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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance

Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

Monadjemi, Shayan [ORNL] (ORCID:0000000293855969)↗
DOE OSTI2022

Integration of the Kromek D3S Detector and Spot Robot For Secondary Inspections

Inspecting vehicles and containers for the presence of nuclear material is a challenging task for border control and security. When performed manually by inspectors, this task also has an associated risk of exposing the inspectors to unknown radiation. With the advent of agile, easy-to-program, quadruped robots like the Boston Dynamics Spot, automation of secondary inspection can improve the efficiency of the inspection process and alleviates the radiation risks to inspectors. In this project, Brookhaven National Laboratory and the University of Massachussetts at Lowell explored how to automate a simple secondary inspection mission. The Spot robot comes with its own software development kit (SDK) that allows clients/users to write custom code in the Python programming language to control the robot. Spot also has a payload computer called Spot-CORE, which runs the Ubuntu Linux operating system and allows users to integrate external sensors, such as a radiation detector, with Spot. In this study, the Kromek D3S detector has been integrated with Spot via the Spot-CORE, allowing Spot to capture gamma spectra and neutron counts for a specified acquisition period. Two custom routines, search and confirmation, have been developed and executed in this specified order. The search routine directs Spot to go around the nearest obstacle, e.g., vehicle and container, in a preset distance and step to collect gamma and neutron gross counts with the D3S detector. The radiation data and the robot location corresponding to each step are stored and fed to the confirmation routine at the end of the search. The confirmation routine then navigates Spot to the locations of the highest gamma or neutron counts to perform a long, e.g., one minute, measurement, and gives the operators the signature gamma spectra and neutron counts at the hotspots. This paper presents a detailed description of this automated system along with results of the preliminary tests in identifying the location and signature of a 137Cs radiation source in a vehicle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

The B737 MAX 8 Accidents as Operational Experiences With Automation Transparency

Automation transparency has come into prominence in the human-machine systems literature, with researchers offering definitions, models, design frameworks, and empirical findings. However, missing from the literature are reflections on the actual operational experiences of human crews interacting with intelligent automation conceived from multiple transparency perspectives. The Boeing 737 MAX 8 accidents present a tragic case of a catastrophic failure of automation transparency. We explore this case with an emphasis on extracting design and regulatory insights for the nuclear power and other safety-critical domains.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Light Water Reactor LEU+ Lattice Optimization

Commercial light water reactor (LWR) operators and fuel vendors in the United States are exploring potential changes to nuclear fuel that include low-enriched uranium plus (LEU+) designs to further improve operational economics (e.g., extend cycle length). LEU+ fuel is fuel with a maximum enrichment between 5 wt% and 10 wt% 235 U; it allows for higher assembly burnup but likely requires additional reactivity control, e.g., increased burnable absorbers. This report examines possible LEU+ fuel lattice design changes using the lattice physics code, SCALE/Polaris. An optimization driver called the Metaheuristic Optimization Tool (MOT) is used to automate domain space exploration and optimization of LEU+ lattice designs. Heuristics from previous LWR lattice optimization studies were used to construct the objective function and define the domain space for optimization. This work successfully demonstrated that the optimization algorithms of MOT can generate feasible, nonproprietary LEU+ lattice designs (GE14 10 × 10 and Westinghouse 17 × 17) that meet the constraints of traditional LWR lattices while extending cycle length.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Power Converter Circuit Design Automation using Parallel Monte Carlo Tree Search

The tidal waves of modern electronic/electrical devices have led to increasing demands for ubiquitous application-specific power converters. A conventional manual design procedure of such power converters is computation- and labor-intensive, which involves selecting and connecting component devices, tuning component-wise parameters and control schemes, and iteratively evaluating and optimizing the design. To automate and speed up this design process, we propose an automatic framework that designs custom power converters from design specifications using Monte Carlo Tree Search. Specifically, the framework embraces the upper-confidence-bound-tree (UCT), a variant of Monte Carlo Tree Search, to automate topology space exploration with circuit design specification-encoded reward signals. Moreover, our UCT-based approach can exploit small offline data via the specially designed default policy and can run in parallel to accelerate topology space exploration. Further, it utilizes a hybrid circuit evaluation strategy to substantially reduce design evaluation costs. Empirically, we demonstrated that our framework could generate energy-efficient circuit topologies for various target voltage conversion ratios. Compared to existing automatic topology optimization strategies, the proposed method is much more computationally efficient --- the sequential version can generate topologies with the same quality while being up to 67% faster. Here, the parallelization schemes can further achieve high speedups compared to the sequential version.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Pressurized Water Reactor Gadolinia Pin Location Optimization

This report presents the results of lattice optimization studies performed to find optimum locations for gadolinia burnable absorber (BA) rods in pressurized water reactor (PWR) lattice fuel designs. Initial excess reactivity suppression allows core designers to further improve operational economics by extending cycle length. Gadolinia BAs are commonly used in boiling water reactor assembly designs for this purpose. In recent years, gadolinia absorbers have been used in PWR designs owing to their longer effectiveness for reactivity suppression compared with common BAs used in PWR assemblies. This report examines the optimum gadolinia pin placement in 17 × 17 PWR lattices at different fuel and gadolinia concentrations for optimized lattice performance, using the SCALE/Polaris lattice physics code. The completed work is continuation of the Light Water Reactor LEU+ Lattice Optimization (ORNL/TM-2021/2366) project. An optimization driver called the metaheuristic optimization tool (MOT) is used to automate domain space exploration and optimization of the lattice designs. Heuristics from previous light-water reactor (LWR) lattice optimization studies were used to construct the objective function and define the domain space for optimization. This work successfully demonstrated that the optimization algorithms of MOT can generate feasible, nonproprietary PWR lattice designs with gadolinia.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles: Preprint

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed Automated Transit Network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level are the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Exploring Domain-Wall Pinning in Ferroelectrics via Automated High-Throughput Atomic Force Microscopy

Domain-wall dynamics in ferroelectric materials are strongly position-dependent, since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of wall pinning using scanning-probe microscopy techniques. The pinning centers and pre-existing domain walls are usually sparse within the image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large-area epitaxial PbTiO 3 film on cubic KTaO 3 was investigated to quantify the electric-field-driven dynamics of the polar–strain domain structures using ML-controlled automated piezoresponse force microscopy. Analysis of 1500 switching events reveals that domain-wall displacement depends not only on field parameters but also on the local ferroelectric–ferroelastic configuration. For example, twin boundaries in polydomains regions, like a 1 – /c+ ∥ a 2 – /c – , stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20 to 30 V, whereas single-variant boundaries, like the a 2 + /c + ∥ a 2 – /c – stack, are already activated at 20 V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. Here, this microstructure-specific rule set forms the foundation for the design of ferroelectric memories.

automated scanning probe microscopy↗
DOE OSTI2022

First Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

Accurate, reliable nuclear data is essential for the success of Federal missions such as nonproliferation, nuclear forensics, homeland security, national defense, space exploration, clean energy generation, and scientific research. Data access is also key to innovative commercial developments such as new medicines, automated industrial controls, energy exploration, energy security, nuclear reactor design, and isotope production. The United States Nuclear Data Program (USNDP) is the domestic custodian of nuclear data. In its April 2022 meeting, the DOE/NSF Nuclear Science Advisory Committee was charged with preparing two reports on nuclear data. In this first report, we review recent accomplishments of the USNDP and discuss complementary and collaborative international efforts. Detailed descriptions of nuclear data needs for basic science, nonproliferation, national security, nuclear energy together with medical and space applications are also presented. Lastly, a set of specific cross-cutting nuclear data needs with relevance for multiple applications areas are also identified for further discussion in a follow-on report planned for release at the end of January 2023.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Data Management for Digital Twin Implementation: Enhancing Existing SCADA Architecture for INL's CITRC Test Distribution System

The United States power grid will continue to experience stress from aging infrastructure, extreme weather, and increasing loads. In order to strengthen resiliency, researchers need to run tests and simulations that model real-life infrastructure. Idaho National Laboratory (INL) is creating a digital twin of its Critical Infrastructure Test Range Complex (CITRC). CITRC is an at-scale grid testbed which can be configured in utility-realistic distribution scenarios for a variety of tests, such as advanced grid protection and control. A digital twin of this infrastructure would support in-depth tests/simulations without risking physical consequences, before trialing devices under test at-scale. This project focuses on the data acquisition part of constructing a digital twin. It investigates the Supervisory Data Acquisition and Control (SCADA) system of CITRC and explores ways to utilize this system for the twin. Future “ideal” at-scale implementations such as upgraded equipment, modern communication protocols, and automation applications are also explored.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

The non-adiabatic nanoreactor: towards the automated discovery of photochemistry

The ab initio nanoreactor has previously been introduced to automate reaction discovery for ground state chemistry. In this work, we present the nonadiabatic nanoreactor, an analogous framework for excited state reaction discovery. We automate the study of nonadiabatic decay mechanisms of molecules by probing the intersection seam between adiabatic electronic states with hyper-real metadynamics, sampling the branching plane for relevant conical intersections, and performing seam-constrained path searches. We illustrate the effectiveness of the nonadiabatic nanoreactor by applying it to benzene, a molecule with rich photochemistry and a wide array of photochemical products. Our study confirms the existence of several types of S 0 /S 1 and S 1 /S 2 conical intersections which mediate access to a variety of ground state stationary points. We elucidate the connections between conical intersection energy/topography and the resulting photoproduct distribution, which changes smoothly along seam space segments. The exploration is performed with minimal user input, and the protocol requires no previous knowledge of the photochemical behavior of a target molecule. We demonstrate that the nonadiabatic nanoreactor is a valuable tool for the automated exploration of photochemical reactions and their mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

The Automated Mobility District Implementation Catalog: Insights from Ten Early-Stage Deployments

Major disruptive technologies are set to redefine the way in which people view travel, particularly in dense urban areas. Already, ride-hailing services have redefined mobility expectations of a new generation of urban dwellers in some places around the country. Over the next few decades, the proliferation of autonomous vehicles, which are generally referred to in the industry as “automated vehicles (AVs),” will be enhanced by the next generation of shared mobility. This combination of AV operations with on-demand service will provide convenience of mobility similar to that being exhibited in today’s transportation networking companies (TNCs). Shared, automated, public mobility resulting from the cross-hybridization of AVs with on-demand mobility service will bring economic and system efficiencies. Economic efficiencies may be realized by less vehicle ownership and more vehicle “usership.” Many companies are already exploring avenues for shared automated mobility through fleet operations as the wave of the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Integrating Cyber-Informed Engineering into Process Automation

As organizations increasingly automate their core missions and essential functions to address business risks and enhance efficiency, process automation becomes pivotal. This shift, involving minimal or no manual intervention, significantly impacts an organization's cyber-risk landscape. While automation drives efficiencies, it also introduces new cyber risks if not properly managed. Cyber-Informed Engineering (CIE) provides a proactive framework for managing these digital risks, enhancing cyber-resilience in process automation. This document supports organizations in applying CIE principles to mitigate the cyber risks associated with automation. The outlined approach can be independently implemented to improve any organization’s cyber-resilience, ensuring that the advantages of automation do not result in unaddressed or unmanaged digital risks. It serves as a starting point, offering considerations for integrating CIE principles and practices into organizational processes. CIE is presented as an iterative process, fostering continuous improvement and reinforcing the engineering and operational cultures to manage digital risks effectively. The document is structured as follows: Section 1 provides background on CIE and process automation, and their integration. Section 2 explores the twelve CIE principles in the context of process automation, highlighting key questions, engineering considerations, and implications for digital risk management. Section 3 synthesizes the findings and offers recommendations to advance resilience by design.

42 - ENGINEERING↗
DOE OSTI2020

Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry

Reaction times, activation energies, branching ratios, yields, and many other quantitative attributes are important for precise organic syntheses and generating detailed reaction mechanisms. Often, it would be useful to be able to classify proposed reactions as fast or slow. However, quantitative chemical reaction data, especially for atom-mapped reactions, are difficult to find in existing databases. Therefore, we used automated potential energy surface exploration to generate 12,000 organic reactions involving H, C, N, and O atoms calculated at the ω B97X-D3/def2-TZVP quantum chemistry level. We report the results of geometry optimizations and frequency calculations for reactants, products, and transition states of all reactions. Additionally, we extracted atom-mapped reaction SMILES, activation energies, and enthalpies of reaction. We believe that this data will accelerate progress in automated methods for organic synthesis and reaction mechanism generation—for example, by enabling the development of novel machine learning models for quantitative reaction prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Autonomous platform for solution processing of electronic polymers

The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently processing electronic polymer solutions into thin films with specific properties remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore processing pathways for achieving high-conductivity, low-defect electronic polymers films. Leveraging importance-guided Bayesian optimization, Polybot efficiently navigates a complex 7-dimensional processing space. In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. The experimental campaign yields scale-up fabrication recipes, producing transparent conductive thin films with averaged conductivity exceeding 4500 S/cm. Feature importance analysis and morphological characterizations reveal key design factors. This work signifies a significant step towards transforming the manufacturing of electronic polymers, highlighting the potential of AI-driven automation in material science.

Wang, Chengshi [Argonne National Laboratory (ANL),↗
DOE OSTI2020

An Optimization-Based Planning Tool for On-Demand Mobility Service Operations

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the National Renewable Energy Laboratory (NREL) recently developed the Automated Mobility District (AMD) toolkit. The AMD toolkit—comprising of a travel micro-simulation model and an energy estimation model—estimates the mobility and energy impacts of a given shuttle configuration within an AMD. Early-stage AMD deployments need to find optimal operational configurations that include: (a) passenger capacity of an AES, (b) time-dependent routes, and (c) fleet size (AES units) to satisfy the demand for the region. This research extends the AMD toolkit functionality by developing an optimization-based planning module that will assist in the operations of AES units. We developed a constrained mixed-integer program accounting for passenger waiting time, battery range, and passenger capacity of AES units. For scalability, we demonstrated the Tabu search-based solution technique for a real-world network—a proposed AMD deployment in Greenville, South Carolina, USA. Compared to rule-based operations, our developed solution yields higher travel time and energy savings for the network at different demand levels. The sensitivity analyses for waiting time thresholds indicate nonlinearity in the system performance, underscoring the need to meet shared-use mobility user-level expectations. The developed optimization framework can be adapted and extended to accommodate different categories of shared-use on-demand mobility services.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Exploring Microsimulation Process for Energy Impact Evaluation of Connected and Automated Vehicles

In this paper, the authors present a microsimulation-based methodological approach for evaluating the energy impact of connected and automated vehicles (CAVs). They use an open-source micro-simulator, SUMO, and provide a way to set up a simulation environment that emulates real-world traffic dynamics. They also employ the Intelligent Driver Model to represent human drivers and calibrate its driving behavior using real-world traffic data and driving statistics. The authors conduct extensive simulation studies considering different penetration rates of CAVs, different car-following models, and varying car-following model parameters. Using the state-of-the-art Future Automotive System Technology Simulator (FASTSim), they estimate the fuel economy of each vehicle and analyze the energy impact of the given CAV implementation. Finally, the authors analyze the possible factors affecting the simulation results, and also discuss limitations and future work.

ADVANCED PROPULSION SYSTEMS↗