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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

XDesign : An open-source software package for designing X-ray imaging phantoms and experiments

Here, the development of new methods or utilization of current X-ray computed tomography methods is impeded by the substantial amount of expertise required to design an X-ray computed tomography experiment from beginning to end. In an attempt to make material models, data acquisition schemes and reconstruction algorithms more accessible to researchers lacking expertise in some of these areas, a software package is described here which can generate complex simulated phantoms and quantitatively evaluate new or existing data acquisition schemes and image reconstruction algorithms for targeted applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Controlling Nanoparticle Orientations in the Self-Assembly of ‘Patchy’ Quantum Dot-Gold Heterostructural Nanocrystals

Self-assembly of nanocrystals is a promising route for creating macroscale materials that derive function from the properties of their nanoscale building blocks. While much progress has been made assembling nanocrystals into different superlattices, controlling the relative orientations of nanocrystals in those lattices remains a challenge. Here, we combine experiments with computer simulations to study the self-assembly of patchy heterostructural nanocrystals (HNCs), consisting of near-spherical quantum dots decorated with regular arrangements of small gold satellites, into close-packed superlattices with pronounced orientational alignment of HNCs. Our simulations indicate that the orientational alignment is caused by van der Waals interactions between gold patches and is sensitive to the interparticle distance in the superlattice. We demonstrate experimentally that the degree and type of orientational alignment can be controlled by changing ligand populations on HNCs. This study provides guidance for the design and fabrication of nanocrystal superlattices with enhanced structural control.

ZHu, Hua↗
DOE OSTI2022

Low-synch Gram–Schmidt with delayed reorthogonalization for Krylov solvers

The parallel strong-scaling of iterative methods is often determined by the number of global reductions at each iteration. Low-synch Gram-Schmidt algorithms are applied here to the Arnoldi algorithm to reduce the number of global reductions and therefore to improve the parallel strong-scaling of iterative solvers for nonsymmetric matrices such as the GMRES and the Krylov-Schur iterative methods. In the Arnoldi context, the factorization is "left-looking" and processes one column at a time. Among the methods for generating an orthogonal basis for the Arnoldi algorithm, the classical Gram-Schmidt algorithm, with reorthogonalization (CGS2) requires three global reductions per iteration. A new variant of CGS2 that requires only one reduction per iteration is presented and applied to the Arnoldi algorithm. Delayed CGS2 (DCGS2) employs the minimum number of global reductions per iteration (one) for a one-column at-a-time algorithm. The main idea behind the new algorithm is to group global reductions by rearranging the order of operations. DCGS2 must be carefully integrated into an Arnoldi expansion or a GMRES solver. Numerical stability experiments assess robustness for Krylov-Schur eigenvalue computations. Performance experiments on the ORNL Summit supercomputer then establish the superiority of DCGS2 over CGS2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Extraction of valuable chemicals from food waste via computational solvent screening and experiments

About 1.3 billion tons of global food production end up in landfills and composting, leading to significant anthropogenic greenhouse gas (GHG) emissions. Extracting antioxidant and antimicrobial chemicals (flavonoids, phenolic acids, etc.) from food waste is an economically lucrative valorization strategy but is hindered by efficient solvent selection. Here we perform in silico high throughput screening to identify high solubility solvents for key phenolics and reveal more than 100+ higher-performing solvents than the traditional ethanol and methanol. Solubilities of nine shortlisted solvents are measured and found in reasonable agreement with model predictions. Analysis of the Conductor like Screening Model for Real Solvents (COSMO-RS) σ-profiles and Hansen Solubility Parameters reveals that polarity and hydrogen bonding make dimethylformamide (DMF) an excellent single solvent. We showcase the replacement of high-solubility toxic solvents with green mixtures and demonstrate the approach to potato peel waste. As a result, our work provides a blueprint for solvent selection and generates new insights into extraction from food waste.

09 BIOMASS FUELS↗
DOE OSTI2021

Ultrafast x-ray pump x-ray probe transient absorption spectroscopy: A computational study and proposed experiment probing core-valence electronic correlations in solvated complexes

Femtosecond X-ray pump X-ray probe experiments are currently possible at free electron lasers such as the linac coherent light source (LCLS), which opens new opportunities for studying solvated transition metal-ligand complexes. In order to make the most effective use of these kinds of experiments, it is necessary to determine which chemical properties an X-ray pump pulse will measure. In this paper, we have combined electron cascade calculations and excited-state time-dependent density functional theory calculations to predict the initial state prepared by an X-ray pump and the subsequent X-ray probe spectra at the Fe K-edge in the solvated model transition metal complex, K 4 Fe II (CN) 6 . We find several key spectral features that report on the ligand-field splitting, solvent-solute interactions, and the 3p and 3d electron interactions. We then show how these features could be measured in an experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

On-demand data analytics in HPC environments at leadership computing facilities: Challenges and experiences

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Harney, John↗
DOE OSTI2017

Pillars of the Mantle: Imaging the Interior of the Earth with Adjoint Tomography. In: PEARC17 Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, Article No. 75

In this work, we investigate global seismic tomographic models obtained by spectral-element simulations of seismic wave propagation and adjoint methods. Global crustal and mantle models are obtained based on an iterative conjugate-gradient type of optimization scheme. Forward and adjoint seismic wave propagation simulations, which result in synthetic seismic data to make measurements and data sensitivity kernels to compute gradient for model updates, respectively, are performed by the SPECFEM3D_GLOBE package [1] [2] at the Oak Ridge Leadership Computing Facility (OLCF) to study the structure of the Earth at unprecedented levels. Using advances in solver techniques that run on the GPUs on Titan at the OLCF, scientists are able to perform large-scale seismic inverse modeling and imaging. Using seismic data from global and regional networks from global CMT earthquakes, scientists are using SPECFEM3D_GLOBE to understand the structure of the mantle layer of the Earth. Visualization of the generated data sets provide an effective way to understand the computed wave perturbations which define the structure of mantle in the Earth.

Pugmire, David↗
DOE OSTI2022

Three-Photon Optical Pumping for Trapped Ion Quantum Computing

In this report we describe the testing of a novel scheme for state preparation of trapped ions in a quantum computing setup. This technique optimally would allow for similar precision and speed of state preparation while allowing for individual addressability of single ions in a chain using technology already available in a trapped ion experiment. As quantum computing experiments become more complicated, mid-experiment measurements will become necessary to achieve algorithms such as quantum error correction. Any mid-experiment measurement then requires the measured qubit to be re-prepared to a known quantum state. Currently this involves the protected qubits to be moved a sizeable distance away from the qubit being re-prepared which can be costly in terms of experiment length as well as introducing errors. Theoretical calculations predict that a three-photon process would allow for state preparation without qubit movement with similar efficiencies to current state preparation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments— including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers—is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.

computational science and engineering↗
DOE OSTI2025

Residual stress distribution in an additively manufactured complex structure by neutron diffraction measurement

Residual stress in an aerodynamically shaped Ni-based superalloy airfoil fabricated by laser powder bed fusion was measured by neutron diffraction. The experiment was conducted by considering the complex shape, implementing computer aided experiment planning, and automatic alignment at each rapid measurement. The 3-dimensional (3D) residual stress distribution in the airfoil is presented in this work, which lacks symmetry due to the complex geometry of the airfoil. In conclusion, the results provide theoretical thermal processing models a complete residual stress dataset of simulation validation on 3D shape complex structure.

Residual stress↗
DOE OSTI2024

A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems

We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with model-based image reconstruction algorithms have been proposed. This novel approach enables high quality real-time reconstructions from streaming experimental data, thus providing feedback to users, while requiring fewer yet a fixed number of projections compared to the filtered back projection method. In this paper, we propose a novel machine learning criterion that can terminate a streaming neutron tomography scan once sufficient information is obtained based on the current set of measurements. Our decision criterion uses a quality score which combines a reference-free image quality metric computed using a pre-trained deep neural network with a metric that measures differences between consecutive reconstructions. The results show that our method can reduce the measurement time by approximately a factor of five compared to a baseline method based on filtered back projection for the samples we studied while automatically terminating the scans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Distributed Computing for the Project 8 Experiment

The Project 8 collaboration aims to measure the absolute neutrino mass or improve on the current limit by measuring the tritium beta decay electron spectrum. We present the current distributed computing model for the Project 8 experiment. Project 8 is in its second phase of data taking with a near continuous data rate of 1Gbps. The current computing model uses DIRAC (Distributed Infrastructure with Remote Agent Control) for its workflow and data management. A detailed meta-data assignment using the DIRAC File Catalog is used to automate raw data transfers and subsequent stages of data processing. The DIRAC system is deployed on containers managed using a Kubernetes cluster to provide a scalable infrastructure. A modified DIRAC Site Director provides the ability to submit jobs using Singularity on opportunistic High-Performance Computing (HPC) sites.

Distributed Computing, Kubernetes, DIRAC, Project ↗
DOE OSTI2018

An Experimental and Computational Study of a Single Diesel Droplet Impinging on a Dry Surface

This work presents experiments and computational simulations of a single diesel droplet impinging on a dry surface to provide a better understanding of the dynamics of the impingement process. The experimental work was carried out at several substrate surface temperatures where a single diesel droplet was dropped from a precision syringe pump to provide a specific droplet diameter and impact velocity. The effect of the surface temperature on the temporal evolution of the droplet spreading factor, dynamic contact angle, and contact line velocity was studied. The experimental study on the temporal evolution of the droplet-wall interaction was used to develop a dynamic contact angle model for computational simulations. Using an in-house multiphase flow solver, simulations of the single diesel droplet impingement were performed. The simulation results of the spreading factor were compared to the experimental results and the effects of liquid temperature and contact angle model parameters were investigated. The development of a dynamic contact angle model for diesel droplets enhances the ability to study diesel droplet impingement, an important factor in diesel fuel injection. With an accurate dynamic contact angle model, future studies can be performed at scales and velocities representative of the conditions present in internal combustion engines.

Markt, D.↗