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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Continuous Emulation and Multiscale Visualization of Traffic Flow Using Stationary Roadside Sensor Data

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Lastly, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

42 ENGINEERING↗
DOE OSTI2020

GrAMME: Semisupervised Learning Using Multilayered Graph Attention Models

Modern data analysis pipelines are becoming increasingly complex due to the presence of multiview information sources. While graphs are effective in modeling complex relationships, in many scenarios, a single graph is rarely sufficient to succinctly represent all interactions, and hence, multilayered graphs have become popular. Though this leads to richer representations, extending solutions from the single-graph case is not straightforward. Consequently, there is a strong need for novel solutions to solve classical problems, such as node classification, in the multilayered case. In this article, we consider the problem of semisupervised learning with multilayered graphs. Though deep network embeddings, e.g., DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective. To this end, we propose to use attention models for effective feature learning and develop two novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the interlayer dependences for building multilayered graph embeddings. Using empirical studies on several benchmark data sets, we evaluate the proposed approaches and demonstrate significant performance improvements in comparison with the state-of-the-art network embedding strategies. The results also show that using simple random features is an effective choice, even in cases where explicit node attributes are not available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information

Efficient processing of large-scale time series data is an intricate problem in machine learning. Conventional sensor signal processing pipelines with hand engineered feature extraction often involve huge computational cost with high dimensional data. Deep recurrent neural networks have shown promise in automated feature learning for improved time-series processing. However, generic deep recurrent models grow in scale and depth with increased complexity of the data. This is particularly challenging in presence of high dimensional data with temporal and spatial characteristics. Consequently, this work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to efficiently process complex multi-dimensional time series data with spatial information. Here, the cellular recurrent architecture in the proposed model allows for location-aware synchronous processing of time series data from spatially distributed sensor signal sources. Extensive trainable parameter sharing due to cellularity in the proposed architecture ensures efficiency in the use of recurrent processing units with high-dimensional inputs. This study also investigates the versatility of the proposed DCRNN model for classification of multi-class time series data from different application domains. Consequently, the proposed DCRNN architecture is evaluated using two time-series datasets: a multichannel scalp EEG dataset for seizure detection, and a machine fault detection dataset obtained in-house. The results suggest that the proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A 24-Channel Digitizer With a JESD204B-Compliant Serial Interface for High-Speed Detectors

In this report a 24-channel application-specific integrated circuit (ASIC) for the readout of high-speed CMOS active pixel sensors for charged particle detection is presented. The chip comprises 24 preamplifiers, 24 distinct 12-bit, 25 MSPS Pipelined analog-to-digital converters (ADCs) with self-calibration, an internal phase-locked loop (PLL), and a 3 Gbps serial interface that conforms to the JESD204B standard. To simplify interfacing with a variety of sensors, the ASIC also includes an automatic offset calibration loop. The high level of integration of the ASIC reduces overall system cost and area, and exploiting the signal characteristics of the image sensor allows the ADC to be optimized for reduced power dissipation. The use of an integrated serializer and an industry standard protocol simplifies integration of the ASIC into a complete camera system. The ASIC, called the High-Speed Image Preprocessor Targeted for Electron Readout, or HIPSTER, with a die area of 64.26 mm 2 , is packaged in a 480-ball grid array (BGA) and is fabricated in 180-nm CMOS technology. HIPSTER achieves typical differential nonlinearity (DNL) < 0.55 LSB, input-referred thermal noise of $114.5 μV-rms, and a bit error rate (BER) of better than 10-14. The power dissipation is 98 mW/channel.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Multiscale System Modeling of Single-Event-Induced Faults in Advanced Node Processors

Integration-technology feature shrink increases computing-system susceptibility to single-event effects (SEE). While modeling SEE faults will be critical, an integrated processor’s scope makes physically correct modeling computationally intractable. Without useful models, presilicon evaluation of fault-tolerance approaches becomes impossible. To incorporate accurate transistor-level effects at a system scope, we present a multiscale simulation framework. Charge collection at the 1) device level determines 2) circuit-level transient duration and state-upset likelihood. Circuit effects, in turn, impact 3) register-transfer-level architecture-state corruption visible at 4) the system level. Furthermore, the physically accurate effects of SEEs in large-scale systems, executed on a high-performance computing (HPC) simulator, could be used to drive cross-layer radiation hardening by design. We demonstrate the capabilities of this model with two case studies. First, we determine a D flip-flop’s sensitivity at the transistor level on 14-nm FinFet technology, validating the model against published cross sections. Second, we track and estimate faults in a microprocessor without interlocked pipelined stages (MIPS) processor for Adams 90% worst case environment in an isotropic space environment.

42 ENGINEERING↗
DOE OSTI2021

Cooling and Timing Tests of the ATLAS Fast TracKer VME Boards

The Fast TracKer (FTK) is an ATLAS trigger upgrade built for full-event, low-latency, high-rate tracking. The FTK core, made of 9U VME boards, performs the most demanding computational task. The associative memory board (AMB) serial link processor and the auxiliary card (AUX), plugged on the front and back sides of the same VME slot, constitute the processing unit (PU), which finds tracks using hits from eight layers of the inner detector. The PU works in pipeline with the second stage board (SSB), which finds 12-layer tracks by adding extra hits to the identified tracks. In the designed configuration, 16 PUs and four SSBs are installed in a VME crate. The high power consumption of the AMB, AUX, and SSB (respectively, of about 250, 70, and 160 W per board) required the development of a custom cooling system. Even though the expected power consumption for each VME crate of the FTK system is high compared with a common VME setup, the 8 FTK core crates will use ≈60 kW, which is just a fraction of the power and the space needed for a CPU farm performing the same task. We report on the integration of 32 PUs and eight SSBs inside the FTK system, on the infrastructures needed to run and cool them, and on the tests performed to verify the system processing rate and the temperature stability at a safe value.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information

Detecting and anticipating global proliferation expertise and capability evolution from unstructured, noisy, and incomplete public data streams is a highly desired, but extremely challenging task. Here, in this article, we present our pioneering data-driven approach to support the non-proliferation mission to detect and explain the evolution of proliferation expertise and capability development globally from terabytes of publicly available information (PAI), focusing on our knowledge extraction pipeline and descriptive analytics. We first discuss how we fuse nine open-source data streams, including multilingual data, to convert 4 TB of unstructured data to structured knowledge and encode dynamically evolving proliferation expertise representations—content and context graphs. For this, we rely on natural language processing (NLP) and deep learning (DL) models to perform information extraction, topic modeling, and distributed text representation (aka embedding) learning. We then present interactive, usable, and explainable descriptive analytics to refine domain knowledge and present it in a human-understandable form. Finally, we introduce future work avenues that will leverage our dynamic knowledge representations and descriptive analytics to enable predictive and prescriptive inferences to achieve real-time domain understanding and contextual reasoning about global proliferation expertise and capability evolution.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

ERSAP: Toward Better NP Data-Stream Analytics With Flow-Based Programming

This paper presents an reactive, actor-model and FBP paradigm based framework that we develop to design data-stream processing applications for HEP and NP. This framework encourages a functional decomposition of the overall data processing application into small mono-functional artifacts. Artifacts that are easy to understand, develop, deploy and debug. The fact that these artifacts (actors) are programmatically independent they can be scaled and optimized independently, which is impossible to do for components of the monolithic application. One of the important advantages of this approach is fault tolerance where independent actors can come and go on the data-stream without forcing the entire application to crash. Furthermore, it also makes it is easy to locate the faulty actor in the data pipeline. Due the fact that the actors are loosely coupled, and that the data (inevitably) carries the context, they can run on heterogeneous environments, utilizing different accelerators. This paper describes the main design concepts of the framework and presents a ?proof of concept? application design and deployment results obtain processing on-beam calorimeter streaming data.

Gyurjyan, V.↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
DOE OSTI2020

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism

Here, we present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks. Deep learning-based emerging scientific workflows often require model training with large, high-dimensional samples, which can make training much more costly and even infeasible due to excessive memory usage. We solve these challenges by extensively applying hybrid parallelism throughout the end-to-end training pipeline, including both computations and I/O. Our hybrid-parallel algorithm extends the standard data parallelism with spatial parallelism, which partitions a single sample in the spatial domain, realizing strong scaling beyond the mini-batch dimension with a larger aggregated memory capacity. We evaluate our proposed training algorithms with two challenging 3D CNNs, CosmoFlow and 3D U-Net. Our comprehensive performance studies show that good weak and strong scaling can be achieved for both networks using up to 2K GPUs. More importantly, we enable training of CosmoFlow with much larger samples than previously possible, realizing an order-of-magnitude improvement in prediction accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Real-Time Omnidirectional Stereo Rendering: Generating 360° Surround-View Panoramic Images for Comfortable Immersive Viewing

Surround-view panoramic images and videos have become a popular form of media for interactive viewing on mobile devices and virtual reality headsets. Viewing such media provides a sense of immersion by allowing users to control their view direction and experience an entire environment. When using a virtual reality headset, the level of immersion can be improved by leveraging stereoscopic capabilities. Stereoscopic images are generated in pairs, one for the left eye and one for the right eye, and result in providing an important depth cue for the human visual system. For computer generated imagery, rendering proper stereo pairs is well known for a fixed view. However, it is much more difficult to create omnidirectional stereo pairs for a surround-view projection that work well when looking in any direction. One major drawback of traditional omnidirectional stereo images is that they suffer from binocular misalignment in the peripheral vision as a user's view direction approaches the zenith / nadir (north / south pole) of the projection sphere. This paper presents a real-time geometry-based approach for omnidirectional stereo rendering that fits into the standard rendering pipeline. Our approach includes tunable parameters that enable pole merging - a reduction in the stereo effect near the poles that can minimize binocular misalignment. Results from a user study indicate that pole merging reduces visual fatigue and discomfort associated with binocular misalignment without inhibiting depth perception.

360° panorama↗
DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Make the Fastest Faster: Importance Mask Synthesis for Interactive Volume Visualization using Reconstruction Neural Networks

Visualizing a large-scale volumetric dataset with high resolution is challenging due to the substantial computational time and space complexity. Recent deep learning-based image inpainting methods significantly improve rendering latency by reconstructing a high-resolution image for visualization in constant time on GPU from a partially rendered image where only a portion of pixels go through the expensive rendering pipeline. However, existing solutions need to render every pixel of either a predefined regular sampling pattern or an irregular sample pattern predicted from a low-resolution image rendering. Both methods require a significant amount of expensive pixel-level rendering. In this work, we provide Importance Mask Learning (IML) and Synthesis (IMS) networks, which are the first attempts to directly synthesize important regions of the regular sampling pattern from the user’s view parameters, to further minimize the number of pixels to render by jointly considering the dataset, user behavior, and the downstream reconstruction neural network. Our solution is a unified framework to handle various types of inpainting methods through the proposed differentiable compaction/decompaction layers. Experiments show our method can further improve the overall rendering latency of state-of-the-art volume visualization methods using reconstruction neural network for free when rendering scientific volumetric datasets. Our method can also directly optimize the off-the-shelf pre-trained reconstruction neural networks without elongated retraining.

Large-scale data↗
DOE OSTI2020

Towards Interactive, Reproducible Analytics at Scale on HPC Systems

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case-applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow-reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-Time streams of HDF5 data.

containers↗
DOE OSTI2023

Visualizing Comparisons of Bill of Materials

Protecting critical infrastructure from cyber attacks, natural disasters, and other disruptions is a priority of the U.S. Government. Critical infrastructure includes providing electricity to homes and businesses, supplying natural gas for heating, and producing renewable energy sources. A loss of these services, as seen in the Solarwinds supply chain attack in 2020 , Texas snowstorm of 2021, the Colonial Pipeline cyber incident of 2021, and the Washington power substation attacks in 2022 result in high costs to consumers, disruption of everyday life, and even death. To protect the infrastructure, we first have to know what equipment we are protecting. The complexity of distributed manufacturing and development coupled with the increasing prevalence of cyber and supply chain attacks necessitates a greater understanding of the hardware and software components that comprise equipment in critical infrastructure. When a vulnerability in a single software library can have disastrous consequences, it is vital to understand critical equipment and systems at a granular level. This need has led to increased energy around the development and incorporation of bill-of-materials (BOM) into existing asset management practices to aid in mitigating, and responding to future attacks \cite{noauthor_software_nodate}. While much of the current research is devoted to creating BOMs, it is equally important to develop methodologies for leveraging BOMs to answer questions, such as: How has my software changed? Are two pieces of equipment equivalent? Does this piece of equipment that just arrived match my historical information? In this work, we demonstrate how BOMs can be represented by graph structures. We then describe how these structures can be fed into a graph comparison algorithm to produce a novel interactive visualization that allows us to not only identify differences in BOMs, but show exactly where they are in the product.

Jones, Rebecca D.↗
DOE OSTI2022

Genome and proteome analyses show the gaseous alkane degrader Desulfosarcina sp. strain BuS5 as an extreme metabolic specialist

The metabolic potential of the sulfate-reducing bacterium Desulfosarcina sp. strain BuS5, currently the only pure culture able to oxidize the volatile alkanes propane and butane without oxygen, was investigated via genomics, proteomics and physiology assays. Complete genome sequencing revealed that strain BuS5 encodes a single alkyl-succinate synthase, an enzyme which apparently initiates oxidation of both propane and butane. The formed alkyl-succinates are oxidized to CO 2 via beta oxidation and the oxidative Wood–Ljungdahl pathways as shown by proteogenomics analyses. Strain BuS5 conserves energy via the canonical sulfate reduction pathway and electron bifurcation. An ability to utilize long-chain fatty acids, mannose and oligopeptides, suggested by automated annotation pipelines, was not supported by physiology assays and in-depth analyses of the corresponding genetic systems. Consistently, comparative genomics revealed a streamlined BuS5 genome with a remarkable paucity of catabolic modules. These results establish strain BuS5 as an exceptional metabolic specialist, able to grow only with propane and butane, for which we propose the name Desulfosarcina aeriophaga BuS5. This highly restrictive lifestyle, most likely the result of habitat-driven evolutionary gene loss, may provide D. aeriophaga BuS5 a competitive edge in sediments impacted by natural gas seeps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗