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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 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Integrating Control Methods and Digital Twins for Advanced Nuclear Reactors

Advanced nuclear reactors offer new capabilities such as the ability to adapt to variable energy demand, operate autonomously with remote supervision, be deployable in rural locations, be compact in size, afford lower power ratings, and rely on novel techniques for increased operational safety. However, realizing these capabilities requires intelligent control systems that can track changing power demands and make autonomous decisions based on these demands. The unique aspects of advanced reactors (e.g., strict regulatory requirements, harsh operating environments, high consequences, highly coupled dynamics, evolving knowledge, and limited operating histories) directly impact the design and deployment of control systems for these reactors. The present work identifies and evaluates these aspects so as to develop a set of control system requirements to guide future research and development. To meet these requirements, a layered control system approach is proposed that integrates digital twins with different control paradigms. This work aims to demonstrate how different methods interface with enabling solutions, and to identify any gaps that need to be researched.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Data from the Development Evolution of a Vehicle for Custom Control

In order to develop custom controllers intended to operate vehicles on a live highway, a series of data collection-focused tests were performed at increasing stages of complexity. Modern vehicles with features like Adaptive Cruise Control (ACC) feature a rich set of sensors and drive-by-wire mechanisms. The presented stages of data collection begins with the analysis of raw data provided by various vehicles, and eventually results in spoofing Controller Area Network (CAN) protocols for sending control commands to operate a vehicle. This paper covers the data and technical efforts needed at various stages. The raw data and tools to plot the data are also publicly available.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2018

Efficient and Collision-Free Anticipative Cruise Control in Randomly Mixed Strings

Connected intelligent vehicle following can improve safety and efficiency compared to today's road transport, but real traffic in the near future will not provide an ideal setting for its deployment. Unconnected human-driven vehicles will follow variable behavior patterns that combine with differences in dynamic capability to create heterogeneous scenarios. In this paper, connected automated vehicles employ model predictive control for following in traffic that may include both heavy and passenger vehicles at quasi-random positions. Adding further realism, some quasi-randomly mixed vehicles are completely unconnected and equipped with the reactive intelligent driver model using pseudorandom parameters. A mixed-integer quadratic programming formulation adapts the predictive algorithm to diverse powertrain operating point constraints. The preceding vehicle's control input is estimated from velocity and brake light observations and used to probabilistically generate a preview for the ego vehicle. Here, a terminal constraint designed using particle kinematics prevents collisions due to shortsightedness. Results are simulated at various heavy and predictive vehicle concentrations in the presence of packet loss. Linear fuel economy improvements between 1.4% and 1.9% per 10% increase in predictive vehicle penetration rate are shown.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab’s Rapid-Cycling Synchrotron (RCS) operating at 15 Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster’s gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

Kafkes, Diana (ORCID:000000021716463X)↗

DOE OSTI2018

Feasibility Study for the Autonomous Radiological Sensing with UAS and an Open Source UAS Autopilot Simulator

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radiation detectors, radiological data, radiation,↗

DOE OSTI2021

BOOSTR: A Dataset for Accelerator Control Systems

The Booster Operation Optimization Sequential Time-series for Regression (BOOSTR) dataset was created to provide a cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, Fermilab's Rapid-Cycling Synchrotron (RCS) operating at 15~Hz. BOOSTR provides a time series from 55 device readings and settings that pertain most directly to the high-precision regulation of the Booster's gradient magnet power supply (GMPS). To our knowledge, this is one of the first well-documented datasets of accelerator device parameters made publicly available. We are releasing it in the hopes that it can be used to demonstrate aspects of artificial intelligence for advanced control systems, such as reinforcement learning and autonomous anomaly detection.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

AI & Physics-Based Bad Command/Data Detection in Large Power Electronics Systems: Multi-Port Autonomous Reconfigurable Solar Power Plant (MARS)

Detection of bad data from measurement sensors and bad commands from control centers need to be carried out to avoid instabilities within large power electronics systems. Towards the same, in this paper, model and data driven methods are proposed to identify anomalies in measured data and commands received by large power electronics systems. The large power electronics system considered in this paper is a multi-port autonomous reconfigurable solar power plant (MARS), which consists of photovoltaic (PV) and energy storage systems (ESSs) that connect to high-voltage direct current (HVdc) system and transmission ac power grid. The proposed algorithms in the MARS power plant to detect bad data from measurements and bad commands from control centers are evaluated in simulations and hardware-in-the-loop (HIL) tests. Furthermore, it has been observed that the proposed algorithms are able to detect bad measurements and commands in all the use cases evaluated.

14 SOLAR ENERGY↗

DOE OSTI2016

Supervisory Control System for Multi-Modular Advanced Reactors

The proposed supervisory control system (SCS) may provide considerable benefits to advanced small modular reactors, including reduced plant staffing, optimized maintenance activities, greater plant availability, and higher operating efficiency. The SCS makes risk-informed decisions based on (1) a probabilistic assessment of the likelihood of success given the status of the plant/systems and component health, and (2) a deterministic assessment between plant operating parameters and reactor protection parameters to prevent unnecessary trips and challenges to plant safety system—one measure of SCS success. The probabilistic portion of the decision-making engine of the SCS is based on the control actions associated with an advanced liquid-metal reactor (ALMR) Power Reactor, Innovative, Small Module (PRISM). Within the SCS, the probabilistic assessment provides a ranking of viable control actions; however, certain instructions generated by the probabilistic model only include an abstract notion of action without specifications. For instance, one instruction may be to reduce power without specifying how much reduction is needed. The prognostic/diagnostic models incorporate the health of components into the decision-making process. Once the control options are identified and ranked based on the likelihood of success, the SCS transmits the options to the deterministic portion of the platform. The performance-based system models assess and rank each of the probabilistically identified control actions by taking into account the physical behavior (current and projected) of the system. The performance-based decision making module receives inputs from the probabilistic decision-making module and the ERM module to generate a single solution. Interfaces to these modules are defined later in the section. A utility theory algorithm factors into the decision making by estimating the distance from and approach to a trip setpoint for each control option. If the magnitude of a negative utility value increases rapidly as the system approaches the trip setpoint, that option is not likely to be the preferred option. This can lead to a re-ranking of the control options. The SCS then transmits a control signal(s) to a component or system and informs the operator of actions taken based on the action chosen. The SCS successfully coupled probabilistic and performance-based system models to arrive at optimal control decisions based on the actual status of the plant and components. The automatic, autonomous, and real-time performance requirements for a control system were met by the SCS. The value of coupling probabilistic and performance-based system models was demonstrated by the re-ranking of control options based on the use of a utility algorithm. The use of ERM monitors provides added value to the SCS as demonstrated by the re-ranking of control options based on a components degraded state.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

Here, we present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗

DOE OSTI2020

Autonomous Strategies for Improved Performance and Reliability of Li‐Ion Batteries

Abstract The battery community has devoted significant effort to improve the performance and reliability of Li‐ion batteries through the development of new materials. The integration of autonomous function provides an alternative strategy to further extend performance. Autonomous material systems mimic the ability of biological systems to self‐protect, sense, regulate, and heal in response to damage and other environmental changes. When incorporated in batteries, these materials enable autonomic restoration of battery performance upon degradation, as well as autonomic shutdown or extinguishment in response to thermal runaway. Successful strategies for improving Li‐ion battery performance include controlled release of microencapsulated functional additives and the integration of self‐healing or adaptive interlayers and binders. These methods have been effective for stabilizing the solid electrolyte interface, preventing battery fires, restoring electrode conductivity, and suppressing Li dendrite growth. Challenges and opportunities exist in the further application of autonomous strategies in the battery environment. A greater variety of encapsulated additives with precise triggered‐release are needed to address specific battery degradation mechanisms. Incorporation of self‐healing polymers in solid‐state batteries facilitates improvement in interfacial and mechanical stability. The integration of autonomous strategies with battery management systems potentially enables enhanced control over multiple battery cycling parameters.

Zhao, Lihong↗

DOE OSTI2022

DINGO: Digital assistant to grid operators for resilience management of power distribution system

With increasing adverse weather events and disasters, enabling resiliency of the power distribution system (PDS) is becoming increasingly important. Here in this work, resiliency is defined as the systems ability to keep supplying critical loads even with multiple contingencies. Resiliency may depend on: (a) advanced tools to assist operators in situational awareness and decision making with the increasing volume of data generated by the PDS, (b) visualization and ease of interaction with system resources and information, especially during extreme events and resulting human operator stress, and (c) flexible resources and autonomous control. Operators and support engineers need to interact with the system for key information and take action under stress, given the requirement for decisions in a short time. Integrated technological solutions are prevailing steps to support the most appropriate decision during critical times to serve essential loads. In order to meet the required goals, a Real-time Resiliency Monitoring and Operational Decision Support (RT-RMOD) tool have been developed. It supports various functionalities, including real-time monitoring, resilience assessment, and proactive decision support. However, this work makes advanced feature additions to the tool by developing data-enabled resilience management algorithms for (i) outage detection and localization, (ii) Resiliency-metric driven restoration and reconfiguration, and (iii) NLP based digital assistant for operators called DINGO (DIgital assistaNt to Grid Operators) to interact with Advanced Distribution Management System (ADMS) and RT-RMOD. The developed algorithm was validated for multiple cases of weather events using a real-world, off-grid microgrid system modeled in a real-time simulator, sensor data, and software tools.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

High-dimensional multi-fidelity Bayesian optimization for quantum control

Abstract We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse problems in the quantum control of prototypical quantum systems. Our approach automatically constructs time-dependent control fields that enable transitions between initial and desired final quantum states. Most importantly, our BO approach gives impressive performance in constructing time-dependent control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide detailed descriptions of our machine learning methods as well as performance metrics for a variety of machine learning algorithms. Taken together, our results demonstrate that BO is a promising approach to efficiently and autonomously design control fields in general quantum dynamical systems.

Computer Science↗

DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2020

Synchronverter-based Control of Multi-Port Autonomous Reconfigurable Solar Plants (MARS)

Control of integrated photovoltaic (PV) plants with energy storage systems (ESSs) has become an important research and development topic in recent times. In this context, a Multi-port Autonomous Reconfigurable Solar (MARS) plant that integrates PV and ESS to alternating current transmission grid and high-voltage direct current (HVdc) link is studied in this paper. With penetration of power electronic based resources in the grid, the grid’s capability to recover from frequency or voltage disturbances are reduced. Therefore, one of the vital objectives of any new grid integrated power electronic resource is to provide advanced control functions like voltage and frequency support to the grid during disturbances. In this research work, a detailed implementation of a synchronverter-based control algorithm of MARS is presented. The proposed control algorithm and the MARS control architecture are evaluated through simulations on PSCAD/EMTDC simulation platform to showcase the performance in different operating conditions. In addition, they are evaluated in Opal-RT offline simulation models which can also be used to perform control-hardware-in-the-loop (cHIL) tests.

Marthi, Phani Ratna Vanamali↗

DOE OSTI2020

3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Coordination of thermostatically controlled loads

Apparatus and methods for a market-based control framework to coordinate a group of autonomous thermostatically controlled loads (TCL) to achieve system-level objectives with pricing incentives is disclosed. In one example of the disclosed technology, a method of providing power to a load via a power grid by submitting bids to a coordinator includes determining an energy response relating price data for one or more energy prices to quantity data for power to be consumed by the load, sending a bid for power for a finite time period based on the energy response to the coordinator, and receiving a clearing price based on: the bid, on bids received from a plurality of additional loads, and a feeder power constraint. In some examples, the energy response is based at least in part on an equivalent thermal parameter model and a control policy indicating one or more power states for the load.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Coordination of thermostatically controlled loads

Lian, Jianming↗