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DOE OSTI2026

Associated Particle Imaging of Neutron Inelastic Scatter: 3-D Reconstruction, Capabilities, and Challenges

Associated particle imaging (API) offers unique advantages for 3-D imaging of neutron inelastic scatter, including single-view tomographic imaging and data acquisition when access is limited to only one side of the interrogated object. However, widespread adoption of neutron inelastic scatter imaging has been impeded by several inherent challenges, most prominently spatial resolution, self-attenuation, and statistical noise. Here, in this work, the capabilities and challenges of neutron inelastic scatter imaging are investigated. Instead of focusing on a single imaging application, we identify imaging principles that hold for various neutron inelastic scatter imaging techniques. The primary challenges for 3-D imaging are characterized. The inherent spatial resolution in the time-of-flight (TOF) dimension is derived based on the known system timing resolution and scan geometry. Three reconstruction algorithms are described and demonstrated, including the introduction of modern iterative reconstruction incorporating a physics-based system model. Simulation is leveraged to demonstrate imaging capability with varying coincidence count levels and system timing resolution. An example of measured data with both back-scatter and forward-scatter detector positioning is presented. System design characteristics and their effects on image quality are identified. The imaging framework presented in this article has the potential to facilitate growth of 3-D neutron inelastic scatter API by identifying applications that are a good match for the technique and by targeting system development resources toward the requirements of a specific imaging task.

Associated particle imaging (API)↗

DOE OSTI2024

Can we rely on drought–ending “miracles” in the Colorado River Basin?

Unexpected and large spring precipitation events in the Colorado River Basin (CRB) that significantly alleviated an otherwise severe water shortage have been observed for over a century, such as the “Miracle May” of 2015. Although these events are often termed as “drought-busting” or “miracle events” by water managers and the media, they have not been extensively researched or characterized. In this collaborative study with water managers across the CRB, we propose a definition for these hard-to-predict, ultra-high precipitation events occurring during the late-snow or snowmelt season. This characterization provides a framework for quantifying the frequency and intensity of extreme dry-to-wet springtime transitions. Despite limitations of climate model simulations due to uncertainties and the inhomogeneous qualities, our findings suggest that such transitions may become less frequent and less intense in a warming climate. Finally, in view of the potentially wetter but less-snowy climate in the basin, the need for future research to more quantitatively assess these “miracle events” is emphasized.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗

DOE OSTI2024

Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface

Aerosol-cloud interactions (ACIs) are vital for regulating Earth’s climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Robust Optimal Experimental Design of Infinite-Dimensional Bayesian Nonlinear Inverse Problems

Abstract. We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse problem. However, the optimal design is dependent on elements of the inverse problem such as the simulation model, the prior, or the measurement error model. ROED aims to produce an optimal design that is aware of the additional uncertainties encoded in the inverse problem and remains optimal even after variations in them. We follow a worst-case scenario approach to develop a new framework for robust optimal design of nonlinear Bayesian inverse problems. The proposed framework (a) is scalable and designed for infinite-dimensional Bayesian nonlinear inverse problems constrained by PDEs; (b) develops efficient approximations of the utility, namely the expected information gain; (c) employs eigenvalue sensitivity techniques to develop analytical forms and efficient evaluation methods of the gradient of the utility with respect to the uncertainties against which we wish to be robust; and (d) employs a probabilistic optimization paradigm that properly defines and efficiently solves the resulting combinatorial max-min optimization problem. The effectiveness of the proposed approach is illustrated for optimal sensor placement problem in an inverse problem governed by an elliptic PDE.

Chowdhary, Abhijit↗

DOE OSTI2025

Influence of local river hydraulics on Kootenai River white sturgeon ( Acipenser transmontanus ) habitat selection during four spawning years, 2017–2020

Understanding fine-scale habitat selection of endangered Kootenai River white sturgeon ( Acipenser transmontanus) is an important component for monitoring and recovery efforts. Fine-scale habitat selection and quantifying temporal changes in suitable habitat contributes to the work of addressing recruitment failure within the Kootenai River population. Habitat suitability indices were developed using over 96 000 acoustic telemetry sturgeon detections and two-dimensional hydrodynamic model simulations near Bonners Ferry, Idaho, USA. The selected habitat was assessed to develop habitat suitability indices for sturgeon; females undergoing spawn migrations and non-spawners. The most frequented locations were 8–9 m deep and water velocities of 0.3–0.7 m·s −1 . These observations suggest sturgeon with different spawning capabilities selected similar habitat. Weighted usable area was calculated to understand temporal variability in habitat quality, which showed a positive relationship with increases in flow. Results help understand the habitat limiting factors in regulated hydrologic regimes; provide biologists insight for monitoring efforts in discrete habitat conditions; guidance for water managers and the regulation of upstream water resources; and guidance to restoration practitioners for in-stream structure designs.

Dudunake, Taylor J. (ORCID:0000000176502419)↗

DOE OSTI2024

Interdependent Water And Power Infrastructure Model

The approach used is the Multi-Agent System (MAS) paradigms, where systems components are represented as agents, interacting both with each other, and with the environment in which they evolved. Agents behaviors correspond to components in the real Integrated Water-Power System. The model simulate actions and interactions of these (autonomous) agents to analyze their effects on the overall system. Agents in the water system capture components of water collection, treatment, transportation, distribution and use (e.g. pipe, canal, pump, water demands for agriculture, etc.). Agents in the power system capture components of power generation, transportation, distribution and use (electricity demands, sources, etc.).

Toba, Danho Ange Lionel [Idaho National Laboratory↗

DOE OSTI2025

pnnl-predictive-phenomics/emll

This codebase was designed to tackle the problem of how to directly utilize experimental omics measurements with a genome scale metabolic model of the organism of the study. Most models only loosely utilize known experimental quantities to constrain model simulation.

John, Peter St. [@NVIDIA]↗

DOE OSTI2024

Assessment of Electric Grid Transmission System Simulator for Human Factors Research

Over the past decades, various technologies have been developed for electric grid operations to support clean energy, meet rising electricity demands, and address infrastructure concerns. However, the human factors aspect is often overlooked during rapid integration. Questions persist about how these technologies impact human performance. Simulators play a critical role in supporting investigation of human factors design concepts and conducting comprehensive usability testing to evaluate human performance and assess human reliability. This paper aims to address human factors research simulator requirements and conduct a comparative study of six different simulators. A detailed evaluation reveals that the evaluated simulators lack the ability to customize user interfaces. Additionally, their user interface designs do not fulfill the basic human factors design principles, potentially leading to increased response variability and reduced statistical power when conducting controlled experimental research. In the future, it is essential to develop scripting tools to integrate customizable user interfaces and simulation models, ensuring meeting research requirements.

Li, Ruixuan↗

DOE OSTI2025

Asynchronous-many-task systems: Challenges and opportunities - Scaling an AMR astrophysics code on exascale machines using Kokkos and HPX

Dynamic and adaptive mesh refinement is pivotal in high-resolution, multi-physics, multi-model simulations, necessitating precise physics resolution in localized areas across expansive domains. Today’s supercomputers’ extreme heterogeneity presents a significant challenge for dynamically adaptive codes, highlighting the importance of achieving performance portability at scale. Our research focuses on astrophysical simulations, particularly stellar mergers, to elucidate early universe dynamics. Here, we present Octo-Tiger, leveraging Kokkos, HPX, and SIMD for portable performance at scale in complex, massively parallel adaptive multi-physics simulations. Octo-Tiger supports diverse processors, accelerators, and network backends. Experiments demonstrate exceptional scalability across several heterogeneous supercomputers including Perlmutter, Frontier, and Fugaku, encompassing major GPU architectures and x86, ARM, and RISC-V CPUs. Parallel efficiency of 47.59% (110,080 cores and 6880 hybrid A100 GPUs) on a full-system run on Perlmutter (26% HPCG peak performance) and 51.37% (using 32,768 cores and 2048 MI250X) on Frontier are achieved.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Dynamic Temporal Graph Sequence Data for Resilience-Oriented Distribution Network Reconfiguration

This dataset comprises temporal dynamic graph sequences generated from power grid simulations focused on grid reconfiguration to enhance resilience. The simulations model failure propagation under varying conditions, with nodes assigned distinct failure probabilities. For each time step, the dataset captures the evolution of node states (functional or failed) and features critical to grid operations, such as pv_output, load_profile, load_dispatch, dg_output, loss, and voltage. Node types include sources, normal loads, and nodes with specific equipment like PVs, micro turbines, or shunt capacitors. The dataset is structured to support the training of dynamic graph neural networks, facilitating research on node feature prediction and edge dynamics under failure scenarios. Three distinct configurations are included, providing a robust foundation for modeling power grid resilience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

High-Resolution Fire Weather Index Data for the Conterminous US (1980–2099), Version 1

This dataset presents a suite of high-resolution fire weather index datasets calculated from observation (gridMet, Livneh, Daymet V4), reanalysis (AgERA5), downscaled hydro-climate projections over the conterminous United States (CONUS) based on multiple selected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Aside from the daily FWI datasets, we also include a set of FWI extreme indicators at annual, seasonal, and monthly scales, including 1) fwixx: maximum FWI; 2) fwisa: mean FWI. For annual fwisa, it refers to the season with the maximum seasonal average; 3) fwils: Length of fire season over a specified period, where fire season is defined as the days exceeding the median value of the normalized FWI during the reference period (1980-1984); 4) fwixd: Number of extreme fire weather days over a specified period, where extreme day is defined as the day with FWI > the 95th percentile of the FWI during the reference period (1980-1984). All FWI datasets cover 1980-2020 baseline and the model simulated products including the downscaled products additionally include 2021-2099 near-future periods under the high-end (SSP585) emission scenario.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Modeling In-Reactor Temperature Gradients in Lithium Aluminate Ceramic Pellets

In reactor thermal performance of lithium aluminate ceramic pellets are calculated to determine operating temperatures if internal components shift during irradiation. A 2D conduction model was setup to model this scenario with additional physics, including thermal expansion of components to reactor temperatures, creep down of the cladding due to differential pressure between the inside of the rod and reactor coolant system, and net radiation exchange between components. The non-linear coupling of the governing equations presented convergence challenges due to the very different effects from a small temperature change on the radiation vs radial and circumferential conduction equations. Initial temperature guesses for the multidimensional solution were provided from conservative 1D models simulating bounding scenarios. Convergence was greatly improved by taking small steps towards the root solutions and averaging with initial guesses. The final method robustly converged all 132 points at an axial plane after a few dozen iterations. The temperature results show that the additional physics have a secondary effect as the dominant heat transfer mechanism is radial conduction across the gas gap followed by circumferential conduction around components. Circumferential conduction in the lithium aluminate pellets is enhanced by the relatively high thermal conductivity of this ceramic.

Carstens, Nathan A.↗

DOE OSTI2024

Toward a long-lifetime polarized photoelectron gun for the Ce+BAF positron source

The addition of spin-polarized, continuous-wave (c.w.) positron beams to the 12 GeV Continuous Electron Beam Accelerator Facility (CEBAF) would provide a significant capability to the experimental nuclear physics program at Jefferson Lab. Based on bremsstrahlung and pair-production in a high-Z target, the positron source requires a 120 MeV spin-polarized c.w. electron beam of several milliamperes. While the beam dynamics of the high-current electron beam are tenable, sustaining this current for weeks of user operations requires an unprecedented charge lifetime from a high-polarization GaAs-based photocathode. A promising approach to exceed the kilocoulomb charge lifetime barrier is reducing the ion back-bombardment fluence at the photocathode. By increasing the laser size and managing the emittance growth with an adequate cathode/anode design, significantly enhanced charge lifetime may be achieved. Based upon a new simulation model that qualitatively explains the lifetime data previously measured at different spot sizes, we describe the practical implications on the parameter space available for a kilocoulomb-lifetime polarized photogun design.

Bruker, M.↗

DOE OSTI2024

Residential Water Heating Demand Side Management (DSM) - South Africa

The electricity crisis in South Africa has deteriorated significantly, with the country experiencing frequent and prolonged rolling blackouts. These outages have severe economic repercussions, leading to decreased growth and productivity. Demand Side Management (DSM), particularly focusing on electric water heaters due to their significant energy consumption and peak demand contribution, is identified as a key strategy. The study aims to assess opportunities for DSM programs targeting water heating to reduce energy consumption and peak demand. It entails developing a bottom-up simulation model to establish a baseline scenario of water heating electricity load demand in 2023 and 2033, identifying technologies for energy reduction, estimating the impacts of a selected number of measures and providing recommendations to inform policy makers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Toward a long-lifetime polarized photoelectron gun for the Ce+BAF positron source

Bruker, M.↗

DOE OSTI2024

Multi-Task with Procter and Gamble (CRADA No. NFE-10-02672)

The purpose of this Cooperative Research and Development Agreement (CRADA) between UT-Battelle, LLC (the “Contractor) and Procter & Gamble Company (the “Participant”) is the development of a research partnership to create new tools, tests and analytical methods to improve the performance, safety and/or environmental quality of chemicals, advanced materials, food products and manufacturing processes. The Participant operates in three global business units: Beauty, Health and Well-Being and Household Care. Some of its worldwide products include Head and Shoulders®, Pantene®, Gillette® razors and personal care products, Crest®, Dawn®, Tide®, Bounty®, Duracell® batteries; and Iams® pet food among others. At its core, however, the Participant is a science driven company. It supports one of the most robust industrial research and development (R&D) programs in the world. The Participant uses this rich foundation of science to drive innovation across all of its product lines. But the innovation process is not confined in-house The Participant pursues an “open innovation” policy, seeking partnerships with scientists and researchers in universities and national laboratories where it can contribute its extensive knowledge assets and collaborate to advance scientific understanding. The research under this multi-task CRADA was directed under the following general task areas and, throughout the duration of this CRADA the work statement was modified to match the needs of the Parties and the direction of the research. (1) Software modeling, simulation and development; (2) Manufacturing Technologies; (3) Supply Chain Optimization, (4) Advanced Materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

MRCI Subtask 2.4/2.5: Regional/Subregional Analysis and Risk Assessment Final Technical Summary Report

The objective of the Midwest Regional Carbon Initiative (MRCI) project is to implement a collaborative Regional Initiative (RI) to accelerate the deployment of carbon capture, and storage (CCS) in the Midwest-Northeastern quadrant of the United States. This report is a Technical Summary report describing work performed on Tasks 2.4 (Conducting Regional/Subregional Analysis) and 2.5 (Assessing and Managing Risk) during the MRCI project. In Task 2.4, detailed numerical reservoir simulation models were developed for selected carbon storage (CS) systems identified under Task 2.1 (Battelle, 2021a) in the MRCI study area. The objective of Task 2.4 is to demonstrate a dynamic modeling methodology for evaluating the suitability of the (selected) CS systems in the MRCI region for hosting a commercial-scale storage project. In this study, CO2 injectivity was evaluated for different CS systems with an annual injection rate of 1 million metric tonnes (MMT) of CO2 considered as the minimum requirement for a commercial scale project. The objective of Task 2.5 is to assess key risks associated with storing CO2 in the different CS systems across the MRCI region and to demonstrate a method(s) for assessing these risks that may be used by developers of future CO2 storage projects in the region. The risk analysis was limited to evaluating two types of leakage risks (i.e., wellbore leakage, flow across unfractured caprock) at three modeled sites considered in Task 2.4.

MRCI,Report,Summary,Technical Challenges,dynamic m↗