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At least 433 records · Page 24

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DOE OSTI2021

Computational general relativistic force-free electrodynamics: II. Characterization of numerical diffusivity

Scientific codes are an indispensable link between theory and experiment; in (astro-)plasma physics, such numerical tools are one window into the universe’s most extreme flows of energy. The discretization of Maxwell’s equations – needed to make highly magnetized (astro)physical plasma amenable to its numerical modeling – introduces numerical diffusion. It acts as a source of dissipation independent of the system’s physical constituents. Understanding the numerical diffusion of scientific codes is the key to classifying their reliability. It gives specific limits in which the results of numerical experiments are physical. We aim at quantifying and characterizing the numerical diffusion properties of our recently developed numerical tool for the simulation of general relativistic force-free electrodynamics by calibrating and comparing it with other strategies found in the literature. Our code correctly models smooth waves of highly magnetized plasma. We evaluate the limits of general relativistic force-free electrodynamics in the context of current sheets and tearing mode instabilities. We identify that the current parallel to the magnetic field (j ∥ ), in combination with the breakdown of general relativistic force-free electrodynamics across current sheets, impairs the physical modeling of resistive instabilities. We find that at least eight numerical cells per characteristic size of interest (e.g., the wavelength in plasma waves or the transverse width of a current sheet) are needed to find consistency between resistivity of numerical and of physical origins. High-order discretization of the force-free current allows us to provide almost ideal orders of convergence for (smooth) plasma wave dynamics. The physical modeling of resistive layers requires suitable current prescriptions or a sub-grid modeling for the evolution of j ∥ .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Design of a Multi-Chemistry Battery Pack System for Behind-the-Meter Storage Applications

Battery management systems (BMS) are essential for a battery pack's safe operation and longevity. This paper presents an active balancing method-based BMS for different cell chemistry structures to be used in behind-the-meter storage (BTMS) applications. The proposed system utilizes modular isolated dual active bridge (DAB) DC/DC converters to actively balance the battery pack through a low voltage (LV) bus. A supervisory controller monitors all the cell voltage, current, and state of charge (SOC) values. Based on the estimation of the SOCs, reference currents for the DAB converters are generated by the supervisory controller. Detailed modeling and the control approach of the modular DAB converters are presented in the paper. Moreover, the control strategy of the supervisory control is also analyzed. The proposed method and structure can be extended to any combination of the number of cells to design the battery pack. Simulation results are provided for a system consisting of three cells in parallel to form a cell block and three cell blocks in series to form the battery module. Experimental results are provided for three modular DAB converters operating with a LiFeMnPO4 prismatic cell with 3.2V, 20Ah rated values.

battery management systems↗

DOE OSTI2022

WPGNN and PLayGen (Wind Plant Graph Neural Network and Plant Layout Generator) [SWR-21-90]

WPGNN is the graph neural network machine learning based surrogate model and software that provides a streamlined approximation of wind plant wake models. It can rapidly estimate the energy production of the plant and turbines for any arbitrarily sized wind plant and layout under any inflow condition. Associated tools include graphing and visualization capabilities as well as a wind plant generator capable of creating randomized realizations of canonical wind plant layouts. The WPGNN architecture and application is extendable to multiple wake models, turbine technologies and features, and supports downstream optimizations of wind plant layouts and control strategies. In addition to the WPGNN, we include the code for the plant layout generator (PLayGen) playgen.py. This generator can produce random realizations of realistic wind plant layouts from one of the four canonical styles: cluster, single string, multiple string, or parallel string. The PLayGen_demo.ipynb notebook provides a demonstration of how to use the generator tool.

Harrison-Atlas, Dylan↗

DOE OSTI2018

Causes and Consequences of Spatial Within-Host Viral Spread

The spread of viral pathogens both between and within hosts is inherently a spatial process. While the spatial aspects of viral spread at the epidemiological level have been increasingly well characterized, the spatial aspects of viral spread within infected hosts are still understudied. Here, with a focus on influenza A viruses (IAVs), we first review experimental studies that have shed light on the mechanisms and spatial dynamics of viral spread within hosts. These studies provide strong empirical evidence for highly localized IAV spread within hosts. Since mathematical and computational within-host models have been increasingly used to gain a quantitative understanding of observed viral dynamic patterns, we then review the (relatively few) computational modeling studies that have shed light on possible factors that structure the dynamics of spatial within-host IAV spread. These factors include the dispersal distance of virions, the localization of the immune response, and heterogeneity in host cell phenotypes across the respiratory tract. While informative, we find in these studies a striking absence of theoretical expectations of how spatial dynamics may impact the dynamics of viral populations. To mitigate this, we turn to the extensive ecological and evolutionary literature on range expansions to provide informed theoretical expectations. We find that factors such as the type of density dependence, the frequency of long-distance dispersal, specific life history characteristics, and the extent of spatial heterogeneity are critical factors affecting the speed of population spread and the genetic composition of spatially expanding populations. For each factor that we identified in the theoretical literature, we draw parallels to its analog in viral populations. We end by discussing current knowledge gaps related to the spatial component of within-host IAV spread and the potential for within-host spatial considerations to inform the development of disease control strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ antiimmune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation. We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine, tumor necrosis factor alpha (TNFα), promote pathogenesis through a parallel feed-forward circuit that promotes inflammation. These results are consistent with previous studies showing the role of over-stimulation of the inflammatory response to SARS-CoV in pathogenesis. We conclude that the critical balance between immune response and inflammation can be manipulated to improve the outcome of the infection. Further, our study provides two potential therapeutic strategies for mitigating the effects of SARS-CoV infection, and may provide insight into treatment strategies for Middle East Respiratory Syndrome Coronavirus (MERS-CoV).

network inference↗

DOE OSTI2023

Reinforcement Learning for Load-balanced Parallel Particle Tracing

We explore an online reinforcement learning (RL) paradigm to dynamically optimize parallel particle tracing performance in distributed-memory systems. Our method combines three novel components: (1) a work donation algorithm, (2) a high-order workload estimation model, and (3) a communication cost model. First, we design an RL-based work donation algorithm. Our algorithm monitors workloads of processes and creates RL agents to donate data blocks and particles from high-workload processes to low-workload processes to minimize program execution time. The agents learn the donation strategy on the fly based on reward and cost functions designed to consider processes' workload changes and data transfer costs of donation actions. Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations. Third, we design a communication cost model that considers both block and particle data exchange costs, helping RL agents make effective decisions with minimized communication costs. We demonstrate that our algorithm adapts to different flow behaviors in large-scale fluid dynamics, ocean, and weather simulation data. Our algorithm improves parallel particle tracing performance in terms of parallel efficiency, load balance, and costs of I/O and communication for evaluations with up to 16,384 processors.

Distributed and parallel particle tracing↗

DOE OSTI2022

Developing an ELM Ecosystem Dynamics Model on GPU with OpenACC

Porting a complex scientific code, such as the E3SM land model (ELM), onto a new computing architecture is challenging. The paper presents design strategies and technical approaches to develop an ELM ecosystem dynamics model with compiler directives (OpenACC) on NVIDIA GPUs. The code has been refactored with advanced OpenACC features (such as deepcopy and routine directives) to reduce memory consumption and to increase the levels of parallelism through parallel loop reconstruction and new data structures. As a result, the optimized parallel implementation achieved more than a 140-time speedup (50 ms vs 7600 ms), compared to a naive implementation that uses OpenACC routine directive and parallelizes the code across existing loops on a single NVIDIA V100. On a fully loaded computing node with 44 CPUs and 6 GPUs, the code achieved over a 3.0-times speedup, compared to the original code on the CPU. Furthermore, the memory footprint of the optimized parallel implementation is 300 MB, which is around 15% of the 2.15 GB of memory consumed by a naive implementation. This study is the first effort to develop the ELM component on GPUs efficiently to support ultra-high-resolution land simulations at continental scales.

Schwartz, Peter↗

DOE OSTI2018

A modular synthetic approach for band-gap engineering of armchair graphene nanoribbons

Despite the great promise of armchair graphene nanoribbons (aGNRs) as high-performance semiconductors, practical band-gap engineering of aGNRs remains an unmet challenge. Given that width and edge structures are the two key factors for modulating band-gaps of aGNRs, a reliable synthetic method that allows control of both factors would be highly desirable. We report a simple modular strategy for efficient preparation of N = 6 aGNR, the narrowest member in the N = 3p (p: natural number) aGNR family, and two unsymmetrically edge-functionalized GNRs that contain benzothiadiazole and benzotriazole moieties. The trend of band-gap transitions among these GNRs parallels those in donor-acceptor alternating conjugated polymers. In addition, post-functionalization of the unsymmetrical heterocyclic edge via C-H borylation permits further band-gap tuning. Therefore, this method opens the door for convenient band-gap engineering of aGNRs through modifying the heteroarenes on the edge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

OsCAldOMT1 is a bifunctional O-methyltransferase involved in the biosynthesis of tricin-lignins in rice cell walls

Abstract Lignin is a phenylpropanoid polymer produced in the secondary cell walls of vascular plants. Although most eudicot and gymnosperm species generate lignins solely via polymerization of p -hydroxycinnamyl alcohols (monolignols), grasses additionally use a flavone, tricin, as a natural lignin monomer to generate tricin-incorporated lignin polymers in cell walls. We previously found that disruption of a rice 5-HYDROXYCONIFERALDEHYDE O -METHYLTRANSFERASE ( OsCAldOMT1 ) reduced extractable tricin-type metabolites in rice vegetative tissues. This same enzyme has also been implicated in the biosynthesis of sinapyl alcohol, a monolignol that constitutes syringyl lignin polymer units. Here, we further demonstrate through in-depth cell wall structural analyses that OsCAldOMT1 -deficient rice plants produce altered lignins largely depleted in both syringyl and tricin units. We also show that recombinant OsCAldOMT1 displayed comparable substrate specificities towards both 5-hydroxyconiferaldehyde and selgin intermediates in the monolignol and tricin biosynthetic pathways, respectively. These data establish OsCAldOMT1 as a bifunctional O -methyltransferase predominantly involved in the two parallel metabolic pathways both dedicated to the biosynthesis of tricin-lignins in rice cell walls. Given that cell wall digestibility was greatly enhanced in the OsCAldOMT1 -deficient rice plants, genetic manipulation of CAldOMT s conserved in grasses may serve as a potent strategy to improve biorefinery applications of grass biomass.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multiphysics analysis system for heat pipe cooled micro-reactors employing PRAGMA-OpenFOAM-ANLHTP

A multiphysics analysis system for neutronics/thermo-mechanical/heat-pipe analysis of heat pipe cooled micro-reactors was developed using the PRAGMA code as the neutronics engine. PRAGMA, which used to be a GPU-based continuous-energy MC code for power reactor applications, now has an extended geometry package to handle geometries with unstructured meshes generated by the ANSYS Design-Modeler and Meshing. The NVIDIA ray tracing engine OptiX was exploited for efficient neutron transport on unstructured geometry. On the multiphysics side, the open-source CFD tool OpenFOAM and one-dimensional heat pipe analysis code ANLHTP were adopted. The manager-worker system based on the MPI dynamic process management (DPM) model enables efficient coupling of codes employing different parallelization schemes. With all the features, the multiphysics analysis of the one-sixth symmetrical MegaPower 2D core was performed and it demonstrated the benefit of the tight integration of the three-way coupling system and one-to-one geometry coupling strategy. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Integrating Marine Hydrokinetic and Offshore Wind Energy: A Review of Technologies, Deployment, and Challenges

Together, offshore wind (OSW) and marine hydrokinetic (MHK) technologies have vast potential to expand the world’s access to abundant energy resource. With more than 60 GW of offshore wind energy capacity and 527 MW of ocean energy deployed globally by 2023, there is a significant amount of available resources; however, technical and non-technical challenges prevent the combined large-scale deployment of these technologies. There is still a lack of research that provides a parallel review of both MHK and OSW technologies in order to better understand their synergistic working principles. This paper aimed to address that research gap by presenting a comprehensive side-by-side review of the worldwide technological landscape, global deployment trends, integration strategies, and modeling approaches for MHK and OSW. A particular focus has been given on analyzing existing modeling and simulation techniques, assessing integration and control strategies, and comparing technologies based on water depth. Furthermore, this study provides important insights into the readiness levels of both technologies by highlighting ongoing international projects. By addressing these issues, this review will give researchers and industry stakeholders an outline for assessing the maturity of OSW and MHK systems and facilitating their transition to large-scale, sustainable deployment.

16 - TIDAL AND WAVE POWER↗

DOE OSTI2012

A modern solver framework to manage solution algorithms in the Community Earth System Model

Global Earth-system models (ESM) can now produce simulations that resolve ~50 km features and include finer-scale, interacting physical processes. In order to achieve these scale-length solutions, ESMs require smaller time steps, which limits parallel performance. Solution methods that overcome these bottlenecks can be quite intricate, and there is no single set of algorithms that perform well across the range of problems of interest. This creates significant implementation challenges, which is further compounded by complexity of ESMs. Therefore, prototyping and evaluating new algorithms in these models requires a software framework that is flexible, extensible, and easily introduced into the existing software. We describe our efforts to create a parallel solver framework that links the Trilinos library of solvers to Glimmer-CISM, a continental ice sheet model used in the Community Earth System Model (CESM). We demonstrate this framework within both current and developmental versions of Glimmer-CISM and provide strategies for its integration into the rest of the CESM.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving net- works spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with promi- nent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a “Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named “Relative Selectivity" that is used to se- lect between different query processing strategies. Our experiments performed on real online news, network traffic stream and a syn- thetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.

graphs, cybersecurity↗

DOE OSTI2015

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a ``Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named ``Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over non-incremental, selectivity agnostic approaches.

dynamic graphs↗

DOE OSTI2013

Multi-cycle boiling water reactor fuel cycle optimization

In this work a new computer code, BWROPT (Boiling Water Reactor Optimization), is presented. BWROPT uses the Parallel Simulated Annealing (PSA) algorithm to solve the out-of-core optimization problem coupled with an in-core optimization that determines the optimum fuel loading pattern. However it uses a Haling power profile for the depletion instead of optimizing the operating strategy. The result of this optimization is the optimum new fuel inventory and the core loading pattern for the first cycle considered in the optimization. Several changes were made to the optimization algorithm with respect to other nuclear fuel cycle optimization codes that use PSA. Instead of using constant sampling probabilities for the solution perturbation types throughout the optimization as is usually done in PSA optimizations the sampling probabilities are varied to get a better solution and/or decrease runtime. The new fuel types available for use can be sorted into an array based on any number of parameters so that each parameter can be incremented or decremented, which allows for more precise fuel type selection compared to random sampling. Also, the results are sorted by the new fuel inventory of the first cycle for ease of comparing alternative solutions. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Furthermore, model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Closing the Nanographene Gap: Surface‐Assisted Synthesis of Peripentacene from 6,6′‐Bipentacene Precursors

Abstract The thermally induced cyclodehydrogenation reaction of 6,6′‐bipentacene precursors on Au(111) yields peripentacene stabilized by surface interactions with the underlying metallic substrate. STM and atomic‐resolution non‐contact AFM imaging reveal rectangular flakes of nanographene featuring parallel pairs of zig‐zag and armchair edges resulting from the lateral fusion of two pentacene subunits. The synthesis of a novel molecular precursor 6,6′‐bipentacene, itself a synthetic target of interest for optical and electronic applications, is also reported. The scalable synthetic strategy promises to afford access to a structurally diverse class of extended periacenes and related polycyclic aromatic hydrocarbons as advanced materials for electronic, spintronic, optical, and magnetic devices.

Rogers, Cameron↗

DOE OSTI2017

A global view of structure–function relationships in the tautomerase superfamily

The tautomerase superfamily (TSF) consists of more than 11,000 nonredundant sequences present throughout the biosphere. Characterized members have attracted much attention because of the unusual and key catalytic role of an N-terminal proline. These few characterized members catalyze a diverse range of chemical reactions, but the full scale of their chemical capabilities and biological functions remains unknown. To gain new insight into TSF structure–function relationships, we performed a global analysis of similarities across the entire superfamily and computed a sequence similarity network to guide classification into distinct subgroups. Our results indicate that TSF members are found in all domains of life, with most being present in bacteria. The eukaryotic members of the cis-3-chloroacrylic acid dehalogenase subgroup are limited to fungal species, whereas the macrophage migration inhibitory factor subgroup has wide eukaryotic representation (including mammals). Unexpectedly, we found that 346 TSF sequences lack Pro-1, of which 85% are present in the malonate semialdehyde decarboxylase subgroup. The computed network also enabled the identification of similarity paths, namely sequences that link functionally diverse subgroups and exhibit transitional structural features that may help explain reaction divergence. A structure-guided comparison of these linker proteins identified conserved transitions between them, and kinetic analysis paralleled these observations. Phylogenetic reconstruction of the linker set was consistent with these findings. Our results also suggest that contemporary TSF members may have evolved from a short 4-oxalocrotonate tautomerase–like ancestor followed by gene duplication and fusion. Our new linker-guided strategy can be used to enrich the discovery of sequence/structure/function transitions in other enzyme superfamilies.

Biochemistry & Molecular Biology↗