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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Intermetallic alloy structure–activity descriptors derived from inelastic X-ray scattering

Synchrotron spectroscopy and Density Functional Theory (DFT) are combined to develop a new descriptor for the stability of adsorbed chemical intermediates on metal alloy surfaces. This descriptor probes the separation of occupied and unoccupied d electron density in platinum and is related to shifts in Resonant Inelastic X-ray Scattering (RIXS) signals. Simulated and experimental spectroscopy are directly compared to show that the promoter metal identity controls the orbital shifts in platinum electronic structure. The associated RIXS features are correlated with the differences in the band centers of the occupied and unoccupied d bands, providing chemical intuition for the alloy ligand effect and providing a connection to traditional descriptions of chemisorption. The ready accessibility of this descriptor to both DFT calculations and experimental spectroscopy, and its connection to chemisorption, allow for deeper connections between theory and characterization in the discovery of new catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

PipeSight: A High-Performance Computing Platform for Pipeline Integrity Management

The Phase I feasibility study completed as part of this project has led to a number of innovative technologies being developed and has laid the foundation for a successful Phase II effort to commercialize a platform for managing the integrity of pipelines for the damage mechanisms of the new, hybrid-energy based economy. To ground the development efforts and direction of the project, an extensive market research and customer discovery effort was undertaken early in Phase I. Through this effort, a number of pipeline owners and operators were interviewed, and the following key findings were discovered about the pipeline industry: • Small pipeline operators do not have the central engineering groups necessary to perform their own independent analysis of inspection data, but instead rely on summarized tally sheets provided to them by inspection service providers. • The time it takes to go from an inspection to a completed engineering assessment, even for small segments of pipeline, can take anywhere from 30-120 days. During this delay, critical threats can (and have been known to) cause failures. • Uncertainty is often not accounted for in the assessment of pipeline integrity. The tally sheets provided by third-party service providers are almost always deterministic in nature, identifying threats that present a concern only to the current (not the future) integrity of the pipeline. • It is uncommon to apply the latest technologies to perform advanced assessments of damaged pipelines. There is a desire to use more advanced analysis capabilities to assess threats. Many pipeline operators indicated that they would often excavate a pipeline to perform an inspection and find that the damage was not as bad as they anticipated, thus using limited resources unnecessarily. Companies are not consistent in their use of inspection data to determine corrosion rates, and those that do only calculate deterministic corrosion rates. • The industry has prominently relied on time-based inspections but has recently started to transition to risk-based inspections. However, there appears to be no uniform guidance on how to do so while properly accounting for all sources of uncertainty. • Companies are not storing inspection data in a manner that allows for the ready determination of temporal trends. • Predictive maintenance principles and practices are beginning to be used by early adopters • Some pipelines are being re-purposed to transport different process fluids than they were designed for, e.g., H 2 and CO 2 rich process streams to serve the new hybrid-energy based economy, which are presenting new integrity concerns for the existing pipeline network that crisscrosses the United States. As a result of these discoveries, we were able to target the development efforts in Phase I to best serve the needs of the industry. In Phase I, we developed a way to correlate multiple large-scale scans of the pipeline to determine a probabilistic corrosion rate that accounts for all sources of error and uncertainty in the inspection process. This probabilistic corrosion rate can be used to predict the future thickness distribution of the pipe wall. We demonstrate how this analysis may be performed in an analytical fashion and has been implemented in such a manner that it can be readily distributed using GPU computing through integration of the Kokkos programming model. We also make a very novel extension of the analytical corrosion rate model to Bayesian Networks (an explainable AI technique) that can account for non-parametric distributions of corrosion rates. With the predictions made above for the probabilistic corrosion rate and corresponding future distribution of the pipe wall thickness, we can assess the integrity of the pipeline through the use of a probabilistic engineering assessment. We developed a novel screening data analysis approach that can rapidly identify ‘hotspots’ (local thin areas) where the integrity of the pipeline is a concern. Once more, we implemented this screening approach in C++ to leverage GPU computing via the Kokkos programming model. After the critical hotspots are identified, we developed a program that can automatically generate an advanced finite element model of the damaged regions. Since the number of damaged regions that require advanced analysis can number in the thousands, we integrated an open-source container-native workflow engine for orchestrating parallel jobs on the cloud. Initially, these advanced numerical models were only designed to account for loading due to internal pressure. However, in a slight pivot from the initial Phase I proposal, we developed a complete pipe stress analysis program (called Simflex) which can simulate the complete pipeline and its response to thermal expansion, pressure, thermal bowing, weight, wind, earthquake, support displacement, support friction and external forces. This pipe stress analysis program was written generically, to handle any piping system, but contains the features needed to model long pipelines (i.e., it incorporates a model for soil mechanics and can account for the nonlinear boundary conditions necessary to simulate long underground pipelines). This pipe stress analysis program can simulate any segment of the pipeline (simple or complex) under any set of conditions and loads, to determine the supplemental loads (axial forces and bending moments) at the location of damage. This enables the most accurate state of stress to be accounted for in the pipeline, which can prove critical when evaluating the integrity of a damaged region. In the process of developing the technologies to perform the integrity assessment of the pipeline, we also extended one of the industry standard approaches for performing the assessment of local thin areas that extend more in the circumferential direction than the longitudinal direction of the pipeline. This approach was presented to the API 579-1/AS ME FFS-1 steering committee in November 2021 for consideration in the next edition of the industry standard for Fitness-For-Service (expected to be released in 2023). To help pipeline operators make decisions with the results on any integrity assessment, we developed a new approach to the life-cycle management of pipelines which uses a Bayesian Decision Network. The network is designed to help pipeline operators plan and prioritize inspection activities and ultimately make smarter, more cost-effective decisions. The Bayesian approach accounts for all sources of uncertainty and carries them through to the final optimal decisions, providing a probabilistic framework for optimizing inspection intervals. The proof-of-concept networks developed in the feasibility study are complete, verified, and are focused on a subset of the pipeline. To expand this novel approach to the scale necessary for an entire network of pipelines in Phase II, we will leverage the DOE-funded Bengi solver for industrial-scale decision making with Bayesian Networks [22]. Once implemented, we will be able to provide the pipeline industry with a much-needed tool for optimal inspection planning using truly explainable artificial intelligence (XAI). To handle all of these advanced capabilities into a cloud-based platform, the architecture of the Equity Engineering Cloud (EEC) was extended to include Argo Workflows, a framework capable of distributing and managing a massive number of jobs that consume their own resources, such that thousands of serial finite element simulations can be run in parallel. As part of this substantial undertaking, we also integrated Argo Continuous Delivery (CD) into the EEC, to aid with the rapid prototyping and iterations that will be imperative to the success of the PipeSight platform’s Agile development process in Phase II. As part of the pipe stress analysis program, we also developed a custom visualizer that leverages the DOE-funded VTK visualization library. We added custom contouring capabilities and a means for interacting visually with both the inputs and outputs of the pipe stress analysis program. We also developed routines for automating the post-processing of the finite element simulations to determine if any failure criteria are met and to visualize the deformations, stresses and strains in ParaView using the exodus II file format (a subset of netCDF).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimal, Reliable Building-Integrated Energy Storage (Cooperative Research and Development Final Report)

The team will advance the commercial readiness of behind-the-meter (BTM) energy storage (ES) systems by employing health-conscious controls that guarantee lifetime and optimize the ES system's value stream when integrated with onsite renewable energy generation. Specifically, the team will develop ES controls that increase the net present value (NPV) of photovoltaics (PV) by 50% in markets where net-metering policies are being replaced by variable electricity pricing structures. The team will also reduce the risk of achieving a 10-year ES warranty lifetime by at least one order of magnitude. The successful two-year project will develop the controls and system enabling Eaton to commercialize the technology by 2021. The developments achieved through this project may enable wide scale adoption of stationary energy storage benefitting the public.

25 ENERGY STORAGE↗
DOE OSTI2024

Enabling the broader adoption of fusion simulation on complex geometry

This project addressed a key barrier to advanced fusion and nuclear simulation: the difficulty of performing high-fidelity Monte Carlo neutronics directly on complex, real-world CAD geometry. Traditional workflows require engineers to rebuild CAD models as simplified constructive solid geometry, a time-consuming and error-prone process that limits design iteration and broader adoption of simulation tools. The goal of this Phase I SBIR was to make CAD-based neutronics practical, accessible, and robust for industrial and research users. During the project, Coreform significantly enhanced the Direct Accelerated Geometry Monte Carlo (DAGMC) workflow and fully integrated it into Coreform Cubit as a first-class capability. Major achievements include optimized material assignment and surface meshing workflows, substantial performance improvements to geometry imprinting and preparation, native export of DAGMC models, and new visualization tools to support OpenMC source definition and lost-particle debugging. Coreform also expanded Cubit’s capabilities as a full OpenMC preprocessor, including the ability to convert OpenMC constructive solid geometry models back into CAD for visualization, multiphysics coupling, and debugging. In collaboration with Argonne National Laboratory, the project delivered comprehensive new DAGMC documentation and training materials, transforming DAGMC from a research-oriented tool into a production-ready workflow. Results were disseminated through tutorials, conference training, and multiple well-attended webinars demonstrating integrated CAD-based neutronics and multiphysics workflows. Overall, this project demonstrated that high-fidelity Monte Carlo simulations can be performed directly on complex CAD geometry, reducing setup time, improving usability, and enabling faster, more informed design decisions for fusion and nuclear energy systems.

42 ENGINEERING↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Stable Positron Acceleration in Thin, Warm, Hollow Plasma Channels

Hollow plasma channels are attractive for lepton acceleration for providing intrinsic emittance preservation regimes. However, beam breakup instabilities dominate the dynamics. Here, we show that thin, warm hollow channels can sustain large-amplitude plasma waves ready for high-quality positron acceleration. We verify that the combination of warm electrons and thin hollow channels enables positron focusing structures. Such focusing wakefields unlock beam breakup damping mechanisms. We demonstrate that such channels emerge self-consistently during the long-term plasma dynamics in the blowout's regime aftermath, allowing for experimental demonstration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Establishing a silica gel zone in well annulus and evaluating its performance in blocking vertical water flow

Wells are often constructed for monitoring purposes with relatively long screen lengths (e.g., >10 m). Vertical water flows can occur within the artificial or natural filterpack annulus that surrounds the screened interval, bypassing packer assemblies installed inside the wellbore. Attempts to isolate discrete vertical zones during groundwater sampling are unsuccessful when annular flow occurs and lead to remedy decisions based on biased or incorrect interpretations. Blocking vertical annular water flow and contaminant transport will help obtain more accurate concentrations of contaminants from sampling in targeted depth intervals. The application of silica gels formed from the injected colloidal silica CS suspensions is a novel approach to minimize or prevent movement of vertical movement of groundwater in the surrounding filterpack annulus. In this work, we tested the feasibility of injecting CS suspensions to target locations and developed a modified CS formulation that is injectable and prevents gravity sinking. We studied the distribution and penetration of silica gel at laboratory scale in mock well annulus with surrounding formations. We evaluated the performance of the silica gel in blocking vertical water flow in the annulus and in minimizing chemical transport through the gel zone. CS suspension formulations have been defined that are ready for injection, stay in target locations, and form gel within desired time frames. Injection of CS suspensions achieved uniform distribution in a well annulus filter pack, fully occupied the annulus pore space, and penetrated the formation surrounding the filter packer with a sufficient distance to create a hydraulic annular seal when the injection was applied at a sufficient rate. The depth of penetration into the formation was dependent on the permeability contrast between the filter pack and the surrounding formation. Silica gel that formed in the annulus blocked vertical water flow and stopped the chemical transport through the gel zone. In conclusion, this research reveals that using CS suspension injection and sequential gelation (CS-GEL) is a promising technology for blocking vertical water flow and chemical transport through the filter pack in targeted zones within the annulus of long-screened well systems.

Colloidal silica suspension↗
DOE OSTI2020

DOE Hanford Tank Side Cesium Removal System (TSCR) Update: Fabrication and Factory Acceptance Testing - 20300

The Tank Side Cesium Removal (TSCR) System is a modular, quickly-deployable, and passively safe system for efficiently removing solids and cesium from alkaline liquid waste raffinate created by fuel processing on the Hanford site. This paper provides an overview of the project with particular emphasis on completion of fabrication and factory acceptance testing (FAT). All TSCR System components are inside three enclosures known as the Process Enclosure, Ancillary Enclosure, and Control Enclosure. The Process Enclosure is seismic design category II (SDC-II) and built to withstand the rigors of transportation and lifting, high winds, volcanic ash fall, and heavy precipitation (snow) over a temperature range of -25 to 115 deg. F. The Process Enclosure and Control Enclosure were fabricated in AVANTech's Columbia, SC facility, while the Ancillary Enclosure was fabricated in AVANTech's Richland, WA facility. The design and preliminary documented safety analysis were completed in December 2019, with all work being performed in accordance with ASME NQA-1 quality assurance requirements. The Process Enclosure, Ion Exchange Columns (IXCs), and several other TSCR components have a nuclear safety function and are designated as safety significant structures, systems, and components (SS-SSCs). The NQA-1 Commercial Grade Dedication (CGD) process was used to identify critical characteristics in order to accept materials and items used for fabrication of several safety significant TSCR components. This paper also addresses unique aspects and challenges of implementing CGD in DOE facilities and systems. After fabrication and preliminary testing, the Process and Control Enclosures were shipped to AVANTech's Richland facility where the complete TSCR System was setup and connected in a represented layout and configuration that will be used on the Hanford site. Test tanks and pumps were put in place and connected to the TSCR Process Enclosure in a manner that mimicked interfacing Double Shell Tanks in the 241-AP Tank Farm. The TSCR System was then tested/operated under conditions equivalent to those planned for onsite operation. Testing was separated into five separate phases as defined below: - Phase 1: Confirmation of TSCR System readiness for the FAT; - Phase 2: Verification of TSCR System operation under standard conditions; - Phase 3: Demonstration of TSCR System operation under non-standard conditions; - Phase 4: Demonstration of ion exchange media removal from an IXC; and - Phase 5: Completion of TSCR System lift plans and site mobilization. The Hanford Tank Operations Contractor, Washington River Protection Solutions (WRPS), participated in all phases of testing. Testing at AVANTech's Richland Facility allowed WRPS personnel to familiarize themselves with TSCR operations in a radiologically clean facility prior to onsite mobilization. Engineering and operations personnel gained valuable experience that allowed them to better refine onsite operational protocols and procedures - including handling and movement of IXCs with a forklift. This paper describes the technologies used by the TSCR System as well as how these technologies are deployed and operated to advance the Hanford mission of safely, efficiently, and effectively treating tank waste. It also provides detailed information on fabrication and FAT, including operational data from simulated tank waste runs. A project schedule for WRPS' planned construction acceptance testing, operational acceptance testing, and actual tank waste treatment is provided. This presentation should be of great interest to parties responsible for the design, testing, and mobilization of modular NQA-1 technologies for the treatment of raffinate and similar liquid wastes containing high concentrations of radioactive cesium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Challenges and Lessons Learned from an Analysis of Three Zero Energy Buildings

Zero energy buildings, or zero energy ready buildings, which are designed and operated by public or private commercial property owners can play an important role in reducing carbon emissions. This paper discusses lessons learned and key takeaways from an in-depth analysis of three zero energy buildings that took an integrated design and construction approach to significantly reduce energy use. Two of these projects are new construction and one is a retrofit to zero energy. Findings are based on project literature review, data analysis, and in-depth interviews with the building design teams and staff who were involved during the design, construction, and operation of the buildings. The paper addresses value proposition and cost data in such a way that other building owners can replicate the strategies and technology solutions in response to regulatory mandates or organizational goals. It also provides details on operational improvements taken at each zero energy building to enhance energy performance and increase the potential for load flexibility, and discusses challenges and lessons gleaned from design teams and building staff. The findings serve as a reference for building owners, designers, engineers, contractors, or others interested in, or involved with, the design, construction, or use of new or existing buildings. The paper also includes recommended pathways for widespread adoption of zero energy strategies that can be applied in various locations.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2021

Precision measurement of electron-electron scattering in GaAs/AlGaAs using transverse magnetic focusing

Electron-electron (e-e) interactions assume a cardinal role in solid-state physics. Quantifying the e-e scattering length is hence critical. In this paper we show that the mesoscopic phenomenon of transverse magnetic focusing (TMF) in two-dimensional electron systems forms a precise and sensitive technique to measure this length scale. Conversely we quantitatively demonstrate that e-e scattering is the predominant effect limiting TMF amplitudes in high-mobility materials. Using high-resolution kinetic simulations, we show that the TMF amplitude at a maximum decays exponentially as a function of the e-e scattering length, which leads to a ready approach to extract this length from the measured TMF amplitudes. The approach is applied to measure the temperature-dependent e-e scattering length in high-mobility GaAs/AlGaAs heterostructures. The simulations further reveal current vortices that accompany the cyclotron orbits - a collective phenomenon counterintuitive to the ballistic transport underlying a TMF setting.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Roadmap for Assessing Fuel Reprocessing in Fluoride-Based Salts

This joint report assesses the pyroprocessing of used nuclear fuel from molten fluoride salts being conducted between Idaho National Laboratory and Argonne National Laboratory. The goal of this report is to identify the research and development gaps needed to reduce the technical risks of extending pyroprocessing technologies and unit operations to molten fluoride salts used in molten salt reactors (MSRs). Assessing and performing the tasks outlined in this report will help mitigate the technical risks and design appropriate flowsheets for reprocessing fuel and coolant salts. These suggested tasks will provide necessary data to implement the development of unit operations. Sections are highlighted to identify processes that need to be assessed and unit operations that may be used in fluoride-based chemistries. Research and development needs are listed and discussed including re-fluorination methods, electrowinning for separations, reference electrode development, materials compatibility, salt purification/re-fluorination, and waste disposition. This report will examine the technical challenges associated with pyroprocessing in molten fluoride-based salt and outline approaches to increase the technical readiness level of pyroprocessing in molten fluoride salts.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

2025 Advances in NekRS: Supporting improved performance for nuclear applications

This report presents several 2025 advancements in NekRS, a high-fidelity spectral element CFD code developed at Argonne National Laboratory to support the NEAMS thermal-hydraulics program. The forthcoming v25 release consolidates several of these advances, adding new features for portability across heterogeneous GPU architectures, real-time in situ visualization, improved turbulence modeling, and conjugate heat transfer coupling. Over the past year, NekRS has demonstrated strong scalability and performance on DOE’s leading exascale platforms, including Aurora and Frontier, confirming its readiness for some of the largest and most complex simulations attempted to date. These achievements provide a powerful new platform for high-fidelity data generation, which in turn supports the development and validation of advanced closure models critical for reactor safety and design. Significant algorithmic innovations have also been introduced. A new global runtime h-refinement capability simplifies workflows by reducing mesh preparation burdens and enabling coarse-to-fine restarts. Building on this, a novel multigrid strategy was implemented to accelerate pressure and transport solves at scale, addressing long-standing bottlenecks in exascale CFD. Together, these developments improve both the efficiency and accessibility of high-fidelity simulations for reactor-relevant problems. Collectively, these enhancements represent a major step forward in simulation technology, positioning NekRS as a cornerstone of NEAMS efforts to enable accurate, efficient, and scalable high-fidelity analysis of advanced nuclear systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Securing Grid-interactive Efficient Buildings (GEB) through Cyber Defense and Resilient System (CYDRES)

The DOE CYDRES project is driven by the urgent need to address critical research gaps in the domain of cyber-physical security of smart buildings, including Grid-interactive Efficient Buildings (GEBs). CYDRES, a real-time advanced building resilient platform, aims to enhance the cyber-attack-immune capabilities of buildings through multi-layered prevention, detection, and adaptation mechanisms. CYDRES consists of five key modules: a multi-layer network analyzer, an Automatic Fault Detection, Diagnosis, and Prognosis (AFDDP) framework, an intelligent mode selector, a cyber-resilient control framework, and a situation awareness platform. The Network Analyzer employs a data-driven framework that includes a protocol state learning tool and a CRF (Conditional Random Field) command validator. In Hardware-In-the-Loop (HIL) testbeds, it achieved 100% detection accuracy with a false alarm rate of 3%, validating its efficacy in identifying selected cyber-attacks. The AFDDP framework leverages pattern matching, PCA (Principal Component Analysis)-based strategies, and a DBN (Dynamic Bayesian Network)-based fault diagnosis approach to pinpoint the causes of physical system abnormalities using Building Automation System (BAS) data. In HIL experiments, the AFDDP module attained a detection accuracy of over 95% with a false alarm rate below 7%. Additionally, the fault detector utilized machine learning (Random Forest) and deep learning (Multi-Layer Perceptron) methods with acoustic sensor data to achieve a 100% fault detection accuracy in Heating, Ventilation, and Air-Conditioning (HVAC) equipment. The Mode Selector offered real-time impact analysis, allowing immediate actions to protect BASs in the face of emerging threats. The cyber-resilient control framework included an adaptive Model Predictive Control (MPC) and a measurement compensator, reducing temperature violations by up to 94% and improving the total demand flexibility by up to 70% in HIL experiments. Such HIL experiments covered a cyber-attack case and a physical fault case, showcasing CYDRES’ efficiency in maintaining operational continuity during threats. The situation awareness platform in Grafana enhanced real-time threat detection and response visualization, augmenting the operational awareness for building operators. CYDRES demonstrated high technical effectiveness in various test scenarios, particularly in HIL environments. The project's phased development approach ensured efficient use of resources, highlighting its practical feasibility and readiness for commercialization. By enhancing the security and resilience of building operations, CYDRES represents a significant advance in mitigating risks associated with cyber-physical systems, thereby enhancing public confidence in the safety of modern building infrastructure. Future directions for the project include expanding testing protocols, refining AFDDP methodologies, exploring more comprehensive resilient control strategies, and testing in real commercial buildings.

42 ENGINEERING↗
DOE OSTI2021

Surface processing and discharge-conditioning of high voltage electrodes for the Ra EDM experiment

The Ra EDM experiment uses a pair of high voltage electrodes to search for the atomic electric dipole moment of 225 Ra. We use identical, plane–parallel electrodes with a primary high gradient surface of 200 mm 2 to generate reversible DC electric fields. Our statistical sensitivity is linearly proportional to the electric field strength in the electrode gap. We adapted surface decontamination and processing techniques from accelerator physics literature to chemical polish and clean a suite of newly fabricated large-grain niobium and grade-2 titanium electrodes. Three pairs of niobium electrodes and one pair of titanium electrodes were discharge-conditioned with a custom high voltage test station at electric field strengths as high as +52.5 kV/mm and –51.5 kV/mm over electrode gap sizes ranging from 0.4 mm to 2.5 mm. One pair of large-grain niobium electrodes was discharge-conditioned and validated to operate at ±20 kV/mm with steady-state leakage current ≤ 25 pA (1σ) and a polarity-averaged 98 ± 19 discharges per hour. These electrodes were installed in the Ra EDM experimental apparatus, replacing a copper electrode pair, and were revalidated to ±20 kV/mm. Here, the niobium electrodes perform at an electric field strength 3.1 times larger than the legacy copper electrodes and are ultimately limited by the maximum output of our 30 kV bipolar power supply.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Thin Film Hydrogen Sensor Development, Testing and Integration Into Low Cost Wireless Sensing Systems (SBIR Phase 1 Final Report)

Element One, Inc. is reporting on the results of its DOE SBIR Phase I project for the development and testing of low-cost thin film hydrogen sensors and their subsequent integration into wireless sensing systems and networks. This work is based on Element One’s chemo-chromic sensing technology which indicates hydrogen presence (leaks) colorimetrically and electronically with no power required. The lack of required power makes them particularly suitable for passive Radio Frequency Identification Device (RFID) technology which can be deployed affordably and abundantly to detect leaks in hazardous environments. Element One built upon its experience with chemochromic materials that change color in the presence of hydrogen. Both thin films and pigments have been used, but thin films have the ability to change conductivity by more than three orders of magnitude making them ideal for resistive sensors. Prototype sensors were fabricated, tested and characterized. For the wireless component, Element One partnered with Esensor, Inc. to construct and test a wireless sensing network. The network was tested and validated at the National Renewable Energy Laboratory (NREL) in February and March. The tests involved exposing the wireless system to hydrogen gas concentrations of 1% and 5%. There were no significant problems encountered, and the system is ready for commercial development. The system demonstrated the viability of wireless hydrogen sensing networks for hydrogen leak detection and other applications.

08 HYDROGEN↗
DOE OSTI2023

Coal as Value-Added for Lithium Battery Anodes (Final Report)

Graphite is a basic material used for energy storage in lithium-ion (Li-ion) batteries. More than 70% of worldwide graphite production and nearly 50% of the annual graphite supply for the United States comes from China. Potential economic and national security concerns exist because Li-ion batteries are used not only for the fast-growing electrical vehicle market (15.6% compound annual growth rate [CAGR] from 2018 to 2028 in North America), but also for defense applications including unmanned aerial and ground vehicles. Development of an alternative formulation for Li-ion batteries is key. Building upon previous research confirming the ability of silicon alloys to improve the energy density of anodes, Semplastics developed a novel material based on our X-MAT® polymer-derived ceramic (PDC) technology. The X-MAT anode material is a composite of chemically tailored silicon oxycarbide (SiOC) and domestically sourced coal powder, designed to be a drop-in replacement for graphite within Li-ion battery anodes. Preliminary tests of this material showed more than twice the reversible capacity of graphite anodes (1,000 mAh/g vs. 372 mAh/g), with excellent stability and capacity retention, almost 100% coulombic efficiency, low voltage potential, and low thermal expansion. At full adoption in 2024, we expect the coal utilization to reach 27,300 tons. Through this project, Semplastics proposed to complete development and begin commercialization of this material. The objective was to determine the best formulation for technical performance and economic viability. At the end of the project, the X-MAT anode material is ready for implementation into existing battery manufacturing processes and can have a significant impact on the utilization of coal, with positive effects for the mining sector and the mitigation of carbon dioxide (CO 2 ) emissions.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

The CYBER security – Competency Health and Maturity Progression (CYBER-CHAMP) model: Extending the National Initiative for Cybersecurity Education (NICE) Framework Across Organizational Security

Problem Statement: There is a pervasive talent deficit in the cybersecurity industry that prevents employers from being able to fill their open positions efficiently. A holistic approach to security is required to ensure organizations have adequate prevention and response capabilities in case of a cyberattack. Specifically, industrial control systems (ICS’s) and their operational technology (OT) components have become a constant target for cyberattacks. Research Questions: It is proposed that the NICE Framework should be extended in the following areas: 1) Include guidance regarding the job roles and competencies for both IT and OT professionals. 2) Offer step-by-step solutions, based on the work role mappings from the NICE Framework, to increase cybersecurity through employee training and education. 3) Provide a streamlined, lifecycle approach to building a cybersecurity program. Contribution: The CYBER security – Competency Health and Maturity Progression (CYBER-CHAMP©) model provides a customized solution for businesses to understand their education gaps in organizational security and target areas for improvement. Rationale: The Framework for Improving Critical Infrastructure Cybersecurity v1.1 addresses ICS but does not offer a measurement of cybersecurity maturity or clear methods to ascertain an organization’s current risk profile. In Phases 1 and 5 of the model, measurements are provided to help an organization build their current and target risk profiles. The NICE framework provides a structure for planning an IT cybersecurity workforce, but the OT aspects of cybersecurity are only briefly discussed. The model uses Phases 2-3 to examine the competencies of an organization’s workforce, which includes both IT and OT roles. Current frameworks do not offer next steps to increase an organization’s cybersecurity. During Phase 4, employees’ roles are mapped to training, education, and/or certifications from common vendors. Investigative Approach: The model provides measurements and metrics for both an organization’s status and continual improvement. This improvement methodology includes guidance for creating an overall strategic plan for security improvement via products designed to increase an organization’s operational readiness through workforce competency health. Lessons Learned: Depending on who was participating, there were contradicting answers given in Phase 1 due to different security cultures in the organization. This revelation has influenced the steps listed in the User’s Guide, where Phase 1’s first recommended step is to assemble a team that champions the facilitation and implementation of the model in the organization. During Phase 2, the discovery was made that organizations may be missing roles that are necessary to perform critical cybersecurity functions. By understanding the functional roles and competencies needed, they can contract or hire cybersecurity help to fill these gaps. Implications: Using the model, organizations can discuss quantitative measures for improvement as a business case for advancing their security program. Future research can validate and extend the present theory and model to a variety of environments. It is of interest to investigate additional security roles and knowledge domains that are used to build standardized cybersecurity curriculum.

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DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

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