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At least 703 records · Page 39

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DOE OSTI2021

Conflation of Geospatial POI Data and Ground-level Imagery via Link Prediction on Joint Semantic Graph

With the proliferation of smartphone cameras and social networks, we have rich, multi-modal data about points of interest (POIs) - like cultural landmarks, institutions, businesses, etc. - within a given areas of interest (AOI) (e.g., a county, city or a neighborhood) available to us. Data conflation across multiple modalities of data sources is one of the key challenges in maintaining a geographical information system (GIS) which accumulate data about POIs. Given POI data from nine different sources, and ground-level geo-tagged and scene-captioned images from two different image hosting platforms, in this work we explore the application of graph neural networks (GNNs) to perform data conflation, while leveraging a natural graph structure evident in geospatial data. The preliminary results demonstrate the capacity of a GNN operation to learn distributions of entity (POIs and images) features, coupled with topological structure of entity's local neighborhood in a semantic nearest neighbor graph, in order to predict links between a pair of entities.

Gurav, Rutuja↗

DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗

DOE OSTI2023

Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms

The primary purpose of this document is to outline the progress made on the LDRD titled “Identifying and Explaining Anomalous Activity in Surveillance Video with Compression Algorithms” in FY22 and FY23. In this LDRD, we explored the usage of compression-based analytics to identify anomalous activity in video. We developed a novel algorithm, Spatio-Temporal N-Gram PPM (STNG PPM) that accounts for spatially and temporally aware anomalies in video. We extracted features using motions vectors from video as well as operating on the raw features. STNG PPM is comparable to many deep learning approaches but does not require specialized hardware (GPUs) to run efficiently. We also examine the evaluation metrics and propose novel measures addressing faults in the current evaluation measures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Interpreting and Accelerating Transformers for Jet Tagging

Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Cybersecurity Considerations for Grid-Connected Batteries with Hardware Demonstrations

The share of renewable and distributed energy resources (DERs), like wind turbines, solar photovoltaics and grid-connected batteries, interconnected to the electric grid is rapidly increasing due to reduced costs, rising efficiency, and regulatory requirements aimed at incentivizing a lower-carbon electricity system. These distributed energy resources differ from traditional generation in many ways including the use of many smaller devices connected primarily (but not exclusively) to the distribution network, rather than few larger devices connected to the transmission network. DERs being installed today often include modern communication hardware like cellular modems and WiFi connectivity and, in addition, the inverters used to connect these resources to the grid are gaining increasingly complex capabilities, like providing voltage and frequency support or supporting microgrids. To perform these new functions safely, communications to the device and more complex controls are required. The distributed nature of DER devices combined with their network connectivity and complex controls interfaces present a larger potential attack surface for adversaries looking to create instability in power systems. To address this area of concern, the steps of a cyberattack on DERs have been studied, including the security of industrial protocols, the misuse of the DER interface, and the physical impacts. These different steps have not previously been tied together in practice and not specifically studied for grid-connected storage devices. In this work, we focus on grid-connected batteries. We explore the potential impacts of a cyberattack on a battery to power system stability, to the battery hardware, and on economics for various stakeholders. We then use real hardware to demonstrate end-to-end attack paths exist when security features are disabled or misconfigured. Our experimental focus is on control interface security and protocol security, with the initial assumption that an adversary has gained access to the network to which the device is connected. We provide real examples of the effectiveness of certain defenses. This work can be used to help utilities and other grid-connected battery owners and operators evaluate the severity of different threats and the effectiveness of defense strategies so they can effectively deploy and protect grid-connected storage devices.

25 ENERGY STORAGE↗

DOE OSTI2025

Merger Response of Halo Anisotropy Properties

Anisotropy properties—halo spin, shape, position offset, velocity offset, and orientation—are an important family of dark matter halo properties that indicate the level of directional variation of the internal structures of halos. These properties reflect the dynamical state of halos, which in turn depends on the mass assembly history. In this work, we study the evolution of anisotropy properties in response to merger activity using the IllustrisTNG simulations. We find that the response trajectories of the anisotropy properties significantly deviate from secular evolution. These trajectories have the same qualitative features and timescales across a wide range of merger and host properties. We propose explanations for the behavior of these properties and connect their evolution to the relevant stages of merger dynamics. We measure the relevant dynamical timescales. We also explore the dependence of the strength of the response on time of merger, merger ratio, and mass of the main halo. These results provide insight into the physics of halo mergers and their effects on the statistical behavior of halo properties. This study paves the way toward a physical understanding of scaling relations, particularly to how systematics in their scatter are connected to the mass assembly histories of halos.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Feature extraction for subtle anomaly detection using semi-supervised learning

The demand for automated and effective monitoring techniques has soared with the increased digitization of industrial monitoring systems. State-of-the-art machine learning methods are effectively detecting abrupt changes in system states. However, these methods lack comparable maturity in detecting subtle changes that may be signs of incipient faults. This manuscript argues that the current anomaly detection methods can be enhanced by exploring weak patterns to enable subtle variation detection. Specifically, the concept of semi-supervised learning is employed, with labels representing knowledge about some anomalous conditions of a system. The basic idea is to extract a candidate set of weak patterns discarded by state-of-the-art baselining algorithms. With few labeled anomalous data, the algorithm selects the weak patterns and allows for their possible fusion using the highest sensitivity to the labeled anomalies. Here, the method’s applicability is demonstrated using a representative pressurized water reactor (PWR) model simulated by Dymola.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Solvent Effects on Catalytic Reactions and Related Phenomena at Liquid-Solid Interfaces

Catalytic reactions involve the direct interaction of reactants, intermediates and products with the catalyst surface. We not only need to control the atomic structure and electronic properties of the active site, but also explore the multiple molecular interactions that occur beyond the active site; they play an essential role in altering the binding and reactivity of surface species. In liquid-phase catalysis, solvents provide additional degrees of freedom in the design of the catalytic process for desirable activity and selectivity. Here, the multi-faceted effects of solvents have a profound impact on the catalyst performance by restricting the mass transfer to the site, tuning the chemical potential of the surface species, competing for active sites, stabilizing the initial and transition states, and causing mechanistic changes by participating in the kinetically relevant elementary steps. This review addresses the different aspects of solvent effects, using a few prototype solid-liquid interfaces to illustrate these fundamental features. Recent experimental and computational studies that provide new insight at the molecular level are examined. Solvent structures in the proximity of the catalyst surface are discussed along with their influence in molecular binding and reaction at the solid-liquid interfaces. Furthermore, opportunities to alter such a solid-liquid interaction by tuning the wettability of the catalyst surfaces are explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗

DOE OSTI2020

Classical discrete time crystals

The spontaneous breaking of time-translation symmetry in periodically driven quantum systems leads to a new phase of matter: the discrete time crystal (DTC). This phase exhibits collective subharmonic oscillations that depend upon an interplay of non-equilibrium driving, many-body interactions and the breakdown of ergodicity. However, subharmonic responses are also a well-known feature of classical dynamical systems ranging from predator–prey models to Faraday waves and a.c.-driven charge density waves. This raises the question of whether these classical phenomena display the same rigidity characteristic of a quantum DTC. In this work, we explore this question in the context of periodically driven Hamiltonian dynamics coupled to a finite-temperature bath, which provides both friction and, crucially, noise. Focusing on one-dimensional chains, where in equilibrium any transition would be forbidden at finite temperature, we provide evidence that the combination of noise and interactions drives a sharp, first-order dynamical phase transition between a discrete time-translation invariant phase and an activated classical discrete time crystal (CDTC) in which time-translation symmetry is broken out to exponentially long timescales. Power-law correlations are present along a first-order line, which terminates at a critical point. We analyse the transition by mapping it to the locked-to-sliding transition of a d.c.-driven charge density wave. To conclude, building upon results from the field of probabilistic cellular automata, we conjecture the existence of classical time crystals with true long-range order, where time-translation symmetry is broken out to infinite times.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Increasing thermostability of the key photorespiratory enzyme glycerate 3‐kinase by structure‐based recombination

As global temperatures rise, improving crop yields will require enhancing the thermotolerance of crops. One approach for improving thermotolerance is using bioengineering to increase the thermostability of enzymes catalysing essential biological processes. Photorespiration is an essential recycling process in plants that is integral to photosynthesis and crop growth. The enzymes of photorespiration are targets for enhancing plant thermotolerance as this pathway limits carbon fixation at elevated temperatures. We explored the effects of temperature on the activity of the photorespiratory enzyme glycerate kinase (GLYK) from various organisms and the homologue from the thermophilic alga Cyanidioschyzon merolae was more thermotolerant than those from mesophilic plants, including Arabidopsis thaliana. To understand enzyme features underlying the thermotolerance of C. merolae GLYK (CmGLYK), we performed molecular dynamics simulations using AlphaFold-predicted structures, which revealed greater movement of loop regions of mesophilic plant GLYKs at higher temperatures compared to CmGLYK. Based on these simulations, hybrid proteins were produced and analysed. These hybrid enzymes contained loop regions from CmGLYK replacing the most mobile corresponding loops of AtGLYK. Two of these hybrid enzymes had enhanced thermostability, with melting temperatures increased by 6 °C. One hybrid with three grafted loops maintained higher activity at elevated temperatures. Whilst this hybrid enzyme exhibited enhanced thermostability and a similar Km for ATP compared to AtGLYK, its Km for glycerate increased threefold. This study demonstrates that molecular dynamics simulation-guided structure-based recombination offers a promising strategy for enhancing the thermostability of other plant enzymes with possible application to increasing the thermotolerance of plants under warming climates.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Thermal-Mechanical Analysis of an Additive Manufacturing Ceramic Heat Exchanger for High-Temperature Recuperator in a sCO2 Power System

Supercritical CO2 (sCO2) Brayton power cycle can be configured in a closed-loop power system and has a potentially high cycle efficiency. Compactness and high efficiency of a sCO2 power block make the sCO2 Brayton cycle a versatile power cycle in broad applications. While many heat sources are of sufficient intensity to produce high temperature working fluids to achieve high cycle efficiency, the thermal-mechanical stability of traditional materials (e.g., steels and nickel-based superalloys) used in construction of heat exchangers and turbine components limits the operating conditions and thus thermodynamic efficiency of the system. This effort seeks to establish the viability of ceramic heat exchanger technologies for the most extreme operating conditions envisioned for power generation and other high temperature processes. Heat exchangers constructed from ultra-high temperature ceramics, a class of extreme environment materials featuring melting points (Tmp.) above 3000 degrees C, is particularly appealing for sCO2 Brayton cycles given their ultra-low creep rates and very high retained strength at low homologous temperatures (i.e., T < 0.5 Tmp., or at least 1500 degrees C). To translate these materials properties to ultra-high temperature heat exchangers, innovations are required in ceramic manufacturing techniques to realize the complex architectures featured in compact heat exchangers with high power density. With appropriate processing, ZrB2-SiC based compositions can be sintered to near full density and shaped into complex topologies via ceramic additive manufacturing methods. This paper analyzes heat exchanger designs and explores thermal-mechanical implications of the operating environment. Thermal flow, heat transfer, and conjugate mechanical analyses provide insights into benefits and risks associated with the design approach.

additive manufacturing↗

DOE OSTI2024

Tandem Predictions for HPC Jobs

At the core of the predictive analytics applied to High Performance Computing (HPC), the most prominent tasks are the prediction of job runtimes and the prediction of job queue times, both of which have the potential for informing HPC users during their every-day decision making. Accurate runtime predictions can help users better choose so-called wallclock times at job submission, decreasing the odds of their jobs waiting in queues longer than necessary. The accurate and timely queue time predictions offered for the available partitions can inform the favorable selection of partitions for running jobs. This potential is well understood as we see in the abundance of research studies that propose solutions for these tasks, including the work published in the last several years. These tasks are seemingly receptive to the Machine Learning (ML) solutions, considering that there is no shortage of training data where HPC centers over time run millions and millions of jobs. However, we study the existing research literature, as well as look for examples in the toolchains supported on the exemplar HPC facilities, and, surprisingly, do not find any practical solutions that are ready to be adopted. We interpret this as a manifestation of the shortage of UX/UI efforts that support HPC analytics and also as a sign that the research has not come to the consensus on solving these tasks. In this study, we aim to shed new light on the long-running task of job queue time prediction by exploring the utility of runtime predictions in improving prediction accuracy and, actually, predicting these two metrics together, in tandem. In other words, we show how runtime predictions become valuable input in the queue time modeling. We challenge the existing approaches to feature engineering for the queue time prediction and describe promising results we obtained for a large dataset of HPC jobs from a supercomputer at the National Renewable Energy Laboratory.

HPC↗

DOE OSTI2025

Investigation of the distribution of laser-induced damage precursors in etched grating sidewalls fabricated using inductively coupled plasma reactive-ion etching

Inductively coupled plasma reactive-ion etching for grating fabrication is explored with emphasis on the type and distribution of process-induced defects that can act as precursors to laser-induced damage initiation. To provide flexibility in the ability to perform a variety of material characterization methods, grating-like features with large pitch were fabricated in silica monolayers. The results indicate that the etch process introduces surface fluorine and subsurface carbon-containing contamination, which reduces the laser-induced damage threshold. The local relative concentrations of the laser-damage precursors introduced during the etch step of the manufacturing process were investigated using angle-resolved damage testing, taking advantage of the shift of the location of the peak electric-field intensity incident on the grating sidewall. The results suggest an increased level of etch-related defects near the base of the sidewall, with directly fluorinated silicon identified as the primary defect.

Dent, R. [Univ. of Rochester, NY (United States). ↗

DOE OSTI2021

Securing Distributed Energy Resource Integration

The penetration of distributed energy resources (DER) is growing at much higher rates than predicted 20 years ago. Far from being used only in residential settings, DER are now installed on distribution and transmission circuits. In this position, they do not have the same properties as traditional generators and are more flexible in many cases. The growing penetration and range of uses for DER motivate the need to reliably and safely integrate them into the grid. Operators must be able to rely on them not only for normal operation, but also during abnormal conditions like black starts or adverse cyber scenarios. To that end, we study the communications, device interfaces, and potential consequences of DER operation under abnormal and adversarial conditions. The weaknesses of communications networks are studied based on the industrial protocols used, and the benefits of security features are examined. The device interfaces are found to be vulnerable to attack based on the requirements in the IEEE-1547 standard for DER interconnection and interoperability, which is expected to be adopted in the next ten years. In addition to exploring the requirements of the standard, we show that these vulnerabilities and others do exist and can be used maliciously in a modern storage system DER. Consequences of these vulnerabilities range from exacerbated grid instability, to simultaneous loss of large portions of DER penetration, to physical damage to inverters or DER themselves and other sensitive equipment. We tie these outcomes to specific attacker actions in an effort to give operators a better threat intelligence view that allows them to prioritize mitigations. Finally, we discuss mitigations that could prevent many of the adversarial scenarios described. Some solutions can be added to existing infrastructure, while others may require longer term planning for grid modernization with consideration for security.

25 ENERGY STORAGE↗

DOE OSTI2025

Tandem Predictions for HPC Jobs: Preprint

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Using Dark Fiber and Distributed Acoustic Sensing to Characterize a Geothermal System in the Imperial Valley, Southern California

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ~28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity ($V_s$) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high $V_s$ and low $V_p$/$V_s$ ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Vp/Vs imaging↗

DOE OSTI2022

In situ feature analysis for large-scale multiphase flow simulations

The study of multiphase flow is essential for designing chemical reactors such as fluidized bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing reactor performance and stability. An FBR allows scientists to conduct different types of chemical reactions involving multiphase materials, especially interaction between gas and solids. During such complex chemical processes, the formation of void regions in the reactor, generally termed as bubbles, is an important phenomenon. The study of these bubbles has a deep implication in predicting the reactor’s overall efficiency. But physical experiments needed to understand bubble dynamics are costly and non-trivial due to the technical difficulties involved and harsh working conditions of the reactors. Therefore, to study such chemical processes and bubble dynamics, a state-of-the-art computational simulation MFIX-Exa is being developed. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena, the large-scale output data prohibits the use of traditional post hoc analysis capabilities in both storage and I/O time. Herein, to address these issues and allow the application scientists to explore the bubble dynamics in an efficient and timely manner, we have developed an end-to-end analytics pipeline that enables in situ detection of bubbles, followed by a flexible post hoc visual exploration methodology of bubble dynamics. The proposed method enables interactive analysis of bubbles, along with quantification of several bubble characteristics, enabling experts to understand the bubble interactions in detail. Positive feedback from the experts has indicated the efficacy of the proposed approach for exploring bubble dynamics in very-large-scale multiphase flow simulations.

97 MATHEMATICS AND COMPUTING↗