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DOE OSTI2023

TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones

Tropical cyclones (TCs) are among the most destructive weather systems. Realistically and efficiently detecting and tracking TCs are critical for assessing their impacts and risks. In particular, the eye is a signature feature of a mature TC. Therefore, knowing the eyes’ locations and movements is crucial for both operational weather forecasts and climate risk assessments. Recently, a multilevel robustness framework has been introduced to study the critical points of time-varying vector fields. The framework quantifies the robustness (i.e., structural stability) of critical points across varying neighborhoods. By relating the multilevel robustness with critical point tracking, the framework has demonstrated its potential in cyclone tracking. An advantage is that it identifies cyclonic features using only 2D wind vector fields, which is encouraging as most tracking algorithms require multiple dynamic and thermodynamic variables at different altitudes. A disadvantage is that the framework does not scale well computationally for datasets containing a large number of cyclones. Herein this paper introduces a topologically robust physics-informed tracking framework (TROPHY) for TC tracking. The main idea is to integrate physical knowledge of TC to drastically improve the computational efficiency of multilevel robustness framework for large-scale climate datasets. First, during preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability, thus preserving good candidates for TC tracking. Second, during in-processing, we impose constraints during the multilevel robustness computation to focus only on physics-informed neighborhoods of TCs. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks. In comparison with the observed tracks, we demonstrate that TROPHY can capture TC characteristics (e.g., frequency, intensity, duration, latitudes with maximum intensity, and genesis) that are comparable to and sometimes even better than a well-validated TC tracking algorithm that requires multiple dynamic and thermodynamic scalar fields.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Facility-Level Industry Representation for Decarbonization Modeling [Slides]

The largest facilities of energy-intensive materials processing industries are disproportionate contributors to U.S. greenhouse gas (GHG) emissions. In general, industrial energy system modeling for the United States currently represents industrial demands at a much lower resolution than other end-use sector modeling. Therefore, characterizing even a subset of energy-intensive materials processing facilities will capture a significant portion of industrial GHG emissions. In order to further the development of publicly-available data to support modeling of industrial decarbonization, we summarize a set of approaches and results for characterizing the location, energy intensity and mix, process emissions intensity, and general production technology of existing clinker, ammonia, and iron and steel facilities in the United States. We also characterize facilities that represent options for reducing GHG emissions from each industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

2022 AI Testbed Expeditions Report

By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.

36 MATERIALS SCIENCE↗

DOE OSTI2019

The structure of nano-twinned rhombohedral YCuO 2.66 solved by electron crystallography

In the search for frustrated spin interactions, a YCuO 2.66 phase has been synthesized by a treatment under oxygen pressure of YCuO 2.5 . X-ray powder diffraction and electron diffraction studies have been conducted. Electron diffraction shows that the sample is twinned on a 10 nm scale. Precession electron diffraction data obtained from a twinned crystal was treated in order to obtain intensities corresponding to only one of the orientations of the twins. From this data a structure solution was obtained where, as in YCuO 2.5 , the Cu atoms form triangular planes. The Cu atoms are linked in two dimensions by oxygen atoms in the present structure whereas in YCuO 2.5 they are only linked in one-dimensional chains.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Light Water Reactor Sustainability Program: BWR High-Fluence Material Project: Assessment of the Role of High-Fluence on the Efficiency of HWC Mitigation on SCC Crack Growth Rates

As nuclear power plants age, the increasing neutron fluence experienced by stainless steels components affects the materials resistance to stress corrosion cracking and fracture toughness. The purpose of this report is to identify any new issues that are expected to rise as boiling water reactor power plants reach the end of their initial life and to propose a path forward to study such issues. It has been identified that the efficiency of hydrogen water chemistry mitigation technology may decrease as fluence increases for high-stress intensity factors. This report summarizes the data available to support this hypothesis and describes a program plan to determine the efficiency of hydrogen water chemistry as a function of the stress intensity factor applied and fluence. This program plan includes acquisition of irradiated materials, generation of material via irradiation in a test reactor, and description of the test plan. This plan offers three approaches, each with an estimated timetable and budget.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Towards the spatial resolution of metalloprotein charge states by detailed modeling of XFEL crystallographic diffraction

Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2–3 × 10 −4 (Δ E / E ). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (Δ E / E = 3 × 10 −3 ) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Image processing of multiphase images obtained via X-ray microtomography: A review

Easier access to X-ray microtomography (μCT) facilities has provided much new insight from high-resolution imaging for various problems in porous media research. Pore space analysis with respect to functional properties usually requires segmentation of the intensity data into different classes. Image segmentation is a nontrivial problem that may have a profound impact on all subsequent image analyses. This review deals with two issues that are neglected in most of the recent studies on image segmentation: (i) focus on multiclass segmentation and (ii) detailed descriptions as to why a specific method may fail together with strategies for preventing the failure by applying suitable image enhancement prior to segmentation. In this way, the presented algorithms become very robust and are less prone to operator bias. Three different test images are examined: a synthetic image with ground-truth information, a synchrotron image of precision beads with three different fluids residing in the pore space, and a μCT image of a soil sample containing macropores, rocks, organic matter, and the soil matrix. Image blur is identified as the major cause for poor segmentation results. Other impairments of the raw data like noise, ring artifacts, and intensity variation can be removed with current image enhancement methods. Bayesian Markov random field segmentation, watershed segmentation, and converging active contours are well suited for multiclass segmentation, yet with different success to correct for partial volume effects and conserve small image features simultaneously.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Machine learning bandgaps of double perovskites

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Heating effects on pyrogenic organic matter mass loss and temperature in a sand matrix in a lab setting from 2022

This dataset contains data associated with the preprint “A laboratory method for simulating the effects of subsequent fires on pyrogenic organic matter at different exposure depths in a sand matrix” (https://doi.org/10.1101/2024.07.30.605814), which has been accepted for publication in the International Journal of Wildland Fire. The dataset was generated to investigate the effects of subsequent fires on preexisting pyrogenic organic matter (PyOM) at different soil depths. Specifically, we designed a laboratory method to simulate soil heating from above, applying realistic heat flux profiles (High, Low, Control) derived from jack pine (Pinus banksiana Lamb.) log burns. Using a cone calorimeter, these profiles were applied to buried jack pine PyOM (produced at 350°C) in a sand matrix pyrocosm to examine PyOM mass loss at different depths (surface, 1 cm, 5 cm). Each treatment (e.g., High heat flux at 1 cm depth) was applied once to five replicates in a pyrocosm.The dataset includes mass loss fraction of PyOM and time-series temperature profiles (measured with type K thermocouples). Additionally, calibration datasets for high and low heat flux profiles are provided, including programmed heat flux profile data and three sets of calibration results. All datasets are in .csv format and were analyzed in R using code found at https://github.com/MengmengLuo/Reburning-pyrogenic-organic-matter-A-laboratory-method-for-dosing-dynamic-heat-fluxes-from-above. This dataset supports research on PyOM transformations during subsequent fires, and methodological advancements in simulating soil heating. It can be used to explore fire-induced PyOM alterations across different fire intensities and soil depths.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Photofission cross-section ratio measurement of 235 U/ 238 U using monoenergetic photons in the energy range of 9.0–16.6 MeV

Photofission cross-section ratios of 235U and 238U have been measured using monoenergetic photon beams from the High Intensity Gamma-ray Source facility at the Triangle Universities Nuclear Laboratory. These measurements have been performed in small energy steps between 9.0 and 16.6 MeV using a dual-fission ionization chamber. The measured cross-section ratios are compared with the previous experimental data as well as with the recent evaluated nuclear data library ENDF.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

CROCUS Air Quality Dataset from the University of Illinois Chicago (UIC), July 2024

This dataset was collected by the measurement system in the Atmosphere, Climate, and Ecosystems (ACE) Lab at the University of Illinois Chicago (UIC) from July 12 to July 31, 2024, as part of the Community Research on Climate and Urban Science (CROCUS) Urban Integrated Field Laboratory (UIFL) project, led by Argonne National Laboratory.To enhance understanding of urban air quality dynamics in Chicago, and as part of the CROCUS 2024 Urban Canyon Intensive Observation Period (IOP), several instruments were set up to provide continuous measurements of air quality parameters in Chicago during July 2024. These measurements cover both aerosols and gas-phase species. It focuses on particle size distribution (2.5–478 nm) measured by two Scanning Mobility Particle Sizers (SMPS) at a 4-min resolution, total particle number concentrations at a 1-s resolution, and chemical composition from a High-Resolution Time-of-Flight Aerosol Mass Spectrometer (AMS) at a 1-min resolution. Key gas-phase species, including NO, NO₂, SO₂, and O₃, are measured at a 1-min resolution, along with high-resolution NO and dimethyl sulfide (DMS) data from a Chemical Ionization Mass Spectrometer (CIMS). Volatile organic compound (VOC) data for toluene, isoprene, and benzene are provided by a GC-PID with a time resolution of 25 minutes.The data are formatted as NetCDF (.nc) files, making them easily accessible using common software such as MATLAB, R, and Python. Each parameter is stored in an individual dataset, which includes detailed instrument information in the header, as well as the corresponding sample start time and concentration/distribution data for each sample.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. Here, the key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix. In the presence of experimental-like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramér–Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2011

Development of a Fibrinogen-Specific Sandwich Enzyme-Linked Immunosorbent Assay Microarray Assay for Distinguishing Between Blood Plasma and Serum Samples

We have developed a fibrinogen-specific sandwich ELISA microarray assay for use in qualitatively distinguishing between blood plasma and serum samples. Three capture antibodies, 49D2, HPA001900, and F8512, were evaluated in conjunction with 1D6 as detection antibody, and the data show that 49D2 and, to a lesser extent, F8512 successfully identify previously unknown plasma and serum samples based upon a ~28-fold difference in signal intensity between the sample types. This assay has utility in rapidly identifying previously archived clinical samples with incomplete annotation in a high throughput manner prior to proteomics analyses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Monte Carlo validation experiments for the gas Cherenkov detectors at the National Ignition Facility and Omega

The gas Cherenkov detectors at NIF and Omega measure several ICF burn characteristics by detecting multi-MeV nuclear γ emissions from the implosion. Of primary interest are γ bang-time (GBT) and burn width defined as the time between initial laser-plasma interaction and peak in the fusion reaction history and the FWHM of the reaction history respectively. To accurately calculate such parameters the collaboration relies on Monte Carlo codes, such as GEANT4 and ACCEPT, for diagnostic properties that cannot be measured directly. This paper describes a series of experiments performed at the High Intensity γ Source (HIγS) facility at Duke University to validate the geometries and material data used in the Monte Carlo simulations. Results published here show that model-driven parameters such as intensity and temporal response can be used with less than 50% uncertainty for all diagnostics and facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Subdiffraction-resolution live-cell imaging for visualizing thylakoid membranes

© 2018 The Authors The Plant Journal © 2018 John Wiley & Sons Ltd The chloroplast is the chlorophyll-containing organelle that produces energy through photosynthesis. Within the chloroplast is an intricate network of thylakoid membranes containing photosynthetic membrane proteins that mediate electron transport and generate chemical energy. Historically, electron microscopy (EM) has been a powerful tool for visualizing the macromolecular structure and organization of thylakoid membranes. However, an understanding of thylakoid membrane dynamics remains elusive because EM requires fixation and sectioning. To improve our knowledge of thylakoid membrane dynamics we need to consider at least two issues: (i) the live-cell imaging conditions needed to visualize active processes in vivo; and (ii) the spatial resolution required to differentiate the characteristics of thylakoid membranes. Here, we utilize three-dimensional structured illumination microscopy (3D-SIM) to explore the optimal imaging conditions for investigating the dynamics of thylakoid membranes in living plant and algal cells. We show that 3D-SIM is capable of examining broad characteristics of thylakoid structures in chloroplasts of the vascular plant Arabidopsis thaliana and distinguishing the structural differences between wild-type and mutant strains. Using 3D-SIM, we also visualize thylakoid organization in whole cells of the green alga Chlamydomonas reinhardtii. These data reveal that high light intensity changes thylakoid membrane structure in C. reinhardtii. Moreover, we observed the green alga Chromochloris zofingiensis and the moss Physcomitrella patens to show the applicability of 3D-SIM. This study demonstrates that 3D-SIM is a promising approach for studying the dynamics of thylakoid membranes in photoautotrophic organisms during photoacclimation processes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Improving Translation Models for Predicting the Energy Yield of Photovoltaic Power Systems. Cooperative Research and Development Final Report, CRADA Number CRD-13-526

The project under this CRADA will analyze field data of various flat-plate and concentrator module technologies and cell measurements at the laboratory level. The field data will consist of current versus voltage data collected over many years on a latitude tilt test bed for Si, CdTe, amorphous silicon, and CIGS technologies. The concentrator data will be for mirror- and lens-based module designs using multijunction cells. The laboratory data will come from new measurements of cell performance with systematic variation of irradiance, temperature and spectral composition. These measurements will be labor-intensive and the aim will be to cover the widest possible parameter space for as many different PV samples as possible. The data analysis will require software tools to be developed. These tools will be customized for use with the specific NREL datasets and will be unsuitable for commercial release. The tools will be used to evaluate different translation equations against NREL outdoor datasets.

14 SOLAR ENERGY↗

DOE OSTI2013

Full spatial characterization of a nanofocused x-ray free-electron laser beam by ptychographic imaging

The emergence of hard X-ray free electron lasers (XFELs) enables new insights into many fields of science. These new sources provide short, highly intense, and coherent X-ray pulses. In a variety of scientific applications these pulses need to be strongly focused. In this article, we demonstrate focusing of hard X-ray FEL pulses to 125 nm using refractive x-ray optics. For a quantitative analysis of most experiments, the wave field or at least the intensity distribution illuminating the sample is needed. We report on the full characterization of a nanofocused XFEL beam by ptychographic imaging, giving access to the complex wave field in the nanofocus. From these data, we obtain the full caustic of the beam, identify the aberrations of the optic, and determine the wave field for individual pulses. This information is for example crucial for high-resolution imaging, creating matter in extreme conditions, and nonlinear x-ray optics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗