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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Web-based Preprocessing and Visualization of 3D FIB Tomography Data for Nuclear Fuel Characterization

Three-dimensional (3D) focused ion beam (FIB) tomography enables reconstruction of internal nuclear fuel features that can't be fully evaluated through surface imaging alone. This capability supports characterization of fuel constituents and defects under thermal and irradiation conditions relevant to microreactor development. However, large tomography datasets can create data-handling, loading, and visualization challenges, especially when image-stack preparation and file conversion must be completed with separate tools. The Computational Ultraspatial Tomography Toolkit for High-Resolution Object Analysis Tools (CUTTRHOAT) is an open-source web application being developed to display FIB tomography datasets available through the Nuclear Research Data System (NRDS). The current alpha version requires prepared HDF5 datasets and has limited integrated data-preparation capabilities. This project improves CUTTHROAT by adding dataset-folder selection, automatic input detection, dataset scanning, missing-slice identification, blank-slice insertion, and image-stack-to-HDF5 conversion. Two applications will be compared: the baseline CUTTHROAT alpha workflow and the updated application containing the integrated data-handling and preprocessing functions. Evaluation will consider dataset detection accuracy, conversion success, loading time, rendering responsiveness, application stability, and user interaction. Preliminary results demonstrate successful loading of existing HDF5 files and converted image stacks, while testing also identified performance reductions caused by excessive blank-slice generation. The updated workflow reduces reliance on external preparation tools and supports more direct movement from image stacks to color-code 3D visualization. Future work includes refining missing-slice handling, integrating additional preprocessing functions, like a denoising feature, parsing TIFF metadata for automatic voxel scaling, and adding manual X, Y, and Z voxel-spacing inputs for PNG and JPEG.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Mini Micropulse Lidar measurements in vertical and scanning modes

The Atmospheric Radiation Measurement (ARM) program operates ten Sigma Space (currently Droplet Measurement Technologies/DMT) micropulse lidar systems (MPL-4B-IDS/POL-FS series), which detect the backscattered light in two orthogonal linear polarization channels allowing to distinguish between spherical (e.g. water) and non-spherical (e.g. ice) scattering particles. The ARM MPLs have been providing valuable measurements to the aerosol and cloud research community for well over a decade. DMT’s Mini-Micropulse lidar (MiniMPL) is a compact version of the standard MPL. It reduces the power-aperture product to minimize cost, size, weight, power requirements and has all the electronics integrated with optics in a single box. Although as a result, it has a lower detection range than the MPL, limiting the measurements to the troposphere, its performance is promised to match that of the standard MPL within the troposphere. The MiniMPL weighs 13 kg, and the complete system, consisting of an optical transceiver and a laptop running the data acquisition and post-processing software, fits in a storm case, allowing its transport on domestic or international flights. This can be especially valuable for short field campaigns or Intensive Operational Periods, where time is of the essence when replacing a faulty instrument. In addition, the scanning polarization lidar capability (MiniMPL-SCAN option) could be of great interest for satisfying ARM measurement needs for certain research topics related to clouds and precipitation. To evaluate its full potential in comparison with co-located remote sensing instruments including the ARM MPL-4B-IDS/POL-FS at SGP, a miniMPL was hosted at the ARM Southern Great Plains (SGP) mega-site in 2020 for approximately 3 months. During this time the miniMPL was tested in both vertical sampling and scanning modes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

New systems in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

The Cell Utilized Partitioning Model as a Predictive Tool for Optimizing Counter-Current Chromatography Processes

Counter-current chromatography (CCC) is capable of unique elution modes that isolate analytes using the movement of the stationary phase in addition to moving the mobile phase. These modes include elution-extrusion CCC (EECCC) and dual-mode CCC (DM CCC) that are not possible in traditional solid-liquid chromatography systems. Although EECCC and DM CCC are widely used to recover highly retained components, to our knowledge, optimizing the elution process in these modes with predictive models has not been reported. To address this gap, we developed a predictive model for CCC dubbed the Cell Utilized Partitioning (CUP) model. The CUP model accurately predicts the effluents of multicomponent separations in EECCC and DM CCC modes when compared to experimental data. Furthermore, CUP model simulations were extended to investigate the influence of operating and intrinsic parameters on the yield and productivity, and to compare the separation performances of EECCC and DM CCC in various conditions. The results demonstrate that low distribution constants, usually a KD less than 1, and a selectivity > 1.3, under specific flowrate ranges, increase both productivity and yield. From these results, generalized optimization and scaleup guidelines are proposed that can apply to research settings and to industrial processes to maximize preparative CCC performance.

BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
DOE OSTI2024

Real Time-Optimal Power Flow-Based Distributed Energy Resource Management System (DERMS)

This project aims to promote lab-proven clean energy technology to commercially scalable versions of the technology, integrate the technology with broader systems, provide extended performance data, and validate the manufacturability and reliability of the technology. The lab-proven technology, RT-OPF DERMS, was developed and validated through previous U.S. Department of Energy-funded efforts, including Advanced Research Projects Agency-Energy funding under the Network Optimized Distributed Energy Systems program and Holy-Cross Energy High Impact Project. In the Advanced Research Projects Agency-Energy Network Optimized Distributed Energy Systems project, the RT-OPF DERMS was developed and implemented in multiple hardware platforms, demonstrating its performance and capabilities in the lab and field environments. The technology was also evaluated and matured via a participation in the U.S. Department of Energy I-Corps program, whose goal is to pair teams of researchers with industry mentors for an intensive 2-month training in which the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. These activities indicate the high technology maturity and Technology Readiness Level of the RT-OPF DERMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Benefits and challenges of vibroacoustic process monitoring to support operators in nuclear research facilities

According to the International Atomic Energy Agency in 2024, global nuclear energy capacity is projected to increase by 2.5 times by 2050 in their high-case scenario. Research on the laboratory scale of innovative processes within nuclear facilities is ongoing to improve nuclear fuel cycle capabilities. Vibroacoustic process monitoring of these techniques has potential to inform operators regarding process specific metrics, predictive maintenance, and anomalous event detection. Additionally, this type of monitoring has the potential to aid in nuclear safeguards. Challenges regarding vibroacoustic monitoring in these environments include high temperature, high radiation, limited access, and shielded equipment limiting sensor type and placement. Microphones, accelerometers, and temperature sensors were deployed both inside and near these environments during operation to determine environment driven limitations, sound attenuation of containment enclosures, process specific metrics, and future potential of vibroacoustic monitoring in these environments. Overall, this work highlights the benefits and limitations of vibroacoustic process monitoring in nuclear research facilities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Emerging Functionality in Transition-Metal Compounds Driven by Spatial Confinement and Broken Symmetry

This research project investigates the emerging functionality in transition-metal-compounds (TMCs) driven by spatial confinement and broken symmetry. It combines advanced growth capabilities with cutting-edge characterization and first principles theory to probe and control the properties of TMC interfaces, including the utilization and development of state-of-the-art atomically resolved electron microscopy and spectroscopy to determine the structure, composition, and bonding at TMC interfaces. The proposed research will focus on four challenging areas: 1) manipulate interfaces to design new material phases such as magnetic metals with unique polar structure (net dipole) to achieve multiple functionality; 2) explore electronic mismatch or screening at interfaces of insulating/poor metal TMCs to produce novel electronic and magnetic properties; 3) elucidate and exploit the role of defects, both point and extended, on the functionality of interfaces; 4) develop advanced electron microscopy/spectroscopy techniques to explore temperature dependent phase transitions and couple these structural tools with new nonlinear optical probes of the electronic structure. The research team aims to close the materials-by-design loop of make, measure, model, and modify. The program promises to enhance our ability to engineer the desired physical properties at interfaces, superlattices (periodic arrays of films of different compounds), and heterostructures of TMCs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗
DOE OSTI2023

Manufacturing Demonstration Facility: Development and Evaluation of Hybrid Manufacturing Toolpaths

The integration of additive manufacturing (AM) capabilities on Computer Numerical Control (CNC) systems allows for the expansion of additive manufacturing to a wide range of part and tool repair operations. This multi-tasking integration, termed hybrid manufacturing, has been researched by others in the past, and Autodesk has been critical in developing process planning and toolpath algorithms for hybrid systems. Objectives and Tasks: Hybrid manufacturing systems enable both additive and subtractive capabilities in a single manufacturing workcell. These systems have the potential to impact a variety of industries, including the tool and die industry due to their repair, refurbishment, and complex geometry manufacturing capabilities. While there has been significant development of toolpath planning for both subtractive and additive processes independently, there has been little, if any, development of hybrid toolpath planning to integrate both processes during the manufacturing design and toolpath generation stage of a product’s lifecycle. Furthermore, additive toolpath planning has been limited to planar manufacturing, but this limitation could be overcome as hybrid CNC machines have multi-axis control. The objectives of this research include: - Development and demonstration of independent three-, four-, and five-axis toolpath generation algorithms for both additive and subtractive processes, and - Development, demonstration, and integration of three-, four-, and five-axis hybrid process planning and toolpath generation algorithms for hybrid additive and subtractive processes. The team will leverage the widely used Autodesk Fusion 360 product design and manufacturing (CAD/CAM) platform to achieve these objectives. Autodesk will provide the expertise in CAD tools, as well as access to their new CAD/CAM manufacturing tools (3-, 4-, and 5-axis milling, additive toolpath generation). ORNL will provide expertise in additive manufacturing toolpath generation, process planning, and manufacturing validation. By the end of the program, the team will have developed and validated multi-axis milling, additive manufacturing, and hybrid manufacturing on an industrial hybrid CNC system (Mazak 500-VC).

42 ENGINEERING↗
DOE OSTI2021

Expanded Capabilities and Opportunities for Virtual Beamline Code

In the 25 years since LLNL broke ground on the National Ignition Facility (NIF) - a cornerstone of the National Nuclear Security Administration’s Stockpile Stewardship Program - the NIF and Photon Science (NIF&PS) Directorate has steadily pushed the boundaries of laser physics, nonlinear optics, and photonics in service of inertial confinement fusion experiments, and advanced photon sources development. Beyond the 192 beamlines of the primary laser facility with its world-record energy and fusion yields, NIF&PS capabilities have expanded to include chirped-pulse amplification, kilojoule petawatt-class short-pulse systems to generate hard x-ray radiographic probes, high-average power lasers as secondary sources for generation or wake field particle acceleration along directed energy applications, and other scientific pursuits. The Computing Directorate partners with NIF&PS on numerous projects and technologies from diagnostic measurements and high performance control systems to data analysis, information technology infrastructure, and scientific simulation code development. “This strong collaboration enables cutting-edge science, particularly with regard to physics-based modeling and simulation,” says physicist Jean-Michel Di Nicola. One crucial capability for NIF&PS is the Virtual Beamline (VBL) laser simulation code. It can model all the major laser physics and technology involved in the design optimization, commissioning, and operations of advanced laser architectures from tabletop to NIF-scale. VBL provides researchers with high-fidelity models and high-resolution calculations of laser performance predictions—including for the entire NIF laser system, the Advanced Radiographic Capability, parts of the High Repetition-Rate Advanced Petawatt Laser System laser delivered to the Czech Republic, and the Optical Science Laser. After more than two decades of experimentally verified physics and computing enhancements, this unique workhorse code is wrapping up another major milestone: migration from Java to C++ with a flurry of user interface and optimization features, as well as laser physics and high-resolution enhancements thanks to parallel execution on Livermore Computing platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Let’s Get Real: Are Wearable Plant Sensors Ready for Crop Monitoring?

In recent years, the number of publications describing new and exciting developments in wearable plant sensors (WPSs) has skyrocketed. These small, lightweight sensors hold promise to assist precision agriculture and may thus help reduce crop losses, increase resource use efficiency, and automate crop production. However, WPSs are often not adequately tested in environments relevant for crop growth, and the majority of experimental WPS studies reveal a glaring lack of basic knowledge of plant biology. This review aims to bridge the communication gap between WPS developers and the wider plant research community by (1) providing essential physiological and environmental background information for engineers in relation to WPS sensing capabilities, (2) offering a step-by-step guide to conduct sensor tests on plants correctly, and (3) highlighting potential challenges and suggesting WPS applications in the open field, greenhouses, and vertical farming systems. We hope this review facilitates the development of WPSs and guides them to be truly “ready for the world”.

crop monitoring↗
DOE OSTI2023

Design and Modeling of an Open-Source Baseline Floating Marine Turbine: Preprint

Marine energy resources such as river, tidal, and ocean currents can provide abundant, predictable, and clean power to both densely populated and remote communities globally. However, marine turbine technologies are still in an early stage of development. Recently, features have been added to the National Renewable Energy Laboratory's open-source wind turbine modeling tool OpenFAST to support the simulation of axial-flow marine turbines. These new features enable marine energy developers to predict the performance and safety of their turbines and allow researchers and students to advance the field through the exploration of novel designs. Developing and demonstrating the new capabilities of OpenFAST for marine turbines requires a baseline design that can be used as an example and test case. There are limited open-source turbine models suitable for this purpose. One existing baseline design is the Reference Model 1 (RM1), a fixed-bottom, dual-rotor marine turbine that was published in 2014 as part of the Department of Energy's Water Power Reference Model Project. To test and demonstrate OpenFAST's ability to model floating marine turbines, several modifications were made to the RM1. A floating platform and mooring system were designed to support a single RM1 rotor in a wide range of possible current and wave conditions. The system and model are intended to be a stable reference case and starting point for research efforts. The platform features all cylindrical members and familiar design elements from the offshore wind industry; the mooring system uses simple catenary chains. While not optimized for the lowest platform cost, the design is understandable and robust and can serve as a baseline for floating marine turbine technologies. This is a useful starting point for studies to explore new technologies or design methodologies. Additionally, the floating RM1 design enables cross-collaboration among industry, academia, and national lab researchers and serves as an educational platform for newcomers to marine energy. This short paper presents the specifications of the turbine, including its floating support structure and mooring system. The rationale behind the selection of certain design parameters is described. To enable adoption of this baseline case for research and educational efforts, an OpenFAST model of the full system has been made publicly available at https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/MHK_RM1_Floating.

floating↗
DOE OSTI2026

Fabrication and test of a 6-tesla-class high-temperature superconducting dipole magnet at 4.2 K

Superconducting magnets enable energy-frontier accelerators by generating strong magnetic fields to steer and focus the particles. Although high-temperature superconductors such as REBa 2 Cu 3 O x (, RE = rare earth) hold a strong potential for generating a higher magnetic field than Nb-Ti and Nb 3 Sn , the associated magnet and conductor technology for accelerator applications is still in its infancy. The U.S. Magnet Development Program is developing magnet technology in collaboration with industry. Here we report an experiment of making a dipole magnet called C3 using commercial high-temperature superconducting wires. The magnet, following a canted cos θ design, generated a dipole field of 5.99 T at 4.2 K in its clear aperture of 65 mm at 6.795 kA when a resistive voltage of 105 μ V appeared across one of the coils in the magnet. The stored energy was 53 kJ at the peak field. The magnet showed no degradation in the current-carrying capability at 4.2 K after the thermal cycle. We report on the detailed design, fabrication, and performance of the C3 magnet that can be of interest to potential users of this emerging technology. We also discuss issues and research needs to inform future magnet development. The experiment represented another step to addressing if the high-temperature superconducting accelerator magnet technology can increase the discovery capability of future particle accelerators.

Abraimov, Dmytro [National High Magnetic Field Lab↗
DOE OSTI2025

Deep Cyber-Physical Situational Awareness for Energy Systems: A Secure Foundation for Next-Generation Energy Management

This document provides the final report for the CYPRES project. The purpose is (1) to highlight and summarize its major accomplishments and (2) to provide guidance on how its outcomes have informed and can inform important additional research and technology transfer. The goal of CYPRES was the research, development, and demonstration of a security-oriented next generation cyber-physical EMS for electric power systems that detects malicious and abnormal events through the fusion of cyber and physical data. To achieve this, the CYPRES project team researched, developed, and built a prototype of the solution, referred to as the CYPRES EMS. The CYPRES EMS is a proof-of-concept cyber-physical platform that demonstrates the management of the energy system, communications, security, and cyber-physical grid modeling and analytics. As part of the capabilities of the CYPRES EMS, the team designed and developed a suite of power system applications for monitoring, risk analyses, detection, and control that are inherently cyberaware. At its core, the project aimed to research, develop, and demonstrate a security-oriented next-generation cyber-physical Energy Management System (EMS) capable of detecting malicious and abnormal events through the innovative fusion of cyber and physical data. This approach represents a fundamental shift from traditional EMS, reimagining how critical infrastructure can be protected through unified cyber-aware and physics-aware secure data flow pipelines. The project’s cornerstone deliverable, the CYPRES EMS, serves as a proof-of-concept cyber-physical platform that revolutionizes the management of energy systems, communications, security, and cyber-physical grid modeling and analytics. This prototype implements a comprehensive suite of power system applications for monitoring, risk analyses, detection, and control, all designed with inherent cyber awareness. The system’s architecture extends from end-devices in the field through to control center applications, establishing a secure and resilient control framework that addresses the challenges posed by diverse devices of unknown trustworthiness connecting to modern power systems. Through this innovative approach to deep cyber-physical situational awareness, the CYPRES project not only advances the state-of-the-art in energy infrastructure protection but also establishes a new paradigm for how EMS can be designed, deployed, and operated in an increasingly complex threat landscape. The findings and developments from this project provide crucial insights for stakeholders across the energy sector, offering a blueprint for enhancing the reliability and resilience of our nation’s critical energy infrastructure in the face of evolving cyber threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Small Cells with Big Photosynthetic Productivities: Biotechnological Potential of the Picochlorum Genus

The Picochlorum genus is a distinctive eukaryotic green-algal clade that is the focus of several current biotechnological studies. It is capable of extremely rapid growth rates and has exceptional tolerances to high salinity, intense light, and elevated temperatures. Importantly, it has robust stability and high-biomass productivities in outdoor field trials in seawater. These features have propelled Picochlorum into the spotlight as a promising model for both fundamental and biotechnological research. Recently, several genetic tools, including genome editing, were developed for these algae, enabling insights into Picochlorum photophysiology and algal transformations for expanded capabilities. Here, we survey the Picochlorum genus, its genetic toolbox, recently characterized transformants, and discuss the commercial potential of Picochlorum as a salt-water photoautotrophic biocatalyst.

09 BIOMASS FUELS↗