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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗
DOE OSTI2023

How an Autonomous Offshore Power System Can Transform the Ocean Economy - A Hypothetical Case Study Utilizing an Autonomous Offshore Power System in Northern Lights Carbon Capture and Storage Project

An Autonomous Offshore Power System (AOPS) provides in-situ power, energy storage, real-time data and communications support, asset management, and other capabilities at sea. It has applications for all offshore industries: energy, defense and security, aquaculture, science and research, and communications. Furthermore, this paper highlights how an AOPS can reduce cost, complexity, and carbon-intensity for existing offshore operations and enable new capabilities for offshore industry leaders. This new AOPS technology has two primary advantages. First, it unlocks the autonomous, electric future of the ocean economy via ‘local’ power generation and energy storage, in addition to real-time connection to the data cloud. Second, it helps enable a material change in the global energy mix through cost-effective, reliable generation and storage technology for use cases including mobile/static data-gathering and reporting systems, operating equipment, and charging networks for uncrewed surface vessels. AOPS technology will help transform the ocean economy, and thus has implications for offshore industry leaders as they push to reduce costs today and make an autonomous and decarbonized future possible.

16 TIDAL AND WAVE POWER↗
DOE OSTI2013

A stochastic perturbation theory for non-autonomous systems

We develop a perturbation theory for a class of first order nonlinear non-autonomous stochastic ordinary differential equations that arise in climate physics. The perturbative procedure produces moments in terms of integral delay equations, whose order by order decay is characterized in a Floquet-like sense. Both additive and multiplicative sources of noise are discussed and the question of how the nature of the noise influences the results is addressed theoretically and numerically. By invoking the Martingale property, we rationalize the transformation of the underlying Stratonovich form of the model to an Ito form, independent of whether the noise is additive or multiplicative. The generality of the analysis is demonstrated by developing it both for a Brownian particle moving in a periodically forced quartic potential, which acts as a simple model of stochastic resonance, as well as for our more complex climate physics model. The validity of the approach is shown by comparison with numerical solutions. The particular climate dynamics problem upon which we focus involves a low-order model for the evolution of Arctic sea ice under the influence of increasing greenhouse gas forcing ΔF{sub 0}. The deterministic model, developed by Eisenman and Wettlaufer [“Nonlinear threshold behavior during the loss of Arctic sea ice,” Proc. Natl. Acad. Sci. U.S.A. 106(1), 28–32 (2009)] exhibits several transitions as ΔF{sub 0} increases and the stochastic analysis is used to understand the manner in which noise influences these transitions and the stability of the system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Autonomous Microgrid Restoration Using Grid-Forming Inverters and Smart Circuit Breakers

The proliferation of distributed inverter-based resources (IBRs) raises the questions if these IBRs can be used to blackstart microgrids and distribution feeders after major outages. In this paper, we propose and evaluate an autonomous microgrid restoration concept using grid-forming (GFM) IBRs and smart circuit creakers (SCBs). The concept is first explored in simulation platform and then a hardware testbed containing actual GFM inverters is developed to demonstrate these functionalities. A combination of dispatchable virtual oscillator control (dVOC) and droop-based control schemes have been designed for GFM inverter controls in the software simulations and hardware testbed. Subsequently, operation of SCBs using two distinct principles have been demonstrated that can restore or connect portions of the network.

black start↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2017

Tailoring the response of Autonomous Reactivity Control (ARC) systems

The Autonomous Reactivity Control (ARC) system was developed to ensure inherent safety of Generation IV reactors while having a minimal impact on reactor performance and economic viability. In this study we present the transient response of fast reactor cores to postulated accident scenarios with and without ARC systems installed. Using a combination of analytical methods and numerical simulation, the principles of ARC system design that assure stability and avoids oscillatory behavior have been identified. A comprehensive transient analysis study for ARC-equipped cores, including a series of Unprotected Loss of Flow (ULOF) and Unprotected Loss of Heat Sink (ULOHS) simulations, were performed for Argonne National Laboratory (ANL) Advanced Burner Reactor (ABR) designs. With carefully designed ARC-systems installed in the fuel assemblies, the cores exhibit a smooth non-oscillatory transition to stabilization at acceptable temperatures following all postulated transients. To avoid oscillations in power and temperature, the reactivity introduced per degree of temperature change in the ARC system needs to be kept below a certain threshold the value of which is system dependent, the temperature span of actuation needs to be as large as possible.

ARC↗
DOE OSTI2022

Development of a nearly autonomous management and control system for advanced reactors

This presentation discusses the development of a Nearly Autonomous Management and Control (NAMAC) system for advanced reactors. This presentation first introduces the NAMAC-enabled plant control. Next, this presentation introduces major technologies of implementing NAMAC, including operational workflow, digital twin, and advanced machine learning algorithms. To demonstrate the capability of the NAMAC system, NAMAC is connected with a simulator of the Experimental Breeder Reactor II, where NAMAC is activated to provide recommendations during a single loss of flow accident.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Statistical properties of autonomous flows in 2D active nematics

We study the dynamics of a tunable 2D active nematic liquid crystal composed of microtubules and kinesin motors confined to an oil–water interface. Kinesin motors continuously inject mechanical energy into the system through ATP hydrolysis, powering the relative microscopic sliding of adjacent microtubules, which in turn generates macroscale autonomous flows and chaotic dynamics. We use particle image velocimetry to quantify two-dimensional flows of active nematics and extract their statistical properties. In agreement with the hydrodynamic theory, we find that the vortex areas comprising the chaotic flows are exponentially distributed, which allows us to extract the characteristic system length scale. We probe the dependence of this length scale on the ATP concentration, which is the experimental knob that tunes the magnitude of the active stress. In conclusion, our data suggest a possible mapping between the ATP concentration and the active stress that is based on the Michaelis–Menten kinetics that governs the motion of individual kinesin motors.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Northern Arabian Sea Circulation-Autonomous Research (NASCar): A Research Initiative Based on Autonomous Sensors

The Arabian Sea circulation is forced by strong monsoonal winds and is characterized by vigorous seasonally reversing currents, extreme differences in sea surface salinity, localized substantial upwelling, and widespread submesoscale thermohaline structures. Its complicated sea surface temperature patterns are important for the onset and evolution of the Asian monsoon. This article describes a program that aims to elucidate the role of upper-ocean processes and atmospheric feedbacks in setting the sea surface temperature properties of the region. The wide range of spatial and temporal scales and the difficulty of accessing much of the region with ships due to piracy motivated a novel approach based on state-of-the-art autonomous ocean sensors and platforms. The extensive data set that is being collected, combined with numerical models and remote sensing data, confirms the role of planetary waves in the reversal of the Somali Current system. These data also document the fast response of the upper equatorial ocean to monsoon winds through changes in temperature and salinity and the connectivity of the surface currents across the northern Indian Ocean. New observations of thermohaline interleaving structures and mixing in setting the surface temperature properties of the northern Arabian Sea are also discussed.

54 ENVIRONMENTAL SCIENCES↗