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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data Quality Assessment Process for Real-Time Data-Driven Traffic Microsimulation of Smart Corridor

Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2024

An integrated transportation-power system model for a decarbonizing world

Rising demand for electricity from electric vehicles (EVs) will require new paradigms to guarantee reliable and low-cost electricity. This study couples an agent-based travel demand simulator and an electricity grid model to assess the economic costs of supplying power to meet EVs' added demand across the Chicago region. Results suggest that shifting from personal EVs to a fleet of shared, fully-automated all-electric vehicles (SAEVs) could lower per-mile emissions, congestion, and embodied vehicle and charging infrastructure emissions. Further, the results should compel policymakers to shift the cost of providing power onto commercial customers, like electric ride-hail fleets, through price-indexed electricity prices, which can shift charging to off-peak periods or away from resource-scarce hours.

Integrated modeling↗
DOE OSTI2024

Towards Agentic AI on Particle Accelerators

As particle accelerators grow in complexity, traditional control methods face increasing challenges in achieving optimal performance. This paper envisions a paradigm shift: a decentralized multi-agent framework for accelerator control, powered by Large Language Models (LLMs) and distributed among autonomous agents. We present a proposition of a self-improving decentralized system where intelligent agents handle high-level tasks and communication and each agent is specialized control individual accelerator components. This approach raises some questions: What are the future applications of AI in particle accelerators? How can we implement an autonomous complex system such as a particle accelerator where agents gradually improve through experience and human feedback? What are the implications of integrating a human-in-the-loop component for labeling operational data and providing expert guidance? We show two examples, where we demonstrate viability of such architecture.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Interdependent Water And Power Infrastructure Model

The approach used is the Multi-Agent System (MAS) paradigms, where systems components are represented as agents, interacting both with each other, and with the environment in which they evolved. Agents behaviors correspond to components in the real Integrated Water-Power System. The model simulate actions and interactions of these (autonomous) agents to analyze their effects on the overall system. Agents in the water system capture components of water collection, treatment, transportation, distribution and use (e.g. pipe, canal, pump, water demands for agriculture, etc.). Agents in the power system capture components of power generation, transportation, distribution and use (electricity demands, sources, etc.).

Toba, Danho Ange Lionel [Idaho National Laboratory↗
DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2024

A novel open-source cultivation system helps establish the first full cycle chemosynthetic symbiosis model system involving the giant ciliate Zoothamnium niveum

Symbiotic interactions drive species evolution, with nutritional symbioses playing vital roles across ecosystems. Chemosynthetic symbioses are globally distributed and ecologically significant, yet the lack of model systems has hindered research progress. The giant ciliate Zoothamnium niveum and its sulfur-oxidizing symbionts represent the only known chemosynthetic symbiosis with a short life span that has been transiently cultivated in the laboratory. While it is experimentally tractable and presents a promising model system, it currently lacks an open-source, simple, and standardized cultivation setup. Following the FABricated Ecosystems (EcoFABs) model, we leveraged 3D printing and polydimethylsiloxane (PDMS) casting to develop simple flow-through cultivation chambers that can be produced and adopted by any laboratory. The streamlined manufacturing process reduces production time by 86% and cuts cost by tenfold compared to the previous system. Benchmarking using previously established optimal growth conditions, the new open-source cultivation system proves stable, efficient, more autonomous, and promotes a more prolific growth of the symbiosis. For the first time, starting from single cells, we successfully cultivated the symbiosis in flow-through chambers for 20 days, spanning multiple generations of colonies that remained symbiotic. They were transferred from chamber to chamber enabling long-term cultivation and eliminating the need for continuous field sampling. The chambers, optimized for live imaging, allowed detailed observation of the synchronized growth between the host and symbiont. Highlighting the benefit of this new system, we here describe a new step in the first hours of development where the host pauses growth, expels a coat, before resuming growth, hinting at a putative symbiont selection mechanism early in the colony life cycle. With this simple, open-source, cultivation setup, Z. niveum holds promises for comparative studies, standardization of research and wide adoption by the symbiosis research community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Prototype acoustic positioning system for the Pacific Ocean Neutrino Experiment

We present the design and initial performance characterization of the prototype acoustic positioning system intended for the Pacific Ocean Neutrino Experiment. It comprises novel piezo-acoustic receivers with dedicated filtering- and amplification electronics installed in P-ONE instruments and is complemented by a commercial system comprised of cabled and autonomous acoustic pingers for sub-sea installation manufactured by Sonardyne Ltd. We performed an in-depth characterization of the acoustic receiver electronics and their acoustic sensitivity when integrated into P-ONE pressure housings. These show absolute sensitivities of up to -125 dB re V2/μPa2 in a frequency range of 10–40 kHz. We furthermore conducted a positioning measurement campaign in the ocean by deploying three autonomous acoustic pingers on the seafloor, as well as a cabled acoustic interrogator and a P-ONE prototype module deployed from a ship. Using a simple peak-finding detection algorithm, we observe high accuracy in the tracking of relative ranging times at approximately 230–280 μs at distances of up to 1600 m, which is sufficient for positioning detectors in a cubic-kilometer detector and which can be further improved with more involved detection algorithms. The tracking accuracy is further confirmed by independent ranging of the Sonardyne system and closely follows the ship's drift in the wind measured by GPS. The absolute positioning shows the same tracking accuracy with its absolute precision only limited by the large uncertainties of the deployed pinger positions on the seafloor.

Data analysis↗
DOE OSTI2025

Development of a Discrepancy Checker for the Digital Twin in a Supervisory Control System for a Thermal Energy Delivery System

Defined as a virtual representation of a physical object, process, or service, and used to support real-world decision-making, a digital twin (DT) can be utilized to combine classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems, and to enable optimal autonomous operations. However, a DT’s usefulness largely depends on its ability to adequately mirror the state of its physical counterpart, and this adequacy should be reflected by the level of uncertainty in the underlying simulation models when estimating and predicting quantities of interest (QOIs). Moreover, simulation models in a DT may involve multiple fidelities of representations—ranging from physics-based models to data-driven ones—but classical uncertainty quantification (UQ) methods struggle to handle numerous uncertainty sources, nor are they designed for real-time applications. This work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system applied to a thermal energy delivery system (TEDS) at Idaho National Laboratory. The discrepancy checker was developed using metadata from an automated DT development process, and these metadata included different combinations of physical model forms and model parameters, training data and hyperparameters for surrogate models, and design parameters for supervisory control systems. Next, correlations between the uncertainty results and the metadata were established and then applied to the DT operations. The discrepancy checker evaluates the discrepancies between model predictions from virtual and sensor measurements and backtraces them to the corresponding major sources of uncertainty. The discrepancy checker showed reasonable performance in detecting discrepancies and diagnosing sources of uncertainty in testing scenarios.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Demonstrating autonomous controls on hardware test beds is a necessity for successful missions to Mars and beyond

NASA and the Department of Defense are planning for a mission to Mars in the 2030s–2040s using nuclear thermal propulsion (NTP). NTP uses a nuclear reactor to heat flowing hydrogen and create thrust. A serious concern for crewed and uncrewed missions to Mars is the loss of reactor control. The reactor startup and initial rocket impulse are initiated in cislunar or near-earth orbital regions; therefore, radio communications between ground control and the NTP engine should occur in real time. However, radio communications can take more than 20 min, depending on planet positions, to reach Mars orbiters from ground control. To address this delay, local autonomous controls are implemented onboard the NTP engine to ensure acceptable operation. However, autonomous controls have not been demonstrated or implemented in research or power reactor contexts because of safety and reliability concerns. To enable autonomous controls development, demonstration, and validation, Oak Ridge National Laboratory has created a nonnuclear hardware-in-the-loop test bed. Sensors throughout the test bed relay system status and hardware response to the user control algorithm, including measurements of temperature, flow, pressure of a loop, control drum position, and drum speed. This paper discusses the development of this facility and user accessibility.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A Vision–based Robust $\mathcal{H}$ ∞ Gain Scheduling Longitudinal and Lateral Following Controller for Autonomous Vehicles on Urban Curved Roads

Implementing advanced driver assistance systems (ADAS) in congested and intricate urban traffic scenarios poses significant challenges. To address the frequent stop–and–go motions exhibited by autonomous vehicles (AVs) navigating urban roads with changes in curvature, we propose a vision–based robust $\mathcal{H}$ ∞ adaptive cruise control system (ACC) for longitudinal control, plus a lane keeping assist system (LKAS) for lateral control. For the vision-based ACC, a weighted probability objective function for the vehicle following behavior is formulated. We incorporate $\mathcal{H}$ ∞ performance and gain scheduling techniques to mitigate the impact of uncertainty in visual sensor measurements. Furthermore, the optimal time headway is scheduled based on the velocity to ensure traffic flow efficiency and safety during the vehicle following process. For the LKAS, we introduce a road curvature estimation method that integrates lane and vehicle dynamics information to obtain the lateral and heading offsets. Next, the design criterion of the observer–based robust gain scheduling lateral motion controller is established by linear matrix inequality (LMI). Here, a series of experiments conducted within a camera–in–loop platform validate the proposed method.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

VOLTTRON/volttron-pnnl-aems

The Autonomous Energy Management Software (AEMS) system will continuously optimize the operations of the distributed energy resources in the small and medium size commercial building by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. Initially, AEMS system will manage rooftop air conditioners and heat pumps but it can be extended in the future to manage, hot water heaters, storage (battery and thermal), electric vehicle charging and monitoring solar photovoltaic. AEMS support both energy efficiency and grid service features.

Bleeker, Amelia [Pacific Northwest National Labora↗
DOE OSTI2026

IPC-Fusion (Infrastructure Perception and Control (IPC): Multisensor Data Fusion Software) [SWR-25-153]

As part of the National Laboratory of the Rockies' (NLR’s) Infrastructure Perception and Control Laboratory, the IPC-Fusion toolkit provides a probabilistic, scalable, multi-sensor fusion framework that integrates (late-stage fusion) heterogeneous object detection data from traffic sensors to enable robust, real-time tracking of roadway occupants. The algorithmic design of the toolkit is motivated by the need for creating a digital twin of traffic at the edge in a scalable and affordable manner. The software operates by combining object-level measurements (such as position and velocity) from a suite of sensors (such as radar, lidar, camera) using Kalman filtering and probabilistic data association techniques to overcome individual sensor limitations and achieve superior tracking performance in complex traffic zones. The framework addresses key challenges including heterogeneous measurement uncertainties, asynchronous data streams, varying spatiotemporal data resolutions, robust data association, and adaptive object lifecycle management. Validated on real-world traffic intersection data including vehicles and pedestrians, IPC-Fusion demonstrates enhanced tracking reliability across scenarios involving occlusions, sensor failures, and varying traffic densities, supporting the broader IPC initiative's goal of transforming transportation infrastructure through advanced perception capabilities for intelligent transportation systems, traffic safety applications, and autonomous vehicle support.

Sandhu, Rimple [National Laboratory of the Rockies↗
DOE OSTI2026

Zeteotech, LLC TRGR Project Final Report

Reliable detection of aerosolized pathogens is difficult due to need to distinguish between the benign bioaerosols such as dander and pollen and the thousands of pathogens capable of infecting people. Accurate identification of airborne pathogens of concern in the past has required the collection of aerosol samples in a filter, periodic collection of the samples, and processing and identification in the laboratory. This process is labor intensive and expensive. Additionally, this method necessarily has a time to detection window of hours to days depending on the collection frequency. Biological pathogens have an incubation period before the onset of symptoms and severe health effects and/or mortality, people will typically be exposed to them without realizing it. This has resulted in a detect to treat strategy for protection against bio releases. While prophylactic measures can still be effective over these time scales, reducing the time to detection will significantly improve the effectiveness of these measures and subsequently reduce the consequences of a release. Various attempts to reduce the time to detection and identification have been plagued by highly undesirable false-positives which degrade confidence in the system. Zeteotech, LLC has developed a mass spectrometer based bioaerosol sensing system which is capable of autonomously identifying airborne pathogens of concern and alert authorities within minutes instead of hours to days. They have deployed these instruments to protect high-risk facilities by alerting authorities of public health events and intentional bioterrorism events in near real time. This makes it possible to more accurately identify the time and location of the release and minimize the number of people that are exposed through prompt quarantining of affected areas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Design, Build, and Initial Testing of a Portable Methane Measurement Platform

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH 4 ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH 4 ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH 4 ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH4ERS units will be deployed in real-world environments to investigate the utility of each in the future.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2025

Vehicle Automation Benefits and Challenges for Passenger Transport System Beyond Automated Driving

The National Renewable Energy Laboratory has been researching the implementation of fully automated passenger transport systems to be operated within dense urban settings, referred to as Automated Mobility Districts, based on roadway vehicle automation as opposed to track or train-based automation. This research, now in its third phase, is presently addressing the full spectrum of benefits and challenges of full automation of passenger transport systems with respect to fleet electrification and the associated multimodal, large fleet operational management, benefits beyond simply automating the driving tasks. The focal points summarized in the paper and presentation address the benefit-analysis of fleet automation to address added complexities imposed on the multi-fleet operational management when there is a simultaneous implementation of an on-demand service mode that connects with and optimizes the effectiveness of legacy transit systems and new sub-regional autonomous vehicle fleets, with specific emphasis on enhanced ability to better meet peak ridership demand. The research also begins to address challenges in operations arising from lack of personnel present to handle unexpected customer and system needs. Combined, this research articulates vehicle automation benefits and challenges beyond simply automating the driving tasks, addressing additional operational benefits automation provides to address the added complexities imposed by electrification and on-demand modes of operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Online Bayesian State Estimation for Real-Time Monitoring of Growth Kinetics in Thin Film Synthesis

Rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, in this study, we demonstrate an approach to enable real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimation method. We developed a physical model for film growth and applied the direct filter (DF) method for real-time estimation of nucleation and growth rates during pulsed laser deposition (PLD). We validated the approach using simulated and experimental reflectivity data for WSe 2 growth and ultimately deployed the algorithm on an autonomous PLD system during the growth of 1T'-MoTe 2 . The DF robustly estimates growth parameters in real time at early stages of growth, down to 15% monolayer area coverage. This fusion of in situ diagnostics, data assimilation, and physical modeling opens new opportunities in adaptive control of synthesis trajectories toward desired material states.

36 MATERIALS SCIENCE↗