SEARCH · Engineering Papers

Results for “Autonomous Experiments”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Data for: A Synoptic System for Capturing Ecosystem Control Points Across Terrestrial-Aquatic Interfaces

The investigation of how climate change and water level fluctuations impact variable and interconnected ecosystems, like the interfaces between terrestrial and aquatic environments, requires the collection and integration of many data types. We describe an integrative and autonomous environmental monitoring approach that uses environmental sensors and data loggers to monitor surface water, groundwater, soil, and vegetation changes and generate essential data for predictive models. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including a large-scale flood manipulation experiment, collectively generating over three million observations per month. Such sensor networks hold great promise for tracking and comprehending environmental changes where land and water intersect. The sensor system and overall approach to sensor management that we have designed is intended to be widely accessible for research teams spanning in size from an individual investigator to large multi-institution projects. This dataset shows example data generated by the sensor network described above. Data output for data loggers connected to groundwater water quality sondes measuring dissolved oxygen, pH, oxidative redox potential (ORP), groundwater elevation, groundwater salinity and temperature, and groundwater elevation; replicate soil moisture and conductivity probes installed at 10 and 30 cm below the ground surface; rainfall and solar radiation; and mean sap flow from 8 replicate probes. Sensors are installed at upland forest (UP), wetlands (W), and transitional locations between these ecosystems (TR). Example datasets are from Goodwin Islands in the Chesapeake Bay and Portage River along the Lake Erie coastline.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

The Evolution of Airport Terminal Wayfinding Technologies

This work was presented during the Transportation Research Board (TRB) and Airport Cooperative Research Program (ACRP) webinar "Enhancing the Airport Experience with Wayfinding" on May 23, 2024. The process of navigating within airports has evolved over the years, with various technologies emerging to complement static signage. Historically, wayfinding began with static signage and airport staff members providing personal guidance, before progressing into digital signs, interactive kiosks, and displays. In recent years, wayfinding has continued along its digital path using mobile applications, indoor positioning technology, and even robot guides. With ever- larger airport facilities and a growing number of travelers, the future of wayfinding will likely be heavily linked to further digital developments such as facial recognition, augmented-reality technology, and autonomous vehicle navigation. This presentation delves into the history of interior wayfinding in airports, its present state, and the anticipated future. Key wayfinding technologies are discussed with an emphasis on emerging smartphone applications. Other considerations such as legal issues, language barriers, and human-technology interactions are included. A wayfinding framework is proposed, with static wayfinding technologies serving as the base upon which dynamic and personalized digital technologies are built. In this framework, electronic wayfinding technologies do not replace - but rather augment - traditional methods. These technologies can be integrated into existing wayfinding systems for a seamless traveler experience.

airport↗

DOE OSTI2026

Demonstration and analysis of volumetric additive manufacturing via sub-orbital spaceflight testing

Computed Axial Lithography (CAL) represents a significant advancement in the emerging field of Volumetric Additive Manufacturing (VAM). CAL addresses key limitations of traditional photopolymer additive manufacturing technologies, by eliminating the need for layering and support structures. Unlike conventional methods, CAL prints components by illuminating all points within a desired geometry simultaneously, using tomographic reconstruction to form the object in a single step. This unique approach eliminates the relative motion between the object and the precursor material, enabling faster printing speeds and reducing the waste associated with support structures. However, CAL parts require post-processing steps before they can be utilized. CAL's core attributes make it particularly suited for In-Space Manufacturing (ISM), due to its fast fabrication times, wide breadth of materials it can use, and minimized footprint. CAL has been successfully demonstrated in microgravity during parabolic flight experiments. However to fully validate and understand CAL's behaviour in microgravity, all manufacturing and post-processing steps must be integrated. In June 2024, we conducted SpaceCAL Mission 3, testing this entire workflow on a suborbital flight aboard Virgin Galactic's SpaceShipTwo. During ~140 s of microgravity, the system autonomously manufactured and post-processed four parts using PEGDA700 resin. Post-flight analysis showed that 2/4 parts were recognisable, while others were distorted due to bubble formation from residual water droplets, off-axis optical aberrations, and non-uniform solvent rinsing. Despite these limitations, this study represents the first integrated CAL workflow in space, providing an initial experimental demonstration and analysis for closed-loop in-space manufacturing.

Waddell, Taylor [Department of Mechanical Engineer↗

DOE OSTI2024

Redesigning large-scale multimodal transit networks with shared autonomous mobility services

Here, this study addresses a large-scale multimodal transit network design problem, with Shared Autonomous Mobility Services (SAMS) as both transit feeders and an origin-to-destination mode. The framework captures spatial demand and modal characteristics, considers intermodal transfers and express services, determines transit infrastructure investment and path flows, and generates transit routes. A system-optimal multimodal transit network is designed with minimum total door-to-door generalized costs of users and operators, satisfying transit origin-destination demand within a pre-set infrastructure budget. Firstly, the geography, demand, and modes in each zone are characterized with continuous approximation. The decisions of network link investment and multimodal path flows in zonal connection optimization are formulated as a minimum-cost multi-commodity network flow (MCNF) problem and solved efficiently with a mixed-integer linear programming (MILP) solver. Subsequently, the route generation problem is solved by expanding the MCNF formulation to minimize intramodal transfers. The model is illustrated through a set of experiments with the Chicago network comprised of 50 zones and seven modes, under three scenarios. The computational results present savings in traveler journey time and operator cost demonstrating the potential benefits of collaboration between multimodal transit systems and SAMS.

Autonomous vehicles↗

DOE OSTI2023

Autonomous Molecular Structure Imaging with High-Resolution Atomic Force Microscopy for Molecular Mixture Discovery

Due to its single-molecule sensitivity, high-resolution atomic force microscopy (HR-AFM) has proved to be a valuable and uniquely advantageous tool to study complex molecular mixtures, which hold promise for developing clean energy and achieving environmental sustainability. However, significant challenges remain to achieve the full potential of the sophisticated and time-consuming experiments. Automation combined with machine learning (ML) and artificial intelligence (AI) is key to overcoming these challenges. Here we present Auto-HR-AFM, an AI tool to automatically collect HR-AFM images of petroleum-based mixtures. In this study, we trained an instance segmentation model to teach Auto-HR-AFM how to recognize features in HR-AFM images. Auto-HR-AFM then uses that information to optimize the imaging by adjusting the probe-molecule distance for each molecule in the run. Auto-HR-AFM is the initial tool that will lead to fully automated scanning probe microscopy (SPM) experiments, from start to finish. This automation will allow SPM to become a mainstream characterization technique for complex mixtures, an otherwise unattainable target.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Modulation of the heart's electrical properties by the anticonvulsant drug retigabine

Retigabine, currently used as antiepileptic drug, has a wide range of potential medical uses. Administration of the drug in patients can lead to QT interval prolongation in the electrocardiogram and to cardiac arrhythmias in rare cases. This suggests that the drug may perturb the electrical properties of the heart, and the underlying mechanisms were investigated here. Effects of retigabine on currents through human cardiac ion channels, heterologously expressed in tsA-201 cells, were studied in whole-cell patch-clamp experiments. In addition, the drug's impact on the cardiac action potential was tested. This was done using ventricular cardiomyocytes isolated from Langendorff-perfused guinea pig hearts and cardiomyocytes derived from human induced pluripotent stem cells. Further, to unravel potential indirect effects of retigabine on the heart which might involve the autonomic nervous system, membrane potential and noradrenaline release from sympathetic ganglionic neurons were measured in the absence and presence of the drug. Retigabine significantly inhibited currents through hKv11.1 potassium, hNav1.5 sodium, as well as hCav1.2 calcium channels, but only in supra-therapeutic concentrations. In a similar concentration range, the drug shortened the action potential in both guinea pig and human cardiomyocytes. Therapeutic concentrations of retigabine, on the other hand, were sufficient to inhibit the activity of sympathetic ganglionic neurons. We conclude that retigabine- induced QT interval prolongation, and the reported cases of cardiac arrhythmias after application of the drug in a typical daily dose range, cannot be explained by a direct modulatory effect on cardiac ion channels. They are rather mediated by indirect actions at the level of the autonomic nervous system. - Highlights: • We study effects of anticonvulsant retigabine on the heart's electrical properties. • Retigabine does not modulate cardiac ion channels in therapeutic concentrations. • The activity of sympathetic ganglionic neurons is inhibited by retigabine. • Ganglionic block may generate QT interval prolongation and cardiac arrhythmias.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

High Performance Computing Peak Shaving for Microreactor Operation

There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Design and Validation of Mechanical & Autonomous Systems in Harsh Environments [Poster]

The autonomous systems within the Mobile Hot Cell provide advanced capabilities over traditional hot cells, pushing the boundaries of these systems in harsh environments. The degraded state of some radiological devices and the variety of designs present a series of challenges that require innovative tooling and procedural solutions. By leveraging technical knowledge and experience from disposition experts at SwRi and other institutions, initial design concepts were produced using rapid prototyping techniques. These concepts were then validated and optimized in a non-hazardous test bed, resulting in iterative improvements at minimal cost.

07 - ISOTOPES AND RADIATION SOURCES↗

DOE OSTI2021

Segmented Energy Routing for a Modular AC/DC Hybrid System

This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Exploring Large Language Model Agents in Cybersecurity: A Literature Review with Experiments

The accelerated development and integration of large language model (LLM) agents have led researchers and developers to explore their effectiveness in cybersecurity, specifically with penetration testing (pentesting). Recent research efforts have attempted to use LLM agents to automate the process of pentesting because of the cost and time requirements that are required to perform a manual review. However, not all of the tools perform as expected. This paper reviews some of the newest and most popular autonomous pentesting frameworks, highlighting the capabilities and limitations of each one with the goal of providing the components needed to successfully and effectively build an autonomous pentesting agent in the future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Periodicity, chaos, and multiple attractors in a memristor-based Shinriki's circuit

In this contribution, a novel memristor-based oscillator, obtained from Shinriki's circuit by substituting the nonlinear positive conductance with a first order memristive diode bridge, is introduced. The model is described by a continuous time four-dimensional autonomous system with smooth nonlinearities. The basic dynamical properties of the system are investigated including equilibria and stability, phase portraits, frequency spectra, bifurcation diagrams, and Lyapunov exponents' spectrum. It is found that in addition to the classical period-doubling and symmetry restoring crisis scenarios reported in the original circuit, the memristor-based oscillator experiences the unusual and striking feature of multiple attractors (i.e., coexistence of a pair of asymmetric periodic attractors with a pair of asymmetric chaotic ones) over a broad range of circuit parameters. Results of theoretical analyses are verified by laboratory experimental measurements.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

AutoTG: Reinforcement Learning-Based Symbolic Optimization for AI-Assisted Power Converter Design

Power converters are pervasive in modern electronic component design. They can be found in all electronic devices from household appliances and cellphone chargers to vehicles. Currently, designing new circuit topologies is hard because it requires human expertise based on experience and is difficult to automate. However, artificial-intelligence-assisted design can significantly facilitate the development of new power converters and/or improve the final result. Intelligently designed highly efficient power converters can have a significant effect on many important attributes, such as power efficiency, layout size, cost, heat dissemination, energy requirements, etc. We propose Autonomous Topology Generator (AutoTG), a reinforcement-learning-based framework that generates power converter topology candidates based on user specifications, optimized for user preferences. By modeling power converter design as a symbolic optimization problem, we sequentially sample components in an autoregressive manner until new topologies are formed, providing both the topology specification and the sizing (magnitude of each component parameter) of the proposed power converter. Here, we provide an empirical evaluation and show that AutoTG is able to generate varied high-efficiency topologies within component restrictions based on user input and show that previously unknown topologies can be found for further evaluation.

(AI)-based design↗

DOE OSTI2026

Navigating the Path to Autonomy: Real-World Lessons from an Air-Free Self-Driving Laboratory

While autonomous experimentation has promise to accelerate discovery in physcial sciences, the real-world integration of predictive models and experimentation is non-trivial. Here we describe the genesis of a self-driving laboratory (SDL) for air-sensitive chemistry at Argonne National Laboratory and demonstrate the experimental design considerations needed for high-throughput experiments before predictive models can lead to scientific discovery. Our SDL was designed to explore battery electrolyte stability. Our final SDL utilized plate readers in a glovebox with a nitrogen atmosphere to perform kinetic assays and screen hundreds of battery-relevant solvents. However, the roadmap to autonomy and airfree-friendly experimentation required the complex evaluation of several spectroscopic and chromatographic methods. The greatest experimental challenges were (a) developing long-term sampling methods that remained air-free; (b) accelerating kinetics to advance reactivity projections; and (c) ensuring labware compatibility with nonaqueous solvents used in battery chemistry. Our experiences highlight the practical gap between closed-loop aspirations and the realities of chemical discovery, offering lessons on the challenges of transferring every day laboratory workflows to autonomy. These results suggest a more realistic blueprint for autonomy in chemistry—one that balances thoughtful and realistic experimental formulation.

Robertson, Lily A.↗

DOE OSTI2023

Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission

Autonomous experimentation has emerged as an efficient approach to accelerate the pace of material discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials, and nanoparticles, examples of autonomous tools for physical vapor deposition are scarce yet important for the semiconductor industry. Here, we report the design and implementation of an autonomous workflow for sputter deposition of thin films with controlled composition, leveraging a highly automated sputtering reactor custom-controlled by Python, optical emission spectroscopy (OES), and a Bayesian optimization algorithm. We modeled film composition, measured by x-ray fluorescence, as a linear function of plasma emission lines monitored during co-sputtering from elemental Zn and Ti targets in an N 2 and Ar atmosphere. A Bayesian control algorithm, informed by OES, navigates the space of sputtering power to fabricate films with user-defined compositions by minimizing the absolute error between desired and measured optical emission signals. We validated our approach by autonomously fabricating Zn x Ti 1-x N y films that deviate from the targeted cation composition by a relative ±3.5%, even for 15 nm thin films, demonstrating that the proposed approach can reliably synthesize thin films with a specific composition and minimal human interference. Moreover, the proposed method can be extended to more difficult synthesis experiments where plasma intensity lines depend non-linearly on pressure, or the elemental sticking coefficients strongly depend on the substrate temperature.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Skeletides: A Modular, Simplified Physical Model of Protein Secondary Structure

Three-dimensional (3D) models are essential for visualization and conceptual understanding of complex architectures such as protein structure. Although there is a plethora of software platforms that allow digital depictions of protein structure at an atomic level in silico, physical models are needed to convey an intuitive understanding of biomolecular architecture. However, it is a challenge to represent all the relevant features of proteins in a single physical model due to their sheer structural complexity. Here, we describe a modular protein model that focuses only on representation of the secondary structure—the underlying structural skeleton. The simplified model consists of amino acid units, which can be linked together to reproduce the two most fundamental structural features of protein secondary structure: the relative positions of the amino acid alpha carbon atoms, and the intra-main chain hydrogen bonding pattern. We use 3D printing and magnets to create a set of three modular amino acid building blocks, which when linked together into a chain, can faithfully represent alpha helices, beta sheets, and turns. These simple to make models can be used to quickly assemble the alpha carbon trace of an entire protein domain, which conveys a tactile experience of the complexity of protein skeletal architecture. These models highlight the modularity of protein structure: where a single structural unit, the amino acid, can be linked together to form a larger, regular secondary structure. These models also have a propensity to spontaneously organize into alpha helices and beta sheets, as demonstrated by their ability to autonomously assemble when placed in a circulating water tank. These models provide a missing educational tool to expand knowledge of protein structure, foster deeper insight into protein folding, and inspire greater interest in biomacromolecular architecture.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Joint Fleet Sizing and Charging System Planning for Autonomous Electric Vehicles

This paper studies the joint fleet sizing and charging system planning problem for a company operating a fleet of autonomous electric vehicles (AEVs) for passenger and goods transportation. Most of the relevant published papers focus on intracity scenarios and adopt heuristic approaches, e.g., agent based simulation, which do not guarantee optimality. In contrast, we propose a mixed integer linear programming model for intercity scenarios. This model incorporates comprehensive considerations of 1) limited AEV driving range; 2) optimal AEV routing and relocating operations; 3) time-varying origin-destination transport demands; and 4) differentiated operation cost structure of passenger and goods transportation. The proposed model can be computational expensive when the scale of the transportation network is large. We then exploit the structure of this program to expedite its solution. Numerical experiments are conducted to validate the proposed method. Our experimental results show that AEVs in passenger and goods transportation have remarkable planning and operation differences. We also demonstrate that intelligent routing and relocating operations, charging system and vehicle parameters, e.g., charging power, battery capacity, driving speed etc., can significantly affect the economic efficiency and the planning results of an AEV fleet.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2013

Cascade Defect Evolution Processes: Comparison of Atomistic Methods

Determining the defect evolution beyond the molecular dynamics (MD) time scale is critical in bridging the gap between atomistic simulations and experiments. The recently developed self-evolving atomistic kinetic Monte Carlo (SEAKMC) method provides new opportunities to simulate long-term defect evolution with MD-like fidelity. In this study, SEAKMC is applied to investigate the cascade defect evolution in bcc iron. First, the evolution of a vacancy rich region is simulated and compared with results obtained using autonomous basin climbing (ABC) +KMC and kinetic activation-relaxation technique (kART) simulations. Previously, it is found the results from kART are orders of magnitude faster than ABC+KMC. The results obtained from SEAKMC are similar to kART but the time predicted is about one order of magnitude faster than kART. The fidelity of SEAKMC is confirmed by statistically relevant MD simulations at multiple higher temperatures, which proves that the saddle point sampling is close to complete in SEAKMC. The second is the irradiation-induced formation of C15 Laves phase nano-size defect clusters. In contrast to previous studies, which claim the defects can grow by capturing self-interstitials, we found these highly stable clusters can transform to <111> glissile configuration on a much longer time scale. Finally, cascade-annealing simulations using SEAKMC is compared with traditional object KMC (OKMC) method. SEAKMC predicts substantially fewer surviving defects compared with OKMC. The possible origin of this difference is discussed and a possible way to improve the accuracy of OKMC based on SEAKMC results is outlined. These studies demonstrate the atomistic fidelity of SEAKMC in comparison with other on-the-fly KMC methods and provide new information on long-term defect evolution in iron.

Xu, Haixuan↗