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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2025

WRPS Hanford Update [Slides]

The purpose of this report is to coordinate with the staff from WRPS, the current contract holder for operations at the Hanford site to better inform design decisions for the Autonomous systems for Hanford waste tank handling project. The project goals are to support risk reduction associated with monitoring, inspection and mapping of Hanford tank underground pits and includes: 1. A pit mock-up demonstration of Idaho National Laboratory’s (INL) Autonomous Pit Exploration System (APES) technology, which has two configurations: A. The Autonomous Robotic Arm, which is a proposed collaboration with Florida International University (FIU) and has capabilities to visually inspect, monitor, map and conduct simple tool manipulation tasks in the tank pits, and B. The robotic crawler configuration, which has capabilities to deploy to the bottom of the pits and conduct closer and bottom-up visual inspections. 2. An Implementation Plan that identifies Hanford site-wide application of the INL remote robotics technologies to enhance the performance of the tank farm systems and the characterization of the waste they contain to mitigate risk and optimize the overall waste mission. 3. A first-of-a-kind environmental digital twin will be produced. Digital twins to date require permanently installed sensors in order to produce asset specific predictions. This project will use the intermittent signals during inspections from deployed sensors on robotic systems to produce the data sets used by AI/ML to enable predictions of issues on tanks.

42 - ENGINEERING↗

DOE OSTI2025

Towards philosophical reasoning with agentic LLMs: Socratic method for scientific assistance

As large language models (LLMs) become central tools in science, improving their reasoning capabilities is critical for meaningful and trustworthy applications. We introduce a Socratic agent for scientific reasoning, implemented through a structured system prompt that guides LLMs via classical principles of inquiry. Unlike typical prompt engineering or retrieval-based methods, our approach leverages definition, analogy, hypothesis elimination, and other Socratic techniques to generate more coherent, critical, and domain-aware responses. We evaluate the agent across diverse scientific domains and benchmark it on the abstraction and reasoning corpus challenge dataset, achieving 97.15% under a fixed prompting protocol and without fine-tuning or external tools. Expert evaluation shows improved reasoning depth, clarity, and adaptability over conventional LLM outputs, suggesting that structured prompting rooted in philosophical reasoning can improve the scientific utility of language models.

LLM reasoning↗

DOE OSTI2025

Development and Validation of Dense Discrete Phase Flow Models for Concentrating Solar Power Particle Receivers

This report summarizes a recent project aimed at developing and validating the necessary tools to enable more accurate modeling of denser and more complex particle flows in next-generation particle receivers used in concentrating solar power towers. A newly developed CFD/DEM simulation capability was created by coupling existing the modeling and simulation tools Sierra and LAMMPS. This new capability permitted the inclusion of additional physics for particle drag and particle collisions to model

14 SOLAR ENERGY↗

DOE OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

WigglyRivers: A tool to characterize the multiscale nature of meandering channels

Channel sinuosity is ubiquitous along river networks, producing complex patterns that encapsulate and influence morphodynamic processes and ecosystem services. Accurately characterizing these patterns is challenging with traditional curvature-based algorithms. Here, in this study, we present WigglyRivers, a Python package that builds on existing wavelet-based methods to create an unsupervised meander identification and characterization tool. The package uses planimetric information the user provides or from the USGS’s High-Resolution National Hydrography Dataset to characterize individual reaches or entire river networks. WigglyRivers also includes a supervised river identification tool for manually selecting individual meandering features. Here, we provide examples of idealized river transects and show the capabilities of WigglyRivers. We also use the supervised identification tool to validate the unsupervised identification on river transects across the continental US. WigglyRivers is a tool to understand better the multiscale characteristics of river networks and the link between river geomorphology and river corridor connectivity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Catalight─An Open-Source Automated Photocatalytic Reactor Package Illustrated through Plasmonic Acetylene Hydrogenation

An open-source and modular Python package, Catalight, is developed and demonstrated to automate (photo)catalysis measurements. (Photo)catalysis experiments require studying several parameters to evaluate performance, including the temperature, gas flow rate and composition, illumination power, and spectral profile. Catalight orchestrates measurements over this complicated parameter space and systematically stores, analyzes, and visualizes the results. To showcase the capabilities of Catalight, we perform an automated apparent activation barrier measurement of acetylene hydrogenation over a plasmonic AuPd catalyst on an Al 2 O 3 support, simultaneously varying laser power, wavelength, and temperature in a multiday experiment controlled by a simple Python script. Our chemical results unexpectedly show an increased activation barrier upon light excitation, contrary to previous findings for other plasmonic reactions and catalysts. We show that the reaction rate order with respect to both acetylene and hydrogen remains unchanged upon illumination, suggesting that molecular surface coverage is not changed by light. By analyzing the inhomogeneity of the laser-induced heating, we attribute these results to a partial photothermal effect combined with a photochemical/hot electron-driven mechanism. In conclusion, our findings highlight the capabilities of a new experiment automation tool; explore the photocatalytic mechanism for an industrially relevant reaction; and identify systematic sources of error in canonical photocatalysis experimental procedures.

Catalysts↗

DOE OSTI2024

FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

There is growing support and interest in postsecondary interdisciplinary environmental education which integrate concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here are a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise for explaining changing water usage than discussing tradeoffs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as more nuanced understanding of student comprehension of complex interdisciplinary concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2026

The inertial confinement fusion experimental platform and diagnostics for studies of nuclear reactions relevant to nuclear astrophysics

High energy density plasmas generated in laser-driven inertial confinement fusion implosions provide unparalleled laboratory conditions for studying stellar-relevant nuclear reactions: plasma environment; hot and dense; uniquely high achievable neutron flux. These experiments have the potential to address long-standing questions about plasma effects on nuclear reactions hitherto experimentally inaccessible, including nuclear rates with thermally distributed reactants, plasma screening, and reactions involving nuclei in excited states. The National Ignition Facility (NIF) and OMEGA lasers are two primary facilities for executing experiments of this type. Existing and future nuclear diagnostics, along with supporting diagnostics to characterize the platform, enable exploitation of these plasmas for such nuclear astrophysics-relevant experiments. Here, this review describes the nuclear diagnostic capabilities currently available for these types of experiments at the NIF and OMEGA, including neutron time-of-flight spectrometers, charged-particle detectors, gamma detectors and radiochemistry diagnostics, and briefly summarizes other available diagnostic capabilities used for platform characterization. Enabling tools not yet available are also identified, including a rapid radioactive sample retrieval system, a low-energy neutron spectrometer and a high-efficiency gamma spectrometer.

National Ignition Facility↗

DOE OSTI2024

Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets

This paper presents an exploration into the development and validation of data analysis approaches for Phasor Measurement Units (PMUs) using open-source datasets and tools. Various methods for event detection, event classification, frequency response, and oscillation analysis were tested. We leverage the capabilities of Archive Walker (AW), the Frequency Response Analysis Tool (FRAT), and the Oscillation Baselining and Analysis Tool (OBAT), all open-source tools, for efficient processing and analysis of synchrophasor data. The open-source Transmission Signature Library (TSL) dataset was employed as a dataset for a comprehensive evaluation to assess the performance and reliability of the proposed methods.

PMU, event analysis, oscillation, Frequency Respon↗

DOE OSTI2025

Relativistic Magnetic Reconnection in Astrophysical Plasmas: A Powerful Mechanism of Nonthermal Emission

Magnetic reconnection—a fundamental plasma physics process, where magnetic field lines of opposite polarity annihilate—is invoked in astrophysical plasmas as a powerful mechanism of nonthermal particle acceleration, able to explain fast-evolving, bright high-energy flares. Near black holes and neutron stars, reconnection occurs in the relativistic regime, in which the mean magnetic energy per particle exceeds the rest mass energy. This review reports recent advances in our understanding of the kinetic physics of relativistic reconnection (RR): ▪ Kinetic simulations have elucidated the physics of plasma heating and nonthermal particle acceleration in RR. ▪ The physics of radiative RR, with its self-consistent interplay between photons and reconnection-accelerated particles—a peculiarity of luminous, high-energy astrophysical sources—is the new frontier of research. ▪ RR plays a key role in global models of high-energy sources, in terms of both global-scale layers and reconnection sites generated as a by-product of local magnetohydrodynamic instabilities. We summarize themes of active investigation and future directions, emphasizing the role of upcoming observational capabilities, laboratory experiments, and new computational tools.

acceleration of particles↗

DOE OSTI2026

Biosynth Pipeline v1.0

BioPKS Pipeline is a computational pipeline for retrosynthetic design of small molecule biosynthesis pathways (e.g. retrobiosynthesis). It combines capabilities by interfacing with existing retrobiosynthesis tools- RetroTide (developed at LBNL) and DORAnet to create pathways that combine multiple biosynthesis approaches- both megasynthase assembly line enzymes and single step enzymes.

Backman, Tyler [Lawrence Berkeley National Laborat↗

DOE OSTI2025

Effect of Plutonium on Uranium Oxide Microstructural Fingerprint

The analysis of particulates from environmental sampling is routinely performed for nuclear forensics applications. In order to increase the tools available for nuclear forensics applications, capability development materials (CDMs), or reference particulates, are necessary for developing and benchmarking new analytical methods. Historically, these CDM particulates have been produced with highly controlled and characterized isotopic and size parameters for applications in developing and benchmarking particle sizers and mass spectrometry analytical methods [1- 4]. However, the development of CDMs with highly characterized particle morphology and phase of the particles is vital for aiding in the development and benchmarking of particle analytical methods for those parameters. In many cases, key properties such as crystallographic phase, morphology, and microstructure, may be correlated to processing history of environmental sampling particulates [5]. To that end, this work investigates determining the structural fingerprint of produced Pu-doped uranium oxide CDMs using transmission electron microscopy (TEM). The particulates, one of which shown in Figure 1, were synthesized to Fig. 1. Single particulate of uranium oxide fabricated using the THESEUS technique. develop capabilities for environmental sampling investigations. These particles are monodisperse at diameter of 1 μm and a range of plutonium concentrations from 0- 1,000 ppm. Given their small diameter, TEM analysis was ideal for studying the particulate structural fingerprint in detail. Previous investigations confirmed the external homogeneity of the particulates at different plutonium concentrations, however this analysis focuses on determining the grain structure, phase distribution, and porosity of the particulates.

Mayer, Jack [Univ. of Florida, Gainesville, FL (Un↗

DOE OSTI2024

Utah FORGE: Interferometric Synthetic Aperture Radar Data from 2023 and 2024

The dataset comprises Interferometric Synthetic Aperture Radar (InSAR) data from the TerraSAR-X and TanDEM-X satellite missions, covering the Utah FORGE site. This data includes interferometric pairs created using GMT-SAR processing software, chosen for their short orbital separations between May 1, 2023, and June 30, 2024. Included are various data and metadata, including Digital Elevation Models, unit vectors, and correlation coefficients. The dataset is packaged in several compressed tar files and formatted in NetCDF. To utilize this dataset, users will need software capable of handling NetCDF files and tools for decompressing tar files.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Development and Commercialization of a Nanosegrated Oxygen Evolution Reaction Electrocatalyst (CRADA)

This collaborative project leverages advanced characterization tools, catalyst treatment and electrochemical testing capabilities at LBNL to help commercialize a bimetallic nanosegrated catalysts for oxygen evolution reaction in proton-exchange membrane water electrolyzers. This project also has three industrial partners who are involved in catalyst scale-up synthesis, large electrode fabrication and large electrode electrochemical testing. This project investigates a broad range of topics including electrocatalysis, ink rheology and electrode fabrication and testing. This knowledge could help bridge the performance and durability gap from laboratory-developed materials (such as electrocatalysts) to applications in commercial devices. The research results also bring fundamental understanding of possible degradation mechanisms that occur under extremely oxidative potentials including material and morphological changes. The project will help accelerate the deployment of proton-exchange membrane water electrolyzers. The public benefits could include creating jobs and increasing public awareness of potential clean technologies for green hydrogen production. Ultimately, the project will help national wide effort of flighting climate change and achieving carbon neutrality by 2050.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the software release referred to as ADDER v1.1.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software for Depletion and Fuel Management

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

DistOPF: Advanced Solutions for Distribution Optimal Power Flow Analysis - DistOPF v0.2 Documentation

To achieve an affordable and reliable energy system, research on power distribution system is often focused on integration of distributed generators, energy storage solution, EV charging, smart meters, and other advanced assets that may benefit from or require more advanced control and optimization techniques. Despite this focus on advanced distribution system topics, early researchers and grid scientists often start from scratch when developing optimization programs for power distribution systems. This report introduces DistOPF, a Python package that consolidates years of research into a versatile and modular tool. DistOPF provides researchers with essential capabilities to solve distribution system Optimal Power Flow (OPF) problems using standard network models. Additionally, it offers a platform to benchmark both new and existing algorithms against established test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗