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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Chiral assemblies of pinwheel superlattices on substrates

The unique topology and physics of chiral superlattices make their self-assembly from nanoparticles highly sought after yet challenging in regard to (meta)materials. In this report we show that tetrahedral gold nanoparticles can transform from a perovskite-like, low-density phase with corner-to-corner connections into pinwheel assemblies with corner-to-edge connections and denser packing. Whereas corner-sharing assemblies are achiral, pinwheel superlattices become strongly mirror asymmetric on solid substrates as demonstrated by chirality measures. Liquid-phase transmission electron microscopy and computational models show that van der Waals and electrostatic interactions between nanoparticles control thermodynamic equilibrium. Variable corner-to-edge connections among tetrahedra enable fine-tuning of chirality. The domains of the bilayer superlattices show strong chiroptical activity as identified by photon-induced near-field electron microscopy and finite-difference time-domain simulations. The simplicity and versatility of substrate-supported chiral superlattices facilitate the manufacture of metastructured coatings with unusual optical, mechanical and electronic characteristics.

42 ENGINEERING↗
DOE OSTI2018

Framework and methodology for supply chain lifecycle analytics

The various technologies presented herein relate to pertaining to identifying and mitigating risks and attacks on a supply chain. A computer-implemented representation of a supply chain is generated comprising nodes (locations) and edges (objects, information). Risk to attack and different attack vectors can be defined for the various nodes and edges, and further, based upon the risks and attacks, (difficulty, consequence) pairs can be determined. One or more mitigations can be generated to increase a difficulty of attack and/or reduce consequence of an attack. The one or more mitigations can be constrained, e.g., by cost, time, etc., to facilitate determination of how feasible a respective mitigation is to implement with regard to finances available, duration to implement, etc. A context-free grammar can be utilized to identify one or more attacks in the supply chain. Further, the risks can undergo a ranking to enable mitigation priority to be determined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Preparations for quantum simulations of quantum chromodynamics in 1+1 dimensions. I. Axial gauge

Here, tools necessary for quantum simulations of 1+1 dimensional quantum chromodynamics are developed. When formulated in axial gauge and with two flavors of quarks, this system requires 12 qubits per spatial site with the gauge fields included via nonlocal interactions. Classical computations and D-wave’s quantum annealer advantage are used to determine the hadronic spectrum, enabling a decomposition of the masses and a study of quark entanglement. Color “edge” states confined within a screening length of the end of the lattice are found. IBM’s seven-qubit quantum computers, ibmq_jakarta and ibm_perth, are used to compute dynamics from the trivial vacuum in one-flavor QCD with one spatial site. More generally, the Hamiltonian and quantum circuits for time evolution of 1+1 dimensional SU(N c ) gauge theory with Nf flavors of quarks are developed, and the resource requirements for large-scale quantum simulations are estimated.

1+1 dimensions↗
DOE OSTI2021

Facile ab initio approach for self-localized polarons from canonical transformations

Electronic states in a crystal can localize due to strong electron-phonon (e-ph) interactions, forming so-called small polarons. Methods to predict the formation and energetics of small polarons are either computationally costly or not geared toward quantitative predictions. Here we show a formalism based on canonical transformations to compute the polaron formation energy and wave function using ab initio e-ph interactions. Comparison of the calculated polaron and band-edge energies allows us to determine whether charge carriers in a material favor a localized small polaron over a delocalized Bloch state. Due to its low computational cost, our approach enables efficient studies of the formation and energetics of small polarons, as we demonstrate by investigating electron and hole polaron formation in alkali halides and metal oxides and peroxides. We outline refinements of our scheme and extensions to compute transport in the polaron hopping regime.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Validating the simulation of beam-ion charge exchange in MAST Upgrade

Abstract Simulation of the impact of charge-exchange (CX) reactions on beam ions in the Mega Amp Spherical Tokamak (MAST) Upgrade was compared to measurements carried out with a fission chamber (neutron fluxes) and a fast ion deuterium-alpha (FIDA) diagnostic. A simple model was developed to reconstruct the outer-midplane neutral density based on measurements of deuterium-alpha emission from edge neutrals, and on Thomson scattering measurements of electron density and temperature. The main computational tools used were the ASCOT orbit-following code and the FIDASIM code for producing synthetic FIDA signals. The neutral density reconstruction agrees qualitatively with SOLPS-ITER modelling and yields a synthetic passive FIDA signal that is consistent with measurement. When CX losses of beam ions are accounted for, predicted neutron emission rates are quantitatively more consistent with measurement. It was necessary to account for CX losses of beam ions in simulations to reproduce the measured passive FIDA signal quantitatively and qualitatively. The results suggest that the neutral density reconstruction is a good approximation, that CX with edge neutrals causes significant beam-ion losses in MAST Upgrade, typically 20% of beam power, and that the ASCOT fast-ion CX model can be used to accurately predict the redistribution and loss of beam ions due to CX.

Physics↗
DOE OSTI2025

Aerodynamic Characterization of 3D Scanned Wind Turbine Blades Using Experimental and Computational Methods

This study presents an aerodynamic characterization of 3D scanned wind turbine blades using both experimental and computational methods. The research was conducted by Gulf Wind Technology and Sandia National Laboratories. The primary objective was to investigate the aerodynamic impacts of leading-edge manufacturing defects on wind turbine blades. The study utilized the Stratasys NEO 800 3D Printer for high-precision manufacturing and the GWT Accelerator Wind Tunnel for experimental testing. Computational simulations were performed using COMSOL Multiphysics to model the wind tunnel and analyze flow characteristics and OpenFOAM to study the aerodynamic impacts of leading-edge defects. OpenFAST was used to estimate how these defects can lead to revenue losses for wind farm operators as high as 6%. The results demonstrated significant aerodynamic performance variations due to defects, with detailed analysis provided through wind tunnel and CFD data. The findings contribute to the understanding of defect impacts on wind turbine blade performance and offer insights for future design improvements.

17 WIND ENERGY↗
DOE OSTI2018

Topological quantum computation based on chiral Majorana fermions

Here, the chiral Majorana fermion is a massless self-conjugate fermion which can arise as the edge state of certain 2D topological matters. It has been theoretically predicted and experimentally observed in a hybrid device of a quantum anomalous Hall insulator and a conventional superconductor. Its closely related cousin, the Majorana zero mode in the bulk of the corresponding topological matter, is known to be applicable in topological quantum computations. Here we show that the propagation of chiral Majorana fermions leads to the same unitary transformation as that in the braiding of Majorana zero modes and propose a platform to perform quantum computation with chiral Majorana fermions. A Corbino ring junction of the hybrid device can use quantum coherent chiral Majorana fermions to implement the Hadamard gate and the phase gate, and the junction conductance yields a natural readout for the qubit state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Extracting Higher Central Charge from a Single Wave Function

A (2+1)D topologically ordered phase may or may not have a gappable edge, even if its chiral central charge c_ is vanishing. Recently, it was discovered that a quantity regarded as a "higher" version of chiral central charge gives a further obstruction beyond c_ to gapping out the edge. Here, in this Letter, we show that the higher central charges can be characterized by the expectation value of the partial rotation operator acting on the wave function of the topologically ordered state. This allows us to extract the higher central charge from a single wave function, which can be evaluated on a quantum computer. Our characterization of the higher central charge is analytically derived from the modular properties of edge conformal field theory, as well as the numerical results with the ν = 1/2 bosonic Laughlin state and the non-Abelian gapped phase of the Kitaev honeycomb model, which corresponds to U(1) 2 and Ising topological order, respectively. The Letter establishes a numerical method to obtain a set of obstructions to the gappable edge of (2+1)D bosonic topological order beyond c_, which enables us to completely determine if a (2+1)D bosonic Abelian topological order has a gappable edge or not. We also point out that the expectation values of the partial rotation on a single wave function put a constraint on the low-energy spectrum of the bulk-boundary system of (2+1)D bosonic topological order, reminiscent of the Lieb-Schultz-Mattis-type theorems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

A graph theoretic framework for representation, exploration and analysis on computed states of physical systems

A graph theoretic perspective is taken for a range of phenomena in continuum physics in order to develop representations for analysis of large scale, high-fidelity solutions to these problems. Of interest are phenomena described by partial differential equations, with solutions being obtained by computation. The motivation is to gain insight that may otherwise be difficult to attain because of the high dimensionality of computed solutions. We consider graph theoretic representations that are made possible by low-dimensional states defined on the systems. These states are typically functionals of the high-dimensional solutions, and therefore retain important aspects of the high-fidelity information present in the original, computed solutions. Our method is rooted in regarding each state as a vertex on a graph and identifying edges via processes that are induced either by numerical solution strategies, or by the physics. Correspondences are drawn between the sampling of stationary states, or the time evolution of dynamic phenomena, and the analytic machinery of graph theory. A collection of computations is examined in this framework and new insights to them are presented through analysis of the corresponding graphs.

42 ENGINEERING↗
DOE OSTI2022

The Case for Optimized Edge-Centric Tractography at Scale

The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Polarization-dependent resonant inelastic x-ray scattering of β-Ga2O3: An experimental and computational study

The bulk electronic structure of β-Ga2O3 single crystals was investigated using oxygen K-edge x-ray emission and absorption spectroscopy as well as resonant inelastic soft x-ray scattering. Spectra were obtained for different orientations of the crystal planes with respect to the polarization vector of the incident x-ray beam. The spectra are analyzed with calculations based on density functional theory and using the Bethe–Salpeter equation in the ocean code to take the core–hole interaction into account. These calculations correctly capture all the main features in the experimental spectra, demonstrating the potential of the approach to predict the electronic properties of similar compounds. We find a pronounced anisotropy as a function of the excitation polarization vector and significant differences in the spectral contributions from the inequivalent oxygen atoms of β-Ga2O3.

Pyatenko, Elizaveta↗
DOE OSTI2016

Surface nanopattern formation due to current-induced homoepitaxial nanowire edge instability

Physical nanopatterning based on a precise control of macroscopic forcing is an essential tool of nanoscale science and technology. Using an externally applied electric field as the macroscopic force, we report here a computational study on the formation of surface nanopatterns consisting of single-layer homoepitaxial islands as a result of a morphological instability that can occur under edge electromigration conditions on the straight edge of a single-layer nanowire grown epitaxially on a crystalline substrate. Direct dynamical simulations based on a model that has been validated experimentally for the Ag/Ag system show that the current-induced nanowire edge instability causes the breakup of the nanowire and leads to the formation of uniformly distributed islands, arranged in linear or V-shaped arrays, which are uniformly sized with nanoscale dimensions. The simulation results are supported by linear stability theory and demonstrate that the geometrical features of the island patterns and the island sizes can be controlled precisely by controlling the electric field direction with respect to the nanowire axis and the electric field strength. Our findings have important implications for developing physical nanopatterning approaches toward enabling future nanofabrication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Unraveling the intrinsic atomic physics behind x-ray absorption line shifts in warm dense silicon plasmas

Here, we present a novel free-energy density functional theory (DFT)-based methodology for optical property calculations of warm dense matter to cover a wide range of thermodynamic conditions and photon energies including the entire x-ray range. It uses Mermin–Kohn–Sham density functional theory with exchange-correlation (XC) thermal effects taken into account via a fully temperature dependent generalized gradient approximation XC functional. The methodology incorporates a combination of the ab initio molecular dynamics (AIMD) snapshotted Kubo–Greenwood optic data with a single-atom in simulation cell calculations to close the photon energy gap between the L and K edges and extend the K-edge tail toward many-keV photon energies. This gap arises in the standard scheme due to a prohibitively large number of bands required for the Kubo–Greenwood calculations with AIMD snapshots. Kubo–Greenwood data on snapshots provide an accurate description of optic properties at low photon frequencies slightly beyond the L-edge and x-ray absorption near edges structure (XANES) spectra, while data from periodic calculations with single atoms cover the tail regions beyond the edges. To demonstrate its applicability to mid-Z materials where the standard DFT-based approach is not computationally feasible, we have applied it to opacity calculations of warm dense silicon plasmas. These first-principles calculations revealed a very interesting phenomon of red-to-blue shift in K–L (1s → 2p) and K-edge absorptions along both isotherm and isochore, which are absent in most continuum-lowering models of traditional plasma physics. This new physics phenomenon can be attributed to the underlying competition between the screening of deeply bound core electrons and the screening of outer-shell electrons caused by warm-dense-plasma conditions. We further demonstrate that the ratio of 1s → 2p to the K-edge x-ray absorptions can be used to characterize warm-dense-plasma conditions. Eventually, based on our absorption calculations, we have established a first-principles opacity table (FPOT) for silicon in a wide range of material densities and temperatures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Machine Learning for Computational Heterogeneous Catalysis

Abstract Big data and artificial intelligence has revolutionized science in almost every field – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning tools to investigate the vast materials space. The goal of using these tools is to establish a deeper understanding of the relations between materials properties and activity, selectivity and stability – the important figures of merit in catalysis. Based on these insights, catalyst design principles can be established, which hopefully lead us to discover highly efficient catalysts to solve pressing issues for a sustainable future and the synthesis of highly functional materials, chemicals and pharmaceuticals. The inherent complexity of catalytic reactions quests for machine learning methods to efficiently navigate through the high‐dimensional hyper‐surfaces in structure optimization problems to determine relevant chemical structures and transition states. In this review, we show how cutting edge data infrastructures and machine learning methods are being used to address problems in computational heterogeneous catalysis.

Schlexer Lamoureux, Philomena↗
DOE OSTI2021

Sensor-Assisted Fault Mitigation in Quantum Computation

We propose a method for assisting fault mitigation in quantum computation through the use of sensors, co-located near physical qubits. Specifically, we consider using transition edge sensors co-located on silicon substrates hosting superconducting qubits to monitor for energy injection from ionizing radiation that has been demonstrated to increase decoherence in transmon qubits. We generalize from these two physical device concepts and explore the potential advantages of co-located sensors to assist fault mitigation in quantum computation. In the simplest scheme, co-located sensors beneficially assist rejection of calculations potentially affected by environmental disturbances. Investigating the potential computational advantage further required development of an extension to the standard formulation of quantum error correction. In a specific case the standard three-qubit, bit flip quantum correction code, we show that given a 20% overall error probability per qubit, approximately 90% of repeated calculation attempts are correctable. However, when sensor-detectable errors account for 45% of overall error probability, the use of colocated sensors uniquely associated with independent qubits, boosts the fraction of correct final-state calculations to 96% at the cost of rejecting 7% of repeated calculation attempts.

Orrell, John L.↗
DOE OSTI2017

Effects of Channel Implant Variation on Radiation-Induced Edge Leakage Currents in n-Channel MOSFETs

The effects of radiation-induced defects and statistical variation in the dose and energy of MOSFET channel implants in a modern bulk CMOS technology are modeled using a process simulator in combination with analytical computations. The model integrates doping profiles obtained from process simulations and experimentally determined defect potentials into implicit surface potential equations. Solutions to these equations are then used to model radiation-induced edge leakage currents in 90-nm bulk CMOS n-channel MOSFETs. The results indicate that slight variations in the channel implant parameters can have a significant impact on the doping profile along the shallow trench isolation sidewall and thus the radiation-induced edge leakage currents.

42 ENGINEERING↗
DOE OSTI2016

Clustering VoIP caller for SPIT identification

Abstract The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.

Azad, Muhammad Ajmal↗
DOE OSTI2023

Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific Computing and Machine Learning

We present Adrastea, an efficient FPGA design environment for developing scientific machine learning applications. FPGA development is challenging, from deployment, proper toolchain setup, programming methods, interfacing FPGA kernels, and more importantly, the need to explore design space choices to get the best performance and area usage from the FPGA kernel design. Adrastea provides an automated and scalable design flow to parameterize, implement, and optimize complex FPGA kernels and associated interfaces. We show how virtualization of the development environment via virtual machines is leveraged to simplify the setup of the FPGA toolchain while deploying the FPGA boards and while scaling up the automated design space exploration to leverage multiple machines concurrently. Adrastea provides an automated build and test environment of FPGA kernels. By exposing design space hyper-parameters, Adrastea can automatically search the design space in parallel to optimize the FPGA design for a given metric, usually performance or area. Adrastea simplifies the task of interfacing with the FPGA kernels with a simplified interface API. To demonstrate the capabilities of Adrastea, we implement a complex random forest machine learning kernel with 10,000 input features while achieving extremely low computing latency without loss of prediction accuracy, which is required by a scientific edge application at SNS. We also demonstrate Adrastea using an FFT kernel and show that for both applications Adrastea is able to systematically and efficiently evaluate different design options, which reduced the time and effort required to develop the kernel from months of manual work to days of automatic builds.

Young, Aaron↗