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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 1,657 records · Page 92
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.

Architecture optimization, reuse, performance, ene↗
DOE OSTI2023

A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments

To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.

Sundararajan, Aditya↗
DOE OSTI2024

Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net-Load Forecasting

Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

Bhattacharjee, Kaustav↗
DOE OSTI2013

Graph Analytics for Signature Discovery

Within large amounts of seemingly unstructured data it can be diffcult to find signatures of events. In our work we transform unstructured data into a graph representation. By doing this we expose underlying structure in the data and can take advantage of existing graph analytics capabilities, as well as develop new capabilities. Currently we focus on applications in cybersecurity and communication domains. Within cybersecurity we aim to find signatures for perpetrators using the pass-the-hash attack, and in communications we look for emails or phone calls going up or down a chain of command. In both of these areas, and in many others, the signature we look for is a path with certain temporal properties. In this paper we discuss our methodology for finding these temporal paths within large graphs.

Signature Science↗
DOE OSTI2015

Social Sensor Analytics: Making Sense of Network Models in Social Media

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013 and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.

event detection↗
DOE OSTI2024

Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

Chae, Junghoon↗
DOE OSTI2021

Outcomes and Insights From Simplified Analytic Trajectory Optimization for a Tethered Underwater Kite

This letter formulates and solves a periodic trajectory optimization problem for a tethered underwater kite. The goal is to maximize the average mechanical power harvested by the kite. The type of kite considered in this letter extracts electricity from ocean currents by moving cross-current as its reel away from its base station, and consumes electricity to reel back. The problem of optimizing this kite’s trajectory is challenging due to the high dimensionality and nonlinearity of its dynamics. To tackle this challenge, the literature often separates the problem into two subproblems focusing on optimizing the cross-current and the reel-in/reel-out components of the trajectory, respectively, which may be sub-optimal. In contrast, this letter solves for the combined cross-current and reel-in/reel-out trajectory by linearizing the dynamics of the kite around a zero-power reference equilibrium trajectory in spherical coordinates. This allows the trajectory optimization problem to be solved analytically for simple sinusoidal input perturbations from equilibrium. Here we use linear quadratic regulation to enable the nonlinear kite model to track the optimized trajectory. The result is a computationally efficient approach that achieves an attractive Loyd factor of 19.9%, while providing important insights into the nature of the optimal trajectory.

42 ENGINEERING↗
DOE OSTI2024

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics

Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. Here we demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)↗
DOE OSTI2017

Consumption Behavior Analytics-Aided Energy Forecasting and Dispatch

For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Spectral Analytics of Solar Photovoltaic Power Output for Optimal Distributed Energy Resource Utilization

This paper examines the spectral analytics of solar photovoltaic (PV) power in order to understand its frequency content. This information is then used to illustrate that the different frequency components of PV generation can be consumed locally by controlling local distributed energy resources in residential/commercial buildings. The solar PV generation signal is divided into three components: high frequency (second-level), medium frequency (minute-level), and low-frequency that correlate with the solar activity. One year of solar PV power data is analyzed with 1-second resolution to find the ideal bounds for the different frequency bands. Results show that by employing intelligent control of Heating, Ventilation and Air-Conditioning (HVAC) systems, HVAC loads are able to accommodate the low and medium frequency components of the PV generation while local energy storage systems can be used to offset the high frequency components. This demonstrates the ability to spatially-local consumption of PV generation using controllable loads so as to minimize impact on the grid, reduce size of storage devices, and increase solar PV penetration levels.

Olama, Mohammed M.↗
DOE OSTI2018

Analytical (S)TEM Studies of Defects Associated with PID in Stressed Si PV Modules

We report the results of analytical transmission electron microscopy studies of defects associated with potentialinduced degradation (PID) of a laboratory stressed multicrystalline silicon mini-module. Shunt defects were first identified using dark lock in thermography (DLIT) and scanning electron microscopy (SEM) electron beam induced current (EBIC) imaging. Transmission electron microscopy (TEM) crosssection samples were then prepared of selected shunt defects using conventional focused ion beam (FIB) lift out techniques and examined by TEM and scanning transmission electron microscopy (STEM). The defects were identified as {111} planar defects and compositional mapping of the defects revealed elevated levels of both Na, expected from previous work, and also surprisingly O at some of the defect cores.

14 SOLAR ENERGY↗
DOE OSTI2025

Visual Analytics of Crosstalk in Quantum Hardware

Crosstalk remains a major obstacle to building scalable and fault-tolerant quantum computers. Conventional diagnostic techniques-often based on numerical simulation or statistical modeling-struggle to scale with hardware complexity and offer limited interpretability. In this work, we present a visual analytics framework for diagnosing qubit crosstalk using lightweight, circuit-based models integrated with an interactive user interface. Our approach quantifies correlations between active and idle qubits under parameterized single- and twoqubit operations, enabling detection of both spatial and gateinduced crosstalk. The system incorporates qubit topology and gate performance data to support sector-based exploration and correlation mapping. This tool assists users in identifying correlated error sources, informing qubit placement strategies, and guiding noise-aware circuit design.

Chae, Junghoon [ORNL] (ORCID:0000000206016746)↗
DOE OSTI2015

Rapid and semi-analytical design and simulation of a toroidal magnet made with YBCO and MgB 2 superconductors

Recent advances in second-generation (YBCO) high-temperature superconducting wire could potentially enable the design of super high performance energy storage devices that combine the high energy density of chemical storage with the high power of superconducting magnetic storage. However, the high aspect ratio and the considerable filament size of these wires require the concomitant development of dedicated optimization methods that account for the critical current density in type-II superconductors. In this study, we report on the novel application and results of a CPU-efficient semianalytical computer code based on the Radia 3-D magnetostatics software package. Our algorithm is used to simulate and optimize the energy density of a superconducting magnetic energy storage device model, based on design constraints, such as overall size and number of coils. The rapid performance of the code is pivoted on analytical calculations of the magnetic field based on an efficient implementation of the Biot-Savart law for a large variety of 3-D “base” geometries in the Radia package. The significantly reduced CPU time and simple data input in conjunction with the consideration of realistic input variables, such as material-specific, temperature, and magnetic-field-dependent critical current densities, have enabled the Radia -based algorithm to outperform finite-element approaches in CPU time at the same accuracy levels. Comparative simulations of MgB 2 and YBCO-based devices are performed at 4.2 K, in order to ascertain the realistic efficiency of the design configurations.

2G↗
DOE OSTI2017

An Analytical Time–Domain Expression for the Net Ripple Produced by Parallel Interleaved Converters

We apply modular arithmetic and Fourier series to analyze the superposition of N interleaved triangular waveforms with identical amplitudes and duty-ratios. Here, interleaving refers to the condition when a collection of periodic waveforms with identical periods are each uniformly phase-shifted across one period. The main result is a time-domain expression which provides an exact representation of the summed and interleaved triangular waveforms, where the peak amplitude and parameters of the time-periodic component are all specified in closed-form. Analysis is general and can be used to study various applications in multi-converter systems. This model is unique not only in that it reveals a simple and intuitive expression for the net ripple, but its derivation via modular arithmetic and Fourier series is distinct from prior approaches. The analytical framework is experimentally validated with a system of three parallel converters under time-varying operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Compression Analytics for Classification and Anomaly Detection within Network Communication

Here, the flexibility of network communication within Internet protocols is fundamental to network function, yet this same flexibility permits the possibility of malicious use. In particular, malicious behavior can masquerade as benign traffic, thus evading systems designed to catch misuse of network resources. However, perfect imitation of benign traffic is difficult, meaning that small unintentional deviations from normal can occur. Identifying these deviations requires that the defenders know what features reveal malicious behavior. Herein we present an application of compression-based analytics to network communication that can reduce the need for defenders to know a priori what features they need to examine. Motivating the approach is the idea that compression relies on the ability to discover and make use of predictable elements in information, thereby highlighting any deviations between expected and received content. We introduce a so-called “slice compression” score to identify malicious or anomalous communication in two ways. First, we apply normalized compression distances (NCDs) to classification problems and discuss methods for reducing the noise by excising application content (as opposed to protocol features) using slice compression. Second, we present a new technique for anomaly detection, referred to as slice compression for anomaly detection (SCADe). A diverse collection of datasets are analyzed to illustrate the efficacy of the proposed approaches. While our focus is network communication, other types of data are also considered to illustrate the generality of the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Descriptive Analytics-Based Anomaly Detection for Cybersecure Load Forecasting

As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more and more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this paper is to develop descriptive analytics-based methods for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and a hybrid implementation of IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. HIIS is also capable of improving true positive rates and reducing false positive rates significantly comparing with IS. The proposed HIIS can serve as an online cybersecure load forecasting scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Attached and Attaching Three-Dimensional Jets from Circular Nozzles: Analytical Model

Here we evaluate velocity profiles of three dimensional attached jets emerging from circular nozzles. These jets lose momentum due to interactions with nearby surfaces and are important to evaluating flows in mixing vessels and to suspending solids and trapped gases in radioactive waste tanks. Despite the industrial importance of attached jets, simple, complete, and quantitative expressions for their velocity profiles remain elusive. Here we present a quantitative analytical model of the three dimensional velocity profiles of attached jets inclusive of a local skin coefficient of friction. We compare these expressions to experimental velocity profiles at moderate and high nozzle Reynolds numbers to find reasonable quantitative agreement.

Jet, attached jet, offset jet, reattaching offset ↗