SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
We present a new hybrid quantum algorithm to estimate molecular excited and charged states on near-term quantum computers following any VQE-based ground state estimation.
Motivated by the significant biological functions of polysulfides in life processes and important applications of sodium polysulfides in battery sciences, herein we report a joint experimental and computational study on the electronic and geometric structures of a series of NaSn- (n = 5-9) clusters. The cryogenic, size-selective, negative ion photoelectron spectroscopy (NIPES) is employed to obtain their photoelectron spectra, in which distinctive spectral features with electron binding energy (EBE) up to 6.4 eV are unraveled. The EBE of the first peak in each spectrum for NaSn- (n =5-9), assigned to the transition from the ground state of anion to the ground state of each neutral radical, is observed to increase with cluster size. The vertical detachment energies (VDEs), determined from the first peak maximum, are 3.43 ± 0.02, 3.57 ± 0.02, 3.82 ± 0.03, 3.86 ± 0.02, and 4.00 ± 0.02 eV and the adiabatic detachment energies (ADEs), determined by the onset of the first peak, are 3.27 ± 0.05, 3.44 ± 0.05, 3.65 ± 0.05, 3.75 ± 0.05, and 3.93 ± 0.05 eV, for n = 5-9, respectively. A plural of low-lying isomers of anions are screened and identified with density functional theory calculations, showing that all featuring preference for a chain-like polysulfide moiety electrostatically interacted with a sodium cation. The calculated VDEs and ADEs using the high-level CCSD(T)/aug-cc-pVTZ method are in excellent agreement with experimental results, further confirming the identified isomers as most stable ones. Further analyses based on the excited-state transition, molecular orbitals and natural population charge are performed to assign all observed spectral bands along the binding energy axis, suggesting that the electron detachment process and observed excitations mainly derived from the polysulfide chain of NaSn- clusters. This work provides a fundamental understanding of the intrinsic molecular properties of sodium polysulfide systems, which widely exist in life science and sodium-sulfur cells.
Not Available
Not provided.
A high-fidelity, low-Mach computational fluid dynamics simulation tool that includes evaporating droplets and variable-density turbulent flow coupling is well-suited to ascertain transmission probability and supports risk mitigation methods development for airborne infectious diseases such as COVID-19. A multi-physics large-eddy simulation-based paradigm is used to explore droplet and aerosol pathogen transport from a synthetic cough emanating from a kneeling humanoid. For an outdoor configuration that mimics the recent open-space social distance strategy of San Francisco, maximum primary droplet deposition distances are shown to approach 8.1 m in a moderate wind configuration with the aerosol plume transported in excess of 15 m. In quiescent conditions, the aerosol plume extends to approximately 4 m before the emanating pulsed jet becomes neutrally buoyant. A dose–response model, which is based on previous SARS coronavirus (SARS-CoV) data, is exercised on the high-fidelity aerosol transport database to establish relative risk at eighteen virtual receptor probe locations.
Cosmic Reionization On Computers is a long-term program of numerical simulations of cosmic reionization. Its goal is to model fully self-consistently (albeit not necessarily from the first principles) all relevant physics, from radiative transfer to gas dynamics and star formation, in simulation volumes of up to 100 comoving Mpc, and with spatial resolution approaching 100 pc in physical units. In this method paper, we describe our numerical method, the design of simulations, and the calibration of numerical parameters. Using several sets (ensembles) of simulations in 20 h {sup –1} Mpc and 40 h {sup –1} Mpc boxes with spatial resolution reaching 125 pc at z = 6, we are able to match the observed galaxy UV luminosity functions at all redshifts between 6 and 10, as well as obtain reasonable agreement with the observational measurements of the Gunn-Peterson optical depth at z < 6.
We compare the results from several sets of cosmological simulations of cosmic reionization, produced under the Cosmic Reionization On Computers project, with existing observational data on the high-redshift Lyα forest and the abundance of Lyα emitters. We find good consistency with the observational measurements and previous simulation work. By virtue of having several independent realizations for each set of numerical parameters, we are able to explore the effect of cosmic variance on observable quantities. One unexpected conclusion we are forced into is that cosmic variance is unusually large at z > 6, with both our simulations and, most likely, observational measurements still not fully converged for even such basic quantities as the average Gunn-Peterson optical depth or the volume-weighted neutral fraction. We also find that reionization has little effect on the early galaxies or on global cosmic star formation history, because galaxies whose gas content is affected by photoionization contain no molecular (i.e., star-forming) gas in the first place. In particular, measurements of the faint end of the galaxy luminosity function by the James Webb Space Telescope are unlikely to provide a useful constraint on reionization.
Atmospheric pressure plasmas (APPs) are at the core of diverse technological applications in materials processing, chemical synthesis, resource recovery, water treatment, and medicine, among others. APPs span a wide range of power density, from low-power non-thermal to high-power thermal discharges, and typically involve interactions with a stream of working gas, processing material, or gas environment. The article provides an overview of Computational Fluid Dynamics (CFD) approaches, from mathematical models to software strategies, for the analysis of APP flows. Its focus is flows with large variations in ionization degree and significant fluid dynamic-thermal-electromagnetic coupling, as particularly found in mid- to high-power discharges. The advances achieved and challenges faced by CFD of APP flows have been driven by established and emerging applications, and can be broadly characterized in terms of model fidelity and numerical accuracy. Fidelity refers to the degree of underlying phenomena captured by the model, whereas accuracy to the precision of the numerical solution of the model. Two distinct numerical accuracy challenges are addressed: the capture of instability and pattern formation phenomena, and of plasma-gas interaction and turbulence; as well as two representative fidelity challenges: radiative transport under nonequilibrium conditions, and nonequilibrium electron and particle kinetics. The purpose of this article is to offer guidance to researchers, from modelers and code developers to open-source and commercial software users, working on CFD analyses of APP flows within technological contexts.
Abstract Synchrotron x-ray imaging techniques, like x-ray computed tomography (CT) and radiography have proven instrumental in expanding the communities knowledge of complex transport and reaction kinetics in electrochemical devices such as fuel cells and electrolyzers. This work presents the development of novel x-ray CT imaging techniques for operando visualization of water within low temperature fuel cells at spatial resolutions spanning the micro and nano scales. The design of operando sample holders, for both micro x-ray CT and nano CT experiments is described in depth, and prototypes of these sample holders were evaluated across a set of requirements, the most important of which are x-ray transmissibility, electrical conductivity and mechanical stability. Water segmentation from micro x-ray CT data was enabled by an image subtraction method, where the image without water is subtracted from the one with water. Through iterative experimentations, the operando nano CT cell was developed to optimize mechanical compression, electric conductivity and gas flow. While three-dimensional fuel cell reconstructions were shown possible, there remain challenges to overcome at typical lower energies (8 keV) due to beam damage, whereas it is not as significant for higher energies (>17.5 keV).
Not Available
Abstract not provided
Explore the source record for details and available documents.
Purpose: This study demonstrates a methodology for tracking changes in metastatic bone disease using trajectories in material basis space in serial dual energy computed tomography (DECT) studies. Methods: This study includes patients with bone metastases from breast cancer that had clinical surveillance CT scans using a General Electric CT750HD in dual energy mode. A radiologist defined regions-of-interested (ROI) for bone metastasis, normal bone, and marrow across the serial DECT scans. Our approach employs a Radon transform to forward-projection the basis images, namely, water and iodine, into sinogram space. This data is then repartitioned into fat/bone and effective density/Z image pairs using assumed energy spectrums for the x-ray energies. This approach both helps remove negative material densities and avoids adding spectrum-hardening artifacts. These new basis data sets were then reconstructed via filtered back-projection to create new material basis pair images. The trajectories of these pairs were then plotted in the new basis space providing a means to both visualize and quantitatively measure changes in the material properties of the tumors. Results: ROI containing radiologist defined metastatic bone disease showed well-defined trajectories in both fat/bone and effective density/Z space. ROI that contained radiologist defined normal bone and marrow did not exhibit any discernible trajectories and were stable from scan to scan. Conclusions: The preliminary results show that changes in material composition and effective density/Z image pairs were seen primarily in metastasis and not in normal tissue. This study indicates that by using routine clinical DECT it may be possible to monitor therapy response of bone metastases because healing or worsening bone metastases change material composition of bone. Additional studies are needed to further validate these results and to test for their correlation with outcome.
Here, dielectric microstructures have generated much interest in recent years as a means of accelerating charged particles when powered by solid state lasers. The acceleration gradient (or particle energy gain per unit length) is an important figure of merit. To design structures with high acceleration gradients, we explore the adjoint variable method, a highly efficient technique used to compute the sensitivity of an objective with respect to a large number of parameters. With this formalism, the sensitivity of the acceleration gradient of a dielectric structure with respect to its entire spatial permittivity distribution is calculated by the use of only two full-field electromagnetic simulations, the original and ‘adjoint’. The adjoint simulation corresponds physically to the reciprocal situation of a point charge moving through the accelerator gap and radiating. Using this formalism, we perform numerical optimizations aimed at maximizing acceleration gradients, which generate fabricable structures of greatly improved performance in comparison to previously examined geometries.
Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomes included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.
X-ray computed tomography (CT) is a nondestructive evaluation technique that may be used to image complex internal features without the line-of-sight restrictions inherent in conventional optical and contact (stylus) metrology instrumentation. Applications of CT metrology include material defects (porosity), dimensions and surface texture. However, scan artifacts, such as streaking, caused primarily by beam hardening and scatter, limit the accuracy of CTbased metrology. Reduction of these artifacts would improve the accuracy material properties and dimensional metrology information. This paper reports on the development of additively manufactured (AM) Ti5553 and 316L measurement phantoms with integral lattice (lattice) structures specifically designed to exhibit these artifacts. The phantoms were scanned in three CT systems and a scanning-probe CMM measurement. Surfaces generated from uncorrected CT data exhibited “dimple” defects that are not present in the CMM measurement. Correction methods were developed in Livermore Tomography Tools (LTT) software and this paper reports on the successful correction of these artifacts. This work is expected to have application in areas where characterization of defects, dimensions and surface texture using CT are critical.
In order to add more capabilities to Halide, we have designed a new framework called Tiramisu and integrated this framework into Halide. Since Tiramisu enables Halide to target heterogeneous architectures, our development efforts have been refocused on Tiramisu. Most high-performance computer systems today are complex and increasingly heterogeneous; they may have CPUs, GPUs and FPGAs. Achieving best performance requires taking full advantage of all these different architectures. To address this issue, we have designed Tiramisu, an optimization framework that enables Halide (and other DSLs) to target heterogeneous architectures. Tiramisu is an optimization framework that takes as input a high level, architecture-independent representation of code and a set of scheduling and data mapping commands that guide code transformation. The input can either be generated by a domain-specific language (DSL) compiler such as Halide or directly written by a programmer. Tiramisu then applies the user-specified code and data-layout transformations and generates an architecture-specific, low-level intermediate representation (IR) that takes advantage of modern architectural features such as multicore parallelism, non-uniform memory (NUMA) hierarchies, clusters, and accelerators like GPUs and FPGAs. We integrated Tiramisu within Halide and implemented a representative set of benchmarks to evaluate this integration. Tiramisu is now open source and is available for public use (http://tiramisu-compiler.org/). A paper about Tiramisu was published, it shows that Tiramisu extends Halide with many new capabilities and that Tiramisu can generate efficient code for multicores, GPUs, FPGAs and distributed heterogeneous systems. The performance of code generated by the Tiramisu backends matches or exceeds hand optimized reference implementations. For example, the multicore backend matches the highly optimized Intel MKL library on many kernels and shows speedups reaching 4x over the original Halide. In addition to making Tiramisu more robust, we have used Tiramisu to implement a set of representative tensor operation for constructing baryon building blocks required for multi baryon contractions in LQCD. In order to implement this code, we needed to generalize Tiramisu in two ways: first we needed to support indirect array accesses, and second, we needed to add support for complex numbers to Tiramisu. The code generated by Tiramisu is 6x faster than the reference code. Our efforts towards an MPI based multi-node version of tiramisu have matured and the resulting code scales well on multiple nodes (tests up to 512 KNL nodes have been undertaken).
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.