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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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Learning-based anticipatory control advances the level of autonomy of microreactor control systems, a key consideration for the unattended operation of fission batteries. In this work, an anticipatory control system is shown to provide accurate load-following for a microreactor by utilizing a model predictive control framework.
The Behavior, Energy, Autonomy, and Mobility Comprehensive Regional Evaluator (BEAM CORE) is an open-source, modular ecosystem of highly refined, agent-based modeling tools developed by Lawrence Berkeley National Laboratory and the National Laboratory of the Rockies. Organizations such as metropolitan planning organizations, agencies, and companies can use BEAM CORE to analyze freight movement and optimize logistics solutions, assess the impacts of emerging freight technologies or e-commerce trends, model dynamic population growth and evolution, and understand the drivers and impacts of electric vehicle adoption across households in a given region. Users can choose from a flexible suite of modeling modules according to their needs and priorities. The modules integrate with travel demand models to support enhanced analysis of diverse scenarios involving freight movement, vehicle technologies, and other key factors relevant to regional planning.
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.
Yearly, hundreds of new scientists and engineers start their independent research programs at universities, government laboratories or other institutions. Every individual embarks on this pathway with some idea about what research directions they wish to undertake, but often, with little insight into how their research career may evolve over time. Additionally, there are relatively few descriptions of how the research programs of other scientists and engineers have developed over time. In most cases where such overviews are available, they are written towards the end of a researcher's career, at a point far from the early development of the program. In this work, I provide my personal thoughts and observations on the development and evolution of the first 16 years of my research program as a case study. This overview does not intend to suggest this particular case as an exemplar, but rather, provides a single example of how one program evolved. In the course of the description, I try to identify general observations and suggestions that might aid a new researcher in the chemical sciences or engineering in the development of their research program. Specifically, the focus is on a program where the newly independent investigator has significant autonomy to select research topics of her/his choice (e.g., many universities, some government or corporate labs), although it is recognized that in many organizations, a researcher must fit into and adapt to the specific needs of the host institution. A particular aspect of my research program is that it blends research on (1) materials chemistry, (2) catalysis and reaction engineering, and (3) separations. As such, I will draw from specific examples of my own work in these fields as part of the discussion. However, the reader should note that this perspective is not a traditional overview of the full breadth of the title research topic, although my group has written such reviews and perspectives on a variety of the subjects covered here.1-6 Instead, here I discuss how my program evolved in stages, including (1) the search for a position, (2) launching your laboratory, (3) development of research themes and finishing with (4) a summary of advice for researchers starting their independent laboratories.
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.
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.
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.
Side-by-side comparisons were made over solstice-to-solstice changes in sun and sky conditions between an optical louver system (OLS) and a conventional Venetian blind set at a horizontal slat angle and located inboard of a south-facing, small-area, clerestory window in a full-scale office testbed. Daylight autonomy (DA), window luminance, and ceiling luminance uniformity were used to assess performance. The performance of both systems was found to have significant seasonal variation, where performance under clear sky conditions improved as maximum solar altitude angles transitioned from solstice to equinox. Although the OLS produced fewer hours per day of DA on average than the Venetian blind, the OLS never exceeded the designated 2000 cd/m2 threshold for window glare. In contrast, the Venetian blind was found to exceed the visual discomfort threshold over a large fraction of the day during equinox conditions. Notably, these peak periods of visual discomfort occurred during the best periods of daylighting performance. Luminance uniformity was analyzed using calibrated high dynamic range luminance images. Under clear sky conditions, the OLS was found to increase the luminance of the ceiling as well as produce a more uniform distribution. Furthermore, compared to conventional venetian blinds, the static optical sunlight redirecting system studied has the potential to significantly reduce the annual electrical lighting energy demand of a daylit space and improve the quality from the perspective of building occupants by consistently transmitting useful daylight while eliminating window glare.
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.
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.
Highlights: • Failures in environmental reporting compromise the effectiveness of the environmental license process • The infrastructure of environmental agencies must be improved • The participation of society is essential for monitoring industries - Abstract: The crushed stone industries' activities are characterized by the extraction and processing of stones for use as aggregates in construction. The sector causes environmental impacts that can be mitigated by the implementation of control instruments, namely, environmental licensing, whose purpose is to carry out monitoring of potentially polluting activities. In this paper, it was analyzed the environmental studies of three industries located in Monsenhor Gil, Piauí, Brazil, elaborated as requisites for obtaining the environmental and mineral licenses. There were made visits to the State Department of Environment and Water Resources (SEMAR) and to the National Department of Mineral Production (DNPM) to verify the environmental reports and the environmental and mineral licensing processes. There were also made visits to the analyzed industries, with the aim of comparing the information contained in the studies, with the reality and verifying the implementation of the mitigating measures proposed in them, through the observation of the industries operation, and interviews with the responsible managers. Furthermore, it was made a research in the scientific literature, to know the licensing process in other countries, to compare with the Brazilian reality. From the research, in Brazilian industries, it was observed that, although all the legal steps to obtain the licenses have been fulfilled, the environmental reports have elaboration failures, such as the lack of a multidisciplinary team and the fact that some data diverge from the reality, decreasing the reliability of the evaluation of the environmental impacts caused by the projects. Also, the environmental replacement cost is not accounted in the final value of the product, and any of the industries have started the implementation of the proposed mitigation measures since they do not have mineral depletion areas. SEMAR was delayed in issuing licenses within the period established by law, and there are no periodic inspections by both the environmental agency and DNPM. It should also be pointed out that the community did not claim public hearings and there is no participation in environmental education projects, factors that compromise the effectiveness of the environmental licensing process. Regarding the process in other parts of the world, in some countries, such as Uruguay and Paraguay, the agency responsible for licensing and inspection is federal. In Argentina, as in Brazil and USA, states have autonomy to legislate on environmental issues. There is also divergence in the period of execution of the licenses, being 12 months in Brazil, while in Thailand it can be up to 56 days. In Peru, the society participation is much more representative than in Brazil, and there is differentiation in the collection form of environmental compensation fees, among countries. The failures pointed out in the Brazilian process, as well as the system of other countries, can serve as parameters for the improvement of the process' effectiveness, both in Brazil and in other parts of the world, aiming at the conservation of the environment.
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.
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.
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.
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.
Cryogenic pressurized hydrogen (H 2 ) vessels promise maximum storage density with potential to enabling practical H 2 vehicles with maximum driving autonomy and minimum cost of ownership. This paper adds a more complete evaluation of the benefits of cryogenic vessel technology by establishing a methodology for evaluating fill density for any initial vessel thermodynamic state. This is accomplished by assessing 24 cryogenic pressure vessel fill experiments with a liquid hydrogen (LH 2 ) pump manufactured by Linde and installed at the Lawrence Livermore National Laboratory (Livermore, CA) campus. The LH 2 piston pump takes LH 2 from the station Dewar at near ambient pressure (3 bar) and very low temperature (24.6 K) and pressurizes it to the vessel pressure in two stages of compression, up to 875 bar, although the rating of the cryogenic vessel used for these experiments limits fill pressure to 345 bar. Experiments spanned initial vessel temperatures from ambient to 22 K, enabling pump testing over a broad range of conditions. Experimental results validate many of the virtues that make LH 2 pumping a promising technology for H 2 dispensing, both for filling cryogenic as well as ambient temperature vessels: high throughput (1.67 kg/min, 100 kg/h), unlimited capacity for back to back refueling, low electricity consumption at the station due to high density of LH 2 minimizing compression work, and highest fill density, up to 80 g/L (estimated) when dispensing cryogenic hydrogen at 700 + bar. Analysis of the experiments shows that fill density can be predicted with reasonable accuracy (±0.7 g/L) by assuming 10 kJ/kg K cryogenic vessel inlet entropy (pump delivery hose outlet entropy). Fill lines are finally generated on a H 2 phase diagram, producing charts that can be used for rapidly determining fill density for any vessel condition at the moment of fill initiation.
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.
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.