Learning the dynamical response of nonlinear non-autonomous dynamical systems with deep operator neural networks
Not provided.
Automation & Control Systems↗
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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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Power Quality and Load Capacity Evaluations of an Electric Vehicle for Multi-Robot System Applications
This paper evaluates the capability of a fully electric pickup truck, using the Ford F-150 Lightning as an example, to provide power to the circuit of a multi-robot system. The case study was conducted on a simulated INL Autonomous Pit Exploration System (APES) designed for the inspection of nuclear waste tank pits. Through a series of controlled tests, the vehicle’s power delivery consistency, load-handling capability, and battery performance were assessed under various conditions. First of all, the load test demonstrated that the vehicle provided stable power with low distortion and no unexpected interruptions. Second, during the operational limit test, the 240V system sustained loads up to 7.4 kW before tripping, providing insights into its operational limits. Last but not least, during a simulated full-scale APES operation, the vehicle’s battery depleted by only 6% over an hour, indicating sufficient capacity for extended use while retaining reserve power for transportation needs. This study highlights the potential of electric vehicles as reliable power sources for field operations, contributing to the advancement of sustainable technologies by reducing reliance on traditional fossil fuel generators and promoting the integration of clean energy solutions in remote and challenging environments.
Electric vehicle↗
IEEE SusTech 2025 Presentation
This paper evaluates the capability of a fully electric pickup truck, using the Ford F-150 Lightning as an example, to provide power to the circuit of a multi-robot system. The case study was conducted on a simulated INL Autonomous Pit Exploration System (APES) designed for the inspection of nuclear waste tank pits. Through a series of controlled tests, the vehicle’s power delivery consistency, load-handling capability, and battery performance were assessed under various conditions. First of all, the load test demonstrated that the vehicle provided stable power with low distortion and no unexpected interruptions. Second, during the operational limit test, the 240V system sustained loads up to 7.4 kW before tripping, providing insights into its operational limits. Last but not least, during a simulated full-scale APES operation, the vehicle’s battery depleted by only 6\% over an hour, indicating sufficient capacity for extended use while retaining reserve power for transportation needs. This study highlights the potential of electric vehicles as reliable power sources for field operations, contributing to the advancement of sustainable technologies by reducing reliance on traditional fossil fuel generators and promoting the integration of clean energy solutions in remote and challenging environments.
42 - ENGINEERING↗
Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)
Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.
97 MATHEMATICS AND COMPUTING↗
Universal Passive Synchronization Method for Grid-Forming Inverters Without Mode Transition
Power systems are transforming with increasing levels of inverter-based resources (IBRs). This transformation requires critical roles of grid-forming (GFM) inverters replacing synchronous generators for bulk power system stabilization and ancillary services, also allowing flexible power system operation, such as microgrid that is operated by multiple GFM IBRs to achieve system resilience against contingencies. To realize the resilient power systems allowing flexible in-and-out operation of GFM IBRs potentially programmed with different primary controls, a synchronization method universally applicable, i.e., independent of control types, would be beneficial to ease the integration process, but it has not been actively studied. To fill the gap, this paper proposes a universal synchronization method that achieves a passive synchronization to enable a smooth transition in a grid with off-nominal system parameters, i.e., voltage and frequency. The logic proposed requires no modification on the primary control, thus applicable to any type of GFMs with a voltage reference input. To validate the concept, a simulation of an IEEE 13-bus benchmark system modified with 3 GFM inverters is presented. It simulates an inverter-driven black start scenario in which GFM inverters autonomously turn on and connect to the grid under heavy loading, using the synchronization logic. The case study demonstrates that GFM inverters can tune their voltage reference to smoothly synchronize without severe transients, and contribute to a seamless black start of the grid under unbalanced load conditions. Two GFM methods - Droop and dispatchable virtual oscillator control - are used for the demo to validate feasibility and interoperability of the passive synchronization.
black start↗
Real-time process monitoring and automated control for direct ink write 3D printing of frontally polymerizing thermosets
Additive manufacturing (AM) enables the fabrication of complex geometries, yet its application to thermosets remains limited by post-processing requirements. Frontal ring-opening metathesis polymerization (FROMP) offers a promising alternative, enabling energy-efficient, in situ curing of freestanding thermoset structures. This study presents a real-time process monitoring and automated control system for direct ink writing (DIW) of FROMP thermosets. By integrating thermochromic leuco dyes and computer vision, we enable real-time polymerization front tracking, allowing autonomous printing parameter adjustments for consistent geometries across resin formulations. The system’s accuracy was validated against manual tracking, demonstrating precise front velocity detection. Its adaptability was confirmed by printing freestanding mechanical springs with different resins, achieving consistent geometries and mechanical properties despite front velocity variations. These findings highlight the potential of automated DIW control for scalable, repeatable, and material-agnostic 3D printing of thermosets.
Mejia, Edgar Brian [Sandia National Laboratories (↗
Adaptive Reinforcement Learning Control for Power Distribution in Multi-Output Resonant Converters
This paper presents an adaptive reinforcement learning (ARL)-based control framework for efficient power distribution in a multi-output resonant converter for UAV applications. The proposed system is based on a high-frequency isolated resonant architecture, where a single energy source supplies multiple propulsion loads through independently controlled output rectifiers, addressing the need for coordinated multi-motor power management. The ARL framework dynamically allocates output power by learning optimal phase-shift control actions under varying load demands and operating conditions. The agent autonomously determines control parameters that maximize conversion efficiency while ensuring accurate power sharing among multiple outputs. In addition, the proposed approach enables adaptive operation without requiring detailed system modeling or manual tuning. Experimental results demonstrate stable and efficient performance over a wide range of operating conditions, confirming the effectiveness and robustness of the learning-based control strategy for multi-output resonant converter system.
Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
Virtual Oscillator Grid-Forming Inverters: State of the Art, Modeling, and Stability
Not provided.
Engineering↗
INTERSECT Architecture Specification: Use Case Design Patterns (V.0.5)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the federated microservice architecture specification. This document represents the science use case design pattern catalog of the INTERSECT Open Architecture Specification.
42 ENGINEERING↗
INTERSECT Architecture Specification: Microservice Architecture (V.0.5)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the microservice architecture specification. This document represents the microservice architecture specification of the INTERSECT Open Architecture Specification.
97 MATHEMATICS AND COMPUTING↗
INTERSECT Architecture Specification: System-of-Systems Architecture (V.0.9)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation.
97 MATHEMATICS AND COMPUTING↗
INTERSECT Architecture Specification: Microservice Architecture (V.0.9)
Oak Ridge National Laboratory (ORNL)’s Self-driven Experiments for Science / Interconnected Science Ecosystem (INTERSECT) architecture project, titled “An Open Federated Architecture for the Laboratory of the Future”, creates an open federated hardware/software architecture for the laboratory of the future using a novel system of systems (SoS) and microservice architecture approach, connecting scientific instruments, robot-controlled laboratories and edge/center computing/data resources to enable autonomous experiments, “self-driving” laboratories, smart manufacturing, and artificial intelligence (AI)-driven design, discovery and evaluation. The project describes science use cases as design patterns that identify and abstract the involved hardware/software components and their interactions in terms of control, work and data flow. It creates a SoS architecture of the federated hardware/software ecosystem that clarifies terms, architectural elements, the interactions between them and compliance. It further designs a federated microservice architecture, mapping science use case design patterns to the SoS architecture with loosely coupled microservices, standardized interfaces and multi programming language support. The primary deliverable of this project is an INTERSECT Open Architecture Specification, containing the science use case design pattern catalog, the federated SoS architecture specification and the microservice architecture specification. This document represents the microservice architecture specification of the INTERSECT Open Architecture Specification.
97 MATHEMATICS AND COMPUTING↗
A nuclear power plant digital twin for developing robot navigation and interaction
As robot technologies are rapidly improving, an increasing number of new ideas on utilizing robots for automated operation and maintenance tasks in nuclear power plants (NPPs) are being studied. However, due to safety concerns, researchers hardly found opportunities to test their new robot solutions on physical NPPs. In that sense, an efficient and realistic simulation environment plays a vital role in the development of automation systems for an NPP. In this paper, we propose the design of a 3D digital twin system capable of simulating NPP in real-time. This system obtains the data from a full-scope NPP simulator to reproduce the operating conditions of the plant. In addition, a scenario of a team of robots performing inspection tasks like temperature and pressure measurements will highlight its usability. This system enables development of intelligent robot swarms to deploy for inspection and maintenance purposes. It will positively impact autonomous control and operations for many types of reactors by reducing uncertainty in autonomous control and providing the tools necessary for remote intervention.
Energy & Fuels↗
Universal Utility Data Exchange (UUDEX) – Security and Administration: Cybersecurity of Energy Delivery Systems (CEDS) Research and Development
A critical component of the Universal Utility Data Exchange (UUDEX) approach is the integrated security contained within its processing. This document describes how that security is designed and expected to be implemented by UUDEX Implementations (U-Implementations), including the UUDEX Server (U-Server) and UUDEX Clients (U-Clients). The UUDEX security hierarchy consists of three levels: 1. The UUDEX Instance (U-Instance) itself, which sits at the top of the hierarchy and contains the U-Server, the UUDEX Identity Authority (U-Identity Authority), and the UUDEX Administrator (U-Administrator) functions; 2. A group of one or more UUDEX Participants (U-Participants) that present “organizations” that participate in the U-Instance and contains the UUDEX Administrator Participant (U-U-Administrator Participant) function; 3. A group of one or more UUDEX Endpoints (U-Endpoints) that represent the individual UUDEX Publish Clients (U-Publish Client) responsible for supplying data to the U-Instance that is consumed by UUDEX Subscriber Clients (U-Subscriber Clients). U-Endpoints can be either autonomous devices that publish and subscribe data such as data exchange servers found in supervisory control and data acquisition and energy management systems, or they can be tied to users of applications that, for example, submit DOE OE-417 disturbance reports. U-Participants and U-Endpoints can be organized into UUDEX Groups (U-Groups). Any number of U-Participants or U-Endpoints can be members of a U-Group. A given U-Participant or U-Endpoint can be a member of multiple U-Groups, but a U-Group cannot contain other U-Groups. For example, a U-Group could be created to contain all U-Participant Transmission Operators within the purview of a Reliability Coordinator, and another U-Group could be created to contain all U-Participant Generator Operators within the purview of a Reliability Coordinator. U-Participants that are both Transmission Operators and Generator Operators would be members of both U-Groups. U-Participants, U-Endpoints, and U-Groups are used in the access control structures to provide access to individual UUDEX Subjects (U-Subjects). U-Groups are created by the U-Administrator and are managed by the U-Administrator or the designated U-Group Managers. U-Endpoints can be assigned UUDEX Roles (U-Roles) that can be used to further restrict access. U-Roles are assigned to individual U-Endpoints. For example, a U-Role of “Security Analyst” could be used to restrict which U-Endpoints can publish or subscribe security incident reports and vulnerability notifications, while a U-Role of “Transmission Planner” can be used to restrict which U-Endpoints can publish power system model updates. U-Role definitions are created by the U-Administrator, but the U-Roles are assigned to U-Endpoints by their respective UUDEX Participant Administrators (U-Participant Administrator). Because all information required to make security decisions is either included within the U-Endpoint’s X.509 digital certificate or stored in a datastore on the U-Server, all security decisions are performed and enforced within the U-Server. This reduces the complexity of the U-Client code and minimizes the chance for compromise of the integrity of the UUDEX security features.
97 MATHEMATICS AND COMPUTING↗
Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood
Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.
Lebakula, Viswadeep↗
Supervisory Algorithm for Autonomous Hemodynamic Management Systems
Future military conflicts will require new solutions to manage combat casualties. The use of automated medical systems can potentially address this need by streamlining and augmenting the delivery of medical care in both emergency and combat trauma environments. However, in many situations, these systems may need to operate in conjunction with other autonomous and semi-autonomous devices. Management of complex patients may require multiple automated systems operating simultaneously and potentially competing with each other. Supervisory controllers capable of harmonizing multiple closed-loop systems are thus essential before multiple automated medical systems can be deployed in managing complex medical situations. The objective for this study was to develop a Supervisory Algorithm for Casualty Management (SACM) that manages decisions and interplay between two automated systems designed for management of hemorrhage control and resuscitation: an automatic extremity tourniquet system and an adaptive resuscitation controller. SACM monitors the required physiological inputs for both systems and synchronizes each respective system as needed. We present a series of trauma experiments carried out in a physiologically relevant benchtop circulatory system in which SACM must recognize extremity or internal hemorrhage, activate the corresponding algorithm to apply a tourniquet, and then resuscitate back to the target pressure setpoint. SACM continues monitoring after the initial stabilization so that additional medical changes can be quickly identified and addressed, essential to extending automation algorithms past initial trauma resuscitation into extended monitoring. Overall, SACM is an important step in transitioning automated medical systems into emergency and combat trauma situations. Future work will address further interplay between these systems and integrate additional medical systems.
42 ENGINEERING↗
5G Enabled Energy Innovation: Advanced Wireless Networks for Science (Workshop Report)
Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.
42 ENGINEERING↗
Autonomous Infrared and Small (Wide) Angle X-Ray Scattering (IR-S(W)AXS) Capability
Thin water films are 2-D, nanoconfined layers that form on solid surfaces exposed to humid atmospheres—environments ubiquitous across catalysis, corrosion science, soil science, and subsurface geochemistry. At relative humidity (RH) values below saturation, these films are Å–nm thick and exhibit properties that differ sharply from bulk water, including disrupted H-bonding and impeded mass transport. Owing to their high surface-to-volume ratio, dissolution of the solid can rapidly drive strong supersaturation with respect to secondary phases. Reactivity in thin water films is highly sensitive to film thickness, and critically, thickness evolves during reaction because the hygroscopicity of the interfacial system changes as ions accumulate or diminish in the film and as reaction products transform. To accurately probe and control these dynamics, a capability is needed that can measure and automatically maintain a constant water-film thickness while simultaneously monitoring solid dissolution, nucleation, and growth. This project developed an autonomous Infrared/Small Angle X-ray Scattering-Wide Angle X-ray Scattering (IR/(W)SAXS) for investigating reactivity in thin water films on solid surfaces exposed to humidified gases. The capability consists of an IR spectrometer, a (W)SAXS instrument, and a mass flow controller system for generating variably humidified gas flows to a custom reaction cell. Progress on each of the major components of the capability are detailed below.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗