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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

3D LiMn 2 O 4 nThin Film Deposited by ALD: A Road toward High-Capacity Electrode for 3D Li-Ion Microbatteries

Miniaturized electronics suffer from a lack of energy autonomy. In that context, the fabrication of lithium-ion solid-state microbatteries with high performance is mandatory for powering the next generation of portable electronic devices. Here, the fabrication of a thin film positive electrode for 3D Li-ion microbatteries made by the atomic layer deposition (ALD) method and in situ lithiation step is demonstrated. The 3D electrodes based on spinel LiMn 2 O 4 films operate at high working potential (4.1 V vs Li/Li + ) and are capable of delivering a remarkable surface capacity (approximate to 180 mu Ah cm -2 ) at low C-rate while maintaining more than 40 mu Ah cm -2 at C/2 (time constant = 2 h). Both the thickness of the electrode material and the 3D gain of the template are carefully tuned to maximize the electrode performance. Further, advanced characterization techniques such as transmission electron and X-ray transmission microscopies are proposed as perfect tools to study the conformality of the deposited films and the interfaces between each layer: no interdiffusion or segregation are observed. This work represents a major issue towards the fabrication of 3D-lithiated electrode by ALD-without any prelithiation step by electrochemical technique-making it an attractive solution for the fabrication of 3D Li-ion solid-state microbatteries with semiconductor processing methods.

3D Li-ion micro-batteries↗
DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗
DOE OSTI2024

Exploring the role of judgement and shared situation awareness when working with AI recommender systems

Abstract AI-advised Decision Making is a form of human-autonomy teaming in which an AI recommender system suggests a solution to a human operator, who is responsible for the final decision. This work seeks to examine the importance of judgement and shared situation awareness between humans and automated agents when interacting together in the form of a recommender systems. We propose manipulating both human judgement and shared situation awareness by providing the human decision maker with relevant information that the automated agent (AI), in the form of a recommender system, uses to generate possible courses of action. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of relevant information the participant had, the assessment technique of the proposed solution, and the reliability of the recommender system. Findings indicate that this technique of supporting the human’s judgement and establishing a shared situation awareness is effective in (1) boosting the human decision maker’s situation awareness and task performance, (2) calibrating their trust in AI teammates, and (3) reducing overreliance on an AI partner. Additionally, participants were able to pinpoint the limitations and boundaries of the AI partner’s capabilities. They were able to discern situations where the AI’s recommendations could be trusted versus instances when they should not rely on the AI’s advice. This work proposes and validates a way to provide model-agnostic transparency into recommender systems that can support the human decision maker and lead to improved team performance.

Srivastava, Divya↗
DOE OSTI2026

Perspectives for artificial intelligence in bioprocess automation

Recent advances in artificial intelligence (AI) have rapidly changed the lab automation landscape, promoting self-driving laboratories (SDLs) that enable autonomous scientific discovery. These trends are increasingly applied in bioprocess development, yet bioprocessing faces unique challenges - biological complexity, regulatory and safety requirements, and multiscale experimentation - that distinguish it from other automation domains. Rather than pursuing full autonomy, we foresee that hybrid SDLs, combining AI-driven decision-making with sustained human oversight, represent the most practical near-term trajectory. This review examines three interconnected perspectives: (i) hybrid human-machine decision-making for bioprocessing; (ii) laboratory design considerations in the era of AI; and (iii) scale-up challenges when transitioning from screening to manufacturing. We highlight critical gaps in data standardization and the required community efforts necessary to realize autonomous bioprocess innovation.

Helleckes, Laura Marie↗
DOE OSTI2025

Compact cryomodule for mobile stand-alone superconducting industrial accelerators

Superconducting RF (SRF) accelerators have the potential to be more profitable in comparison with normally conducting accelerators, as long they can be made compact, achieve operational simplicity, autonomy, trans- portability, satisfactory energy efficiency, as well as have reasonable costs. The development of SRF technologies such as Nb 3 Sn coating, conductively-cooled resonators, and high-capacity liquid-helium-free cryocoolers made SRF accelerators mobile and energetically and economically efficient at a beam power of tens of kW. One of the key elements of such an accelerator is the cryomodule, which must have minimal thermal losses to enable operation from low power cryoheads and enable conductive cooling of SRF resonator. In this research we present the design and test results of a compact cryomodule for an industrial stand-alone SRF linear accelerator proto- type, based on 650 MHz elliptical resonators, being developed at Fermi National Accelerator Laboratory.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Comparative analysis of thermal management systems in electric vehicles at extreme weather conditions: Case study on Nissan Leaf 2019 Plus, Chevrolet Bolt 2020 and Tesla Model 3 2020

With the surge in electric vehicle (EV) adoption and the need for extended driving ranges, optimizing energy efficiency, particularly through thermal management, is critical, especially in extreme weather. Managing the substantial energy needed for cabin climate control and battery temperature regulation can increase energy demands by over 50 %, severely limiting range. This study conducts a comparative analysis of thermal management systems (TMS) in three popular EV vehicles, 2020 Chevrolet Bolt, 2019 Nissan Leaf Plus, and 2020 Tesla Model 3, evaluating their distinct TMS configurations and performance under varied weather conditions. Using both numerical simulations and experimental data collected on a controlled test bench at Argonne National Laboratory, we assess how TMS architecture and operational modes influence energy consumption and range. A comprehensive TMS model was developed, integrating cabin and battery thermal sub-models in the Autonomie software platform, to simulate temperature fluctuations and range impacts. Cabin climate was modeled using a mono-zonal approach, while battery cell temperature distribution was estimated through a 2D nodal structure. Each vehicle's distinct TMS setup was evaluated: the Chevrolet Bolt and Tesla Model 3 use a dual evaporator vapor compression cycle with a PTC heater for the cabin and a coolant loop for battery thermal management; the Nissan Leaf Plus employs a heat pump with a PTC heater for the cabin and air-cooling for the battery. Tests conducted at ambient temperatures of 35°C, 22°C, -7°C, and -18°C reveal significant differences in energy use and range reduction across both configurations and conditions. At 35°C, the Tesla Model 3, Chevrolet Bolt, and Nissan Leaf Plus have a range reduction of 8%, 9%, and 13%, respectively, due to air conditioning. In winter, heating technology is paramount; at -7°C, the Nissan Leaf's heat pump configuration achieves a lower range reduction (19.3%) compared to the Tesla and Chevrolet Bolt PTC heaters, which reduce range by 28.3% and 31%, respectively. Further, this study provides valuable insights for automotive engineers, EV technology researchers, and thermal management system designers aiming to enhance electric vehicle performance by understanding how different weather conditions and TMS architectures impact energy consumption and driving range.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Community civic capacities for meaningful engagement in siting infrastructure for the energy transition

To address the driving forces of climate change and to ensure society has reliable and plentiful energy, considerable amounts of new energy infrastructure will need to be built in scores of communities over the near future. Democratic societies give communities considerable authority, influence, and autonomy on land-use decisions and regulatory policy making. Involving community members and stakeholders in decision making about facility siting and hosting is vital to minimize local opposition. But while there is much written about how to engage communities successfully, there is comparatively little attention given to understanding the civic capacities communities need to be able to participate. This paper reviews literatures on civic capacity and presents a new taxonomy based on six categories: leadership, knowledge, resources, civic engagement, social capital, and culture. It then proposes a systems framework to convey how capacities are developed and employed in collaborative decision making processes about siting and hosting energy facilities. Project sponsors, regulators, stakeholder groups, and communities can use these insights to better prepare and empower communities to participate as equal partners in conversations about energy facility siting.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Medium-scale to large-scale implementation of cyber-physical human experiments in live traffic

Autonomous Vehicles (AVs) such as cars and trucks are being developed and tested as Cyber-Physical Human systems while the technology improves. Before these systems can achieve full autonomy, some serve as tools in the form of adaptive cruise control. The CIRCLES Consortium investigates the potential for AVs to increase fuel efficiency of highway traffic by smoothing “stop-and-go” traffic waves that result from normal human driving behavior in congestion. We have performed an experiment to evaluate the real world effects of implementing this strategy. A medium-scale experiment was performed on I-24 near Nashville, TN in August 2021. This was a precursor to a larger experiment that will take place November 2022. We examine how the human part of the experiment will change as we scale up from an 11 vehicle test (four AVs) to 100 AVs. There are many solutions to problems of the medium-scale experiment that would be inconvenient, complicate the experience, or not be practicable. The medium-scale experiment involved 11 cars of which four had a custom control algorithm installed to be engaged by the driver. The large-scale experiment will have 100 cars, all with custom control algorithm installed to act on traffic when the controller is engaged. We examine key choices made for the medium experiment, and how some will be different for the large experiment. Our experience performing the medium-scale experiment has made it clear that repeating our methods from this smaller one are inefficient or impossible if used for the large-scale experiment and will be improved.

McQuade, Sean T.↗
DOE OSTI2023

Designing a transactive electric vehicle agent with customer’s participation preference

The proliferation of electric vehicles (EVs) and their inherent flexibility in charging timings make them an asset to improve grid performance. In contrast to direct control by a utility or autonomous price-based charging, the transactive control framework not only provides benefits to both grid and customers but also ensures customer autonomy. In this work, we design a transactive electric vehicle (TEV) agent that incorporates the EV owner’s willingness to trade-off between savings and amenity in form of a slider, where the EV owner’s amenity is characterized as vehicle readiness. Further, a privacy-preserving bidding formulation is proposed that also represents the customer’s transactive preference. A transactive market mechanism is discussed that integrates the TEV Agents into the local retail market and reconciles with the current day-ahead and real-time market structure. It is demonstrated that the proposed slider is able to provide a preferred trade-off between savings and amenity to individual customers. At the same time, the market mechanism is shown to successfully reduce both peak prices and peak demand. A comparative investigation of V1G and V2G technologies with respect to the battery prices is also discussed. It reveals that the V2G does not offer significant additional benefits with current battery prices, but could be promising if battery costs decline in the future.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Foam-insulated cryo-compressed hydrogen storage vessels for buses

Here, large reductions (40 % vs. compressed gaseous hydrogen vessels and 17 % vs. vacuum insulated cryo-compressed vessels) in hydrogen storage system cost are possible by operating buses with foam-insulated cryo-compressed hydrogen (CcH 2 ) vessels. Detailed thermodynamic analysis reveals that large size and rapid H 2 consumption in buses compensate for the poor performance of foam insulation (∼100 times more conductive than vacuum multilayer insulation), resulting in compact and inexpensive H 2 storage systems with sufficient H 2 capacity for the longest routes (500 km) while maintaining previously identified advantages of CcH 2 systems: increased autonomy, rapid high density refueling, and improved safety, while simultaneously avoiding vacuum insulation stability problems resulting from composite resin outgassing.

Cryo-compressed↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimization-based approaches to control of connected and automated vehicles: Principles, complexities, applications, challenges, and outlook

Safe and optimal motion control for connected and automated vehicles (CAVs) poses a fundamental optimization challenge at the intersection of system complexity, environmental uncertainty, and stringent real-time constraints. Existing surveys address this challenge in isolation – focusing either on specific control techniques or individual uncertainty sources – without providing a unified framework that characterizes the trade-offs among computational tractability, performance verifiability, and adaptive generalization across paradigms. This review addresses that gap by presenting a cohesive analytical framework concentrated on the decision-making and trajectory optimization layers of the CAV autonomy stack. We systematically analyze three major optimization paradigms – first-principles model-based optimization, data-driven methods, and hybrid synergistic architectures – evaluating each against four core complexity axes: problem formulation, constraint handling, optimality guarantees, and robustness. Key applications including platooning, trajectory planning, collision avoidance, and cooperative control are examined to reveal recurring methodological patterns and critical operational constraints that limit real-world performance. Our synthesis identifies verifiable hybrid architectures, incentive-aligned multi-agent cooperation, and hardware-algorithm co-design as the defining research frontiers, and distills a targeted agenda for developing CAV control systems that are simultaneously safe, computationally efficient, and deployable in the full complexity of real-world traffic environments.

Muzahid, Abu Jafar Md [University of Tennessee, Kn↗
DOE OSTI2022

Joint routing of conventional and range-extended electric vehicles in a large metropolitan network

Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. However, switching from electric to conventional fossil fuel modes also results in higher costs and emissions and lower efficiency. This paper analyzes this heterogeneous vehicle routing problem and describes two solution methods: an exact branch-price-and-cut algorithm and an iterated tabu search metaheuristic. From a methodological perspective, we find that the exact algorithm consistently obtains tight lower bounds that also serve to certify the metaheuristic solutions as near-optimal. From a policy standpoint, we examine a large-scale real-world case study concerning parcel deliveries in the Chicago metropolitan area and quantify various operational metrics including energy costs and vehicle miles traveled. We find that by deploying roughly 20% of range -extended vehicles with a modest all-electric range of 33 miles, parcel distributors can save energy costs by up to 17% while incurring less than 0.5% increase in vehicle miles traveled. Increasing the range to 60 miles further reduces costs by only 4%, which can alternatively be achieved by decreasing the average service time by 1 minute or increasing driver working time by 1 hour. In conclusion, our study reveals several key areas of improvement on which vehicle manufacturers, distributors, and policy makers can focus their attention.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Well-to-Wheels Analysis of Zero-Emission Plug-In Battery Electric Vehicle Technology for Medium- and Heavy-Duty Trucks

Conventional diesel medium- and heavy-duty vehicles (MHDVs) create large amount of air emissions. With the advancement in technology and reduction in the cost of batteries, plug-in battery electric vehicles (BEVs) are increasingly attractive options for improving energy efficiency and reducing air emissions of MHDVs. In this paper, we compared the well-to-wheels (WTW) greenhouse gases (GHGs) and criteria air pollutant emissions of MHD BEVs with their conventional diesel counterparts across weight classes and vocations. We expanded the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model to conduct the WTW analysis of MHDVs. The fuel economy for a wide range of MHDV weight classes and vocations, over various driving cycles, was evaluated using a high-fidelity vehicle dynamic simulation software (Autonomie). The environmental impacts of MHD BEVs are sensitive to the source of electricity used to recharge their batteries. The WTW results show that MHD BEVs significantly improve environmental sustainability of MHDVs by providing deep reductions in WTW GHGs, nitrogen oxides, volatile organic compounds, and carbon monoxide emissions, compared to conventional diesel counterparts. Increasing shares of renewable and natural gas technologies in future national and regional electricity generation are expected to reduce WTW particulate matters and sulfur oxide emissions for further improvement of the environmental performance of MHD BEVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Automated calculation and convergence of defect transport tensors

Defect diffusion is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant end-user intervention. These two bottlenecks prevent high-throughput implementations essential to propagate model-form uncertainty from interatomic interactions to predictive simulations. In order to address these issues, we extend a massively parallel accelerated sampling scheme, autonomously controlled by Bayesian estimators of statewide sampling completeness, to build atomistic kinetic Monte Carlo models on a state-space irreducible under exchange and space group symmetries. Focusing on isolated defects, we derive analytic expressions for drift and diffusion coefficients, providing a convergence metric by calculating the Kullback–Leibler divergence across the ensemble of diffusion processes consistent with the sampling uncertainty. The autonomy and efficacy of the method is demonstrated on surface trimers in tungsten and Hexa-interstitials in magnesium oxide, both of which exhibit complex, correlated migration mechanisms.

36 MATERIALS SCIENCE↗
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

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗