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DOE OSTI2022

Toward holistic understanding of the ITER-like resonant magnetic perturbation (RMP) ELM control on KSTAR

Here, KSTAR has clarified a set of unresolved 3D physics issues utilizing the ITER-like in-vessel, three-row, resonant magnetic perturbation (RMP) configurations. Since RMP-driven, edge-localized-modes (ELMs)-crash control elevates the divertor heat flux peak through its impact on edge plasma parameters and transport, a series of intentionally misaligned RMP configurations (IMCs) have been explored to investigate the relationship between RMP ELM control and divertor heat fluxes, while searching for an ideal IMC that could be favorable in both aspects. First of all, the contrasting influence of kink vs anti-kink phasing on the ELM-crash suppression has been articulated, demonstrating the synergistic benefit of 'kink' phasing on ELM-crash-suppression. On the other hand, the three-row IMC in the anti-kink phasing becomes more insensitive to the ELM-crashes at the sub-marginal level of RMP, consistent with theory. Meanwhile, the divertor 'wetted' area of ELM-crash-suppression gets narrower than that of ELM-crash-mitigation, suggesting that ELM-crash-mitigation remains advantageous over ELM-crash-suppression in terms of time-averaged divertor thermal loading. In comparison, based on a set of two-row IMCs, no evidence of divertor heat flux broadening was found during ELM-crash-suppression, supporting a hypothesis that the dispersal of the divertor heat flux in three-row IMCs cannot be driven by helically structured two-row RMPs alone. Among ITER-like three-rows, lower two-row RMPs have been found to be much more effective in suppressing the ELM-crashes than upper two-row RMPs. Although it is quite preliminary, the up/down asymmetric dependence of RMP coupling may be generically attributed to lower-single-null plasmas. Such a holistic understanding of RMP-driven, ELM-crash-control in KSTAR is expected not only to elucidate various subtle points in the vicinity of ELM-crash-suppression, but also to clarify the relevant divertor thermal loading issues for ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Integrating environmental understanding into freshwater floatovoltaic deployment using an effects hierarchy and decision trees

Abstract In an era of looming land scarcity and environmental degradation, the development of low carbon energy systems without adverse impacts on land and land-based resources is a global challenge. ‘Floatovoltaic’ energy systems—comprising floating photovoltaic (PV) panels over water—are an appealing source of low carbon energy as they spare land for other uses and attain greater electricity outputs compared to land-based systems. However, to date little is understood of the impacts of floatovoltaics on the hosting water body. Anticipating changes to water body processes, properties and services owing to floatovoltaic deployment represents a critical knowledge gap that may result in poor societal choices and water body governance. Here, we developed a theoretically-derived hierarchical effects framework for the assessment of floatovoltaic impacts on freshwater water bodies, emphasising ecological interactions. We describe how the presence of floatovoltaic systems may dramatically alter the air-water interface, with subsequent implications for surface meteorology, air-water fluxes and physical, chemical and biological properties of the recipient water body. We apply knowledge from this framework to delineate three response typologies—‘ magnitude’ , those for which the direction and magnitude of effect can be predicted; ‘ direction’ , those for which only the direction of effect can be predicted; and ‘ uncertain’ , those for which the response cannot be predicted—characterised by the relative importance of levels in the effects hierarchy. Illustrative decision trees are developed for an example water body response within each typology, specifically, evaporative water loss, cyanobacterial biomass, and phosphorus release from bed sediments, and implications for ecosystem services, including climate regulation, are discussed. Finally, the potential to use the new understanding of likely ecosystem perturbations to direct floatovoltaic design innovations and identify future research priorities is outlined, showcasing how inter-sectoral collaboration and environmental science can inform and optimise this low carbon, land-sparing renewable energy for ecosystem gains.

Environmental Sciences & Ecology↗

DOE OSTI2026

Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces

Coastal soils are dynamic systems where unique microbial niches are shaped by the intensity and duration of flooding between the terrestrial and aquatic boundaries of the terrestrial-aquatic interface (TAI). We aimed to understand the soil microbial community (16S rRNA gene) along the TAIs of a freshwater versus estuarine region and how it relates to organic matter (OM, via Fourier Transform Ion Cyclotron Resonance Mass Spectrometry). We studied the TAI gradients along a transect from upland (forested), transition (stressed forest), to wetland at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. Microbial communities differed significantly by region, transect position, and site. Contrary to expectations, given their dynamic hydrologies, transitions represented midpoints in microbial richness and diversity. We identified a core microbiome conserved across all transect positions within a region, highlighting potential microbial functions most resilient to environmental change. Indicator taxa unique to each transect position defined specific niches shaped by soil biogeochemistry. Co-expression networks of feature-level β-nearest-taxon indices revealed positive relationships in bacterial and OM feature contributions to community assembly. Our study provides critical insights into microbial communities at the forefront of hydrological changes in coastal areas that connect the land to lakes and oceans and remain vulnerable to changing weather patterns.

coastal ecosystems↗

DOE OSTI2017

Box tomography: localized imaging of remote targets buried in an unknown medium, a step forward for understanding key structures in the deep Earth

We discuss an application of tomographic methods to the imaging of localized geological structures buried inside the Earth, where neither seismic sources nor receivers are necessarily present. We call this ‘box tomography’. The essential difference between box-tomography and standard tomographic methods is that the numerical modelling (i.e. the ray tracing in traveltime tomography and the wave propagation in waveform tomography or full waveform inversion) is completely confined within the small box-region imaged. Thus, box tomography is a lot more efficient than global tomography (i.e. where we invert for the velocity in the larger volume that encompasses all the sources and receivers), for imaging localized objects. The principal difficulty in the box tomography problem comes from the medium outside the box-region imaged, that masks the target structure. In the data recorded at the surface, the information carried by the waves about the target structure is mixed with that about the surrounding medium. The main focus of this paper is to evaluate how well box tomography can extract useful information to produce accurate images of remote buried objects, especially in the difficult situation where the velocity distribution above the target structure is not known a priori. We implemented traveltime box tomography, where first arrival times are inverted using ray theory, and, waveform box tomography or full waveform box inversion, where seismograms are inverted using either scattering integrals or adjoint methods. Whatever the method employed, we found that, in the simple situation where the velocity structure is known around the target region, box-tomography is faster and more accurate than global tomography. Most importantly, through examples and simple analysis, we show that box-tomography can produce accurate images of localized structures even though the velocity distribution is not a priori known around the box-region imaged. In the last section of this paper, we evaluate the usage of box-tomography for imaging geological structures in the deep Earth. We performed 2-D synthetic experiments in a fluid planet and we argue that box tomography can be employed for refining our current global tomographic models within arbitrary regions inside the Earth. This has important implications for understanding the composition and dynamics of the Earth’s interior.

Masson, Yder↗

DOE OSTI2018

False alarms and the IMS infrasound network: Understanding the factors influencing the creation of false events

The International Monitoring System (IMS) infrasound network has been designed to acquire the necessary data to detect and locate explosions in the atmosphere with a yield equivalent to 1 kiloton of TNT anywhere on Earth. A major associated challenge is the task of automatically processing data from all IMS infrasound stations to identify possible nuclear tests for subsequent review by analysts. This paper is the first attempt to quantify the false alarm rate (FAR) of the IMS network, and in particular to assess how the FAR is affected by the numbers and distributions of detections at each infrasound station. To ensure that the results are sufficiently general, and not dependent entirely on one detection algorithm, the assessment is based on two detection algorithms that can be thought of as end members in their approach to the trade-off between missed detections and false alarms. The results show that the FAR for events formed at only two arrays is extremely high (ranging from 10’s to 100’s of false events per day across the IMS network, depending on the detector tuning). It is further shown that the FAR for events formed at three or more IMS arrays is driven by ocean-generated waves (microbaroms), despite efforts within both detection algorithms for avoiding these signals, indicating that further research into this issue is merited. Overall, the results highlight the challenge of processing data from a globally sparse network of stations to detect and form events. On the basis of these results, it is shown that further work on understanding signal and noise characteristics is needed before attempting to automatically form events detected at only two arrays on the global network. In particular, more work is required to reduce false alarms caused by the detection of microbarom signals.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Understanding intercalation compounds for sodium-ion batteries and beyond

We report that intercalation compounds are popular candidate electrode materials for sodium-ion batteries and other ‘beyond lithium-ion’ technologies including potassium- and magnesium-ion batteries. We summarize first-principles efforts to elucidate the behaviour of such compounds in the layered and spinel structures. Trends based on the size and valence of the intercalant and the ionicity of the host are sufficient to explain phase stability and ordering phenomena, which in turn determine the equilibrium voltage profile. For the layered structures, we provide an overarching view of intercalant orderings in prismatic coordination based on antiphase boundaries, which has important consequences for diffusion. We examine details of stacking sequence transitions between different layered structures by calculating stacking fault energies and discussing the nature of dislocations. A better understanding of these transitions will likely aid the development of batteries with improved cyclability. This article is part of a discussion meeting issue ‘Energy materials for a low carbon future’.

25 ENERGY STORAGE↗

DOE OSTI2021

First-principles calculations of metal surfaces. II. Properties of low-index platinum surfaces toward understanding electron emission

The stability of low-index platinum surfaces and their electronic properties is investigated with density functional theory, toward the goal of understanding the surface structure and electron emission, and identifying precursors to electrical breakdown, on nonideal platinum surfaces. Propensity for electron emission can be related to a local work function, which, in turn, is intimately dependent on the local surface structure. The (1×N) missing row reconstruction of the Pt(110) surface is systematically examined. The (1×3) missing row reconstruction is found to be the lowest in energy, with the (1×2) and (1×4) slightly less stable. In the limit of large (1×N) with wider (111) nanoterraces, the energy accurately approaches the asymptotic limit of the infinite Pt(111) surface. Furthermore, this suggests a local energetic stability of narrow (111) nanoterraces on free Pt surfaces that could be a common structural feature in the complex surface morphologies, leading to work functions consistent with those on thermally grown Pt substrates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Understanding magnetotransport signatures in networks of connected permalloy nanowires

The change in electrical resistance associated with the application of an external magnetic field is known as the magnetoresistance (MR). The measured MR is quite complex in the class of connected networks of single-domain ferromagnetic nanowires, known as “artificial spin ice,” due to the geometrically induced collective behavior of the nanowire moments. Here, we have conducted a thorough experimental study of the MR of a connected honeycomb artificial spin ice, and we present a simulation methodology for understanding the detailed behavior of this complex correlated magnetic system. Finally, our results demonstrate that the behavior, even at low magnetic fields, can be well described only by including significant contributions from the vertices at which the legs meet, opening the door to new geometrically induced MR phenomena.

36 MATERIALS SCIENCE↗

DOE OSTI2019

$β^-$ decay of $T_z$ = +$\frac{11}{2}$ isotopes $\mathrm{^{37}Al}$ and $\mathrm{^{39}Si}$: Understanding Gamow-Teller strength distribution in neutron-rich nuclei

$β^-$ decay of $T_z$ = +$\frac{11}{2}$ nuclei $\mathrm{^{37}Al}$ and $\mathrm{^{39}Si}$ was studied to obtain information about excited states in the daughter and granddaughter nuclei. New information on excited states has been obtained for $\mathrm{^{37,39}P}$ whereas level schemes for $\mathrm{^{37}Si}$ and $\mathrm{^{37,39}S}$ were confirmed and expanded on as compared to the most recent investigations. For the nuclei studied, the valence proton and neutron occupy different major shells with opposite parities, hence, the $β^-$ decay preferentially and primarily populates core-excited $1p1h$ intruder states with a parity opposite to the ground state of the daughter nucleus. These low-lying intruder states shed light on the $N = 20$ shell gap and its evolution with neutron number. The $β^-$ decay of very neutron-rich nuclei also illustrates how the Gamow-Teller (GT) strength distribution changes with neutron excess. Comparing $β^-$ decay of $\mathrm{^{37,39}Si}$ presented here with $\mathrm{^{38,40}Si}$ from a previous study, we clearly see an even-odd effect in the GT strength distribution with the decay from even-even nuclei showing strong low-lying strength whereas for the odd $A$ being very fragmented. Finally, configuration interaction shell-model calculations with the state-of-art SDPFSDG-MU effective interaction were performed to understand the structure of the $1p1h$ states and explore the GT strength distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Understanding trends in lithium binding at two-dimensional materials

Layered structure and peculiar electronic properties of two-dimensional (2D) materials foster the concept of utilizing them as main components of lithium-ion batteries. Understanding basic physical mechanisms governing the interaction of Li with 2D crystals is of key importance to succeeding in a rational design of cathode and anode materials with superior functionalities. In this study density functional theory was applied to reveal the microscopic picture of Li interaction with 15 2D crystals, including several transition metal oxides and dichalcogenides, carbides of Group XIV elements, functionalized graphene, silicene, and germanene, as well as black phosphorus and Ti2C MXene. We found that the general trend in Li binding can be estimated from positions of conduction band minima of 2D materials, since the energy of the lowest empty electronic states shows a nice correlation with the strength of Li adsorption. At variance to the majority of studied surfaces where the electron transferred from Li is spread across the substrate, in monolayers of carbides of Group XIV elements the interaction with Li and the charge transfer are well localized. This gives rise to their capability to accommodate Li structures with a nearly constant binding energy of alkaline atoms over Li coverages ranging from well-separated adatoms to a full monolayer. © 2018 American Physical Society.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Understanding Performance Portability of Bioinformatics Applications in SYCL on an NVIDIA GPU

Our goal is to have a better understanding of performance portability of SYCL kernels on a GPU. Toward this goal, we migrate representative kernels in bioinformatics applications from CUDA to SYCL, evaluate their performance on an NVIDIA GPU, and explain the performance gaps through performance profiling and analyses. We hope that the findings provide valuable feedback to the development of the SYCL ecosystem.

Jin, Zheming↗

DOE OSTI2021

Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis

This study aims to effectively analyze and visualize the concept to concept network derived from the COVID-19 Open Research Dataset (CORD-19) dataset, where we have more than 48,000 concepts with more than 300,000 relationships between concepts. In analyzing networks, we focus on finding relationship patterns between the coronavirus disease 2019 (COVID-19) concepts and other concepts. Given the node and edge datasets, we construct directional graphs and calculate all pair shortest paths based on multiple edge weight schemes. However, statistical metrics are not sufficient to identify specific relationships represented in the network. Therefore, we also propose a visual analytics approach to effectively understand the knowledge graph. Our highly interactive visual analytics allows users to effectively analyze the evolving graphs and (COVID-19) concept nodes and other nodes related to the COVID-19 nodes. We envision that this study will pave the path to develop strategies to provide more accurate and scalable predictive analysis on knowledge graphs related to CORD19 and other biomedical knowledge graphs.

Lim, Seung-Hwan↗

DOE OSTI2017

Big Data Meets HPC Log Analytics: Scalable Approach to Understanding Systems at Extreme Scale

Today's high-performance computing (HPC) systems are heavily instrumented, generating logs containing information about abnormal events, such as critical conditions, faults, errors and failures, system resource utilization, and about the resource usage of user applications. These logs, once fully analyzed and correlated, can produce detailed information about the system health, root causes of failures, and analyze an application's interactions with the system, providing valuable insights to domain scientists and system administrators. However, processing HPC logs requires a deep understanding of hardware and software components at multiple layers of the system stack. Moreover, most log data is unstructured and voluminous, making it more difficult for system users and administrators to manually inspect the data. With rapid increases in the scale and complexity of HPC systems, log data processing is becoming a big data challenge. This paper introduces a HPC log data analytics framework that is based on a distributed NoSQL database technology, which provides scalability and high availability, and the Apache Spark framework for rapid in-memory processing of the log data. The analytics framework enables the extraction of a range of information about the system so that system administrators and end users alike can obtain necessary insights for their specific needs. We describe our experience with using this framework to glean insights from the log data about system behavior from the Titan supercomputer at the Oak Ridge National Laboratory.

Park, Byung↗

DOE OSTI2022

Hybrid Analysis of Fusion Data for Online Understanding of Complex Science on Extreme Scale Computers

The current practice for fusion scientists running first principle simulations on high performance computing plat-forms is to either run their simulations and output their data for post-hoc analysis, or to place in situ analytics into their code. In this paper we examine a complex workflow using XGC fusions simulation run on the Oak Ridge Leadership Computing Facility's supercomputer Summit, which also involve three anal-yses as part of the results necessary for scientific discovery. We discuss the challenges faced when implementing these algorithms and present an original hybrid staging technique to help enable the physicists to make discoveries during the execution of the simulation. By creating this infrastructure, we can examine complicated physics results, which may not have been possible without the infrastructure. For example, our work enables the online visualization of turbulent homoclinic tangle around the magnetic X-point, breaking the last confinement surface. This visualization could help fusion scientists to better understand and improve the turbulence spread of plasma exhaust heat, which is crucial toward realizing plasmas beyond the currently accessible physics regimes of present-day tokamak reactors. The physics of turbulent homoclinic tangle will be reported in a future physics publication, by utilizing the original online analysis/visualization framework presented in this paper.

Suchyta, Eric↗

DOE OSTI2018

Understanding and Analyzing Interconnect Errors and Network Congestion on a Large Scale HPC System

Today's High Performance Computing (HPC) systems are capable of delivering performance in the order of petaflops due to the fast computing devices, network interconnect, and back-end storage systems. In particular, interconnect resilience and congestion resolution methods have a major impact on the overall interconnect and application performance. This is especially true for scientific applications running multiple processes on different compute nodes as they rely on fast network messages to communicate and synchronize frequently. Unfortunately, the HPC community lacks state-of-practice experience reports that detail how different interconnect errors and congestion events occur on large-scale HPC systems. Therefore, in this paper, we process and analyze interconnect data of the Titan supercomputer to develop a thorough understanding of interconnects faults, errors and congestion events. We also study the interaction between interconnect, errors, network congestion and application characteristics.

Kumar, Mohit↗

DOE OSTI2021

Sheaves as a Framework for Understanding and Interpreting Model Fit

As data grows in size and complexity, finding frameworks which aid in interpretation and analysis has become critical. This is particularly true when data comes from complex systems where extensive structure is available, but must be drawn from peripheral sources. In this paper we argue that in such situations, sheaves can provide a natural framework to analyze how well a statistical model fits at the local level (that is, on subsets of related datapoints) vs the global level (on all the data). The sheaf-based approach that we propose is suitably general enough to be useful in a range of applications, from analyzing sensor networks to understanding the feature space of a deep learning model.

Kvinge, Henry J.↗

DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗

DOE OSTI2023

Towards Geospatial Knowledge Graph Infused Neuro-Symbolic AI for Remote Sensing Scene Understanding

Deep learning has proven its effectiveness in numerous tasks for remote sensing scene understanding. However there is an increasing interest to explore fusion of domain-specific background information to the deep neural network to further improve its performance. Remote sensing researchers are also working towards developing models that generalize and adapt to multiple applications. Generalization challenges coupled with the scarcity of large corpora of high-quality noise-free labelled data, have together fueled an interest for leveraging background information. Knowledge graphs serve as excellent choice to represent domain-specific information in a structured, standardized and extensible manner. Integrating symbolic knowledge representations in the form of Knowledge Graph Embedding (KGE) to perform neuro-symbolic reasoning is an emerging research direction promising significant impacts. This vision paper seeks to position ideas and provoke early thoughts toward advancing neuro-symbolic artificial intelligence in the context of geospatial challenges. Specifically, it conceptualizes and elaborates on an architecture for infusing geospatial knowledge from knowledge graph in a deep neural network pipeline. As guiding case studies - land-use land-cover classification, object detection and instance segmentation can benefit from infusing spatio-contextual information with remote sensing imagery. The discussion further reflects on and articulates the challenges and explainable AI opportunities anticipated when scaling and maintaining large-scale geospatial knowledge graphs.

Potnis, Abhishek↗