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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating Control Systems with GitOps: A Path to Automation and Reliability

GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN and Diamond Light Source among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab’s control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.

Gonzalez, M. [Fermilab]↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2024

Using a particle-in-cell model for accelerator control room applications

Many accelerator control rooms rely on envelope models to simulate beam dynamics because they are fast and accurate at tracking the beam core. Particle-in-Cell (PIC) models, however, can track particles inside and outside the core and, with the improvements of computers, are now fast enough to be used in control rooms. The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory is currently developing a tool to use a Particle-in-Cell model for control room applications. This report covers the progress so far and the future goals of using PyORBIT, a Particle-in-Cell simulation model, in the SNS control room.

Cathey, Brandon↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Advanced Rooftop Unit Control

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. Advanced rooftop unit controls (ARC) generally consist of a supply fan VFD, and controls to implement demand controlled ventilation (DCV) and air-side economizing, as a retrofit to improve energy efficiency of existing rooftop units (RTUs).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

A New Approach to Robot Motor Control

This poster details the motor control improvements to Fermilab's Remote Viewing Robot (RVR). It is a robot tasked with remotely investigating issues within the accelerator tunnels at Fermilab. Initially controlled by a single Raspberry Pi that housed all the robot s operations, the RVR will now use a Raspberry Pi Pico W for its motor control. This was achieved using Pulse Width Modulation (PWM) to allow for precise speed and torque control for the robot. This enhances the robot s reliability. By addressing the challenge of navigating high radiation environments, the upgrade helps assist the RVR s goal of decreasing the need for human intervention in accelerator tunnels.

Lopez, Jacob↗
DOE OSTI2024

Lightweight Embedded Controller in Advanced FPGA SoC for Radar Signal Processing [Poster]

The objective of the project is to demonstrate that critical control functions can be implemented using little resources in modern microelectronics. A finite state machine (Figure 1) is implemented onto a field programmable gate array (FPGA). The functionality of the system is demonstrated by sending binary instructions to the controller. The controller transmits patterns through an LED, controls an electromechanical device, and uses pulse-width modulation (PWM) for radar functions.

42 ENGINEERING↗
DOE OSTI2024

Transmission Operator Workflows for Real-Time Reliability Studies: A Review of Control Room Practices and Naturalistic Decision Making

This report provides an overview of real-time reliability study tools and their use by power system operators in the control room environment. After introducing some of the nuances of the control room environment and the differences in perspectives between power system engineers and operators, the roles and responsibilities of key entities involved in RTCA workflows are introduced. These are specifically the transmission system operator (TOP) and reliability coordinator (RC), which are required to run tools such as real-time contingency analysis (RTCA) as part of a real-time reliability assessment every 30 minutes, as dictated by a series of standards issued by the North American Electric Reliability Corporation (NERC). The process by which power systems operators operate the grid is discussed in terms of naturalistic decision making (NDM) and the recognition-primed decision-making (RPD) model. This cognitive model describe how experts working in high-risk, high-stress environments make safety-critical decisions under uncertainty and time pressure. For power system operators, the mental simulations involved in the traditional RPD model are supplemented by physics-based simulations using numerical tools, such as RTCA, to improve situational awareness and effectiveness of control actions. Next, a generic workflow is introduced to describe operator decision making for running RTCA tools and responding to system violations on a pre-contingent basis. The types of analysis performed and control actions chosen by power system operators are described in detail. The overall high-level workflow is then expanded in subsequent sections, with special attention given to high-voltage violations, low-voltage violations, and thermal overloads. Each type of violation is described in detail, with explanations of common causes, impacts on equipment and customers, and mitigation strategies. An additional workflow diagram is provided for each type of violation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Peer-to-peer communication control for resilient operations of networked cyberphysical systems

This report includes two main accomplishments of the peer-to-peer communication control for resilient operation of networked microgrids project in FY24, which include a scheme for cyberattack-aware coordination of networked microgrids for supporting voltages of bulk power systems and a scheme for price signal-based operations of EV-rich networked microgrids with mixed ownership. First, the cyberattack-aware scheme enables networked microgrids to distributedly determine the amount of reactive power injection to support the voltage of bulk power system (BPS) in a fair manner. In this scheme, a risk-informed algorithm is presented to generate the peer-to- peer (P2P) communication graph with minimal risk of attack on communication links. To deal with cyberattacks on MG controllers, the resilient consensus algorithm (CA) is utilized for MG controllers to robustly estimate the total reactive power headroom, from which the MGs can accurately provide the needed amount of reactive power injection for supporting the voltage of BPS. The CA implementation and performance within the P2P communication framework are demonstrated on the IEEE 39-bus system with 6 microgrids contained in the distribution feeder under different cyberattack scenarios. Second, the price-based scheme enables the usage of the real-time price signal for the operations of electric vehicle (EV)-rich networked-microgrids with mixed ownership, in which not all the microgrids can communicate with the distribution system operator (DSO). In this scheme, a max consensus is introduced to enable the real-time price signal to be propagated from the DSO to all the microgrids, from which each microgrid controller will manage the DERs to balance the load demand and the power injection from the EV charging stations within its microgrid. Numerical results over one day with 288 slots of 5-minute intervals on the modified 123-node test feeder including 3 microgrids with high penetration of EV are presented to evaluate how the price signal affects the operations of networked microgrids under different charging strategies of the EV charging stations. The result indicates that our proposed EVCS (dis)charging strategy, which leverages the flexibility of EVs to support the grid through discharging during peak demand, proves to be a cost-effective solution that reduces operational costs while improving the social welfare of EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Scalable Control Co-design for Resilient-by-Design Cyber Physical Systems

Critical infrastructure networks, such as power and transportation networks, are often modelled as cyber-physical systems. With ever increasing complexity of these systems, there is a need for newer and more relevant metrics and design tools that will co-optimize the physical system components and control policies to guarantee resilience against cyber and natural threats. To this end, a simulation-based control co-design computational framework that will concurrently determine the system and control parameters of a cyber-physical system to meet pre-specified resilience, operational and economic objectives has been developed. The capabilities of the developed co-design engine are demonstrated by designing the physical components and control parameters of a microgrid system that will meet its resiliency objectives when subjected to various cyber and physical threats.

42 ENGINEERING↗
DOE OSTI2025

ComStock Measure Documentation: Thermostat and Lighting Control for Load Shedding

This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding and lighting control for load shedding measures to reduce the HVAC and lighting load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 3%-10% daily peak demand reduction performance for applicable buildings, and 0.97% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ComStock Measure Documentation: Thermostat and Lighting Control for Load Shedding + Photovoltaics With 40% Rooftop Coverage

This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding, lighting control for load shedding, and PV with 40% rooftop coverage measures to reduce the net building load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand, while applying the fixed rooftop PV application for onsite electricity generation. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 5%-15% daily peak demand reduction performance for applicable buildings, and around 1% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

14 SOLAR ENERGY↗
DOE OSTI2025

Controls Status for Fermilab’s PIP-II Project

Testing of Cryomodule first articles at PIP-II's on-site Cryomodule Test Facility (CMTF), where EPICS based controls are already being used, has been invaluable to the project. In addition to validating hardware and controls components, it has provided opportunities to build buy-in and illicit meaningful feedback early-on. A device naming convention has also been significantly refined with an internal working group and the PIP-II integration team. Compressed air and Process Control Water (PCW) systems have been installed, and operations has been engaged in Phoebus display development in preparation for a transition to operations for these systems by 2026. Cryoplant hardware has been put in place, and collaboration between controls and PIP-IIs cryo group for IOC and display development is well underway for commissioning of cryoplant systems to begin early 2026. The project has also received beneficial occupancy of the High-Bay Building in October 2025, keeping us on schedule for Warm Front End commissioning to begin in 2027.

Crisp, Dan [Fermilab]↗
DOE OSTI2026

Potential of Data Center Controls in Grid Services

The rapid proliferation of large data centers brings both challenges and opportunities for grid reliability. The data center resources and their potential flexibility have the potential to contribute resources to grid operations. Through capabilities like energy shifting and resource coordination, data centers can help reduce their net demand on the transmission network, as well as provide additional grid services to support reliable operation on the grid. While transient and long-term grid planning and operations are the scenarios that draw most attention, the quasi-steady state timeseries (QSTS) operation of data centers and grid bring interesting scenarios that can help evaluate the data center controls to aid grid services. This work is focused on modeling data centers for QSTS applications – incorporating the AI data center load profiles and building on the PNNL digital twin model for the thermal management loads to enable simulation studies to reveal the impact of data center controls on grid performance. This includes the integration of a QSTS battery and natural gas generator model to incorporate local resource impacts to the system. The simulation study is performed with a modified IEEE 24-Bus transmission system. Scenarios are focused on evaluating the data center load impacts on the transmission system and leveraging both data center and local generation controls to mitigate those impacts and provide additional grid services. The data center controls revealed the ability to contribute to two main kinds of grid services: preventing congestion on a weak grid by coordinating the data center resources with the collocated BESS and onsite generation; and the ability to help the grid operations during stressed times of operation like during a contingency. Leveraging these and other capabilities has the potential to help data centers become grid responsive assets, aiding in both their integration into the power system and grid reliability.

power grid simulation↗
DOE OSTI2026

FOCAL Campaign II/III: Applying Active Hull Controls Using Tuned-mass Dampers/Hull Flexibility and Internal Loads

Campaign II and III of the Floating Offshore-wind Controls Advanced Laboratory Experimental Program (FOCAL) aimed to generate a dataset enabling the validation of the performance and loads of a scaled hull, with and without structural hull control. The floating platform was subjected to a variety of wave environments and controlled using tuned-mass dampers (TMDs) tuned to two of the systems natural frequencies (Platform Pitch and Tower-bending). The floating platform is fully instrumented to record a variety of parameters in real time such as platform dynamics, accelerations, and loads at different points in the structure. The test data considered was generated at the University of Maine's Harold Alfond Wind and Wave (W2) testing facility. This testing was focused only on validation of wave loading, and wind conditions were not considered. As such the platform does not support a working turbine, and instead supports a structure designed to have the same mass properties as the 1:70 IEA 15MW Reference turbine. The Load Cases (LC) considered in this testing campaign are as follows: LC 1.X - Platform Static Offset (TMD off); LC 2.X - Platform Free-decays (TMD off); LC 3.X - Wave Cases (Regular Wave, Irregular Wave, Pink Noise Wave) with and without TMDs active. Detailed properties on the model system are found in the following reference: Lenfest E., Floating Offshore-wind Controls Advanced Laboratory (FOCAL) Experimental Program - Campaigns 2 and 3: 1:70 Model-scale Testing of the IEA-Wind 15MW Reference Turbine and the VolturnUS-S Hull. UMaine ASCC Report Number 23-56-1183.

17 WIND ENERGY↗
DOE OSTI2025

Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning

Wave energy converters (WECs) harness the untapped power of ocean waves to generate renewable energy, offering a promising solution to sustainable energy. An optimal WEC control strategy is essential to maximize power capture that dynamically adjusts system parameters in response to rapidly changing sea states. This study presents a novel control approach that leverages neural networks to estimate sea states from onboard WEC measurements such as position, velocity, and force. Using a point absorber WEC device as a test platform, our proposed approach estimates sea states in real-time and subsequently adjusts PID controller gains to maximize energy extraction. Simulation results across diverse sea conditions demonstrate that our strategy eliminates the need for external wave monitoring equipment while maintaining power capture efficiency. The results show that our neural network-based control technique can improve power capture by 25.6% while significantly reducing system complexity. This approach offers a practical alternative for WEC deployments where direct wave measurements are either infeasible or cost prohibitive.

PIDcontrol↗
DOE OSTI2024

StructuredFuzzer: Fuzzing Structured Text-Based Control Logic Applications

Rigorous testing methods are essential for ensuring the security and reliability of industrial controller software. Fuzzing, a technique that automatically discovers software bugs, has also proven effective in finding software vulnerabilities. Unsurprisingly, fuzzing has been applied to a wide range of platforms, including programmable logic controllers (PLCs). However, current approaches, such as coverage-guided evolutionary fuzzing implemented in the popular fuzzer American Fuzzy Lop Plus Plus (AFL++), are often inadequate for finding logical errors and bugs in PLC control logic applications. They primarily target generic programming languages like C/C++, Java, and Python, and do not consider the unique characteristics and behaviors of PLCs, which are often programmed using specialized programming languages like Structured Text (ST). Furthermore, these fuzzers are ill suited to deal with complex input structures encapsulated in ST, as they are not specifically designed to generate appropriate input sequences. This renders the application of traditional fuzzing techniques less efficient on these platforms. To address this issue, this paper presents a fuzzing framework designed explicitly for PLC software to discover logic bugs in applications written in ST specified by the IEC 61131-3 standard. The proposed framework incorporates a custom-tailored PLC runtime and a fuzzer designed for the purpose. We demonstrate its effectiveness by fuzzing a collection of ST programs that were crafted for evaluation purposes. We compare the performance against a popular fuzzer, namely, AFL++. The proposed fuzzing framework demonstrated its capabilities in our experiments, successfully detecting logic bugs in the tested PLC control logic applications written in ST. On average, it was at least 83 times faster than AFL++, and in certain cases, for example, it was more than 23,000 times faster.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Cold Climate Field Study of the Effect of Defrost Controls on the Integrated Performance of a Ductless Air-Source Heat Pump

Residential heat pumps have advanced over the past decade to allow for operation at colder temperatures. However, the challenges of frost accumulation and defrosting the outdoor coil remain. The goal of this study was to evaluate the impact of the control algorithms that determine when a heat pump needs to defrost and when the base pan heater runs on the overall heating efficiency of the heat pump. In this study, which occurred during the 2023–2024 heating season, we measured the performance of a ductless air-source heat pump installed in Fairbanks, Alaska, USA. The heat pump was instrumented to measure the electrical input and the thermal output, as well as selected internal variables and indoor and outdoor environmental conditions. The heat pump was first operated with factory default control algorithms associated with the initiation of defrost and control of the base pan heater. These factory default algorithms focused on aggressively defrosting the outdoor coil and keeping the base pan ice-free. In the middle of the winter, these algorithms were changed to focus on reducing defrost cycles and increasing efficiency, while the heat pump continued to be operated and monitored. The results showed that significant increases in efficiency are possible by improving the defrost and base pan heater control algorithms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Recent progress in atomic-scale controlled plasma processing

Atomic-scale control in plasma processing is becoming increasingly critical for fabricating of advanced semiconductor devices, particularly as the industry shifts toward three-dimensional (3D) architectures and high-aspect-ratio (HAR) structures. This review presents a comprehensive overview of recent developments in atomic-scale controlled plasma processes, organized along two key directions: the hierarchical structure of plasma–surface interactions and the generational evolution of atomic layer processing (ALP) technologies. We examined the gas phase, where molecular design enables selective generation of ions and radicals; the boundary layer, where transport phenomena govern species delivery into nanoscale features, and the surface, where temperature-dependent reactions and cyclic processing determine etching selectivity and precision. Building on this foundation, we outline five generations of ALP—from thermal atomic layer deposition to transport-aware, temporally and structurally decoupled processes—highlighting the increasing sophistication of process control. The review further explores the transition from empirical recipe development to science-based, data-driven methodologies. By integrating quantum-chemical modeling, advanced diagnostics, and machine learning, we demonstrated how predictive models can link plasma species composition to process outcomes, enabling autonomous and adaptive control strategies. Finally, this review discusses the broader societal implications of plasma process innovation through the E4 quartet: energy and resource efficiency, environmental sustainability, evolutionary advancement, and educational promotion. These principles guide the development of sustainable and intelligent atomic-scale manufacturing technologies that are not only technically advanced but also socially responsible.

Ishikawa, Kenji [Nagoya Univ. (Japan)] (ORCID:0000↗