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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Design and Construction of Detector and Data Acquisition Elements for Proton Computed Tomography

Proton computed tomography (pCT) offers an alternative to x-ray imaging with potential for three-dimensional imaging, reduced radiation exposure, and in-situ imaging. Northern Illinois University (NIU) is developing a second-generation proton computed tomography system with a goal of demonstrating the feasibility of three-dimensional imaging within clinically realistic imaging times. The second-generation pCT system is comprised of a tracking system, a calorimeter, data acquisition, a computing farm, and software algorithms. The proton beam encounters the upstream tracking detectors, the patient or phantom, the downstream tracking detectors, and a calorimeter. The schematic layout of the PCT system is shown. The data acquisition sends the proton scattering information to an offline computing farm. Major innovations of the second generation pCT project involve an increased data acquisition rate ( MHz range) and development of three-dimensional imaging algorithms. The Fermilab Particle Physics Division and Northern Illinois Center for Accelerator and Detector Development at Northern Illinois University worked together to design and construct the tracking detectors, calorimeter, readout electronics and detector mounting system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Raman Lidar (RL) Instrument Handbook

The Raman lidar at the ARM Climate Research Facility (ACRF) Southern Great Plains (SGP) Central Facility (SGPRL) is an active, ground-based laser remote sensing instrument that measures height and time resolved profiles of water vapor mixing ratio and several cloud- and aerosol-related quantities. The system is a non-commercial custom-built instrument developed by Sandia National Laboratories specifically for the ARM Program. It is fully computer automated, and will run unattended for many days following a brief (~5-minute) startup period. The self-contained system (requiring only external electrical power) is housed in a climate-controlled 8’x8’x20’ standard shipping container.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

2D microwave imaging reflectometer electronics

A 2D microwave imaging reflectometer system has been developed to visualize electron density fluctuations on the DIII-D tokamak. Simultaneously illuminated at four probe frequencies, large aperture optics image reflections from four density-dependent cutoff surfaces in the plasma over an extended region of the DIII-D plasma. Localized density fluctuations in the vicinity of the plasma cutoff surfaces modulate the plasma reflections, yielding a 2D image of electron density fluctuations. Details are presented of the receiver down conversion electronics that generate the in-phase (I) and quadrature (Q) reflectometer signals from which 2D density fluctuation data are obtained. Also presented are details on the control system and backplane used to manage the electronics as well as an introduction to the computer based control program.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Translational research in the MPICH project

The MPICH project is an example of translational research in computer science before that term was well known or even coined. The project began in 1992 as an effort to develop a portable, high-performance implementation of the emerging Message-Passing Interface (MPI) Standard. It has enabled the widespread adoption of MPI as a way to write scalable parallel applications on systems of all sizes including upcoming exascale supercomputers. In this paper, we describe how the translational research process was used in MPICH, how that led to its success, the challenges encountered and lessons learned, and how the process could be applied to other similar projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Early Career Award: An Enabling Computational Framework for Uncertainty Assimilation and Propagation in Complex PDE Systems: Sparse and Low-rank Techniques (Final Report)

This project aims to develop a set of new theories, algorithms, and computational tools that enable ultra-scalable representation and propagation of high-dimensional uncertainties to circumvent the issue of curse-of-dimensionality. The research components are motivated by the fact that highly scalable algorithms are needed to achieve exascale performance for uncertainty quantification and predictive simulation at extreme scales. Although these developments are generic and applicable to a wide range of high-dimensional, complex, stochastic, partial-differential-equation (PDE) systems, this research effort will focus on predictive simulation of high-energy-density lithium batteries. To create knowledge about the precise failure mechanisms of these batteries and ultimately predict their performance and reliability, a truly data-driven, multi-scale, multi-physics framework is needed that allows studying (i) dominant sources of uncertainty in the governing physical models at all scales of relevance and (ii) the interface between the interchanging physics and scales. The impact of the planned mathematical and numerical advancements in prediction of high-dimensional uncertain systems will be broad across areas of engineering and science such as climate modeling, fusion energy, combustion, and nanoscience, among others.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Toward Mixed Analog-Digital Quantum Signal Processing: Quantum AD/DA Conversion and the Fourier Transform

Signal processing stands as a pillar of classical computation and modern information technology, applicable to both analog and digital signals. Recently, advancements in quantum information science have suggested that quantum signal processing (QSP) can enable more powerful signal processing capabilities. However, the developments in QSP have primarily leveraged digital quantum resources, such as discrete-variable (DV) systems like qubits, rather than analog quantum resources, such as continuous-variable (CV) systems like quantum oscillators. Consequently, there remains a gap in understanding how signal processing can be performed on hybrid CV-DV quantum computers. Here we address this gap by developing a new paradigm of mixed analog-digital QSP. We demonstrate the utility of this paradigm by showcasing how it naturally enables analog-digital conversion of quantum signals—specifically, the transfer of states between DV and CV quantum systems. We then show that such quantum analog-digital conversion enables new implementations of quantum algorithms on CV-DV hardware. This is exemplified by realizing the quantum Fourier transform of a state encoded on qubits via the free-evolution of a quantum oscillator, albeit with a runtime exponential in the number of qubits due to information theoretic arguments. Collectively, this work marks a significant step forward in hybrid CV-DV quantum computation, providing a foundation for scalable analog-digital signal processing on quantum processors.

42 ENGINEERING↗
DOE OSTI2017

Exascale Programming Approaches for Accelerated Climate Modeling for Energy

The Accelerated Climate Modeling for Energy (ACME) program is a leading-edge climate and Earth system model designed to address the U.S. Department of Energy (DOE) mission needs. The ACME project involves collaboration between seven National Laboratories, the National Center for Atmospheric Research, four academic institutions, and one private-sector company. The ACME project funds ongoing efforts to ensure the model continues to make efficient use of DOE Leadership Computing Facilities (LCFs) in order to best address the three driving grand challenge science questions:Water cycle: How will more realistic portrayals of features important to the water cycle (resolution, clouds, aerosols, snowpack, river routing, land use) affect river flow and associated freshwater supplies at the watershed scale?Cryosphere systems: Could a dynamical instability in the Antarctic ice sheet be triggered within the next 40 years?Biogeochemistry: What are the contributions and feedbacks from natural and managed systems to current greenhouse gas fluxes, and how will those factors and associated fluxes evolve in the future?

Norman, Matthew↗
DOE OSTI2021

Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems

Uncertainty quantification techniques such as the time-dependent generalized polynomial chaos (TD-gPC) use an adaptive orthogonal basis to better represent the stochastic part of the solution space (aka random function space) in time. However, because the random function space is constructed using tensor products, TD-gPC-based methods are known to suffer from the curse of dimensionality. Here, we introduce a new numerical method called the flow-driven spectral chaos (FSC) which overcomes this curse of dimensionality at the random-function-space level. The proposed method is not only computationally more efficient than existing TD-gPC-based methods but is also far more accurate. The FSC method uses the concept of enriched stochastic flow maps to track the evolution of a finite-dimensional random function space efficiently in time. To transfer the probability information from one random function space to another, two approaches are developed and studied herein. In the first approach, the probability information is transferred in the mean-square sense, whereas in the second approach the transfer is done exactly using a new theorem that was developed for this purpose. The FSC method can quantify uncertainties with high fidelity, especially for the long-time response of stochastic dynamical systems governed by ODEs of arbitrary order. Six representative numerical examples, including a nonlinear problem (the Van-der-Pol oscillator), are presented to demonstrate the performance of the FSC method and corroborate the claims of its superior numerical properties. Finally, a parametric, high-dimensional stochastic problem is used to demonstrate that when the FSC method is used in conjunction with Monte Carlo integration, the curse of dimensionality can be overcome altogether.

(nonlinear) stochastic dynamical systems↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2017

Localization landscape theory of disorder in semiconductors. I. Theory and modeling

Here, we present here a model of carrier distribution and transport in semiconductor alloys accounting for quantum localization effects in disordered materials. This model is based on the recent development of a mathematical theory of quantum localization which introduces for each type of carrier a spatial function called localization landscape. These landscapes allow us to predict the localization regions of electron and hole quantum states, their corresponding energies, and the local densities of states. We show how the various outputs of these landscapes can be directly implemented into a drift-diffusion model of carrier transport and into the calculation of absorption/emission transitions. This creates a new computational model which accounts for disorder localization effects while also capturing two major effects of quantum mechanics, namely, the reduction of barrier height (tunneling effect) and the raising of energy ground states (quantum confinement effect), without having to solve the Schrödinger equation. Finally, this model is applied to several one-dimensional structures such as single quantum wells, ordered and disordered superlattices, or multiquantum wells, where comparisons with exact Schrödinger calculations demonstrate the excellent accuracy of the approximation provided by the landscape theory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Computational Thermodynamics of Materials Zi-Kui Liu and Yi Wang

This authoritative volume introduces the reader to computational thermodynamics and the use of this approach to the design of material properties by tailoring the chemical composition. The text covers applications of this approach, introduces the relevant computational codes, and offers exercises at the end of each chapter. The book has nine chapters and two appendices that provide background material on computer codes. Chapter 1 covers the first and second laws of thermodynamics, introduces the spinodal as the limit of stability, and presents the Gibbs-Duhem equation. Chapter 2 focuses on the Gibbs energy function. Starting with a homogeneous system with a single phase, the authors proceed to phases with variable compositions, and polymer blends. The discussion includes the contributions of external electric and magnetic fields to the Gibbs energy. Chapter 3 deals with phase equilibria in heterogeneous systems, the Gibbs phase rule, and phase diagrams. Chapter 4 briefly covers experimental measurements of thermodynamic properties used as input for thermodynamic modeling by Calculation of Phase Diagrams (CALPHAD). Chapter 5 discusses the use of density functional theory to obtain thermochemical data and fill gaps where experimental data is missing. The reader is introduced to the Vienna Ab Initio Simulation Package (VASP) for density functional theory and the YPHON code for phonon calculations. Chapter 6 introduces the modeling of Gibbs energy of phases with the CALPHAD method. Chapter 7 deals with chemical reactions and the Ellingham diagram for metal-oxide systems and presents the calculation of the maximum reaction rate from equilibrium thermodynamics. Chapter 8 is devoted to electrochemical reactions and Pourbaix diagrams with application examples. Chapter 9 concludes this volume with the application of a model of multiple microstates to Ce and Fe3Pt. CALPHAD modeling is briefly discussed in the context of genomics of materials. The book introduces basic thermodynamic concepts clearly and directs readers to appropriate references for advanced concepts and details of software implementation. The list of references is quite comprehensive. The authors make liberal use of diagrams to illustrate key concepts. The two Appendices at the end discuss software requirements and the file structure, and present templates for special quasi-random structures. There is also a link to download pre-compiled binary files of the YPHON code for Linux or Microsoft Windows systems. The exercises at the end of the chapters assume that the reader has access to VASP, which is not freeware. Readers without access to this code can work on a limited number of exercises. However, results from other first principles codes can be organized in the YPHON format as explained in the Appendix. This book will serve as an excellent reference on computational thermodynamics and the exercises provided at the end of each chapter make it valuable as a graduate level textbook. Reviewer: Ram Devanathan is Acting Director of Earth Systems Science Division, Pacific Northwest National Laboratory, USA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Towards Ab-Initio Simulations of Crystalline Defects at the Exascale Using Spectral Quadrature Density Functional Theory

Defects in crystalline solids play a crucial role in determining properties of materials at the nano, meso- and macroscales, such as the coalescence of vacancies at the nanoscale to form voids and prismatic dislocation loops or diffusion and segregation of solutes to nucleate precipitates, phase transitions in magnetic materials via disorder and doping. First principles Density Functional Theory (DFT) simulations can provide a detailed understanding of these phenomena. However, the number of atoms needed to correctly simulate these systems is often beyond the reach of many widely used DFT codes. The aim of this article is to discuss recent advances in first principles modeling of crystal defects using the spectral quadrature method. The spectral quadrature method is linear scaling with respect to the number of atoms, permits spatial coarse-graining, and is capable of simulating non-periodic systems embedded in a bulk environment, which allows the application of appropriate boundary conditions for simulations of crystalline defects. In this article, we discuss the state-of-the-art in ab-initio modeling of large metallic systems of the order of several thousand atoms that are suitable for utilizing exascale computing resourses.

36 MATERIALS SCIENCE↗