and in the centers of interpost spaces. Finally, it is concluded that at low concentrations the confinement in a dense array of nanoposts induces the localized nematic order first in the narrowing regions and, as the concentration further increases, the nematic order spreads over the whole region. The formation of a well-ordered phase at low concentrations of hard rods in a dense array of nanoposts can provide a new route to the low-concentration preparation of nematic liquid crystals that can be used as anisotropic dispersion media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spin-dependent dark matter scattering in quasi-two-dimensional magnets

We study the prospects of detecting dark matter coupled to the spin of the electron, such that it may scatter and excite magnons—collective excitations of electronic spins. We show that materials exhibiting long-range magnetic order where the spins are coupled only along a plane may act as directional dark matter detectors. These quasi-two-dimensional materials possess anisotropic dispersion relations and structure functions which induce a sidereal modulation in the excitation rate. We calculate the expected signal rate for some candidate (anti)ferromagnets, demonstrating a possible route to the direct detection of spin-dependent dark matter in the keV to MeV mass range.

Dark matter direct detection↗
NASA NTRS1973

A critical survey of wave propagation and impact in composite materials

A review of the field of stress waves in composite materials is presented covering the period up to December 1972. The major properties of waves in composites are discussed and a summary is made of the major experimental results in this field. Various theoretical models for analysis of wave propagation in laminated, fiber and particle reinforced composites are surveyed. The anisotropic, dispersive and dissipative properties of stress pulses and shock waves in such materials are reviewed. A review of the behavior of composites under impact loading is presented along with the application of wave propagation concepts to the determination of impact stresses in composite plates.

Moon, F. C.↗
DOE OSTI2026

Reconfigurable self-assembly of porous anisotropic colloids in nematic liquid crystals

Dispersions of anisotropic nanoparticles in liquid crystalline hosts recently yielded new soft condensed matter states, like the thermally reconfigurable monoclinic and orthorhombic biaxial nematic liquid crystals, with a plethora of unusual phases and phase transformations. Our current study shows that the nanoscale porous nature of colloids with micrometer-range overall dimensions also enables highly reconfigurable orientations and assemblies of the microparticles, allowing for realization of condensed matter states with unusual combinations of low-symmetry nematic or smectic order and fluidity. Much like the anisotropic nanoparticles studied previously, these nanoporous anisotropic colloids exhibit thermally reconfigurable oriented alignment with respect to the far-field director, as well as diverse low-symmetry liquid crystalline phase behaviors. Furthermore, our findings open doors to fundamental and applied uses of low-symmetry molecular-colloidal orientationally ordered states of matter with uninhibited fluidity, as well as liquid crystals with partial positional ordering, like low-symmetry smectics, which could lead to applications in metamaterial designs, electro-optics, photonics, etc.

colloidal dispersion↗
NASA NTRS1989

Higher order dispersion in the propagation of a gravity wave packet

To the first order of approximation, the complex amplitude of a wave packet in an anisotropic and dispersive medium is convected with the group of velocity. However, a gravity wave is a vector wave. Its wave packet must be formed by superposition of various wave numbers with corresponding frequencies, as is the case for scalar waves, and additionally by superposing many eigenmodes which also depend on the wave number. To represent the vector wave packet self-consistently, it is found that a gradient term must be included in the expansion. For a Guassian wave packet, this gradient term is shown to have important implications on the velocity vector as represented by its hodograph. Numerical results show that the hodograph is influenced by the location of the relative position of interest from the center of a Gaussian pulse. Higher order expansion shows that an initial Gaussian wave packet will retain its Gaussian shape as it propagates, but the pulse will spread in all directions with its major axis undergoing a rotation. Numerical results indicate that these higher order dispersive effects may be marginally observable in the atmosphere.

Yeh, K. C.↗
NASA NTRS1979

Dynamics of a stellar bar

The dynamical properties of a prolate bar have been studied by means of a three-dimensional computer model. The bar pattern rotates in the sense of the total angular momentum. The mean particle motion is a rapid streaming in the direction of pattern rotation as seen from a frame that rotates with the bar. Rotation rates that would be inferred from observation are significantly (2-3 times) faster than the pattern rotation speed. Velocity dispersions are anisotropic with the largest component along the bar. Particles oscillate in the bar potential significantly faster than pattern rotation. About 25% of the star orbits are near 2:2:1 resonance with the slow motion along the bar. Particle motion is highly ordered in the bar. Observable properties are described; where comparisons can be made, observable properties are in agreement with observations of brightness contours, velocity fields, and velocity dispersions. The bar has nearly exponential density profiles.

Miller, R. H.↗