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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values corresponding to voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Checkpoint triggering in a computer system

According to an aspect, a method for triggering creation of a checkpoint in a computer system includes executing a task in a processing node of the computer system. A monitoring block size is determined for the checkpoint. A checkpoint interval is determined based on the monitoring block size, a checkpoint bandwidth, and a failure rate of the computer system. Based on determining that the checkpoint interval has elapsed, the checkpoint including state data of the task is created to enable restarting execution of the task upon a restart operation. The state data of the checkpoint is restored from a memory responsive to detecting an error condition at the processing node. Execution of the task is restarted in the processing node based on the state data restored from the memory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗
DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗
DOE OSTI2020

Adaptive multi-level checkpointing

In some examples, with respect to adaptive multi-level checkpointing, a transfer parameter associated with transfer of checkpoint data from a node-local storage to a parallel file system may be ascertained for the checkpoint data stored in the node-local storage. The transfer parameter may be compared to a specified transfer parameter threshold. A determination may be made, based on the comparison of the transfer parameter to the specified transfer parameter threshold, as to whether to transfer the checkpoint data from the node-local storage to the parallel file system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Reliable cyber-threat detection in rapidly changing environments

In some embodiments, a plurality of monitoring nodes each generate a series of current monitoring node values over time that represent a current operation of the industrial asset. An attack detection computer platform may receive the series of current monitoring node values and generate a set of current feature vectors including a current feature for capturing transients (e.g., local transients and/or global transients). The attack detection computer platform may also access an attack detection model having at least one decision boundary that was created using at least one of a set of normal feature vectors and/or a set of attacked feature vectors. The attack detection model may then be executed such that an attack alert signal is transmitted by the attack detection computer platform, when appropriate, based on the set of current feature vectors (including the current feature to capture transients) and the at least one decision boundary.

Abbaszadeh, Masoud↗
DOE OSTI2020

Situation awareness and dynamic ensemble forecasting of abnormal behavior in cyber-physical system

A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abbaszadeh, Masoud↗
DOE OSTI2021

Adaptive neural network management system

A method and computer system for managing a neural network. Data is sent into an input layer in a portion of layers of nodes in the neural network. The data moves on an encode path through the portion such that an output layer in the portion outputs encoded data. The encoded data is sent into the output layer on a decode path through the portion back to the input layer to obtain a reconstruction of the data by the input layer. A determination is made as to whether an undesired amount of error has occurred in the output layer based on the data sent into the input layer and the reconstruction of the data. A number of new nodes is added to the output layer when a determination is present that the undesired amount of the error occurred, enabling reducing the error using the number of the new nodes.

Draelos, Timothy J.↗
DOE OSTI2021

Real time feedback-based optimization of distributed energy resources

An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Dall'Anese, Emiliano↗
DOE OSTI2021

Routing flits in a network-on-chip based on operating states of routers

A system is described that includes an integrated circuit chip having a network-on-chip. The network-on-chip includes multiple routers arranged in a topology and a separate communication link coupled between each router and each of one or more neighboring routers of that router among the multiple routers in the topology. The integrated circuit chip also includes multiple nodes, each node coupled to a router of the multiple routers. When operating, a given router of the multiple routers keeps a record of operating states of some or all of the multiple routers and corresponding communication links. The given router then routes flits to destination nodes via one or more other routers of the multiple routers based at least in part on the operating states of the some or all of the multiple routers and the corresponding communication links.

Bharadwaj, Srikant↗
DOE OSTI2021

Scalable In Situ Computation of Lagrangian Representations via Local Flow Maps

In situ computation of Lagrangian flow maps to enable post hoc time-varying vector field analysis has recently become an active area of research. However, the current literature is largely limited to theoretical settings and lacks a solution to address scalability of the technique in distributed memory. To improve scalability, we propose and evaluate the benefits and limitations of a simple, yet novel, performance optimization. Our proposed optimization is a communication-free model resulting in local Lagrangian flow maps, requiring no message passing or synchronization between processes, intrinsically improving scalability, and thereby reducing overall execution time and alleviating the encumbrance placed on simulation codes from communication overheads. To evaluate our approach, we computed Lagrangian flow maps for four time-varying simulation vector fields and investigated how execution time and reconstruction accuracy are impacted by the number of GPUs per compute node, the total number of compute nodes, particles per rank, and storage intervals. Our study consisted of experiments computing Lagrangian flow maps with up to 67M particle trajectories over 500 cycles and used as many as 2048 GPUs across 512 compute nodes. In all, our study contributes an evaluation of a communication-free model as well as a scalability study of computing distributed Lagrangian flow maps at scale using in situ infrastructure on a modern supercomputer.

Sane, Sudhanshu↗
DOE OSTI2021

Instructions for performing multi-line memory accesses

A system is described that performs memory access operations. The system includes a processor in a first node, a memory in a second node, a communication interconnect coupled to the processor and the memory, and an interconnect controller in the first node coupled between the processor and the communication interconnect. Upon executing a multi-line memory access instruction, the processor prepares a memory access operation for accessing, in the memory, a block of data including at least some of each of at least two lines of data. The processor then causes the interconnect controller to use a single remote direct memory access memory transfer to perform the memory access operation for the block of data via the communication interconnect.

Roberts, David A.↗
DOE OSTI2021

Mapping 2-dimensional meshes on 3-dimensional torus

In a L×M×N 3D torus network of computer nodes, a 2-dimensional plane comprising M×N torus network of nodes mapped into M/2 meshes×2*N torus network of nodes. N can be k*M, k is an integer greater than zero, and M and N are even numbers. Each of M/2 mesh of the 2*N torus is contiguous in the 2D plane. Mapping is performed for each of the L planes of the L×M×N 3D torus network. The M/2 meshes are combined with a remaining torus network dimension comprising L planes, the combining creating another 2*N pattern, wherein an L*M/2×2*N communication pattern is created. Application entities are executed according to the mapped L*M/2×2*N communication pattern.

Chen, Dong↗
DOE OSTI2021

Push-pull circuit with driving assisted by asymmetric charge sharing

A push-pull circuit for an opto-electronic device includes: an output node; a pull-up circuit that, in operation, controls a falling edge rate of an input signal to the opto-electronic device while sharing charge with the output node; and a pull-down circuit that, in operation, controls a rising edge rate of the input signal to the opto-electronic device while sharing charge with the output node.

Zhu, Zhubiao↗
DOE OSTI2021

Method for tuning thermal expansion properties in an additive manufacturing feedstock material

A feedstock material for use in an additive manufacturing apparatus is prepared from a first material and a metal organic framework (MOF). The MOF comprises a plurality of nodes and a plurality of linkers, the plurality of linkers coupled to the plurality of nodes, thereby forming a framework. The MOF has a lower coefficient of thermal expansion than a coefficient of thermal expansion for the first material. As a result, the feedstock material has a reduced coefficient of thermal expansion as compared to the first material alone and thus exhibits low thermal expansion as its temperature is increased. The coefficient of thermal expansion for the MOF may be modified by using a different plurality of nodes and/or a different plurality of linkers, as well as by incorporating guest molecules or atoms into the framework of the MOF.

Burtch, Nicholas C.↗
DOE OSTI2021

Relocation and persistence of named data elements in coordination namespace

An approach is disclosed that relocates a named data element. A request to move a name corresponding to the named data element is received from a first storage area in a Coordination Namespace to a second storage area in the Coordination Namespace. The first storage area has a first level of persistence, and the second storage area has a second level of persistence. The named data element exists in a Coordination Namespace that is allocated in a memory distributed amongst a plurality of nodes that include the local node and one or more remote nodes. The approach then creates a copy of the named data element in the second storage area.

Nair, Ravi↗
DOE OSTI2022

Accelerating shared file checkpoint with local burst buffers

A data management system and method for accelerating shared file checkpointing. Written application data is aggregated in an application data file created in a local burst buffer memory at a compute node, and an associated data mapping built index to maintain information related to the offsets into a shared file at which segments of the application data is to be stored in a parallel file system, and where in the buffer those segments are located. The node asynchronously transfers a data file containing the application data and the associated data mapping index to a file server for shared file storage. The data management system and method further accelerates shared file checkpointing in which a shared file, together with a map file that specifies how the shared file is to be distributed, is asynchronously transferred to local burst buffer memories at the nodes to accelerate reading of the shared file.

Gooding, Thomas↗