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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

Here, in this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system's dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system's status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT's accuracy in forecasting operational transients. The DT's rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

EBR-II↗
DOE OSTI2020

Performance Evaluation of Augmented Teleoperation of Contact Manipulation Tasks

At Argonne National Laboratory, an enhanced telerobotic concept has been suggested by providing augmented virtual fixtures for precise and dexterous manipulation. The proposed technology incorporated a human-robot interface that can assist personnel on the processes of Deactivation and Decommissioning in sites of high radiation. It has been implemented on a dual-arm robot system, known as Baxter, by integrating the Robotic Operating System (ROS) with 3D sensors and visual-haptic operator interface. However, what remained before its practical utilization is the capability for precisely placing the virtual fixtures. To this end, the objectives involved camera calibration, which required hardware fabrication and setup, as well as modification and implementation of ROS. The goal of this Research and Development is to develop an augmented-reality- based operator interface for human-inclusive, intelligent, and autonomous robotic system for the Deactivation and Decommissioning (D and D) of radioactive sites. The objectives are to develop: Collaborative Robot; Multi-modal Mixed-Reality Human-Robot Interface; Intelligent and Autonomous Behaviors. In order to achieve this, Baxter's Head-Kinect camera and Left-Hand camera need to be calibrated to overlap the real environment with the simulated environment. Camera calibration, which is crucial for precisely placing the virtual fixtures, was successful after running the simulations. The real and simulated environments overlapped, permitting the utilization of augmented teleoperation and the dexterous manipulation of Baxter for D and D applications in nuclear sites. Future steps: Performance evaluation of augmented teleoperation of contact manipulation tasks: Multiple-Camera 3D Reconstruction and Automation. Oculus Interface: Augmented reality and machine learning for enhanced telerobotic operation (Augmented Tele-autonomy): Machine-learning techniques for complex and flexible autonomous robotic behaviors. Interactive remote operation of robotic systems. Mesh cutting with simulation and real robot.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction

The operation of the neutron facility relies heavily on beamline scientists. Some experiments can take one or two days with experts making decisions along the way. Leveraging the computing power of HPC platforms and AI advances in image analyses, here we demonstrate an autonomous workflow for the single-crystal neutron diffraction experiments. The workflow consists of three components: an inference service that provides real-time AI segmentation on the image stream from the experiments conducted at the neutron facility, a continuous integration service that launches distributed training jobs on Summit to update the AI model on newly collected images, and a frontend web service to display the AI tagged images to the expert. Ultimately, the feedback can be directly fed to the equipment at the edge in deciding the next-step experiment without requiring an expert in the loop. With the analyses of the requirements and benchmarks of the performance for each component, this effort serves as the first step toward an autonomous workflow for real-time experiment steering at ORNL neutron facilities.

Yin, Junqi↗
DOE OSTI2021

Event-Driven Predictive Approach for Real-Time Volt/VAR control with CVR in solar PV rich Active Distribution Network

The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. In addition, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.

14 SOLAR ENERGY↗
DOE OSTI2023

Editorial: Deep-sea observation equipment and exploration technology

The increasing interest in deep-sea exploration is a crucial catalyst for driving advancements in deep-sea observation technology research. Numerous sophisticated sensors have been developed to facilitate comprehensive studies in deep-sea physics, chemistry, and geology. For example, cutting-edge small and compact seabed samplers are now employed on various platforms, such as Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), and Human-Operated Vehicles (HOVs), enabling observation of the hadal zone. Additionally, Micro-electromechanical systems (MEMS) sensors are routinely utilized for ocean wave observation. This Research Topic aims to foster new insights into ocean exploration and observation technology, including ocean sensor technology, ocean observation platforms, and ocean observation reliability technology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Simultaneous Measurements of Noncommuting Observables: Positive Transformations and Instrumental Lie Groups

We formulate a general program for describing and analyzing continuous, differential weak, simultaneous measurements of noncommuting observables, which focuses on describing the measuring instrument autonomously, without states. The Kraus operators of such measuring processes are time-ordered products of fundamental differential positive transformations, which generate nonunitary transformation groups that we call instrumental Lie groups. The temporal evolution of the instrument is equivalent to the diffusion of a Kraus-operator distribution function, defined relative to the invariant measure of the instrumental Lie group. This diffusion can be analyzed using Wiener path integration, stochastic differential equations, or a Fokker-Planck-Kolmogorov equation. This way of considering instrument evolution we call the Instrument Manifold Program. We relate the Instrument Manifold Program to state-based stochastic master equations. We then explain how the Instrument Manifold Program can be used to describe instrument evolution in terms of a universal cover that we call the universal instrumental Lie group, which is independent not just of states, but also of Hilbert space. The universal instrument is generically infinite dimensional, in which case the instrument’s evolution is chaotic. Special simultaneous measurements have a finite-dimensional universal instrument, in which case the instrument is considered principal, and it can be analyzed within the differential geometry of the universal instrumental Lie group. Principal instruments belong at the foundation of quantum mechanics. We consider the three most fundamental examples: measurement of a single observable, position and momentum, and the three components of angular momentum. As these measurements are performed continuously, they converge to strong simultaneous measurements. For a single observable, this results in the standard decay of coherence between inequivalent irreducible representations. For the latter two cases, it leads to a collapse within each irreducible representation onto the classical or spherical phase space, with the phase space located at the boundary of these instrumental Lie groups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Cyber-Resilient Distributed Autonomous Energy Grid

The aim of Cyber-Resilient Distributed Autonomous Grid research initiative is to advance fundamental science and engineering approaches for cyber-resilient design, control, and operation of a distributed, highly interconnected, and autonomous energy grid of the future. From increasing penetration of DERs, smart homes and building with highly controllable loads at the distribution layer, to the interconnection of bulk renewables at the transmission layer the fundamental nature of the energy grid is changing, and these changes are enabled by rapid increase in dependence on the communication infrastructure and independent third parties for control and operation of the grid. NREL's Integrated Energy Pathways critical objective correctly identifies that there are fundamental cyber-resilience challenges inherent in the grid's evolution, and this effort is establishing strong and novel integrated cyber-resilience framework and approaches to address these new and fundamental challenges.

controls↗
DOE OSTI2022

Communication breakdown: Energy efficiency recommendations to address the disconnect between building operators and occupants

As technology advances, progressive building performance goals are met with ease and occupant behavior plays an increasingly significant role in preventing building operators from achieving those goals. However, removing occupants from participating in building control and operation is counterintuitive to the purpose of building operation. Occupant-centric control (OCC) has been suggested as a means of incorporating the occupant while simultaneously reducing the negative impact their behavior can have on building performance. As a result, operators are now tasked with incorporating OCC into their daily operation. The relationship between building occupants and operators is a critical component to OCC, and balancing occupant comfort and building performance. In this paper, we present findings from an international qualitative study of building operators with a focus on the operator and occupant relationship. This paper identifies the role operators play in OCC, how these relationships develop, and how these relationships impact building operation through two key research questions: What factors influence the quality of relationships between occupants and operators? How can the relationships between operators and occupants be improved? Subsequently, these questions revealed this relationship becomes strained when building performance is prioritized over comfort, occupants are ignored or uneducated, and feedback is negative or sparse. In short: there is a disconnection between operators and occupants. Here we propose several solutions including modifying job requirements to prioritize occupant comfort, educating occupants to make autonomous decisions that are not detrimental to operation, and creating effective communication channels for instances where operator intervention is required.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Space Nuclear Power Autonomous Control Algorithm and Control Element Test Bed

Nuclear thermal rockets are currently NASA’s preferred option for use in a manned mission to Mars in the 2040s. The communication delay between an Earth ground station and a spacecraft heading toward Mars can be up to 20 min. Therefore, controlling the nuclear rocket engine would require either a full-time reactor operator on the mission or an autonomous control system for the reactor. The latter idea of making space nuclear reactors fully autonomous has drawn more interest from stakeholders, but such an autonomous control system must be rigorously tested and validated before it is certified for human use. The cost of a full ground test for a space nuclear reactor is tremendous, so a nonnuclear mock reactor test bed was created to test and validate control elements and control algorithms for space nuclear reactors. The test bed consists of control element hardware that inputs physical measurement data into a reactor emulator to produce the reactor’s performance under steady-state, transient, and fault conditions. The control element hardware consists of six full-sized control drums equipped with servo drives and motors and is instrumented with optical encoders, resolvers, and torque sensors for drum movement characterization. In addition to the drums, a two-phase flow loop was designed and built to mimic the valves and turbomachinery associated with the propellant flow through a nuclear thermal rocket engine; components such as pressure sensors, flow meters, thermocouples, and tachometers are instrumented throughout the loop to characterize the fluid flow, valve, and turbomachinery behavior of the system. The data from the physical hardware (e.g., drum position, propellant flow rates) are input to a nuclear reactor simulator to determine the actual nuclear reactor parameters, and the data are sent back to a control algorithm to complete the control loop. The ability to conduct numerous tests of the control systems and autonomous algorithms can help validate the instrumentation and control aspects for a space nuclear reactor for every possible fault situation.

Wilson, Brandon↗
DOE OSTI2026

Toward Unified Autonomous Scattering Experiments: A Cross-Facility Case Study at ALS and PETRA III

Autonomous experiments rely on the integration of control, data acquisition, analysis, and decision-making frameworks. While such systems have been demonstrated at individual facilities, adapting them to additional instruments remains challenging due to differences in local infrastructure. We present a modular workflow that connects existing open-source tools for data access (Tiled), workflow orchestration (Prefect), analysis and visualization (pyFAI, Plotly Dash), and Gaussian-process-based adaptive sampling (gpCAM) into a unified framework for autonomous scattering experiments. The same configuration operates across two synchrotron beamlines (ALS 7.3.3 and PETRA III P03) with only minimal facility-specific adjustments, as shown in proof-of-concept demonstrations. This validates that a consistent design emphasizing modularity and shared interfaces can ease deployment across diverse experimental environments. The resulting framework provides a flexible foundation for extending autonomous control and analysis capabilities beyond a single beamline or instrument.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed automated transit network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level is the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Passenger Boarding Station and Curbfront Configuration Concepts for On-Demand Services with Small Automated Vehicles: Preprint

This paper explores the various configurations of off-line stations and vehicle berthing that have been found to be fundamentally important parameters in the analysis of system performance, operations, and capacity. The proposed Automated Transit Network system concept, which provides a direct ride between a passenger's origin and destination station, is applicable to both fixed guideway automated transit and to systems with self-driving vehicles. Addressed at a conceptual level are the vehicle/station interface in terms of Americans with Disability Act compliance and station capacity concerns. The concept of open-edge platforms monitored by advanced sensing technology combined with artificial intelligence-powered perception for the purpose of protecting passengers from entering the active vehicle lanes is also discussed. Finally, the paper draws initial conclusions on how these station configurations, operational complexities, and associated costs can be better understood through appropriate research and development.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Neuromorphic Computing for Autonomous Racing

Neuromorphic computing has many opportunities in future autonomous systems, especially those that will operate at the edge. However, there are relatively few demonstrations of neuromorphic implementations on real-world applications, partly because of the lack of availability of neuromorphic hardware and software, but also because of the lack of availability of an accessible demonstration platform. In this work, we propose utilizing the F1Tenth platform as an evaluation task for neuromorphic computing. F1Tenth is a competition wherein one tenth scale cars compete in an autonomous racing task; there are significant open source resources in both software and hardware for realizing this task. We present a workflow with neuromorphic hardware, software, and training that can be used to develop a spiking neural network for neuromorphic hardware deployment to perform autonomous racing. We present initial results on utilizing this approach for this small-scale, real-world autonomous vehicle task.

Patton, Robert↗
DOE OSTI2022

Connected and automated vehicles, driving systems, and control logic for info-rich eco-autonomous driving

A method for controlling automated driving operations of a vehicle includes determining vehicle origin and destination data, and generating a graphical representation of a road network with multiple candidate routes between the vehicle's origin and destination. Road-level data, including speed, turn angle, and/or gradient data, is received for each candidate route, and respective total energy uses are estimated for the vehicle to traverse across the candidate routes. Multiple candidate driving strategies, each having respective speed and acceleration profiles, are determined for the candidate route with the lowest estimated total energy use. An optimal candidate driving strategy is selected through a cost evaluation of the associated speed and acceleration profiles and forward movement simulations of the vehicle over a prediction horizon. Command signals are transmitted to the vehicle's steering and/or powertrain systems to execute control operations based on the optimal driving strategy and the candidate route with the lowest energy use.

Zhao, Junfeng↗
DOE OSTI2024

Polarimetry-Enhanced Imaging towards Autonomous Solar Field and Receiver Inspections

During the typical operation of a Concentrating Solar Power (CSP) plant, a large portion of the energy (~45%) can be lost due to various imperfect conditions, such as blocking, shading, mirror soiling, tracking and canting errors, etc. It is necessary to develop efficient and effective field inspection technology to optically survey and characterize a CSP field, which can be used as an input for autonomous control, maintenance scheduling and spot repair whenever necessary to maximize the overall efficiency of the plant. In this project, We aim to apply polarimetric imaging for CSP collector and receiver inspection and develop polarimetric drone cameras (via integrating polarimetric imagers onto drones) for autonomous field inspection in CSP plants.

14 SOLAR ENERGY↗
DOE OSTI2025

Enhancing Autonomous Control of Microreactors Using Multi-Agent Reinforcement Learning

In order for microreactors to be economically competitive, operation costs will need to be minimized through some degree of autonomous control. Previous work has demonstrated the effectiveness of reinforcement learning (RL) for load-following control in a drum-controlled microreactor. This study extends that work by exploring the potential of RL to independently control each of the reactor’s drums. We compare a single-agent RL approach with a multi-agent RL (MARL) framework, testing them for generalization across different load-following power profiles and control timescales, and for robustness in cases of randomly disabled control drums. Since the point kinetics simulation environment used in this study cannot resolve spatial effects, we assume that in the absence of spatially localized disturbances, optimal drum movements should be symmetrical. We demonstrate that single-agent RL is able to achieve accurate performance only when symmetric actions are ignored; otherwise, it fails to train a useful controller. Meanwhile, the MARL framework performs symmetric actions by design and trains a robust, accurate agent, as evidenced by mean absolute errors in power matching of 0.41% for the training power profile, 0.68% for a profile with half the drums disabled, and 0.21% for a profile on a realistic load-following time horizon.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Trustworthiness modeling and evaluation for a nearly autonomous management and control system

The Nearly Autonomous Management and Control (NAMAC) system supports the advanced reactor operation by recommending control actions to operators based on real-time measurements and digital twins (DTs) learning from the knowledge base. To enable the safe and reliable use of autonomous technologies, NAMAC and its recommendations should be trustworthy to operators and regulators at both the design and operation stages. This study proposes a NAMAC trustworthiness modeling and evaluation framework supported by trustworthiness ontologies and evidence-based approaches. The development-time and run-time ontologies are separately constructed and then converted to Bayesian networks to quantitatively evaluate the NAMAC trustworthiness. This evaluation is demonstrated by collecting and characterizing evidence from NAMAC practices, such as the development and assessment of the NAMAC system, data coverage assessment, and the training and optimizations of neural-network-based DTs. Our proposed approach can aggregate various trustworthiness attributes of complex artificial-intelligence-supported systems for safety-critical applications. It also considers the interaction between different DTs and extends beyond the trustworthiness evaluation of a single DT. In conclusion, the evidence-based method enhances the transparency of the trustworthiness modeling and evaluation processes and helps identify uncertainties and subjectivity involved in the processes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗