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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fine Structure Splitting of Phonon-Assisted Excitonic Transition in (PEA)2PbI4 Two-Dimensional Perovskites

Two-dimensional van der Waals materials exhibit particularly strong excitonic effects, which causes them to be an exceptionally interesting platform for the investigation of exciton physics. A notable example is the two-dimensional Ruddlesden–Popper perovskites, where quantum and dielectric confinement together with soft, polar, and low symmetry lattice create a unique background for electron and hole interaction. Here, with the use of polarization-resolved optical spectroscopy, we have demonstrated that the simultaneous presence of tightly bound excitons, together with strong exciton–phonon coupling, allows for observing the exciton fine structure splitting of the phonon-assisted transitions of two-dimensional perovskite (PEA)2PbI4, where PEA stands for phenylethylammonium. We demonstrate that the phonon-assisted sidebands characteristic for (PEA)2PbI4 are split and linearly polarized, mimicking the characteristics of the corresponding zero-phonon lines. Interestingly, the splitting of differently polarized phonon-assisted transitions can be different from that of the zero-phonon lines. We attribute this effect to the selective coupling of linearly polarized exciton states to non-degenerate phonon modes of different symmetries resulting from the low symmetry of (PEA)2PbI4 lattice.

(PEA)2PbI4↗
DOE OSTI2026

Dimensional Reduction for Sampled Priors and Application to Photometric Redshift Distributions

A typical Bayesian inference on the values of some parameters of interest q from some data D involves running a Markov Chain (MC) to sample from the posterior $p$($q$,$n$|$D$) $\propto$ $\mathcal{L}$($D$|$q$,$n$)$p$(q)$p$($n$), where n are some nuisance parameters with a separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior p(n) is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of p(n) at arbitrary values of n, i.e., one needs to provide a density estimator over the full n space from the provided samples. But the high dimensionality of n hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the n space into a much lower-dimensional space u, which projects away directions in n space that cannot appreciably alter $\mathcal{L}$. The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on p(n) than other proposed solutions to this issue. We demonstrate this “mode projection” technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the Dark Energy Survey, where n is a binned representation of the redshift distribution n(z) of the galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Deep nonparametric estimation of operators between infinite dimensional spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Magnetization Dependent In-plane Anomalous Hall Effect in a Low-dimensional System

Anomalous Hall Effect (AHE) response in magnetic systems is typically proportional to an out-of-plane magnetization component because of the restriction imposed by system symmetries, which demands that the magnetization, applied electric field, and induced Hall current are mutually orthogonal to each other. Here, we report experimental realization of an unconventional form of AHE in a low-dimensional heterostructure, wherein the Hall response is not only proportional to the out-of-plane magnetization component but also to the in-plane magnetization component. By interfacing a low-symmetry topological semimetal (TaIrTe4) with the ferromagnetic insulator (Cr2Ge2Te6), we create a low-dimensional magnetic system, where only one mirror symmetry is preserved. We show that as long as the magnetization has a finite component in the mirror plane, this last mirror symmetry is broken, allowing the emergence of an AHE signal proportional to in-plane magnetization. Our experiments, conducted on multiple devices, reveal a gate-voltage-dependent AHE response, suggesting that the underlying mechanisms responsible for the Hall effect in our system can be tuned via electrostatic gating. A minimal microscopic model constrained by the symmetry of the heterostructure shows that both interfacial spin-orbit coupling and time-reversal symmetry breaking via the exchange interaction from magnetization are responsible for the emergence of the in-plane AHE. Our work highlights the importance of system symmetries and exchange interaction in low-dimensional heterostructures for designing novel and tunable Hall effects in layered quantum systems.

FOS: Physical sciences↗
DOE OSTI2020

MULTI-DIMENSIONAL MODELING OF THE CFR ENGINE FOR THE INVESTIGATION OF SI NATURAL GAS COMBUSTION AND CONTROLLED END-GAS AUTOIGNITION

Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emission limits and the wide range of chemical reactivity in pipeline quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its methane number (analogous to the octane number for liquid SI fuels) can span from 65 - 95. In order to realize diesel-like efficiencies, SI natural gas engines must be designed to operate at high BMEP near knock limits over a wide range of fuel reactivity. This requires a deep understanding regarding the combustion-engine interactions pertaining to flame propagation and end-gas autoignition (EGAI). However, EGAI, if controlled, provides an opportunity to increase SI natural gas engine efficiency by increasing combustion rate and the total burned fuel, mitigating the effects of the slow flame speeds of natural gas fuels which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, in order to study EGAI phenomenon, the present work highlights multi-dimensional computational fluid dynamics (CFD) models of the Cooperative Fuel Research (CFR) engine. The CFR engine models are used to investigate fuel-engine interactions that lead to EGAI with natural gas, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation (EGR). A Three-Pressure Analysis, performed with GT-Power, was used to estimate initial and boundary conditions for the three-dimensional CFD model. CONVERGE CFD v2.4 was used for the three-dimensional CFD modeling where the level set G-Equation model and SAGE detailed chemical kinetics solver were used. An assessment of the different modeling approaches is also provided to evaluate their limitations, advantages and disadvantages, and for which situations they are most applicable. Model validation was performed with experimental data taken with a CFR engine over varying compression ratio, CA50, EGR fraction, and IMEP and shows good agreement in Peak Cylinder Pressure (PCP), PCP crank angle, and the location of the 10%, 50%, and 90% mass fraction burned (CA10, CA50, and CA90, respectively). The models can predict the onset crank angle and pressure rise rate for light, medium, and heavy EGAI under a variety of fuel reactivities and engine operating conditions.

Bestel, Diego↗
DOE OSTI2021

A Flexible Power Method for Solving Infinite Dimensional Tensor Eigenvalue Problems

We propose a flexible power method for computing the leftmost, i.e., algebraically smallest, eigenvalue of an infinite dimensional tensor eigenvalue problem, $H x = \lambda x$, where the infinite dimensional symmetric matrix $H$ exhibits a translational invariant structure. We assume the smallest eigenvalue of $H$ is simple and apply a power iteration of $e^{-H}$ with the eigenvector represented in a compact way as a translational invariant infinite Tensor Ring (iTR). Hence, the infinite dimensional eigenvector can be represented by a finite number of iTR cores of finite rank. In order to implement this power iteration, we use a small parameter $t$ so that the infinite matrix-vector operation $e^{-Ht}x$ can efficiently be approximated by the Lie product formula, also known as Suzuki--Trotter splitting, and we employ a low rank approximation through a truncated singular value decomposition on the iTR cores in order to keep the cost of subsequent power iterations bounded. We also use an efficient way for computing the iTR Rayleigh quotient and introduce a finite size iTR residual which is used to monitor the convergence of the Rayleigh quotient and to modify the timestep $t$. In this paper, we discuss 2 different implementations of the flexible power algorithm and illustrate the automatic timestep adaption approach for several numerical examples.

Beeumen, Roel Van↗
DOE OSTI2021

Method of fabricating a three-dimensionally patterned mechanical energy absorptive material

A three-dimensionally patterned energy absorptive material and fabrication method having multiple layers of patterned filaments extrusion-formed from a curable pre-cursor material and stacked and cured in a three-dimensionally patterned architecture so that the energy absorptive material produced thereby has an engineered bulk property associated with the three-dimensionally patterned architecture.

Duoss, Eric B.↗
DOE OSTI2021

Three dimensional nanoporous membrane having multiple independent, continuous pore systems

According to one embodiment, a composition of matter includes: a first system of continuous voids arranged in a three-dimensional matrix; a second system of continuous voids arranged in the three-dimensional matrix; and a nanoporous barrier separating the first system of continuous voids and the second system of continuous voids. The first system of continuous voids and the second system of continuous voids are interwoven but independent so as to form a plurality of channels through the three-dimensional matrix. Corresponding methods for forming the composition of matter are also disclosed.

Ye, Jianchao↗
DOE OSTI2022

m-CUBES An efficient and portable implementation of multi-dimensional integration for gpus

The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation. Multi-dimensional integration is often time-prohibitive on CPUs. Efficient implementation on many-core architectures is challenging as the workload across the integration space cannot be predicted a priori. We propose m-Cubes, a novel implementation of the well-known Vegas algorithm for execution on GPUs. Vegas transforms integration variables followed by calculation of a Monte Carlo integral estimate using adaptive partitioning of the resulting space. m-Cubes improves performance on GPUs by maintaining relatively uniform workload across the processors. As a result, our optimized Cuda implementation for Nvidia GPUs outperforms parallelization approaches proposed in past literature. We further demonstrate the efficiency of m-Cubes by evaluating a six-dimensional integral from a cosmology application, achieving significant speedup and greater precision than the CUBA library's CPU implementation of VEGAS. We also evaluate m-Cubes on a standard integrand test suite. m-Cubes outperforms the serial implementations of the Cuba and GSL libraries by orders of magnitude speedup while maintaining comparable accuracy. Our approach yields a speedup of at least 10 when compared against publicly available Monte Carlo based GPU implementations. In summary, m-Cubes can solve integrals that are prohibitively expensive using standard libraries and custom implementations. A modern C++ interface header-only implementation makes m-Cubes portable, allowing its utilization in complicated pipelines with easy to define stateful integrals. Compatibility with non-Nvidia GPUs is achieved with our initial implementation of m-Cubes using the Kokkos framework.

Sakiotis, Ioannis↗
DOE OSTI2023

Widefield three-dimensional microscopy with a quantum entanglement light source

Devices, systems and methods for widefield three-dimensional (3D) microscopy with a quantum entanglement light source are described. An example method includes generating a first set of photons and a second set of photons, wherein each of the photons in the first set is quantum entangled with a corresponding photon in the second set, directing the second set of photons toward a sample and simultaneously directing the first set of photons toward a first two-dimensional (2D) detector, detecting, from the sample, a plurality of photons at a second 2D detector, analyzing detections from the first and second 2D detectors to determine coincidence information, and determining one or more characteristics associated with at least a three-dimensional (3D) section of the sample based on collective detections at the first and the second 2D detectors.

Bond, Tiziana C.↗
DOE OSTI2023

Offset-aligned three-dimensional integrated circuit

A three-dimensional integrated circuit includes a first die structure having a first geometry. The first die structure includes a first region that operates with a first power density and a second region that operates with a second power density. The first power density is less than the second power density. The three-dimensional integrated circuit includes a second die structure having a second geometry. A stacked portion of the second die structure is aligned with the first region. The three-dimensional integrated circuit includes an additional die structure stacked with the first die structure and the second die structure. The additional die structure has the first geometry or the second geometry.

Wilkerson, Brett P.↗
DOE OSTI2024

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Impact of High-Dimensional Phase Space Correlations on the Beam Dynamics in a Linear Accelerator

Hadron beams develop intensity-dependent transverse-longitudinal correlations within radio-frequency quadrupole (RFQ) accelerating structures. These correlations are only visible in six-dimensional phase space and are destroyed by reconstructions from low-dimensional projections. In this work, we estimate the effect of artificial decorrelation on the beam dynamics in the Spallation Neutron Source (SNS) linac and Beam Test Facility (BTF). We show that the evolution of a realistic initial distribution and its decorrelated twin converge during the early acceleration stages; thus, low-dimensional projections are probably sufficient for detailed predictions in high-power linacs.

Hoover, Austin↗
DOE OSTI2024

Additive manufacturing of hierarchical three- dimensional micro-architected aerogels

Disclosed here is a method for making a three-dimensional micro-architected aerogel, comprising: (a) curing a reaction mixture comprising a co-sol-gel material (e.g., graphene oxide (GO)) and at least one catalyst to obtain a crosslinked co-sol-gel (e.g., GO hydrogel); (b) providing a photoresin comprising a solvent, a photoinitiator, a crosslinkable polymer precursor, and a dispersion of the crosslinked co-sol-gel (e.g., GO hydrogel); (c) curing the photoresin using projection microstereolithography layer-by-layer to produce a wet gel having a pre-designed three-dimensional structure; (d) drying the wet gel to produce a dry gel; and (e) pyrolyzing the dry gel to produce a three-dimensional micro-architected aerogel (e.g., graphene aerogel). Also disclosed is a photoresin for projection microstereolithography, comprising a solvent, a photoinitiator, a crosslinkable polymer precursor, and a dispersion of a crosslinked co-sol-gel.

Worsley, Marcus A.↗
DOE OSTI2024

Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks

Fractional and tempered fractional partial differential equations (PDEs) are effective models of long-range interactions, anomalous diffusion, and non-local effects. Traditional numerical methods for these problems are mesh-based, thus struggling with the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a promising solution due to their universal approximation, generalization ability, and mesh-free training. In principle, Monte Carlo fractional PINN (MC-fPINN) estimates fractional derivatives using Monte Carlo methods and thus could lift CoD. However, this may cause significant variance and errors, hence affecting convergence; in addition, MC-fPINN is sensitive to hyperparameters. In general, numerical methods and specifically PINNs for tempered fractional PDEs are under-developed. Herein, we extend MC-fPINN to tempered fractional PDEs to address these issues, resulting in the Monte Carlo tempered fractional PINN (MC-tfPINN). To reduce possible high variance and errors from Monte Carlo sampling, we replace the one-dimensional (1D) Monte Carlo with 1D Gaussian quadrature, applicable to both MC-fPINN and MC-tfPINN. We validate our methods on various forward and inverse problems of fractional and tempered fractional PDEs, scaling up to 100,000 dimensions. Our improved MC-fPINN/MC-tfPINN using quadrature consistently outperforms the original versions in accuracy and convergence speed in very high dimensions.

42 ENGINEERING↗
DOE OSTI2022

Targeting One- and Two-Dimensional Ta–Te Structures via Nanotube Encapsulation

Fine control over material synthesis on the nanoscale can facilitate the stabilization of competing crystalline structures. In this report we demonstrate how carbon nanotube reaction vessels can be used to selectively create one-dimensional TaTe 3 chains or two-dimensional TaTe 2 nanoribbons with exquisite control of the chain number or nanoribbon thickness and width. Transmission electron microscopy and scanning transmission electron microscopy reveal the detailed atomic structure of the encapsulated materials. Complex superstructures such as multichain spiraling and apparent multilayer moirés are observed. The rare 2H phase of TaTe 2 (1H in monolayer) is found to be abundant as an encapsulated nanoribbon inside carbon nanotubes. The experimental results are complemented by density functional theory calculations for the atomic and electronic structure, which uncovers the prevalence of 2H-TaTe 2 due to nanotube-to-nanoribbon charge transfer and size confinement. Calculations also reveal new 1T' type charge density wave phases in TaTe 2 that could be observed in experimental studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quasi-Fermi liquid behavior in a one-dimensional system of interacting spinless fermions

We present numerical evidence for a paradigm in one-dimensional interacting fermion systems, whose phenomenology has traits of both Luttinger liquids and Fermi liquids. This state, dubbed a quasi-Fermi liquid, possesses a discontinuity in its fermion occupation number at the Fermi momentum. The excitation spectrum presents particlelike quasiparticles and absence of holelike quasiparticles, giving rise instead to edge singularities. Such a state is realized in a one-dimensional spinless fermion lattice Hamiltonian by fine-tuning the interactions to a regime where they become irrelevant in the renormalization group sense. We show, using uniform infinite matrix products states and finite-entanglement scaling analysis, that the system ground state is characterized by a Luttinger parameter K = 1 and a discontinuous jump in the fermion occupation number. We support the characterization with calculations of the spectral function that show a particle-hole asymmetry reflected in the existence of well-defined Landau quasiparticles above the Fermi level and edge singularities without the associated quasiparticles below. Furthermore, these results indicate that the quasi-Fermi liquid paradigm can be realized beyond the low-energy perturbative realm.

1-dimensional systems↗
DOE OSTI2020

Effects of three-dimensional Cu/Nb interfaces on strengthening and shear banding in nanoscale metallic multilayers

Manipulation of the atomic-scale structure of two-dimensional (2D) interfaces have been shown to provide nanocomposites with enhanced strength, deformability, and radiation damage resistance. In comparison with 2D interfaces, here we investigate the mechanical response of nanocomposites containing three-dimensional (3D) Cu/Nb interfaces consisting of a chemical/structural gradient separating pure Cu and Nb layers, through which the lattice mismatch between face-centered cubic Cu and body-centered cubic Nb is accommodated over a distance of several nanometers. Furthermore, it is demonstrated that 3D interfaces increase the yield and flow strength by 50% and 22%, respectively, over composites containing 2D interfaces at similar layer thicknesses. After 14% compressive strain, the onset of shear banding results in co-deformation of both Cu and Nb phases within and outside of the shear band. We conclude with a discussion of the role of interface structure in shear band formation and growth in 3D Cu/Nb.

3-dimensional interface↗