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DOE OSTI2022

Median bed-material sediment particle size across rivers in the contiguous US

Abstract. Bed-material sediment particle size data, particularly the median sediment particle size (D50), are critical for understanding and modeling riverine sediment transport. However, sediment particle size observations are primarily available at individual sites. Large-scale modeling and assessment of riverine sediment transport are limited by the lack of continuous regional maps of bed-material sediment particle size. We hence present a map of D50 over the contiguous US in a vector format that corresponds to approximately 2.7 million river segments (i.e., flowlines) in the National Hydrography Dataset Plus (NHDPlus) dataset. We develop the map in four steps: (1) collect and process the observed D50 data from 2577 U.S. Geological Survey stations or U.S. Army Corps of Engineers sampling locations; (2) collocate these data with the NHDPlus flowlines based on their geographic locations, resulting in 1691 flowlines with collocated D50 values; (3) develop a predictive model using the eXtreme Gradient Boosting (XGBoost) machine learning method based on the observed D50 data and the corresponding climate, hydrology, geology, and other attributes retrieved from the NHDPlus dataset; and (4) estimate the D50 values for flowlines without observations using the XGBoost predictive model. We expect this map to be useful for various purposes, such as research in large-scale river sediment transport using model- and data-driven approaches, teaching environmental and earth system sciences, planning and managing floodplain zones, etc. The map is available at https://doi.org/10.5281/zenodo.4921987 (Li et al., 2021a).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Process Modeling of U-10wt% Mo Alloys Using Integrated Computational Materials Engineering

Low-enriched uranium alloyed with 10 wt% molybdenum (U-10Mo) has been identified as a promising alternative to highly enriched uranium as fuel for the nation’s high-performance research reactors. Manufacturing the U-10Mo alloy consists of multiple complex thermomechanical processes, which is highly challenging for computational modeling. The integrated computational materials engineering (ICME) concept supports building a microstructure-based framework to investigate the effect of manufacturing processes on the material microstructure. In this report, an ICME model that combines homogenization, hot rolling, annealing, and cold rolling is presented as an example of implementing the ICME concept for modeling the manufacturing processes of the U-10Mo alloy. The integrated model enables information to be passed between the different model components, leading to an improved prediction and a better understanding of microstructure across multiple processes. The integrated model was also implemented to investigate the variation of chemical composition, thickness of the zirconium interlayer, grain growth, and carbide redistribution and fracture of U-10Mo during the thermomechanical processes. With the homogenization model, a molybdenum concentration map can be reconstructed from any given microstructure, and the homogenization time needed to reach a desired homogenization level can also be approximated. The microstructure-based finite-element rolling model, shows that the homogenization process reduces the nonuniformity in the thickness of the zirconium interlayers on the U-10Mo. With the output from the rolling model, a statistical analysis of second-phase hard particles was conducted to quantify the particle redistribution and interparticle spacing between carbides during the deformation. Also, new stringer identification criteria were proposed to predict the correlation between stringer volume fractions and hot-rolling reduction. A macro-finite-element method hot-rolling model showed that higher reduction passes can be achieved within the mill separation force range, and a new “aggressive” rolling schedule was proposed. The model also showed the importance of can material on the quality of rolled foils, with stronger can materials resulting in hot-rolled U-10Mo free of defects (waviness, dog-boning). The deformation-induced recrystallization model demonstrated capability to use inputs from deformation simulations of strain accumulation in U-10Mo samples to predict grain growth behavior of the fuel during thermal treatment stages. Finally, a plane-strain compression mode simulated particle fracture and calculates the void volume fraction generated during cold rolling. The macro-finite-element method cold-rolling model showed that smaller diameter rolls are necessary to achieve the desired foil thickness.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2015

Positive contraction mappings for classical and quantum Schrödinger systems

The classical Schrödinger bridge seeks the most likely probability law for a diffusion process, in path space, that matches marginals at two end points in time; the likelihood is quantified by the relative entropy between the sought law and a prior. Jamison proved that the new law is obtained through a multiplicative functional transformation of the prior. This transformation is characterised by an automorphism on the space of endpoints probability measures, which has been studied by Fortet, Beurling, and others. A similar question can be raised for processes evolving in a discrete time and space as well as for processes defined over non-commutative probability spaces. The present paper builds on earlier work by Pavon and Ticozzi and begins by establishing solutions to Schrödinger systems for Markov chains. Our approach is based on the Hilbert metric and shows that the solution to the Schrödinger bridge is provided by the fixed point of a contractive map. We approach, in a similar manner, the steering of a quantum system across a quantum channel. We are able to establish existence of quantum transitions that are multiplicative functional transformations of a given Kraus map for the cases where the marginals are either uniform or pure states. As in the Markov chain case, and for uniform density matrices, the solution of the quantum bridge can be constructed from the fixed point of a certain contractive map. For arbitrary marginal densities, extensive numerical simulations indicate that iteration of a similar map leads to fixed points from which we can construct a quantum bridge. For this general case, however, a proof of convergence remains elusive.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Enhancing the Range and Reliability of the Spacer Layer Imaging Method

The spacer layer imaging method (SLIM) is widely used to measure the thickness of additive and lubricant films, in lubricant development and evaluation, and for fundamental research into elastohydrodynamic lubrication and tribofilm formation mechanisms. The film thickness measurement, as implemented on several popular tribometers, provides powerful, non-destructive in-situ mapping of film topography with nanometre-scale height sensitivity. However, the results can be highly sensitive to experimental procedure, machine condition, and image analysis, in some cases reporting unphysical film thickness trends. The prevailing image analysis techniques make it challenging to interrogate these errors, often hiding their multivariate nonlinear behaviour from the user by spatial averaging. Herein, several common ‘silent errors’ in the SLIM measurement, including colour matching to incorrect fringe orders, and colour drift due to the optical properties of the system or film itself, are discussed, with examples. A robust suite of novel a priori and a posteriori methods to address these issues, and to improve the accuracy and reliability of the measurement, are also presented, including a novel, computationally inexpensive circle-finding algorithm for automated image processing. In combination, these methods allow reliable mapping of films up to at least 800 nm in thickness, representing a significant milestone for the utility of SLIM applied to elastohydrodynamic contact.

EHL film geometry↗

DOE OSTI2025

Three-dimensional photoluminescence imaging of threading dislocations in GaN by sub-band optical excitation

GaN is rapidly gaining attention for implementation in power electronics but is still impacted by its high density of threading dislocations (TDs), which have been shown to facilitate current leakage through devices limiting their performance and reliability. Here, we discuss a novel implementation of photoluminescence (PL) imaging to study TDs in regions within vertically structured p-i-n GaN (PIN) diodes consisting of metalorganic chemical vapor deposition (MOCVD) epitaxial layers grown on ammonothermal GaN (am-GaN) substrates. PL imaging with a sub-bandgap excitation energy (3.1 eV) reveals TDs with excellent clarity in three dimensions within the am-GaN substrate. Galvanometric-driven PL imaging allows the microstructure of hundreds of devices to be characterized in a single session, enhancing the screening process through the addition of device specific TD location tracking and density mapping. The visibility, structural characteristics, luminescent nature and evolution of TDs through the GaN growth process are described, potentially providing the ability to define TD structures associated with leakage current.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Solar Thermal Energy Planner (STEP 1): A New Decision Support Tool for Solar Industrial Process Heat Applications

Solar thermal technologies are a promising technology to supply low-cost thermal energy to industrial processes, but there are often significant barriers to entry to industrial owners considering these technologies for their energy demands. To overcome this barrier and convey economic value to customers, NREL and Sandia National Laboratories developed Solar Thermal Energy Planner (STEP 1), a new web-based decision support tool for solar industrial process heat systems. At SolarPACES 2024, the STEP 1 tool was still under development; progress, methodologies, and a preliminary case study was presented. With the STEP 1 tool launch in May 2025, in this work, the initial version of the full public tool will be presented with demonstrations of its capabilities using a few case studies. First, the user's process heat needs such as location, process media (e.g., steam, air), process temperature, land availability, electricity and fuel costs, among other parameters. STEP 1 features a mapping interface that allows users to draw land and roof boundaries. The process media and temperature inform technology selection criteria modules that determine the appropriate solar thermal collection technologies, as well as congruent heat transfer media (e.g., hot water, oil, salt). Once the solar thermal technology selected, its nominal thermal production for the given site is characterized using NREL's System Advisor Model (SAM). Then, a modified version of NREL's REopt optimal sizing and dispatch optimization tool determines cost-optimal sizing. Within minutes, the user receives the results of the technoeconomics analysis, including the size and performance of the cost-optimal solar-plus-storage system. The cost of the system is compared to business-as-usual (e.g., an existing, standalone natural gas boiler). Users can download key results to store for sensitivity analyses. Examples of flat plate collector, parabolic trough, and molten salt tower applications with and without PV hybridization for different industrial facility types are presented in this work. The STEP 1 tool aims to reduce barriers to the adoption of solar heating solutions stemming from a lack of familiarity and technical background with solar system design options and costs among industry stakeholders.

14 SOLAR ENERGY↗

DOE OSTI2016

One-dimensional Turbulence Models of Type I X-ray Bursts

Type I X-ray bursts are caused by thermonuclear explosions occurring on the surface of an accreting neutron star in a binary star system. Observations and simulations of these phenomena are of great importance for understanding the fundamental properties of neutron stars and dense matter because the equation of state for cold dense matter can be constrained by the mass-radius relationship of neutron stars. During the bursts, turbulence plays a key role in mixing the fuels and driving the unstable nuclear burning process. This dissertation presents one-dimensional models of photospheric radius expansion bursts with a new approach to simulate turbulent advection. Compared with the traditional mixing length theory, the one-dimensional turbulence (ODT) model represents turbulent motions by a sequence of maps that are generated according to a stochastic process. The light curves I obtained with the ODT models are in good agreement with those of the KEPLER model in which the mixing length theory and various diffusive processes are applied. The abundance comparison, however, indicates that the differences in turbulent regions and turbulent diffusivities result in more 12 C survival during the bursts in the ODT models, which can make a difference in the superbursts phenomena triggered by unstable carbon burning.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Improving Microstructural Quantification in 3D FIB-SEM Tomography

A detailed understanding of 3D microstructural features is crucial during postprocessing of the acquired data, while determining microstructural properties. Focused ion beam and scanning electron microscopy (fib-sem) is an ideal technique for the acquisition and quantification of 3D microstructures in subsurface features. In this work, we evaluated and implemented approaches to quantify 3D morphological features of plutonium oxalate and oxide particles. Image analysis, as well as statistical analysis were utilized to study the effect of process variables on the pore morphology of plutonium oxalate and oxide particles. The primary results were the creation of morphological maps of particles and identify indicators that correlated parameters allowing for discrimination between direct and reverse strike processes. This enabled us to properly quantify and map particles with their images obtained via sem. For this project, the highest correlation was found to be between Volume and Sphericity (R 2 = 0.7432) for the Revere Strike process, and Volume % and Sphericity (R 2 = 0.7450) for the Direct Strike process.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Investigating Sociodemographic Disparities in Cancer Risk Using Web-Based Informatics

Cancer health disparities due to demographic and socioeconomic factors are an area of great interest in the epidemiological community. Adjusting for such factors is important when developing cancer risk models. However, for digital epidemiology studies relying on online sources such information is not readily available. This paper presents a novel method for extracting demographic and socioeconomic information from openly available online obituaries. The method relies on tailored language processing rules and a probabilistic scheme to map subjects’ occupation history to the occupation classification codes and related earnings provided by the U.S. Census Bureau. Using this information, a case-control study is executed fully in silico to investigate how age, gender, parity, and income level impact breast and lung cancer risk. Based on 48,368 online obituaries (4,643 for breast cancer, 6,274 for lung cancer, and 37,451 cancer-free) collected automatically and a generalized cancer risk model, our study shows strong association between age, parity, and socioeconomic status and cancer risk. Although for breast cancer the observed trends are very consistent with traditional epidemiological studies, some inconsistency is observed for lung cancer with respect to socioeconomic status.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

TGE: Machine Learning Based Task Graph Embedding for Large-Scale Topology Mapping

Task mapping is an important problem in parallel and distributed computing. The goal in task mapping is to find an optimal layout of the processes of an application (or a task) onto a given network topology. We target this problem in the context of staging applications. A staging application consists of two or more parallel applications (also referred to as staging tasks) which run concurrently and exchange data over the course of computation. Task mapping becomes a more challenging problem in staging applications, because not only data is exchanged between the staging tasks, but also the processes of a staging task may exchange data with each other. We propose a novel method, called Task Graph Embedding (TGE), that harnesses the observable graph structures of parallel applications and network topologies. TGE employs a machine learning based algorithm to find the best representation of a graph, called an embedding, onto a space in which the task-to-processor mapping problem can be solved. We evaluate and demonstrate the effectiveness of TGE experimentally with the communication patterns extracted from runs of XGC, a large-scale fusion simulation code, on Titan.

Choi, Jong Youl↗

DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗

DOE OSTI2020

Correlation of SWIR imaging with LPBF 304L stainless steel part properties

In the Laser Powder Bed Fusion (LPBF) process, the local thermal history can vary significantly over a part as the heat transfer characteristics and the laser scan path are geometry dependent. The variations introduce the potential for defects that lead to part failure, some of which are difficult to identify non-destructively with common ex-situ evaluation techniques. These defects include significant microstructural and mechanical property differences in the part interiors. In this paper, thermal features are extracted from in-situ Short-Wave Infrared (SWIR) imaging measurements to compile voxel based part representations and understand how the complexities in the thermal history affect part performance. The deviations in thermal features due to different laser processing parameters and complex scan pathing are explored. Empirical correlations are developed to map thermal features with the engineering properties (bulk yield strength, area percentage porosity, and local state) of 304L stainless steel parts manufactured by LPBF. Processing modes (insufficient melting and keyholing) are determined by mapping part property measurements with multiple thermal features. Generating the relationships between thermographic measurements and resulting SLM part properties lays the foundation for in-situ part qualification.

36 MATERIALS SCIENCE↗

DOE OSTI2022

rmap: An R package to plot and compare tabular data on customizable maps across scenarios and time

`rmap` is an R package that allows users to easily plot tabular data (CSV or R data frames) on maps without any Geographic Information Systems (GIS) knowledge. Maps produced by `rmap` are `ggplot` objects and thus capitalize on the flexibility and advancements of the `ggplot2` package and all elements of each map are thus fully customizable. Additionally `rmap` automatically detects and produces comparison maps if the data has multiple scenarios or time periods as well as animations for time series data. Advanced users can load their own shapefiles if desired. `rmap` comes with a range of pre-built color palettes but users can also provide any `R` color palette or create their own as needed. Four different legend types are available to highlight different kinds of data distributions. The input spatial data can be both gridded or polygon data. `rmap` is desgined in particular for comparing spatial data across scenarios and time periods and comes preloaded with standard country, state, and basin maps as well as custom maps compatible with the Global Change Analysis Model (GCAM) spatial boundaries. `rmap` has a growing number of users and its products have been used in multiple multisector dynamics publications as well as a required dependency in other R packages such as `rfasst` and `metis`. `rmap's` automatic processing of tabular data using pre-built map selection, difference map calculations, faceting, and animations offers unique functionality which makes it a powerful and yet simple tool for users looking to explore multi-sector, multi-scenario data across space and time.

58 GEOSCIENCES↗

DOE OSTI2017

ECCI, EBSD and EPSC characterization of rhombohedral twinning in polycrystalline α-alumina deformed in a D-DIA apparatus

Rhombohedral twinning in alumina (aluminium oxide, α-Al 2 O 3 ) is an important mechanism for plastic deformation under high-temperature–pressure conditions. Rhombohedral twins in a polycrystalline alumina sample deformed in a D-DIA apparatus at 965 K and 4.48 GPa have been characterized. Three classes of grains were imaged, containing single, double and mosaic twins, using electron channeling contrast imaging (ECCI) in a field emission scanning electron microscope. These twinned grains were analyzed using electron backscatter diffraction (EBSD). The methodology for twin identification presented here is based on comparison of theoretical pole figures for a rhombohedral twin with experimental pole figures obtained with EBSD crystal orientation mapping. An 85°(02{\overline 2}1) angle–axis pair of misorientation was identified for rhombohedral twin boundaries in alumina, which can be readily used in EBSD post-processing software to identify the twin boundaries in EBSD maps and distinguish the rhombohedral twins from basal twins. Elastic plastic self-consistent (EPSC) modeling was then used to model the synchrotron X-ray diffraction data from the D-DIA experiments utilizing the rhombohedral twinning law. From these EPSC models, a critical resolved shear stress of 0.25 GPa was obtained for rhombohedral twinning under the above experimental conditions, which is internally consistent with the value estimated from the applied load and Schmid factors determined by EBSD analysis.

Kaboli, Shirin (ORCID:0000000307447764)↗

DOE OSTI2026

Mapping Interfacial Solution Forces that Drive Fluorescent Nanoparticle Incorporation into Crystals

Interfacial solution structure governs processes ranging from catalytic and electrochemical reactions to particle aggregation and crystallization. Here, we design bio-inspired nanocomposites by mapping interfacial forces and controlling nanoparticle incorporation during crystal growth. Our analytical model contains measured kinetic barriers for surface approaches and equilibrium binding constants with the crystal surface. We validate this model using fluorescent silica nanoparticles and calcite. Our results show that surface chemistry dictates incorporation pathways: methoxy groups are least favorable, hydroxyls reduce kinetic barriers by interacting favorably with hydration layers, carboxylates bind strongly but must overcome a kinetic barrier, and amines outperform all kinetically and thermodynamically. This framework resolves the interplay between interfacial solution structures, particle forces and dynamics, and growth kinetics, enabling predicative control of nanocomposite formation and multiplexed mixed-particle systems.

Zhang, Mingyi [Pacific Northwest National Laborato↗

DOE OSTI2019

Sequential Learning with Active Partial Labeling for Building Metadata

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts.We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Liquid Sorption-Enhanced Haber–Bosch Process

The use of a liquid sorbent in a traditional Haber-Bosch process enables significant improvements in energy efficiency and potential cost savings for arguably the most important chemical process on the planet. The approach presented in this report employs an incompressible liquid sorbent that absorbs and releases ammonia (NH 3 ) under specific conditions. To achieve this, we investigate reactions of ammonia and pure phosphoric acid (H 3 PO 4 , PA), which rapidly neutralize to form an equilibrated solution of monoammonium phosphate (MAP) and diammonium phosphate (DAP) that functions as a reversible and regenerable sorbent. Through intimate contact of the gas-phase Haber-Bosch reaction mixture with this liquid absorbent, complete equilibrium uptake may be achieved in an appropriately sized separator, and facile separation occurs through the use of independent liquid and gas phases. Following depressurization and release of the ammonia product, only the incompressible fluid needs to be repressurized and returned to the reactor. This study documents proof-of-concept absorption and desorption experiments carried out in 75 mL batch reactors, predominantly charged with precise MAP and DAP mixtures that equilibrate at process-relevant temperatures and pressures. We then assemble the first thermodynamic relationships that underlie this advantaged separation strategy, validated by reactive force field (ReaxFF) interatomic potential simulations, and benchmarked with traditional separation routes via process modeling and technoeconomic analysis. The scale of energy consumption in the century-old Haber-Bosch process is massive, and the elegant liquid sorption approach reported here offers opportunities to enhance its energy efficiency for the next frontier of ammonia synthesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗