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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

High Performance Data Transfer for Distributed Data Intensive Sciences

We report on the development of ZX software providing high performance data transfer and encryption. The design scales in: computation power, network interfaces, and IOPS while carefully balancing the available resources. Two U.S. patent-pending algorithms help tackle data sets containing lots of small files and very large files, and provide insensitivity to network latency. It has a cluster-oriented architecture, using peer-to-peer technologies to ease deployment, operation, usage, and resource discovery. Its unique optimizations enable effective use of flash memory. Using a pair of existing data transfer nodes at SLAC and NERSC, we compared its performance to that of bbcp and GridFTP and determined that they were comparable. With a proof of concept created using two four-node clusters with multiple distributed multi-core CPUs, network interfaces and flash memory, we achieved 155Gbps memory-to-memory over a 2x100Gbps link aggregated channel and 70Gbps file-to-file with encryption over a 5000 mile 100Gbps link.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Multi-omic data integration enables discovery of hidden biological regularities

Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge’ challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.

59 BASIC BIOLOGICAL SCIENCES↗
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↗
DOE OSTI2016

Many Body Methods from Chemistry to Physics: Novel Computational Techniques for Materials-Specific Modelling: A Computational Materials Science and Chemistry Network

Understanding the behavior of interacting electrons in molecules and solids so that one can predict new superconductors, catalysts, light harvesters, energy and battery materials and optimize existing ones is the ``quantum many-body problem’’. This is one of the scientific grand challenges of the 21 st century. A complete solution to the problem has been proven to be exponentially hard, meaning that straightforward numerical approaches fail. New insights and new methods are needed to provide accurate yet feasible approximate solutions. This CMSCN project brought together chemists and physicists to combine insights from the two disciplines to develop innovative new approaches. Outcomes included the Density Matrix Embedding method, a new, computationally inexpensive and extremely accurate approach that may enable first principles treatment of superconducting and magnetic properties of strongly correlated materials, new techniques for existing methods including an Adaptively Truncated Hilbert Space approach that will vastly expand the capabilities of the dynamical mean field method, a self-energy embedding theory and a new memory-function based approach to the calculations of the behavior of driven systems. The methods developed under this project are now being applied to improve our understanding of superconductivity, to calculate novel topological properties of materials and to characterize and improve the properties of nanoscale devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
DOE OSTI2017

An approach to online network monitoring using clustered patterns

Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regard to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ESnet/JLab FPGA Accelerated Transport

To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to data center traffic shaping / steering transport capability featuring data event aware network shaping and forwarding. The keystone of this ESnet+JLab FPGA Accelerated Transport (EJFAT) is the joint development of an AI/ML directed dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB data plane featuring real-time packet redirection and high throughput, and a control plane running on the FPGA host computer that monitors network and compute farm telemetry in order to make dynamic AI/ML guided decisions for destination compute host redirection/load balancing and destination resource provisioning. The LB provides for three-tier horizontal scaling across LB suites, core compute hosts, and CPUs within a host. The LB effectively provides seamless integration of edge/core computing to support direct experimental data processing for immediate use by JLab science programs and others such as the EIC as well as data centers of the future requiring high throughput and low latency for both hot and cooled data for both running experiment data acquisition systems and data center use cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physics guided machine learning using simplified theories

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this Letter, we introduce a modular physics guided machine learning framework to improve the accuracy of such data-driven predictive engines. The chief idea in our approach is to augment the knowledge of the simplified theories with the underlying learning process. To emphasize their physical importance, our architecture consists of adding certain features at intermediate layers rather than in the input layer. To demonstrate our approach, we select a canonical airfoil aerodynamic problem with the enhancement of the potential flow theory. We include the features obtained by a panel method that can be computed efficiently for an unseen configuration in our training procedure. By addressing the generalizability concerns, our results suggest that the proposed feature enhancement approach can be effectively used in many scientific machine learning applications, especially for the systems where we can use a theoretical, empirical, or simplified model to guide the learning module.

42 ENGINEERING↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies: Preprint

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remain untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topologybased system that depends on neural network architecture for impact metrics classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, namely, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates both more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn complex relationships between electrical properties and local topological measurements on the simulated response to contingencies for NREL-SIIP power system.

graph theory↗
DOE OSTI2023

Alerga: Alert Aggregation and Reasoning in GOOSE Simulation Pipeline

IEC 61850 specifies the Generic Object Oriented Substation Event (GOOSE) protocol as one option for low latency communication of substation-related events. Due to its strict timing requirements, GOOSE lacks any form of encryption or authentication and has only minimal integrity guarantees. These absences render the protocol vulnerable to a variety of communication anomalies, including adversarial action. In particular, an adversary with access to the substation network can launch man in the middle (MITM) attacks. We propose Alerga, a set of tools to allow operators to mitigate some of the risks of the protocol while retaining its strengths. To that end, we have developed first a GOOSE simulation pipeline including data generation, anomaly detection, alert handling, causal reasoning and data visualization components. The simulator is designed to be modular, allowing operators to swap components to better fit their network capabilities. The volume of alert traffic on a substation network threatens operators with alert fatigue. In order to combat this, we secondly present a novel form of alert aggregation and processing, offering operators a condensed view of any threats to the system. Thirdly, to facilitate the handling of these threats, our causal reasoning system traces the alerts back to their most likely cause, generating an initial hypothesis for operators to investigate.

alert aggregation↗
DOE OSTI2024

Convolutional Non-Homogeneous Poisson Process and its Application to Wildfire Ignition Risk Quantification for Power Delivery Networks

To quantify wildfire ignition risks on power delivery networks, the current practice predominantly relies on the empirically calculated fire danger indices, which may not well capture the effects of dynamically changing environmental factors. This article proposes a spatio-temporal point process model, known as the Convolutional Non-homogeneous Poisson Process (cNHPP), and applies the model to quantify wildfire ignition risks for power delivery networks. The proposed model captures both the current (i.e., instantaneous) and cumulative (i.e., historical) effects of key environmental processes (i.e., covariates) on wildfire risks, as well as the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated. We apply the proposed approach to estimate wildfire ignition risks on major transmission lines in California, using historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Here, a comprehensive comparison study is performed to show the applicability and predictive capability of the proposed approach.

Non-homogeneous Poisson Process↗
DOE OSTI2024

A Hierarchical OPF Algorithm with Improved Gradient Evaluation in Three-Phase Networks

Linear approximation commonly used in solving alternating-current optimal power flow (AC-OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violation. In this paper, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4,518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

approximation algorithms↗
DOE OSTI2019

Geometric Mapping of Tasks to Processors on Parallel Computers with Mesh or Torus Networks

We present a new method for reducing parallel applications' communication time by mapping their MPI tasks to processors in a way that lowers the distance messages travel and the amount of congestion in the network. Assuming geometric proximity among the tasks is a good approximation of their communication interdependence, we use a geometric partitioning algorithm to order both the tasks and the processors, assigning task parts to the corresponding processor parts. In this way, interdependent tasks are assigned to “nearby” cores in the network. We also present a number of algorithmic optimizations that exploit specific features of the network or application to further improve the quality of the mapping. We specifically address the case of sparse node allocation, where the nodes assigned to a job are not necessarily located in a contiguous block nor within close proximity to each other in the network. Furthermore, our methods generalize to contiguous allocations as well, and results are shown for both contiguous and non-contiguous allocations. We show that, for the structured finite difference mini-application MiniGhost, our mapping methods reduced communication time up to 75% relative to MiniGhost's default mapping on 128K cores of a Cray XK7 with sparse allocation. For the atmospheric modeling code E3SM/HOMME, our methods reduced communication time up to 31% on 16K cores of an IBM BlueGene/Q with contiguous allocation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Teleoperation for Nuclear Applications Considering Communication Delay and Uncertainty - 18205

Teleoperation in remote environment is of great interest in robotics. Advancement in communication technology has enabled safe execution of complex and life threatening tasks in network environment in teleoperation. Using bilateral teleoperation, the operator receives force feedback from the slave which provides an interactive feel of forces and smoother coordination. However, time delay across the communication link in sending the command signal from the master to the slave may cause instability to the system. In this research, we use the continuous pole placement method to address the time delay problem in teleoperation. Compared to approximation based and prediction based methods, this method shows improved robustness. Simulations and experiments show a stable and well synchronized control. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Sparsified time-dependent Fourier neural operators for fusion simulations

This paper presents a sparsified Fourier neural operator for coupled time-dependent partial differential equations (ST-FNO) as an efficient machine learning surrogate for fluid and particle-based fusion codes such as NIMROD (Non-Ideal Magnetohydrodynamics with Rotation - Open Discussion) and GTC (Gyrokinetic Toroidal Code). ST-FNO leverages the structures in the governing equations and utilizes neural operators to represent Green's function-like numerical operators in the corresponding numerical solvers. Once trained, ST-FNO can rapidly and accurately predict dynamics in fusion devices compared with first-principle numerical algorithms. In general, ST-FNO represents an efficient and accurate machine learning surrogate for numerical simulators for multi-variable nonlinear time-dependent partial differential equations, with the proposed architectures and loss functions. The efficacy of ST-FNO has been demonstrated using quiescent H-mode simulation data from NIMROD and kink-mode simulation data from GTC. The ST-FNO H-mode results show orders of magnitude reduction in memory and central processing unit usage in comparison with the numerical solvers in NIMROD when computing fields over a selected poloidal plane. The ST-FNO kink-mode results achieve a factor of 2 reduction in the number of parameters compared to baseline FNO models without accuracy loss.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Time-resolved Proteome Profiling of Normal Lung Development

The biochemical networks mediating normal lung development remain unclear with important implications for ameliorating morbidity and mortality in premature infants. Here we perform integrative proteomics and metabolomics analyses of murine lungs from embryonic to early adult ages to characterize the molecular networks mediating normal lung development. Multi-omics analysis identified 8,932 proteins and 158 polar metabolites providing a deep and comprehensive view of the lung proteome and metabolome during normal development. Analysis of the proteomics data revealed ten discrete expression modules driving lung development and the underlying regulatory and signaling network modulating these expression modules. Of interest our data suggest the action of protein ubiquitination in regulating lung development extends beyond the pseudoglandular stage previously described. Combined analysis of the proteomics and metabolomics supported the notion of increased cell proliferation earlier in lung development. Additionally, comparison of the proteomics data with a recent transcriptome analysis revealed biological processes under post-transcriptional control during lung development. Our study provides a unique resource for understanding normal lung development, and potentially avenues for developing strategies to address disrupted development.

computational network analysis, lung development, ↗
DOE OSTI2021

Optimal Demand Response Incorporating Distribution LMP with PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. Here, the objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Principles of proteome allocation are revealed using proteomic data and genome-scale models

Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained (individual proteins) or coarse-grained (functionally-related protein groups) as demonstrated here. Furthermore, this flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.

59 BASIC BIOLOGICAL SCIENCES↗