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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

The Father Christmas worm

Given here is an overview analysis of the Father Christmas Worm, a computer worm that was released onto the DECnet Internet three days before Christmas 1988. The purpose behind the worm was to send an electronic mail message to all users on the computer system running the worm. The message was a Christmas greeting and was signed 'Father Christmas'. From the investigation, it was determined that the worm was released from a computer (node number 20597::) at a university in Switzerland. The worm was designed to travel quickly. Estimates are that it was copied to over 6,000 computer nodes. However, it was believed to have executed on only a fraction of those computers. Within ten minutes after it was released, the worm was detected at the Space Physics Analysis Network (SPAN), NASA's largest space and Earth science network. Once the source program was captured, a procedural cure, using the existing functionality of the computer operating systems, was quickly devised and distributed. A combination of existing computer security measures, the quick and accurate procedures devised to stop copies of the worm from executing, and the network itself, were used to rapidly provide the cure. These were the main reasons why the worm executed on such a small percentage of nodes. This overview of the analysis of the events concerning the worm is based on an investigation made by the SPAN Security Team and provides some insight into future security measures that will be taken to handle computer worms and viruses that may hit similar networks.

Green, James L.↗
DOE OSTI2019

PASTA: A Parallel Sparse Tensor Algorithm Benchmark Suite

Tensor methods have gained increasingly attention from various applications, including machine learning, quantum chemistry, healthcare analytics, social network analysis, data mining, and signal processing, to name a few. Sparse tensors and their algorithms become critical to further improve the performance of these methods and enhance the interpretability of their output. This work presents a sparse tensor algorithm benchmark suite (PASTA) for single- and multi-core CPUs. To the best of our knowledge, this is the first benchmark suite for sparse tensor world. PASTA targets on: 1) helping application users to evaluate different computer systems using its representative computational workloads; 2) providing insights to better utilize existed computer architecture and systems and inspiration for the future design. This benchmark suite will be publicly released.

HPC, sparse tensor, benchmark↗
DOE OSTI2018

Phenomena identification and ranking table study for thermal hydraulics for Advanced High Temperature Reactor

The Advanced High-Temperature Reactor (AHTR) is a conceptual design of a Fluoride salt-cooled High-temperature Reactor (FHR) utilizing 7 LiF-BeF 2 (66-34 mol%) as its primary coolant. To identify key phenomena that impose potential challenges on thermal hydraulics modeling and simulation of such a reactor system, a thermal hydraulics phenomena identification and ranking table (TH-PIRT) study was performed for the AHTR in a Department of Energy Nuclear Energy University Program (NEUP) integrated research project led by Georgia Institute of Technology. A panel of experts from regulators, industries, national laboratories, and academia was assembled for the study. In this paper, the TH-PIRTs identified by the panel for two events, namely, station blackout and simultaneous withdrawal of all control rods, are summarized and discussed in detail. In addition, the key phenomena that warrant further study and research for AHTR analysis are identified to support the validation of thermal hydraulics system-level analysis codes and computational fluid dynamics simulation tools, as well as future FHR reactor licensing.

42 ENGINEERING↗
DOE OSTI2026

An approach for fast and accurate simulation of phase change material based thermal energy storage in buildings

Latent heat thermal energy storage (LHTES) has significant potential for mitigating peak electricity demand and enabling load shifting in buildings. Phase Change Material embedded heat exchangers (PCM-HX) can significantly improve energy demand management due to high storage capacity. However, PCM-HX evaluation typically depends on computationally expensive fully transient simulations, posing significant challenges for scalable system- and building-level energy assessments across different climates and system architectures. This paper presents a generalized, accurate, and computationally efficient methodology for simulating building energy systems integrated with LHTES. The PCM-HX transient performance is represented by performance maps generated using a Generalized Resistance-Capacitance Model (GRCM) that enables accurate predictions of arbitrary PCM-HXs at low computational cost. The feasibility of the proposed approach was verified using a case study considering a dual-mode heat pump-thermal energy storage (HP-TES) system simulated in Modelica with Spawn of EnergyPlus™ for a DOE prototype small office building in two locations: Tampa, FL, and International Falls, MN. The PCM-HX performance maps provided accurate predictions of PCM-HX transient behavior, with mean absolute percentage deviations within 2–4% compared to GRCM while also achieving at least 1800× reduction in computational time. Moreover, the HP-TES system achieved energy savings of up to 17.4% in Tampa, FL, and 62.2% in International Falls, MN, demonstrating the broader applicability of the proposed methodology across different climate zones. This work highlights the importance of robust PCM-HX models in enabling accurate and computationally efficient building-level simulations and enabling future research opportunities for investigating optimized HP-TES designs and advanced control strategies for grid-interactive buildings.

Modelica↗
DOE OSTI2020

Shear localization in metallic materials at high strain rates

Three factors govern adiabatic shear localization: strain hardening (or softening), strain-rate hardening, and thermal softening. It is typically associated with large shear strains (>1), high strain rates (10 3 –10 7 s –1 ), and high temperatures (40–100% of melting point), all of which happen within narrow regions with widths of about 1–200 μm. It is often an undesirable phenomenon, leading to failure, but there are situations where it is desirable, e. g., the generation of machining chips. Here, we review the development of both theoretical and experimental achievements, from the initiation of shear bands to their propagation with emphasis on three aspects: novel experimental techniques, novel materials, and nano/microstructural effects. The principal characteristics of adiabatic shear bands in metallic materials at the nano- and micro-scale are described. Bands that were formerly identified as transformed actually consist of nanocrystalline/ultrafine grains. These grains result from the breakup of the microstructure by a rotational recrystallization process. The evolution of the microstructure inside shear bands and their interactions for hcp, bcc, and fcc alloys, high-entropy alloys, nanocrystalline alloys, and metallic glasses are analyzed mechanistically. In conclusion, the gaps in the field and opportunities for future research are identified. Modern experimental characterization and computational techniques enable a more profound and predictive understanding of adiabatic shear localization and its avoidance in advanced materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security

Cyber-Physical Systems (CPSs) like the power grid are critically important but also increasingly vulnerable; ensuring reliable system operation in the face of disruptions is becoming more and more challenging. Multi-Level Optimization (MLO) is a powerful way to model adversarial interactions, which naturally makes it applicable to studying CPS security. However, MLO typically does not address underlying system dynamics, and incorporating nonlinear dynamics is generally infeasible. In this paper, we show how to combine MLO with the Koopman Operator (KO) to remedy this. The KO maps nonlinear dynamics to a lifted space in which those dynamics are linear, thus making it ideal for use with MLO. Moreover, the structure of the KO also provides convenient ways to incorporate domain knowledge into the data-driven process of learning the KO representation of a given system. Here we then demonstrate the use of MLO-KO on a small example problem taken from the power grid domain, discuss the scalability and computational cost of MLO-KO, and identify future research directions for this work.

42 ENGINEERING↗
DOE OSTI2018

Assessing High-Throughput Descriptors for Prediction of Transparent Conductors

The growth of materials databases has offered significant quantities of data to mine for new energy materials using high-throughput screening methodologies. One application of interest to energy and optoelectronics is the prediction of new high performing p-type transparent conductors (TCs). Yet, screening methods for such materials have never been validated over the breadth of computed materials properties. In this study, we compile an experimental data set of 74 bulk crystal structures corresponding to known state-of-the-art n-type and p-type TCs and compute a series of corresponding computational descriptor properties. Our goals are to (1) compare computational descriptors to experimentally demonstrated properties of real materials in the data set, (2) determine the ability of ground state, density functional theory (DFT)-based computational screening methodologies to identify these experimentally realized TCs, and (3) use this understanding to estimate reasonable screening cutoffs for four commonly used descriptors. First, stability calculations demonstrate that most materials in the data set have an energy above the convex hull ( E hull ) of <100 meV/atom, and we also propose a Pourbaix analysis technique to estimate moisture stability. Second, we find a lenient cutoff for the DFT PBE band gap of 0.5 eV is sufficient to include a majority of the wide gap candidates and exclude narrow gap compounds. Next, the effective mass, m *, is found to correlate weakly to conductivity in the p-type materials as compared with n-type materials, which may motivate an increase in the m * cutoff as well. Lastly, we perform an uncertainty analysis and literature comparison for the branch point energy (BPE), a qualitative descriptor for dopability. We discover the BPEs of most n-type materials to lie near the conduction band and those of most p-type materials to lie at midgap; this is a significant distinction, implying BPE to be a more definitive descriptor for n-type TC materials. By assessing the validity of this simple screening methodology via comparing experimental data to computational descriptors, we aim to motivate and strengthen future materials discovery efforts in the field of transparent conductors and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Bayesian Model Averaging for Ensemble-Based Estimates of Solvation Free Energies

This paper applies the Bayesian Model Averaging (BMA) statistical ensemble technique to estimate small molecule solvation free energies. There is a wide range methods for predicting solvation free energies, ranging from empirical statistical models to {\it ab initio} quantum mechanical approaches. Each of these methods are based on a set of conceptual assumptions that can affect a method's predictive accuracy and transferability. Using an iterative statistical process, we have selected and combined solvation energy estimates using an ensemble of 17 diverse methods from the SAMPL4 blind prediction study to form a single, aggregated solvation energy estimate. The ensemble design process evaluates the statistical information in each individual method as well as the performance of the aggregate estimate obtained from the ensemble as a whole. Methods that possess minimal or redundant information are pruned from the ensemble and the evaluation process repeats until aggregate predictive performance can no longer be improved. We show that this process results in a final aggregate estimate that outperforms all individual methods by reducing estimate errors by as much as 91% to 1.2 kcal/mol accuracy. We also compare our iterative refinement approach to other statistical ensemble approaches and demonstrate that this iterative process reduces estimate errors by as much as 61%. This work provides a new approach for accurate solvation free energy prediction and lays the foundation for future work on aggregate models that can balance computational cost with predictive accuracy.

solvation, Bayesian model averaging, Electrostatic↗
DOE OSTI2019

Electronic Structure and Optical Properties of Designed Photo-Efficient Indoline-Based Dye-Sensitizers with D–A–π–A Framework

Seven D–A–π–A-based indoline (IND) dyes that were designed via quantitative-structure–property relationship modeling have been comprehensively investigated using computational approaches to evaluate their prospect of application in future dye-sensitized solar cells (DSSCs). An array of optoelectronic properties of the isolated dye and dyes adsorbed on a TiO 2 cluster that simulates the semiconductor were explored by density functional theory (DFT) and time-dependent DFT methods. Light absorption spectra, vertical dipole moment, shift of the conduction band of semiconductor, excited state lifetime, driving force of electron injection, photostability of the excited state, and exciton binding energy were computed. Our study showed that the presence of an internal acceptor such as pyrido[3,4-b]pyrazine (pyrazine) would influence greater the open circuit voltage (VOC), compared to the benzothiadiazole moiety. Considering the balance between the VOC and J SC (short circuit current) along with the all calculated characteristics, the IND3, IND5, and IND10 are the most suited among the designed dyes to be used as potential candidates for the photo-efficient DSSCs. Finally, the present study provides the results of rational molecular design followed by exploration of photophysical properties to be used as a valuable reference for the synthesis of photo-efficient dyes for DSSCs.

14 SOLAR ENERGY↗
DOE OSTI2017

Intrinsic magnetism and spontaneous band gap opening in bilayer silicene and germanene

It has been long sought to create magnetism out of simple non-magnetic materials, such as silicon and germanium. Here we show that intrinsic magnetism exists in bilayer silicene and germanene with no need to cut, etch, or dope. Unlike bilayer graphene, strong covalent interlayer bonding formed in bilayer silicene and germanene breaks the original π-bonding network of each layer, leaving the unbonded electrons unpaired and localized to carry magnetic moments. These magnetic moments then couple ferromagnetically within each layer while antiferromagnetically across two layers, giving rise to an infinite magnetic sheet with structural integrity and magnetic homogeneity. Furthermore, this unique magnetic ordering results in fundamental band gaps of 0.55 eV and 0.32 eV for bilayer silicene and germanene, respectively. The integration of intrinsic magnetism and spontaneous band gap opening makes bilayer silicene and germanene attractive for future nanoelectronics as well as spin-based computation and data storage.

Wang, Xinquan↗
DOE OSTI2025

Integrating magnetic Co-nanopillars in a NbN-based VAN thin film as a multifunctional hybrid metamaterial

NbN thin films are considered as a promising alternative candidate to conventional Al superconducting electrodes for Josephson junction devices, superconducting qubits and quantum logic circuits. Herein, by co-growing ferromagnetic Co with superconducting NbN, self-assembled NbN–Co vertically aligned nanocomposite (VAN) thin films have been successfully deposited on a MgO substrates, with NbN in its cubic superconducting phase. The obtained VAN metamaterials exhibit epitaxial growth of NbN with uniformly dispersed Co nanoplates. Further physical property characterization reveals that the NbN–Co VAN presents ferromagnet properties with strong out-of-plane magnetic anisotropy and optical anisotropy. This hybrid metamaterial system could find future applications in superconducting spintronic devices and quantum computing devices.

Zhang, Yizhi [Purdue Univ., West Lafayette, IN (Un↗
DOE OSTI2020

A Customizable Metric to Provide a Comprehensive Picture of the Mobility Potential of a Location

Accessibility in a geo-spatial context refers to the ease of reaching a variety of opportunities from a given location. Accessibility theories (and resulting metrics) have traditionally focused on quantifying access to specific opportunities (such as jobs), or focused on specific modes (such as car, bike, etc.). Such approaches often fall short of providing a comprehensive picture of the true accessibility potential of a location as a combination of multiple modes to multiple types of destinations. Addressing this drawback, a novel metric labeled the ‘Mobility Energy Productivity (MEP) Metric’ was developed at the National Renewable Energy Laboratory to quantify the mobility potential of a location to connect people to goods, services, and employment using a variety of modes, while accounting for time, energy, and affordability. The MEP metric has been integrated with advance travel behavior models to compute the changes in mobility potential for various future scenarios, such as introduction of automated vehicles, and/or electric vehicles—but does so at the aggregate, or average-citizen level. The MEP in its initial iteration is not customized to the particular socio-economic contingents, or even to an individual whose modal availability or pattern of trip making may substantially differ from the average. Addressing this gap, this research effort extends the MEP framework from a static state to a more tailored and dynamic state, one in which an individual, or group can customize the metric for their unique characteristics, such as modes, activity patterns, and time-of-day preferences. The extended MEP metric framework can now be integrated to assess the customized mobility energy productivity of an individual.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2022

Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning

ABSTRACT In this work, we explore the possibility of applying machine learning methods designed for 1D problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly steps, such as the point spread function deconvolution and the training and application of complex Convolutional Neural Networks of thousands or even millions of parameters. In our approach, we extract features from the galaxy images by analysing the elliptical isophotes in their light distribution and collect the information in a sequence. The sequences obtained with this method present definite features allowing a direct distinction between galaxy types. Then, we train and classify the sequences with machine learning algorithms, designed through the platform Modulos AutoML. As a demonstration of this method, we use the second public release of the Dark Energy Survey (DES DR2). We show that we are able to successfully distinguish between early-type and late-type galaxies, for images with signal-to-noise ratio greater than 300. This yields an accuracy of $86{{\ \rm per\ cent}}$ for the early-type galaxies and $93{{\ \rm per\ cent}}$ for the late-type galaxies, which is on par with most contemporary automated image classification approaches. The data dimensionality reduction of our novel method implies a significant lowering in computational cost of classification. In the perspective of future data sets obtained with e.g. Euclid and the Vera Rubin Observatory, this work represents a path towards using a well-tested and widely used platform from industry in efficiently tackling galaxy classification problems at the peta-byte scale.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Quantum real-time evolution using tensor renormalization group methods

We introduce an approach for approximate real-time evolution of quantum systems using tensor renormalization group (TRG) methods originally developed for imaginary time. We use higher-order TRG to generate a coarse-grained time evolution operator for a 1+1⁢D transverse Ising model with a longitudinal field. We show that the standard tensor norm used for the singular value decomposition-based truncation is degenerate and propose an alternate method to discriminate. We show that it is effective and efficient in evolving Gaussian wave packets for one and two particles in the disordered phase, while ordered phase behavior is more challenging to capture. We compare our algorithm with local simulators for universal quantum computers and discuss possible benchmarking in the near future.

lattice gauge theory↗
DOE OSTI2019

Beyond Hosting Capacity: Using Shortest-Path Methods to Minimize Upgrade Cost Pathways

This paper presents a graph-based forward looking algorithm applied to distribution planning in the context of distributed photovoltaic penetration. We study the target hosting capacity problem where the objective is to find the least-cost sequence of system upgrades to reach a predefined hosting capacity target value. We show that commonly used short-term cost minimization approaches often lead to suboptimal long-term solutions. By comparing our method against such myopic techniques on real distribution systems, we show that our algorithm is able to reduce the overall integration costs by looking at future decisions. Because hosting capacity is hard to compute, this problem requires efficient methods to search the space. We demonstrate that heuristics using domain-specific knowledge can be efficiently used to improve the algorithm performance, such that real distribution systems can be studied.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Atomically Thin CBRAM Enabled by 2-D Materials: Scaling Behaviors and Performance Limits

Reducing the energy and power dissipation of conductive bridge random access memory (CBRAM) cells is of critical importance for their applications in future Internet of Things (IoT) device and neuromorphic computing platforms. Atomically thin CBRAMs enabled by 2-D materials are studied theoretically by using 3-D kinetic Monte Carlo simulations together with experimental characterization. The results indicate the performance potential of attoJoule energy dissipation for intrinsic filament formation and a filament size of a single atomistic chain in such a CBRAM cell. The atomically thin CBRAM cells also show qualitatively different features from conventional CBRAM cells, including complete rupture of the filament in the reset stage and comparable forming and set voltages. The scaling and variability of the CBRAM cells down to sub-nanometer size of the switching layer as realized in the experiment are systematically studied, which indicates performance improvement and increased relative variability as the switching layer scales down. Finally, the results establish the ultimate limits of the size and energy scaling for CBRAM cells and illustrate the unique application of 2-D materials in ultralow power memory devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Improving the efficiency of Rubisco by resurrecting its ancestors in the family Solanaceae

Plants and photosynthetic organisms have a remarkably inefficient enzyme named Rubisco that fixes atmospheric CO 2 into organic compounds. Understanding how Rubisco has evolved in response to past climate change is important for attempts to adjust plants to future conditions. In this study, we developed a computational workflow to assemble de novo both large and small subunits of Rubisco enzymes from transcriptomics data. Next, we predicted sequences for ancestral Rubiscos of the (nightshade) family Solanaceae and characterized their kinetics after coexpressing them inEscherichia coli. Predicted ancestors of C 3 Rubiscos were identified that have superior kinetics and excellent potential to help plants adapt to anthropogenic climate change. Our findings also advance understanding of the evolution of Rubisco’s catalytic traits.

59 BASIC BIOLOGICAL SCIENCES↗