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DOE OSTI2019

Quantifying hydrodynamic collective states of magnetic colloidal spinners and rollers

Ferromagnetic microparticles energized by an alternating magnetic field exhibit fascinating collective behavior ranging from the emergent self-assembled spinners to a variety of self-organized rolling states. Despite their simplicity, quantifying their essentially multibody collective behavior remains elusive due to a multitude of relevant interactions, from short-range collisions to long-range magnetic and hydrodynamic forces. Here we develop a high-performance computational algorithm based on smoothed particle hydrodynamics to quantify the role of individual interactions in the emergent collective state. The computational model provides insight into the role of hydrodynamic interaction on the onset of collective behavior and allows characterization of dynamic regimes that are hard to access experimentally. Comparison with high-resolution experimental data allows validation of the algorithm. Our work expands the scope of modern computational tools for predictive modeling of microscopic active systems and provides insight into the intricate role of hydrodynamic interactions on the onset of collective behavior in living and synthetic active matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Exploring the $A$-V-Sb landscape beyond $A$$\mathrm{V_3}$ $\mathrm{Sb_5}$: A case study on the $\mathrm{KV_6}$ $\mathrm{Sb_6}$ Kagome compound

Kagome compounds have garnered attention in the past few years for their intriguing magnetic properties arising from spin frustration dictated by the geometry of the Kagome sublattice. In this paper, we highlight the success of the unconventional hydride route for the fast and easy synthesis of the Kagome compound $\mathrm{KV_6}$ $\mathrm{Sb_6}$. Here, high-temperature in situ powder x-ray diffraction (PXRD) studies proved to be useful in hinting at the existence of $\mathrm{KV_6}$ $\mathrm{Sb_6}$, identifying its synthesis conditions, and understanding the reaction mechanism. The crystal structure for $\mathrm{KV_6}$ $\mathrm{Sb_6}$ was determined from high-resolution PXRD data. The compound has a layered structure [$R\overline{3}m, a = 5.5318$ (9) Å, $c = 34.23$ (3)Å 3 $Z = 3$] at room temperature] and features a Kagome bilayer of V atoms. $\mathrm{KV_6}$ $\mathrm{Sb_6}$ is isostructural to the previously reported $\mathrm{RbV_6}$ $\mathrm{Sb_6}$ and $\mathrm{CsV_6}$ $\mathrm{Sb_6}$ compounds. $\mathrm{KV_6}$ $\mathrm{Sb_6}$ is thermally stable in vacuum up to 1173 K, as evident from the high-temperature in situ PXRD and differential scanning calorimetric analysis. Investigation of magnetic properties for $\mathrm{KV_6}$ $\mathrm{Sb_6}$ between 2 and 300 K reveals temperature-independent paramagnetism and an absence of superconductivity, like the Rb and Cs analogs. Furthermore, we compare the magnetic properties of K V 3 Sb 5 , another ternary Kagome compound, synthesized via two different methods: the hydride route and the traditional route from elements. Low-temperature transport property measurements of $\mathrm{KV_6}$ $\mathrm{Sb_6}$ indicate metallic behavior and an intrinsically low thermal conductivity of $\mathrm{1.0 W K^-1}$ $\mathrm{m^-1}$ at 300 K. The layered structure of $\mathrm{KV_6}$ $\mathrm{Sb_6}$ makes it an attractive candidate for deintercalation and doping studies to tune both magnetic and transport properties, laying a foundation for further studies.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Harnessing the power of an X-ray laser for serial crystallography of membrane proteins crystallized in lipidic cubic phase

Serial femtosecond crystallography (SFX) with X-ray free-electron lasers (XFELs) has proven highly successful for structure determination of challenging membrane proteins crystallized in lipidic cubic phase; however, like most techniques, it has limitations. Here we attempt to address some of these limitations related to the use of a vacuum chamber and the need for attenuation of the XFEL beam, in order to further improve the efficiency of this method. Using an optimized SFX experimental setup in a helium atmosphere, the room-temperature structure of the adenosine A 2A receptor (A 2A AR) at 2.0 Å resolution is determined and compared with previous A 2A AR structures determined in vacuum and/or at cryogenic temperatures. Specifically, the capability of utilizing high XFEL beam transmissions is demonstrated, in conjunction with a high dynamic range detector, to collect high-resolution SFX data while reducing crystalline material consumption and shortening the collection time required for a complete dataset. The experimental setup presented herein can be applied to future SFX applications for protein nanocrystal samples to aid in structure-based discovery efforts of therapeutic targets that are difficult to crystallize.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

167-PFlops Deep Learning for Electron Microscopy: From Learning Physics to Atomic Manipulation

An artificial intelligence system called MENNDL, which used 25,200 Nvidia Volta GPUs on Oak Ridge National Laboratory’s Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity toward nanofabricating materials automatically. Finally, MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Combined Transmission and Distribution Test System to Study High Penetration of Distributed Solar Generation

Transmission and distribution (T&D) standard test systems are currently defined separately, and are critical for researchers and engineers to test new solutions. Growing installation of energy resources in distribution and sub-transmission systems is creating higher interactions between T&D systems. This paper proposes a standard test system to study T&D interactions for high penetration of solar photovoltaic (PV) generation. The proposed test system can be used to study and propose solutions to problems such as reverse power flows and voltage control affected by PV variability. The proposed test system combines the transmission IEEE 118 bus system, with the distribution IEEE 123-node feeder. The original test systems are significantly enhanced by adding high penetration of solar PV generation with corresponding full-year minute-resolution chronological data. Conventional generation chronology is achieved by adding dispatch and unit commitment data for AC optimal power flow solutions. And chronological load data is added in both aggregated and disaggregated fashions. Sample applications of the data are provided highlighting voltage control challenges, and the proposed system is made available openly online.

Samaan, Nader A.↗

DOE OSTI2018

State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies

Not provided.

Environmental Sciences & Ecology↗

DOE OSTI2019

Modeling spatial dependence and economic hotspots in landowners’ willingness to supply bioenergy crops in the northeastern United States

This paper investigates the spatial heterogeneity of landowners’ willingness to supply three bioenergy crops: switchgrass, Miscanthus , and willow, in the northeastern United States. Spatial heterogeneity might arise for several reasons. For example, landowners closer to bioenergy processing plants might be more likely to be willing to supply bioenergy crops, and landowners who are more willing to supply bioenergy crops may be spatially clustered because they share similar land attributes, demographics, experiences, and/or values. Using high–resolution GIS data related to the location of pellet plants utilizing bioenergy crops and survey data related to landowners’ characteristics including spatial location, we estimate a spatial probit model to explain the variation in individual–specific reservation prices (RPs)—the feedstock price at which landowners become willing to supply a bioenergy crop. We find that respondents’ RP is lower the closer they live to their nearest pellet plant and spatial dependency is only present for switchgrass supply. We also identify three economic hotspots (areas with high potential supply and low RPs) for each bioenergy crop. We believe that bioenergy supply chains could be developed around these hotspots.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Dynamic molecular architecture of the synaptonemal complex

During meiosis, pairing between homologous chromosomes is stabilized by the assembly of the synaptonemal complex (SC). The SC ensures the formation of crossovers between homologous chromosomes and regulates their distribution. However, how the SC regulates crossover formation remains elusive. We isolated an unusual mutation in Caenorhabditis elegans that disrupts crossover interference but not SC assembly. This mutation alters the unique C terminal domain of an essential SC protein, SYP-4, a likely ortholog of the vertebrate SC protein SIX6OS1. We use three-dimensional stochastic optical reconstruction microscopy (3D-STORM) to interrogate the molecular architecture of the SC from wild-type and mutant C. elegans animals. Using a probabilistic mapping approach to analyze super-resolution image data, we detect changes in the organization of the synaptonemal complex in wild-type animals that coincide with crossover designation. We also found that our syp-4 mutant perturbs SC architecture. Our findings add to growing evidence that the SC is an active material whose molecular organization contributes to chromosome-wide crossover regulation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Fast and Accurate Intersections on a Sphere

We introduce a fast, high-precision algorithm for calculating intersections between great circle arcs and lines of constant latitude on the unit sphere. We first propose a simplified intersection point formula with improved speed and numerical robustness over the ones traditionally implemented in geoscience software. We then show how algorithms based on the concept of error-free transformations (EFT) can be applied to evaluate this formula within a relative error bound that is on the order of machine precision. Here, we demonstrate that, with a vectorized and parallelized implementation, this enhanced accuracy is achieved with no compute time overhead compared to a direct calculation in hardware floating point, making our algorithm suitable for performance-sensitive applications like regridding of high-resolution climate data. In contrast, evaluating our formula using high-precision data types like quadruple precision and arbitrary precision, or using the robust intersection computation routines from the Computational Geometry Algorithms Library, leads to significant computational overhead, especially since these alternatives inhibit vectorization. More generally, our work demonstrates how EFT techniques can be combined and extended to implement nontrivial geometric calculations with high accuracy and speed.

Environmental sciences↗

DOE OSTI2021

Lidar - NOAA High Resolution Doppler Lidar (HRDL) - Processed Data

[ Not provided ]

17 WIND ENERGY↗

DOE OSTI2023

Satellite-Based Ranking of the World’s Hottest and Coldest Cities Reveals Inequitable Distribution of Temperature Extremes

The identification of the world’s hottest and coldest cities fascinates both the public and the scientific community. However, the ranking of city temperatures, especially from the perspective of human discomfort, has been difficult. Here we estimated the monthly mean maximum and minimum 1-km resolution urban temperatures of 13,135 cities worldwide (2003–19) from the thermal discomfort perspective by combining in situ measurements, satellite-based land surface temperatures, fine-resolution intracity data, and reanalysis data. Manama, Bahrain, was identified as the hottest city (48.18° ± 1.31°C) and Yakutsk, Russia (–42.96° ± 0.72°C), as the coldest city. The global city temperatures followed a power-law pattern, characterized by cities with <0.3 million inhabitants covering 80% of the top 20% global cities with extreme temperatures. Our study reveals an inequitable pattern of global city temperature extremes and highlights the urgency of developing appropriate strategies to reduce climate change risks in small- and medium-sized cities with low development levels.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.

Downscaling↗

DOE OSTI2026

Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS

Environmental sciences↗

DOE OSTI2016

Spatial Variability of the Background Diurnal Cycle of Deep Convection around the GoAmazon2014/5 Field Campaign Sites

The Amazon rainforest is one of a few regions of the world where continental tropical deep convection occurs. The Amazon’s isolation makes it challenging to observe, but also creates a unique natural laboratory to study anthropogenic impacts on clouds and precipitation in an otherwise pristine environment. Extensive measurements were made upwind and downwind of the large city of Manaus, Brazil during the Observations and Modeling of the Green Ocean Amazon 2014-2015 (GoAmazon2014/5) field campaign. In this study, 15 years of high-resolution satellite data are analyzed to examine the spatial and diurnal variability of convection occurring around the GoAmazon2014/5 sites. Interpretation of anthropogenic differences between the upwind (T0) and downwind (T1-T3) sites is complicated by naturally-occurring spatial variability between the sites. During the rainy season, the inland propagation of the previous day’s sea-breeze front happens to be in phase with the background diurnal cycle near Manaus, but is out of phase elsewhere. Enhanced convergence between the river-breezes and the easterly trade winds generates up to 10% more frequent deep convection at the GoAmazon2014/5 sites east of the river (T0a, T0t/k, and T1) compared to the T3 site which was located near the western bank. In general, the annual and diurnal cycles during 2014 were representative of the 2000-2013 distributions. The only exceptions were in March when the monthly mean rainrate was above the 95th percentile and September when both rain frequency and intensity were suppressed. The natural spatial variability must be accounted for before interpreting anthropogenically-induced differences among the GoAmazon2014/5 sites.

Amazon↗

DOE OSTI2018

Utah FORGE: Area Terrain Slope Data

This archive contains a terrain slope image, in units of degrees, of the Utah FORGE area near Roosevelt Hot springs. The data was derived from 0.5 m resolution LiDAR DEM data and is in a GeoTiff format. It was processed using ArcGIS.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Locating Operational Events of the Cooling Tower of a Nuclear Reactor with a Very Local Seismic Network

Geolocation of emergent seismic signals is challenging at close distances. Here, we used three-component data from a seismic network and a targeted experiment at a research nuclear reactor to locate seismic sources. Utilizing known events collected during the targeted experiment, we were able to infer source locations with seismic amplitudes and polarization characteristics of the data. Although the resolution of the source location is not perfect, the seismic amplitudes and polarization analysis offer useful constraints. For the known events, the source region inferred with our analysis includes the true source locations. Synthetic tests indicate the resolution is largely due to limited data coverage and measurement uncertainties because the synthetic tests show similar results compared with the field data. We identified the source of the unknown event through spectrum cross correlation between the signals from the known events and an unknown event. Our findings were confirmed by operational staff at the facility. When the propagation medium properties (i.e., seismic velocity and quality factor for attenuation) are known, our analysis can be applied to continuous data from a seismic array to infer both source amplitude and location. If the medium properties are not known, a targeted experiment can be conducted to estimate them.

58 GEOSCIENCES↗

DOE OSTI2021

Changes in When and Where People are Spending Time in Response to COVID-19

The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗