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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Resilient Energy Delivery and Control Systems (REDCS) (Final Technical Report)

US critical infrastructure is increasingly the target of cyberattacks, where disturbances could cause considerable damage and disruption. To help provide a new layer of cyber-physical protection for one key energy delivery system, natural gas pipelines, GE Vernova Advanced Research along with partners Florida State University and Intel Corporation created an innovative technology called "Resilient Energy Delivery and Control Systems" (REDCS). This cybersecurity package helps detect anomalies caused by cyberattacks, isolate the subsystem being impacted by the attack, and provide functions that can allow for resiliency – giving better situational awareness to the operators and cybersecurity specialists or in the future perform closed loop control for continued operation while compromised.

03 NATURAL GAS↗
DOE OSTI2023

Common Cause Failure Evaluation of High Safety Significant Safety-related Digital Instrumentation and Control Systems using IRADIC Technology

Digital instrumentation and control (DI&C) systems in nuclear power plants (NPPs) have many advantages over analog systems but also pose different engineering and technical challenges, such as potential threats due to common cause failures (CCFs). This paper proposes an integrated risk assessment technology for DI&C systems (IRADIC) developed by Idaho National Laboratory for dealing with potential software CCFs in DI&C systems of NPPs. The methodology development of the IRADIC technology on the quantitative evaluation of software CCFs in high safety-significant safety-related DI&C systems in NPPs is illustrated in this paper. In IRADIC, qualitative hazard analysis and quantitative reliability and consequence analysis are successively implemented to obtain quantitative risk information, compare with respective risk evaluation acceptance criteria, and provide suggestions for risk reduction and design optimization. A comprehensive case study was also performed and documented in this paper. Results show that the IRADIC technology can effectively identify potential digital-based CCFs, estimate their failure probabilities, and evaluate their impacts to system and plant safety.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Artificial Intelligence/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)

This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Intelligent Optimization of the Digital Low Level RF Control System for LANSCE LINAC

The LINAC at the Los Alamos Neutron Science Center (LANSCE) accelerates protons from 750 keV to its final energy at 800 MeV via 48 radio frequency (RF) modules. However, the startup and recovery process of the low-level RF (LLRF) systems, the primary controls for the RF modules, cost significant time for the beam operation, while the process itself is highly prone to human errors. With the new conversion from the analog LLRF (aLLRF) to digital LLRF (dLLRF) system under the recent LANSCE Modernization Project, new approaches with the new dLLRF capabilities can be achieved to address this issue. We propose to develop an intelligent optimization scheme that can significantly lower the downtime caused by the LLRF systems. This directly address the MFR problem statement that asks for “innovative engineering improvements to ancillary systems such as RF and pulsed power that improve reliability, maintainability, and/or performance.”

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building

As new algorithms incorporate occupancy count information into more sophisticated HVAC control, these technologies offer great potential for reductions in energy costs while enhancing flexibility. This study presents results from a two-year field evaluation of an occupancy-based HVAC control system installed in an office building. Two wings on each of the building’s 2–11 floors were equipped with occupancy counters to learn occupancy patterns. In combination with proprietary machine learning algorithms and thermal modeling, the occupancy data were leveraged to implement optimized start, early closure, and adjustments to fan operation at the air handling unit (AHU) level. This study conducted a holistic evaluation of technical performance, cost-effectiveness analysis, and user satisfaction. Results show the platform reduced weekday AHU run times by 2 h and 35 min per AHU per day during the pandemic time period. Simulation shows that 6.1% annual whole-building savings can be achieved when the building is fully occupied. The results are compared with prior studies, and potential drivers are discussed for future opportunities. The assessment results shed light on the expected in-the-field performance for researchers and industry stakeholders and enabled practical considerations as the technology strives to move beyond research-grade pilot trials into product-grade deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluating an Eco-Cooperative Automated Control System

The paper evaluates an Eco-Cooperative Automated Control (Eco-CAC) system on a large-scale network considering a combination of internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and battery-only electric vehicles (BEVs) in a microscopic traffic simulation environment. We used a novel integrated control system that: (1) routes ICEVs, HEVs, and BEVs in a fuel/energy-efficient manner; (2) selects vehicle speeds based on anticipated traffic network evolution; (3) minimizes vehicle fuel/energy consumption near signalized intersections; and (4) intelligently modulates the longitudinal motion of vehicles along freeways within a cooperative platoon to minimize fuel/energy consumption. The study tested the system using the INTEGRATION software on the Los Angeles (LA), U.S., downtown network for three different demand levels: no congestion, mild congestion, and heavy congestion. The results demonstrated that the Eco-CAC system effectively reduces vehicle fuel and energy consumption, travel time, total delay, and stopped delay in heavily congested conditions. However, different vehicle compositions produced different results. In particular, the maximum energy consumption savings for BEVs (36.9%) for a current vehicle composition occurred at a 10% market penetration rate (MPR) of connected automated vehicles (CAVs) in mild congestion, while the maximum savings for a future vehicle composition (35.5%) occurred at a 50% CAV MPR in no congestion. The system reduced fuel consumption for ICEVs and HEVs by up to 5.4% and 6.3% at a 25% CAV MPR in heavy congestion for current and future vehicle compositions, respectively. However, the system increased total fuel consumption by up to 4.6% at a 50% CAV MPR in no congestion for a current vehicle composition. The study demonstrates that the effectiveness of the Eco-CAC system depends on traffic conditions, including congestion level, network configuration, CAV MPR, and vehicle composition.

Engineering↗
DOE OSTI2026

Deploying MARTe2 and MDSplus for Scalable Real-Time Control Systems: A Proof-of-Concept for the SMART Tokamak

The SMART tokamak is a spherical tokamak with flexible shaping that will need a modular and high-performance real-time control system. This work presents the implementation and validation of the MARTe2 framework at the Princeton Plasma Physics Laboratory (PPPL) as a testbed for supporting SMART (University of Seville, Spain). The system integrates MARTe2, MDSplus for data archiving, and user datagram protocol (UDP)-based communication with both synthetic and physical devices. A deterministic control loop was developed using custom control algorithms, showing the system’s ability to operate under strict real-time constraints. The resulting framework is scalable, maintainable, and adaptable to experimental tokamaks like SMART.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Optimal Co-Design of Integrated Thermal-Electrical Networks and Control Systems for Grid-interactive Efficient District (GED) Energy Systems

This project advances a unified, open-source framework for the optimal co-design of thermal, electrical, and control systems in grid-interactive efficient districts (GEDs). As communities integrate growing levels of distributed energy resources, traditional approaches that model thermal and electrical networks independently lead to reduced efficiency, limited flexibility, and missed opportunities for coordinated operation. To address these challenges, the research team developed a comprehensive suite of physics-based models, control algorithms, and software tools that enable holistic simulation, optimization, and demonstration of district-scale energy systems.

14 SOLAR ENERGY↗
DOE OSTI2025

Evaluation of Data Lake Design for the Accelerator Control System

In this modern world, the user expects faster processing and real-time response to operate accelerator control devices. The existing framework with its infrastructure does not have the ability to satisfy these future needs. Therefore, modernization is required to reach industry standards and develop a modular framework that can provide flexibility and dynamic scalability. The data lake architecture, comprising three layers, ingestion, processing, and data consumption, provides flexibility and scalability to meet current and future demands for the accelerator control system.

Jaikar, Amol [Fermilab]↗
DOE OSTI2025

Comparing Control Performance Between Simulation and Experiment using the Microreactor Automated Control System Testbed

In the advanced reactor domain, a flexible and scalable software/hardware infrastructure is crucial for integrating and validating various control technologies. This study used the Microreactor Automated Control System (MACS) hardware platform as a testbed. MACS was originally designed to mirror Idaho National Laboratory (INL)'s Microreactor Applications Research Validation and Evaluation (MARVEL), a 85-kW thermal fission microreactor. It features control drums for simulated reactivity control; lights that function as a surrogate reactor core, with the brightness being proportional to the reactor power; and light sensors that emulate neutron detectors. To transform MACS into a physical twin of MARVEL for evaluating control methods, the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software was employed. This software integrated the hardware with two models of the MARVEL core, based on Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The study aimed to demonstrate the gap between control theory and actual practice—a gap that often necessitates empirical adjustments such as control gain retuning, filters, time discretization, and integrator anti-windup measures. Controllers were developed based on increasingly complex simulations without hardware, starting from the base MARVEL model and then introducing actuator saturation constraints and sensor noise. The final control strategy was then tested using MACS, and a comparative performance analysis was conducted.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2021

Recent Advances in Precision Clock Synchronization Protocols for Power Grid Control Systems

With the advent of a new Precision Time Protocol specification, new opportunities abound for clock synchronization possibilities within power grid control systems. The third iteration of the Institute of Electrical and Electronics Engineers Standard 1588 specification provides several new features specifically aimed at complex, wide-area deployments in which situational awareness and control require precise time agreement. This paper describes the challenges faced by existing technology, introduces the new time distribution specification, and provides examples to explain how it represents a game-changing innovation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The Automatic Reactor Control System (ARCS) Upgrade, version 2.0 for TREAT

The Transient REActor Test Facility (TREAT) recently underwent an upgraded to the Automatic Reactor Control System (ARCS). The purpose of the upgrade was to institute a new software architecture that is better suited for the programming environment, patched software bugs and applied two new segments for reactor control. This paper will focus on the new segments for reactor control. The two new control segments added to ARCS are called Generic Power and Generic Rods. The original version of ARCS provided only two power related functions, namely periods and ramps, that had to be spliced together to generate any power shape. The Generic Power segment allows the user to input data points to define the function and then the algorithm does its best to create the desired shape. The original ARCS program utilized two direct rod commands (i.e. open loop control), these were a rod stop and clip. A Generic Rod segment extends the ability of the computer to directly command any rod position. The functionality of these new segments was proven during the TREAT outage in 2024.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2022

Quantitative evaluation of common cause failures in high safety-significant safety-related digital instrumentation and control systems in nuclear power plants

Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. Here this paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework's capabilities is documented here in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

AI-Ready Control System for the Fermilab Accelerator Complex

Reliable, high-intensity operation of the Fermilab Accelerator Complex is critical to the success of the Long-Baseline Neutrino Facility and Deep Underground Neutrino Experiment. We describe the requirements and infrastructure necessary to support routine use of artificial intelligence and machine learning (AI/ML) in the accelerator control system. Three capabilities are identified: a machine learning operations (MLOps) framework standardizing the lifecycle of AI/ML automation from data management through deployment and monitoring; a data quality framework defining and enforcing standards required to build trustworthy AI/ML applications; and workflow integration with large language models to assist physicists, engineers, and operators with information retrieval, code development, and routine analysis. Use cases spanning beam diagnostics, beam control, and support system automation illustrate the technical requirements across the complex.

43 PARTICLE ACCELERATORS↗