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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Xyce parallel electronic simulator : users' guide.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: (1) Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers; (2) Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. (3) Device models which are specifically tailored to meet Sandia's needs, including some radiation-aware devices (for Sandia users only); and (4) Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing parallel implementation - which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. The development of Xyce provides a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia has an 'in-house' capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods, parallel solver algorithms) research and development can be performed. As a result, Xyce is a unique electrical simulation capability, designed to meet the unique needs of the laboratory.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

Xyce TM Parallel Electronic Simulator: Users’ Guide, Version 5.3

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been designed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: ■ Capability to solve extremely large circuit problems by supporting large-scale parallel computing platforms (up to thousands of processors). Note that this includes support for most popular parallel and serial computers. ■ Improved performance for all numerical kernels (e.g., time integrator, nonlinear and linear solvers) through state-of-the-art algorithms and novel techniques. ■ Device models which are specifically tailored to meet Sandia’s needs, including some radiation-aware devices (for Sandia users only). ■ Object-oriented code design and implementation using modern coding practices that ensure that the Xyce Parallel Electronic Simulator will be maintainable and extensible far into the future. Xyce is a parallel code in the most general sense of the phrase - a message passing parallel implementation - which allows it to run efficiently on the widest possible number of computing platforms. These include serial, shared-memory and distributed-memory parallel as well as heterogeneous platforms. Careful attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. The development of Xyce provides a platform for computational research and development aimed specifically at the needs of the Laboratory. With Xyce, Sandia has an “in-house” capability with which both new electrical (e.g., device model development) and algorithmic (e.g., faster time-integration methods, parallel solver algorithms) research and development can be performed. As a result, Xyce is a unique electrical simulation capability, designed to meet the unique needs of the laboratory

42 ENGINEERING↗
DOE OSTI2023

Multi-output multilevel best linear unbiased estimators via semidefinite programming

Multifidelity forward uncertainty quantification (UQ) problems often involve multiple quantities of interest and heterogeneous models (e.g., different grids, equations, dimensions, physics, surrogate and reduced-order models). While computational efficiency is key in this context, multi-output strategies in multilevel/multifidelity methods are either sub-optimal or non-existent. In this paper we extend multilevel best linear unbiased estimators (MLBLUE) to multi-output forward UQ problems and we present new semidefinite programming formulations for their optimal setup. Not only do these formulations yield the optimal number of samples required, but also the optimal selection of low-fidelity models to use. While existing MLBLUE approaches are single-output only and require a non-trivial nonlinear optimization procedure, the new multi-output formulations can be solved reliably and efficiently. Here, we demonstrate the efficacy of the new methods and formulations in practical UQ problems with model heterogeneity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Structured Adaptive Mesh Refinement Adaptations to Retain Performance Portability With Increasing Heterogeneity

Adaptive mesh refinement (AMR) is an important method that enables many mesh-based applications to run at effectively higher resolution within limited computing resources by allowing high resolution only where really needed. This advantage comes at a cost, however: greater complexity in the mesh management machinery and challenges with load distribution. With the current trend of increasing heterogeneity in hardware architecture, AMR presents an orthogonal axis of complexity. Additionally, the usual techniques, such as asynchronous communication and hierarchy management for parallelism and memory that are necessary to obtain reasonable performance are very challenging to reason about with AMR. Different groups working with AMR are bringing different approaches to this challenge. Here, we examine the design choices of several AMR codes and also the degree to which demands placed on them by their users influence these choices.

42 ENGINEERING↗
DOE OSTI2015

Distributed database kriging for adaptive sampling (D²KAS)

We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. A finite-volume scheme integrates the macro-scale conservation laws for elastodynamics, which are closed by momentum and energy fluxes evaluated at the micro-scale. In the original approach, molecular dynamics (MD) simulations are launched for every macro-scale volume element. Our adaptive sampling scheme replaces a large fraction of costly micro-scale MD simulations with fast table lookup and prediction. The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. For the latter we use kriging, which estimates an unknown value and its uncertainty (error) at a specific location in parameter space by using weighted averages of the neighboring points. We find that our adaptive scheme significantly improves simulation performance by a factor of 2.5 to 25, while retaining high accuracy for various choices of the algorithm parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Cyber–physical perspective on smart grid design and operation

Emerging computation and communication technologies ubiquitously deployed in smart grids introduce unprecedented opportunities by enabling vigorous development of innovative business models and applications that aim for improving system efficiency, reliability, resiliency, and interoperability. Smart grids are typical cyber–physical systems (CPSs). The heterogeneous and complex nature of smart grids presents significant challenges introduced by the tight coupling of computation, communication, and physical systems. Here, the authors first provide an overview of smart grids from a CPS perspective. Then, the authors discuss the opportunities and challenges in smart grid design and operation. Next, a co‐design framework is introduced and used for designing wide‐area damping controller as one of many CPS applications that can be abstracted from various power system layers. The authors conclude that developing a theoretical modelling framework is critical for understanding and managing the complexity and interoperability of smart grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

An investigation of used electronics return flows: A data-driven approach to capture and predict consumers storage and utilization behavior

Highlights: • We analyzed a data set of HDDs returned back to an e-waste collection site. • We studied factors that affect the storage behavior. • Consumer type, brand and size are among factors which affect the storage behavior. • Commercial consumers have stored computers more than household consumers. • Machine learning models were used to predict the storage behavior. - Abstract: Consumers often have a tendency to store their used, old or un-functional electronics for a period of time before they discard them and return them back to the waste stream. This behavior increases the obsolescence rate of used still-functional products leading to lower profitability that could be resulted out of End-of-Use (EOU) treatments such as reuse, upgrade, and refurbishment. These types of behaviors are influenced by several product and consumer-related factors such as consumers’ traits and lifestyles, technology evolution, product design features, product market value, and pro-environmental stimuli. Better understanding of different groups of consumers, their utilization and storage behavior and the connection of these behaviors with product design features helps Original Equipment Manufacturers (OEMs) and recycling and recovery industry to better overcome the challenges resulting from the undesirable storage of used products. This paper aims at providing insightful statistical analysis of Electronic Waste (e-waste) dynamic nature by studying the effects of design characteristics, brand and consumer type on the electronics usage time and end of use time-in-storage. A database consisting of 10,063 Hard Disk Drives (HDD) of used personal computers returned back to a remanufacturing facility located in Chicago, IL, USA during 2011–2013 has been selected as the base for this study. The results show that commercial consumers have stored computers more than household consumers regardless of brand and capacity factors. Moreover, a heterogeneous storage behavior is observed for different brands of HDDs regardless of capacity and consumer type factors. Finally, the storage behavior trends are projected for short-time forecasting and the storage times are precisely predicted by applying machine learning methods.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Heuristic Computational Model for Predicting Lignin Solubility in Tailored Organic Solvents

Lignin is a random heteropolymer that has been extensively studied as a renewable source of aromatic precursors for high-value chemicals, biofuels, and bioplastics. A key challenge in lignin valorization is the structural and compositional heterogeneity of lignin feedstocks. Solvent-based approaches are commonly used to fractionate lignin to reduce this heterogeneity, but solvent selection can be challenging due to variability in lignin composition. In this work, we developed computational methods to predict good and poor organic solvents as a function of lignin composition. We analyzed 28 different linear pentamer structures, 18 from known libraries and 10 hypothetical polymers, and calculated their activity coefficients in 50 different organic solvents by using the conductor-like screening model for realistic solvents. We used these data to train a regression model that enabled the extensive investigation of the impact of solvent and monolignol compositions on predicted lignin solubility. The exhaustive exploration of solubility trends using model predictions revealed sets of solvents, identified using Kamlet–Taft parameters, that are predicted to promote lignin dissolution regardless of lignin composition. We further identified solvents expected to selectively isolate lignin fractions enriched in certain subunits. Furthermore, these results establish heuristic guidelines for solvent selection that can be used to tailor fractionation processes for lignin feedstocks of distinct composition or to design new processes that isolate fractions with higher proportions of selected subunits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Evaluating the effect of internal aperture variability on transport in kilometer scale discrete fracture networks

The apertures of natural fractures in fractured rock are highly heterogeneous. However, in-fracture aperture variability is often neglected in flow and transport modeling and individual fractures are assumed to have uniform aperture distribution. The relative importance of in-fracture variability in flow and transport modeling within kilometer18 scale field–scale fracture networks has been under a matter of debate for a long time because the flow in each single fracture is controlled not only by in-fracture variability but also by boundary conditions. Computational limitations have previously prohibited researchers from investigating the relative importance of in-fracture variability in flow and transport modeling within large-scale fracture networks. We address this question by incorporating internal heterogeneity of individual fractures into 23 flow simulations within kilometer scale three-dimensional fracture networks, where fracture intensity, P32 (ratio between total fracture area and domain volume) is between 0.027 and 0.031 [1/m]. A recently developed discrete fracture network (DFN) simulation capability, dfnWorks, is used to generate DFNs that include in-fracture aperture variability represented by a stationary log-normal stochastic field with various correlation lengths and variances. The Lagrangian transport parameters, non-reacting travel time and cumulative retention, are calculated along particles streamlines. It is observed that due to local flow channeling early particle travel times are more sensitive to in-fracture variability than the tails of travel time distributions, where no significant effect of the in-fracture transmissivity variations and spatial correlation length is observed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Orchestrating Distributed Resource Ensembles for Petascale Science

Distributed, data-intensive computational science applications of interest to DOE scientific com- munities move large amounts of data for experiment data management, distributed analysis steps, remote visualization, and accessing scientific instruments. These applications need to orchestrate ensembles of resources from multiple resource pools and interconnect them with high-capacity multi- layered networks across multiple domains. It is highly desirable that mechanisms are designed that provide this type of resource provisioning capability to a broad class of applications. It is also important to have coherent monitoring capabilities for such complex distributed environments. In this project, we addressed these problems by designing an abstract API, enabled by novel semantic resource descriptions, for provisioning complex and heterogeneous resources from multiple providers using their native provisioning mechanisms and control planes: computational, storage, and multi-layered high-speed network domains. We used an extensible resource representation based on semantic web technologies to afford maximum flexibility to applications in specifying their needs. We evaluated the effectiveness of provisioning using representative data-intensive ap- plications. We also developed mechanisms for providing feedback about resource performance to the application, to enable closed-loop feedback control and dynamic adjustments to resource allo- cations (elasticity). This was enabled through development of a novel persistent query framework that consumes disparate sources of monitoring data, including perfSONAR, and provides scalable distribution of asynchronous notifications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Performance modeling of microsecond scale biological molecular dynamics simulations on heterogeneous architectures

Performance improvements in biomolecular simulations based on molecular dynamics (MD) codes are widely desired. Unfortunately, the factors, which allowed past performance improvements, particularly the microprocessor clock frequencies, are no longer increasing. Hence, novel software and hardware solutions are being explored for accelerating performance of widely used MD codes. In this paper, we describe our efforts on porting, optimizing and tuning of Large-scale Atomic/Molecular Massively Parallel Simulator, a popular MD framework, on heterogeneous architectures: multi-core processors with graphical processing unit (GPU) accelerators. Our implementation is based on accelerating the most computationally expensive non-bonded interaction terms on the GPUs and overlapping the computation on the CPU and GPUs. This functionality is built on top of message passing interface that allows multi-level parallelism to be extracted even at the workstation level with the multi-core CPUs and allows extension of the implementation on GPU-enabled clusters. We hypothesize that the optimal benefit of heterogeneous architectures for applications will come by utilizing all possible resources (for example, CPU-cores and GPU devices on GPU-enabled clusters). Benchmarks for a range of biomolecular system sizes are provided, and an analysis is performed on four generations of NVIDIA's GPU devices. On GPU-enabled Linux clusters, by overlapping and pipelining computation and communication, we observe up to 10-folds application acceleration in multi-core and multi-GPU environments illustrating significant performance improvements. Detailed analysis of the implementation is presented that allows identification of bottlenecks in algorithm, indicating that code optimization and improvements on GPUs could allow microsecond scale simulation throughput on workstations and inexpensive GPU clusters, putting widely desired biologically relevant simulation time-scales within reach of a large user community. In order to systematically optimize simulation throughput and to enable performance prediction, we have developed a parameterized performance model that will allow developers and users to explore the performance potential of future heterogeneous systems for biological simulations.

Agarwal, Pratul K.↗
DOE OSTI2016

Massively parallel and linear-scaling algorithm for second-order Moller–Plesset perturbation theory applied to the study of supramolecular wires

Here, we present a scalable cross-platform hybrid MPI/OpenMP/OpenACC implementation of the Divide–Expand–Consolidate (DEC) formalism with portable performance on heterogeneous HPC architectures. The Divide–Expand–Consolidate formalism is designed to reduce the steep computational scaling of conventional many-body methods employed in electronic structure theory to linear scaling, while providing a simple mechanism for controlling the error introduced by this approximation. Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI +X programming model, where X is either OpenMP or OpenACC. We demonstrate strong and weak scalability of this implementation on heterogeneous HPC systems, namely on the GPU-based Cray XK7 Titan supercomputer at the Oak Ridge National Laboratory. Using the “resolution of the identity second-order Moller–Plesset perturbation theory” (RI-MP2) as the physical model for simulating correlated electron motion, the linear-scaling DEC implementation is applied to 1-aza-adamantane-trione (AAT) supramolecular wires containing up to 40 monomers (2440 atoms, 6800 correlated electrons, 24 440 basis functions and 91 280 auxiliary functions). This represents the largest molecular system treated at the MP2 level of theory, demonstrating an efficient removal of the scaling wall pertinent to conventional quantum many-body methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Numerical computation of Pop plot

The Pop plot — distance-of-run to detonation versus initial shock pressure — is a key characterization of shock initiation in a heterogeneous explosive. Reactive burn models for high explosives (HE) must reproduce the experimental Pop plot to have any chance of accurately predicting shock initiation phenomena. This report describes a methodology for automating the computation of a Pop plot for a specific explosive with a given HE model. Illustrative examples of the computation are shown for PBX 9502 with three burn models (SURF, WSD and Forest Fire) utilizing the xRage code, which is the Eulerian ASC hydrocode at LANL. Comparison of the numerical and experimental Pop plot can be the basis for a validation test or as an aid in calibrating the burn rate of an HE model. Issues with calibration are discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Implementation of Detailed Polyethylene Pyrolysis Kinetics into CFD Simulations using Machine Learning

Municipal solid waste (MSW) and waste plastics have received significant attention due to the issues of waste generation and storage, as well as their potential as an energy resource. High-density polyethylene (HDPE) makes up a large portion of plastic waste and has been the subject of several conversion studies. However, the mechanisms associated with converting HDPE through pyrolysis and gasification are extensive and complex making them difficult to implement into high-fidelity computational fluid dynamic (CFD) simulations. For this project, a primary pyrolysis mechanism containing 42 unique species and 737 heterogeneous reactions was used to generate kinetic data over a range of operating conditions. A machine learning (ML) model was developed to replicate the results of the detailed pyrolysis mechanism while significantly increasing the computational efficiency. A deep operator network (DeepONet) architecture was adopted to train the model using time steps relevant to CFD simulations. The ML used physics-based loss functions to ensure mass conservation. The ML model has been deployed in simple MFiX CFD simulations, single particle, and an experimental drop tube reactor, and has shown promising performance compared to the original scheme.

Houston, Ross↗
DOE OSTI2017

High-Throughput Computational Screening of Multivariate Metal–Organic Frameworks (MTV-MOFs) for CO 2 Capture

Multivariate metal–organic frameworks (MTV-MOFs) contain multiple linker types within a single structure. Arrangements of linkers containing different functional groups confer structural diversity and surface heterogeneity and result in a combinatorial explosion in the number of possible structures. Here, we carried out high-throughput computational screening of a large number of computer-generated MTV-MOFs to assess their CO 2 capture properties using grand canonical Monte Carlo simulations. The results demonstrate that functionalization enhances CO 2 capture performance of MTV-MOFs when compared to their parent (unfunctionalized) counterparts, and the pore size plays a dominant role in determining the CO 2 adsorption capabilities of MTV-MOFs irrespective of the combinations of the three functional groups (-F, -NH 2 , and -OCH 3 ) that we explored. We also found that the functionalization of parent MOFs with small pores led to larger enhancements in CO 2 uptake and CO 2 /N 2 selectivity than functionalization in larger-pore MOFs. Free energy contour maps are presented to visually compare the influence of linker functionalization between frameworks with large and small pores.

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

A fully coupled method for massively parallel simulation of hydraulically driven fractures in 3-dimensions: FULLY COUPLED PARALLEL SIMULATION OF HYDRAULIC FRACTURES IN 3-D

This study describes a fully coupled finite element/finite volume approach for simulating field-scale hydraulically driven fractures in three dimensions, using massively parallel computing platforms. The proposed method is capable of capturing realistic representations of local heterogeneities, layering and natural fracture networks in a reservoir. A detailed description of the numerical implementation is provided, along with numerical studies comparing the model with both analytical solutions and experimental results. The results demonstrate the effectiveness of the proposed method for modeling large-scale problems involving hydraulically driven fractures in three dimensions.

03 NATURAL GAS↗
DOE OSTI2022

The Wormholes Within: A Study of 1, 3, 5–Triamino–2, 4, 6–Trinitrobenzene Crystal Morphology by Micro and Nano–Scale X–Ray Computed Tomography and its Effects Upon the Shock Sensitivity of Detonation

We report that we understand heterogeneous-explosive detonation only to the extent that we can first measure the salient chemical and microstructural features. Barring an inordinate amount of trial and error, we can tailor the detonation properties of our HE formulations only to the extent that we understand (at least qualitatively) the same effects. As an insensitive high explosive, 1, 3, 5-triamino-2, 4, 6-trinitrobenzene (TATB) burns more heterogeneously than conventional high explosives, making its detonation properties sensitive to microstructure. The Benziger route can produce various grain morphologies depending on the amount of water used in the amination step. So-called dry-aminated (DA) TATB crystals are riddled with a void structure called wormholes, which are washed out ammonium chloride (NH4Cl) inclusions that form during synthesis. So-called wet-aminated (WA) TATB crystals do not contain NH 4 Cl inclusions but possess a very convoluted external structure. Using both micro and nano-scale CT, we explore three DA-TATB lots that reflect evolutionary processing changes, plus one WA-TATB material. Micro CT imaged crystal collections show the exterior grain structure, revealing its roughness and providing a crude particle size measurement. The DA-TATB lots that test most sensitively in the LANL ECOT corner-turning test are roughest, having the appearance of being “corroded”. Nano-scale CT reveals both internal NH 4 Cl inclusions and void structures. The DA-TATB lots that tested intermediate and high sensitivity in ECOT exhibited similar wormhole structures, which were more extensive than that of the low-sensitivity lot. The least sensitive DA-TATB lot also retained more NH 4 Cl, consistent with reduced connectivity of voids to the surface.

1, 3, 5-triamino-2,4↗