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At least 91 records · Page 5

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DOE OSTI2022

Controlling accesses to a branch prediction unit for sequences of fetch groups

An electronic device is described that handles control transfer instructions (CTIs) when executing instructions in program code. The electronic device has a processor that includes a branch prediction functional block and a sequential fetch logic functional block. The sequential fetch logic functional block determines, based on a record associated with a CTI, that a specified number of fetch groups of instructions that were previously determined to include no CTIs are to be fetched for execution in sequence following the CTI. When each of the specified number of fetch groups is fetched and prepared for execution, the sequential fetch logic prevents corresponding accesses of the branch prediction functional block for acquiring branch prediction information for instructions in that fetch group.

Yalavarti, Adithya↗

DOE OSTI2024

Experimental Study for Validation of Resilient Response of Controls - Thrust 2 Controls Assessment

The focus of this project is to design, execute, and analyze experiments around resilience response. As part of the Resilience through Data-Driven, Intelligently Designed Control (RD2C) initiative, this project builds upon the previously designed control algorithms and experiments. It designs detailed uses cases to test RD2C-developed mitigation schemes in specific realistic scenarios, analyze and save the measurement data sets. Having a high-confidence and consistent data set is crucial for identifying relevant metrics to describe the system resilience in specific real-life-like scenarios of power grid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs

This paper proposes a novel model-free/data-driven centralized training and decentralized execution multi-agent deep reinforcement learning (MADRL) framework for distribution system voltage control with high penetration of PVs. The proposed MADRL can coordinate both the real and reactive power control of PVs with existing static var compensators and battery storage systems. Unlike the existing DRL-based voltage control methods, our proposed method does not rely on a system model during both the training and execution stages. This is achieved by developing a new interaction scheme between the surrogate modeling of the original system and the multi-agent soft actor critic (MASAC) MADRL algorithm. In particular, the sparse pseudo-Gaussian process with a few-shots of measurements is utilized to construct the surrogate model of the original environment, i.e., power flow model. This is a data-driven process and no model parameters are needed. Furthermore, the MASAC enabled MADRL allows to achieve better scalability by dividing the original system into different voltage control regions with the aid of real and reactive power sensitivities to voltage, where each region is treated as an agent. This also serves as the foundation for the centralized training and decentralized execution, thus significantly reducing the communication requirements as only local measurements are required for control. Comparative results with other alternatives on the IEEE 123-nodes and 342-nodes systems demonstrate the superiority of the proposed method.

14 SOLAR ENERGY↗

DOE OSTI2025

End-to-end differentiable digital twin for the IOTA/FAST facility

As the design complexity of modern accelerators grows, there is more interest in using controllable-fidelity simulations that have fast execution time or can yield additional insights about accelerator state. One notable example of additional information are gradients of physical observables with respect to design parameters produced by differentiable simulations. The IOTA/FAST facility has recently begun a program to implement and experimentally validate an end-to-end digital twin to serve as a virtual accelerator test stand, allowing for rapid prototyping of new software and experiments with minimal beam time costs. In this contribution we will discuss our plans and progress. Specifically, we will cover the selection and benchmarking of both physics and ML codes for linac and ring simulation, the development of generic interfaces between surrogate and physics-based sections, and presenting the control interface as either a deterministic event loop or a fully asynchronous EPICS soft input/output controller. We will also discuss challenges in model calibration and uncertainty quantification, as well as future plans to implement larger proton accelerators like PIPII and Booster.

Kuklev, N. [Fermilab]↗

DOE OSTI2025

A Predictive Deep-Reinforcement-Learning-Based Connected Automated Vehicle Anticipatory Longitudinal Control in a Mixed Traffic Lane Change Condition

Maintaining safety and efficiency for mixed traffic consisting of connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is an arduous task due to the inherent HDVs’ stochasticity. Especially for longitudinal control, which is the basic function of vehicle automation, prevailing research primarily considers CAV’s car-following control merely the acceleration and deceleration of leading vehicles. However, this approach overlooks the potential disruptions caused by surrounding vehicles executing lane changes, which can significantly impact the control vehicle’s stability and overall safety. Hence, our study introduces a predictive deep reinforcement learning (DRL) longitudinal CAV controller. This innovative approach leverages prediction from a physics-informed neural network as well as the control capability of DRL to better anticipate and mitigate issues arising from lane-changing, enhancing the safety and efficiency of CAVs in such scenarios. Finally, validated by the numerical simulations embedded with the real-world data, the results indicate that the proposed controller significantly enhances the safety and efficiency of CAVs in situations involving lane changes by other vehicles, showcasing its potential as a valuable tool in advancing CAV technology in mixed traffic.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2025

A Secondary Control Framework for Microgrid Interoperability With Vendor-Agnostic Grid-Forming Units: Design, Implementation, and Demonstration via Large-Scale Hardware Setup

The reliable operation of islanded microgrids increasingly depends on secondary controls that restore voltage and frequency to nominal values and ensure accurate active and reactive power sharing. Centralized secondary control architectures achieve high accuracy through global coordination at the cost of single-point failures and limited scalability compared with decentralized/distributed approaches. But a critical gap remains in addressing the interoperability and vendor-agnostic operation of secondary controls in real-world microgrids where heterogeneous diesel generator(s) and grid-forming (GFM) inverter(s) from multiple manufacturers always coexist. Practical and vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids with multiple GFM units have not yet been developed; therefore, this paper proposes an interoperable and vendor-agnostic secondary control framework that operates seamlessly across GFM units from different vendors without relying on proprietary controls and protocols, hardware, or lock-ins. The framework leverages existing communication infrastructures (e.g., Modbus TCP/IP) to enable cost-effective deployment while addressing practical challenges, such as packet loss and quantization errors. Mitigation strategies-including data averaging, situational event-triggered control, and finite-iteration execution-are introduced to enhance reliability under real-world conditions. A generalized modeling and design framework is also presented, supported by robustness analysis to demonstrate independence from vendor-specific implementations. The proposed framework is validated through a large-scale hardware demonstration using a 3-$\phi$, 480-V, 60-Hz, 713-kVA laboratory hardware microgrid involving a heterogeneous diesel generator and multiple GFM inverters, showcasing its effectiveness in achieving stable voltage and frequency restoration and accurate power sharing under practical constraints. The results highlight the framework's potential as a scalable and practical solution for next-generation microgrids requiring openness, standard framework, and interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Latency-agnostic memory controller

A computer-implemented method includes receiving, at a memory controller, a new transaction request referencing a new transaction to be executed on a memory. The memory includes two or more memory groups embodying two or more memory technologies, and the memory controller includes two or more group request queues with a respective group request queue corresponding to each memory group of the two or more memory groups. A memory group is selected, by the memory controller, from among the two or more memory groups. The transaction request is placed, by the memory controller, on the respective group request queue corresponding to the selected memory group. The new transaction is executed on the selected memory group. A new response to the new transaction is received, by the memory controller, from the selected memory group. The new response is returned.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Probing Quantum Telecloning on Superconducting Quantum Processors

Quantum information cannot be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typically referred to as quantum cloning, and quantum teleportation. Quantum telecloning allows approximate copies of quantum information to be constructed by separate parties, using the classical results of a Bell measurement made on a prepared quantum telecloning state. Quantum telecloning can be implemented as a circuit on quantum computers using a classical coprocessor to compute classical feedforward instructions using if statements based on the results of a midcircuit Bell measurement in real time. We present universal symmetric optimal 1 → M telecloning circuits and experimentally demonstrate these quantum telecloning circuits for M = 2 up to M = 10, natively executed with real-time classical control systems on IBM Quantum superconducting processors, known as dynamic circuits. We perform the cloning procedure on many different message states across the Bloch sphere, on seven IBM Quantum processors, optionally using the error suppression technique X–X sequence digital dynamical decoupling. Two circuit optimizations are utilized: one that removes ancilla qubits for M = 2,3 , and one that reduces the total number of gates in the circuit but still uses ancilla qubits. Parallel single-qubit tomography with maximum likelihood estimation density matrix reconstruction is used in order to compute the mixed-state density matrices of the clone qubits, and clone quality is measured using quantum fidelity. These results present one of the largest and most comprehensive noisy intermediate-scale quantum computer experimental analyses on (single qubit) quantum telecloning to date. The clone fidelity sharply decreases to 0.5 for M > 5 , but for M = 2 , we are able to achieve a mean clone fidelity of up to 0.79 using dynamical decoupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2022

Control systems and methods to enable autonomous drilling

A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Buerger, Stephen↗

DOE OSTI2023

Control systems and methods to enable autonomous drilling

Buerger, Stephen↗

DOE OSTI2020

Lessons Learned from Hanford's Purex Tunnel 2 Structural Stabilization Project - 20509

In 2019, U.S. Department of Energy (DOE) contractor CH2M Hill Plateau Remediation Company (CHPRC) completed structurally stabilizing the Plutonium Uranium Extraction Plant (Purex) waste storage Tunnel 2 with engineered grout. This action significantly reduced risk to Hanford workers, the surrounding community and environment by preventing future collapse of the tunnel and possible release of radioactive materials. Decades of plutonium production at the Purex facility required construction of two large underground tunnels for storage of highly contaminated plutonium processing equipment that had failed during the operation of Purex and other facilities across the Hanford Site. Following the partial collapse of Purex Tunnel 1 on May 9, 2017, DOE and CHPRC took immediate action to safely stabilize the collapsed section of the tunnel. The hole in the tunnel was filled the next day as a temporary stabilization measure, and no contamination was detected as a result of the partial collapse of the tunnel. Workers then stabilized Tunnel 1 in its entirety by filling with engineered grout, which also encapsulated the radioactive equipment and railcars within the tunnel. Grouting eliminated the risk of further collapse and stabilized the tunnel contents, all while not precluding future remedial actions or final closure decisions. While conducting an integrity analysis of Tunnel 1 by using construction drawings, photos and other historical documents, project teams also analyzed the much larger Purex Tunnel 2. The analysis found Tunnel 2 did not meet current codes for structural integrity, and it may not be able to bear the weight of the soil above the tunnel. Therefore, it was determined that Tunnel 2 was also at risk of collapse. DoE's independent panel of experts determined grouting to be the best choice for stabilizing Tunnel 2 because it provides the highest level of stability and protection, and does not preclude future remedial actions. DOE authorized CHPRC to commence work and the Purex Tunnel 2 Structural Stabilization Project was launched in December of 2017. The paper will discuss the process of recognizing the following critical aspects and risks associated with stabilizing Tunnel 2, and the actions taken by the project team to mitigate these risks, including: - Tunnel investigation and data collection to assist with future remediation; - On-site batching and mock-ups to provide the large quantity of grout required for stabilizing the tunnel; - Workforce involvement and innovative actions that mitigated the project's most significant potential hazards, including traffic control; - Project team high level of execution translated into successful project execution. Sharing lessons learned from this critical risk-reduction effort can enhance the safety and efficiency of other complex and hazardous projects across the DOE complex. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Real-time observation of toroidal current redistributions induced by three-dimensional MHD phenomena triggering vertical displacement events in tokamak plasmas

Three-dimensional MHD instabilities, including edge-localized modes (ELMs) and internal reconnection events (IREs), have been observed to precipitate loss of vertical stability in tokamak plasmas, resulting in vertical displacement events (VDEs). This vertical destabilization can occur due to toroidal current redistributions and/or shape changes resulting from these phenomena. Using a recently introduced method for rapidly reconstructing the two-dimensional toroidal plasma current density profile in real-time, results are presented that demonstrate the specific current distribution changes that occur during ELMs (on KSTAR) and IREs (on MAST-U) that lead to loss of vertical control. The method most efficiently reconstructs the toroidal current density profile by doing so on a basis of principal components of historical profiles. These principal components isolate dominant current profile dynamics, improving interpretability, increasing speed, and reducing dimensionality of the profile computation. On KSTAR, this computation is executed in the real-time plasma control system at a rate of 10 kHz (limited by available CPU cycle times), allowing the current profile evolution to be assessed at several times over the course of each ELM event. Further, by incorporating the reconstructions into a novel vertical stability metric, the contribution of specific current profile dynamics to the loss of vertical stability can be assessed in real-time for VDE avoidance and improved understanding of the causal relationship between three-dimensional MHD phenomena and VDEs. The success of this method in approximating toroidal current density profiles from kinetic equilibrium reconstructions is also presented ($R^2=0.990$), along with its capability to produce other equilibrium quantities of interest in real-time at high time resolution.

edge-localized modes↗

DOE OSTI2024

Sentinel Devices LLC (CRADA Final Report)

Industrial equipment is a critical component of virtually all at-scale manufacturing and infrastructure. Broadly speaking, modern equipment is predominantly controlled using hardened digital controllers – computers designed to be able to operate continuously for sometimes extremely long periods of time, with little to no maintenance. Due to the nature of how these digital controllers have evolved, they are solely optimized to execute a single task, and do not have the capabilities or resources to monitor or analyze their internal state beyond simple execution of their program. As a result, for many “common sense” situations where individual data points can be easily determined to be out-of-normal, the controllers are unable to identify these incorrect operational modes unless a human has explicitly programmed in detection of this degradation. This project seeks to develop an AI/ML system which can identify incorrect or anomalous trends in industrial data streams, of exactly the kind that would be produced and seen by these digital controllers, with a minimal amount of computing resources. The benefits produced by developing this system would ultimately be self-monitoring and self-reporting infrastructure, capable of identifying and alerting humans to issues as soon as they happen, potentially long before they have the chance to impact the industrial process.

42 ENGINEERING↗

DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗

DOE OSTI2020

A physical unclonable neutron sensor for nuclear arms control inspections

Abstract Classical sensor security relies on cryptographic algorithms executed on trusted hardware. This approach has significant shortcomings, however. Hardware can be manipulated, including below transistor level, and cryptographic keys are at risk of extraction attacks. A further weakness is that sensor media themselves are assumed to be trusted, and any authentication and encryption is done ex situ and a posteriori. Here we propose and demonstrate a different approach to sensor security that does not rely on classical cryptography and trusted electronics. We designed passive sensor media that inherently produce secure and trustworthy data, and whose honest and non-malicious nature can be easily established. As a proof-of-concept, we manufactured and characterized the properties of non-electronic, physical unclonable, optically complex media sensitive to neutrons for use in a high-security scenario: the inspection of a military facility to confirm the absence or presence of nuclear weapons and fissile materials.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2022

Experimental Setup for Grid Control Device Software Updates in Supply Chain Cyber-Security

Supply chain cyberattacks that exploit insecure third-party software are a growing concern for the security of the electric power grid. These attacks seek to deploy malicious software in grid control devices during the fabrication, shipment, installation, and maintenance stages, or as part of routine software updates. Malicious software on grid control devices may inject bad data or execute bad commands, which can cause blackouts and damage power equipment. This paper describes an experimental setup to simulate the software update process of a commercial power relay as part of a hardware-in-the-loop simulation for grid supply chain cyber-security assessment. The laboratory setup was successfully utilized to study three supply chain cyber-security use cases.

Keller, Joseph↗

DOE OSTI2026

Intern Poster Session 08/13: Autonomous Nuclear Robotics: Applications in nuclear waste inspection and hot cell experiments

The nuclear industry is experiencing renewed interest in autonomous robotics, yet most deployed systems remain teleoperated with limited autonomy. This work presents two contributions toward fully autonomous nuclear robotic systems: autonomous waste inspection at the Hanford Site and an autonomous hot cell laboratory framework. Inspections of Hanford's underground waste storage tanks are performed manually at significant cost and personnel exposure. We developed a reinforcement-learning (RL) training pipeline for a custom-built inspection arm. In parallel, we are designing an autonomous laboratory framework for post-irradiation examination in hot cells at the Specimen Preparation Laboratory (SPL) that integrates computer vision, task and motion planning, hardware execution, and operator-in-the-loop control. These systems demonstrate a path toward safer, more efficient nuclear operations by reducing human exposure while maintaining rigorous human oversight at critical decision points.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗