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All sources NASA NTRS DOE OSTI

DOE OSTI2026

Modeling rf sheath formation in turbulent tokamak boundary plasma

During ICRF antenna operation, complex interactions between turbulent density profiles, nonlinear RF sheaths, and RF-induced convective transport are observed to alter plasma density in the tokamak edge [D’Ippolito et al., Nucl. Fusion 38, 1543 (1998)]. In this work, we explore the physics of such interactions via numerical modeling, using a nonlinear EM/plasma/sheath code (VSim) and profiles obtained from a fluid plasma turbulence code (Hermes) in a 3D slab domain containing biased side-wall limiters. RF-rectified sheath formation on antenna and limiter surfaces is observed as electromagnetic waves launched by the antenna are refracted through the turbulent density profile. On transport timescales, such sheath potentials have been shown to influence both the mean species density and its RMS fluctuation spectrum [Smithe et al., these proceedings]. On the faster RF timescales, we demonstrate that the converse is also true – regions of high plasma density near material surfaces give rise to the highest sheath potential amplitudes. When density is turbulent and spatially nonuniform, localized regions of high sheath potential (hotspots) may develop where high-density filaments intersect material surfaces. Such hotspots are of particular concern as sources of impurity sputtering, and we explore their behavior in response to changes both to the local plasma density and to antenna operating parameters and structure. Related results exploring the role of Faraday shields and/or enclosing structures in suppressing high sheath potentials for other devices (e.g. SPARC) will also be shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

A new approach for measuring the carbon and oxygen content of atmospherically relevant compounds and mixtures

Abstract. Due to its complexity, gas- and particle-phase organic carbon in the atmosphere is often classified by its bulk physicochemical properties. However, there is a dearth of robust, moderate-cost approaches to measure the bulk chemical composition of organic carbon in the atmosphere. This is particularly true for the degree of oxygenation, which critically affects the properties and impacts of organic carbon but for which routine measurement approaches are lacking. This gap has limited the understanding of a wide range of atmospheric components, including particulate matter, the mass of which is monitored worldwide due to its health and environmental effects but the chemical characterization of which requires relatively high capital costs and complex operation by highly trained technical personnel. In this work, we demonstrate a new approach to estimate the mass of carbon and oxygen in analytes and mixtures that relies only on robust, moderate-cost detectors designed for use with gas chromatography. Organic compounds entering a flame ionization detector were found to be converted with approximately complete efficiency to CO2, which was analyzed downstream using an infrared detector to measure the mass of carbon analyzed. The ratio of the flame ionization detector (FID) signal generated to CO2 formed (FID∕CO2) was shown to be strongly correlated (R2=0.89) to the oxygen-to-carbon ratio (O∕C) of the analyte. Furthermore, simple mixtures of analytes behaved as the weighted average of their components, indicating that this correlation extends to mixtures. These properties were also observed to correlate well with the sensitivity of the FID estimated by structure activity relationships (quantified as the relative effective carbon number). The relationships between measured FID∕CO2, analyte O∕C, and FID sensitivity allow the estimation of one property from another with <15 % error for mixtures and <20 % error for most individual analytes. The approach opens the possibility of field-deployable, autonomous measurement of the carbon and oxygen content of particulate matter using time-tested, low-maintenance detectors, though such an application would require some additional testing on complex mixtures. With some instrumental modifications, similar measurements on gas-phase species may be feasible. Moreover, the potential expansion to additional gas chromatography detectors may provide concurrent measurement of other elements (e.g., sulfur, nitrogen).

42 ENGINEERING↗

DOE OSTI2023

Comparison of Error Rate Depending on Operator Expertise and Simulator Complexity

This paper analyzes operator's error rate from experiments, depending on the expertise and simulator complexity. This study uses the Rancor Microworld, a simplified simulator developed by INL, and the Compact Nuclear Simulator (CNS), a less simplified simulator developed by the Korea Atomic Energy Research Institute (KAERI). The error rates were measured from simulation data of a total of 72 participants, and the collected error rate data were analyzed using analysis of variance (ANOVA) test.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗

DOE OSTI2025

A methodology for decay heat characterization in molten salt reactors

Accurate decay heat prediction in molten salt reactors (MSRs) faces dual challenges: complex operational uncertainties and the need for interpretable models compatible with engineering workflows. This work presents a hybrid machine learning and segmented polynomial methodology that addresses both requirements through three key innovations. First, a modular data architecture encodes MSR-specific operational parameters (power density: 1-100 W cm -3 , humidity: 0-0.1 wt %, air ingress: 0-0.1 mol %) with uncertainty-aware temporal discretization spanning 15 orders of magnitude. Second, region-optimized machine learning models achieve 92.3 % root mean square error (RMSE) reduction over conventional polynomials while maintaining physical interpretability through automated piecewise equation generation. Third, dual front-end interfaces accelerate safety analyses — a Jupyter environment enables researchers to explore 10,000+ parameter combinations via interactive widgets, while a Streamlit web application reduces design iteration cycles through production-grade visualization tools. Operational deployment demonstrates prediction times of only a couple hundred milliseconds for 10 4 years decay profiles, enabling real-time optimization of spent fuel container designs.

42 - ENGINEERING↗

DOE OSTI2023

AMG Preconditioners based on parallel hybrid coarsening and multi-objective graph matching

We describe preliminary results from a multi-objective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on high-end parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package \texttt{PSCToolkit} to exploit multi-threaded parallelism at the node level on multi-core processors. Our matching proposal balances the need to simultaneously compute high weights and large cardinalities by a new formulation of the weighted matching problem combining both these objectives using a parameter $\lambda$. We compute the matching by a parallel $2/3-\varepsilon$-approximation algorithm for maximum weight matchings. Results with the new matching algorithm show that for a suitable choice of the parameter $\lambda$ we compute effective preconditioners in the presence of anisotropy, i.e., smaller solve times, setup times, iterations counts, and operator complexity.

D'Ambra, Pasqua↗

DOE OSTI2024

Optimization-Based Deconfliction of Distribution System Applications

Power distribution systems are transitioning towards a future with large numbers of controllable grid-edge devices and distributed generation, as well as more complex operational objectives. This evolution requires a new distribution management approach, and modular applications can play a significant role. Thus, new strategies to coordinate independent applications with diverging operational goals are necessary. In this paper, we propose a novel optimization-based deconflictor to facilitate coordination in future advanced distribution management systems.

application deconfliction, power system optimizati↗

DOE OSTI2024

Single-mode squeezed-light generation and tomography with an integrated optical parametric oscillator

Quantum optical technologies promise advances in sensing, computing, and communication. A key resource is squeezed light, where quantum noise is redistributed between optical quadratures. We introduce a monolithic, chip-scale platform that exploits the χ (2) nonlinearity of a thin-film lithium niobate (TFLN) resonator device to efficiently generate squeezed states of light. Our system integrates all essential components—except for the laser and two detectors—on a single chip with an area of one square centimeter, reducing the size, operational complexity, and power consumption associated with conventional setups. Using the balanced homodyne measurement subsystem that we implemented on the same chip, we measure a squeezing of 0.55 decibels and an anti-squeezing of 1.55 decibels. We use 20 milliwatts of input power to generate the parametric oscillator pump field by using second harmonic generation on the same chip. Our work represents a step toward compact and efficient quantum optical systems posed to leverage the rapid advances in integrated nonlinear and quantum photonics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

Previous work on multilevel techniques for compression and reduction of scientific data is extended to the case of data given on unstructured meshes in two and three dimensions. The centerpiece of the work is a decomposition algorithm which is shown to be optimal, in terms of both storage and operational complexity, applicable to unstructured grids in both two and three dimensions, and which implicitly gives a Riesz basis that can be exploited to reduce the data while maintaining rigorous bounds on the loss incurred. The flexibility of the approach is illustrated by applications to potential flow around an airfoil and the effect of compression on quantities of interest relevant to airfoil design; compression of computational simulation of a nonlinear reaction-diffusion system with special attention given to the problem of time series reduction; and, data from a simulation of magnetically confined plasma in a fusion reactor reduced so as to preserve the electric field computed from the data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

Security of cyber-physical systems (CPS) continues to pose new challenges due to the tight integration and operational complexity of the cyber and physical components. To address these challenges, this article presents a domain-aware, optimization-based approach to determine an effective defense strategy for CPS in an automated fashion—by emulating a strategic adversary in the loop that exploits system vulnerabilities, interconnection of the CPS, and the dynamics of the physical components. Our approach builds on an adversarial decision-making model based on a Markov Decision Process (MDP) that determines the optimal cyber (discrete) and physical (continuous) attack actions over a CPS attack graph. The defense planning problem is modeled as a non-zero-sum game between the adversary and defender. We use a model-free reinforcement learning method to solve the adversary’s problem as a function of the defense strategy. We then employ Bayesian optimization (BO) to find an approximate best-response for the defender to harden the network against the resulting adversary policy. This process is iterated multiple times to improve the strategy for both players. We demonstrate the effectiveness of our approach on a ransomware-inspired graph with a smart building system as the physical process. Numerical studies show that our method converges to a Nash equilibrium for various defender-specific costs of network hardening.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

EMT data generation

The integration of inverter-based resources (IBRs) in power systems is accelerating, bringing with it significant benefits such as reduced greenhouse gas emissions, improved grid resilience, and increased energy independence. Despite these advantages, the widespread adoption of IBRs introduces several challenges, including issues related to grid stability, increased operational complexity, and the need for updated regulatory frameworks. To address these challenges, IEEE released Standard 2800 in 2022, which sets forth the necessary interconnection capabilities and performance criteria for IBRs connected to transmission and sub-transmission systems. This standard outlines the performance requirements to ensure the reliable integration of IBRs into the bulk power system. Furthermore, in 2023, the North American Electric Reliability Corporation (NERC) published a reliability guideline for electromagnetic transient (EMT) modeling of BPS-connected IBRs. This guideline provides recommendations for developing EMT model requirements, performing model quality checks, and implementing verification practices specifically for EMT models representing BPS-connected inverter-based resources in reliability studies conducted by transmission planners and planning coordinators. These standards and guidelines have a profound impact on EMT studies for transmission networks, influencing system stability analyses, grid recovery and resynchronization processes, fault ride-through evaluations, protection and coordination strategies, advanced control methodologies, and the inclusion of IBRs in transient models of transmission networks. As a result, the generation of EMT data is crucial for conducting various transient-based studies to understand the impact of IBRs. EMT data generation use cases serve as the basis for scenarios in event detection and identification use cases, providing comprehensive details about EMT data generation for transmission grids with inverter-based resources. These use cases supply sufficient training and validation datasets for subsequent EMT analysis algorithms.

Xia, Qianxue↗

DOE OSTI2020

SRF Fixture Design and HOM analysis

A particle accelerator can either be a normal conducting or a superconducting accelerator. While a superconducting accelerator is a more energy efficient option, they traditionally use a liquid helium bath to achieve the temperatures needed for superconductivity. This makes superconducting accelerators operationally complex and expensive. To make superconducting accelerators a more commercially viable option IARC is working on a cryogen free accelerator which can be seen below.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Regulating Gas Transport in Molecularly Engineered Polymer Membranes (Final Technical Report)

Energy-efficient separation processes are essential for a wide range of applications ranging from clean fuels (e.g., hydrogen purification) and petroleum refining (e.g., natural gas processing) to water purification and carbon capture. Membrane-mediated separations have shown tremendous promise in providing high productivity and high separation efficiency at significantly lower energy consumption, e.g., up to 90% less energy cost than traditional thermally driven processes such as distillation. Polymeric membranes–the dominant separation membrane materials–have yet to reach their full potential due to their limitations in long-term durability (e.g., productivity loss over the period of their lifetime due to physical aging) or insufficient stability under harsh conditions (e.g., high temperature, chemically complex feeds). This research seeks to establish a new paradigm in polymer membrane material design by harnessing crosslinked model networks with well-defined yet finely tailorable microstructure to facilitate fast and selective gas transport and simultaneously enhance membrane stability. Unlike traditional randomly crosslinked polymers, which suffer from structural inconsistencies and consequently suboptimal gas separation performance, crosslinked model network membranes prepared via a precisely controlled end-linking process enables the creation of previously unattainable microstructure tunability, which, in turn, results in versatile crosslinked membranes with high separation performance that meet the needs of various challenging gas separations. Using model network framework as a fundamental tool by applying this concept in diverse polymer categories, this work has led to the development of various innovative crosslinked membrane structures such as unimodal, bimodal and clustered model networks. These advanced crosslinked polymer membranes not only demonstrate exceptional gas separation performance that significantly outperform existing randomly crosslinked membranes, but also possess excellent long-term durability and robust stability under complex operating conditions. From a fundamental perspective, results from this research provide critical mechanistic insights into gas separation in crosslinked polymer membranes, addressing key knowledge gaps and opening new avenues for membrane design to meet various separation needs. The new membrane materials produced from this research enable the use of polymeric membranes for high temperature gas separations, offering substantial energy and cost savings by eliminating the need for repeated cooling-heating cycles in industrial processes.

02 PETROLEUM↗

DOE OSTI2025

Combined Creep and Fatigue Modeling

Simultaneous consideration of creep and fatigue is of paramount importance to accurately predict component life in many industrial applications. For example, growing penetration of renewable energy onto the grid is forcing many power plants to transition from base-load power generation to more complex operation modes, characterized by a combination of base-load generation and frequent start-up and shut-down cycles. The frequent start-stop of power plants is causing a significant toll in the component life due to fatigue. As such, creep alone is not adequate anymore to predict the component life. Most existing life prediction models consider creep or fatigue independently while neglect their interactions. In this presentation, we talk about some recent efforts at NETL to develop a unified modeling framework where both creep and fatigue can be considered. The creep-fatigue interaction mechanism is studied. High-throughput simulations are performed under various hold stress and hold time, and analytical expressions are fitted for the crack growth rates under creep and/or fatigue conditions demonstrating some applications of this newly developed modeling capability.

advanced alloy development↗

DOE OSTI2026

Risks to the DOW Mission From Global Advanced Energy Adoption

Advanced energy technologies have the potential to enhance energy security and resilience; however, they can introduce new vulnerabilities even as they mitigate existing ones. This dichotomy highlights that both action and inaction carry strategic risks in a contested and logistically complex operating environment. Regardless of U.S. civilian or military adoption of such technologies, key allies and adversaries are on adoption paths that will impact the U.S. military at the tactical, operational, and strategic level. The global adoption of advanced energy technologies presents the potential for both positive and negative consequences for U.S. Department of Defense (DOD) missions in the next 10-20 years. Three categories of risk drivers are presented in this analysis: infrastructure-related, adoption-related, and response-related. Each risk type can generate consequences for DOD, including power disruptions, suboptimal DOD mission performance and operational effectiveness, higher costs and shortages for both legacy and advanced energy technologies, and loss of U.S. influence and strategic deterrent value. Specific impacts could include tactical impacts, operational impacts, and strategic impacts. Mitigation strategies could include energy resource and technology planning, cybersecurity, supply chain resilience, workforce development, exercises and simulations, investments in commercialized technology, operational testing and pilot programs, research into emerging technologies, partnerships and working groups, common standards, budget planning, and repurposing infrastructure.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗

DOE OSTI2022

Big Data Analytics for Long-Term Meteorological Observations at Hanford Site

A growing number of physical objects with embedded sensors with typically high volume and frequently updated data sets has accentuated the need to develop methodologies to extract useful information from big data for supporting decision making. This study applies a suite of data analytics and core principles of data science to characterize near real-time meteorological data with a focus on extreme weather events. To highlight the applicability of this work and make it more accessible from a risk management perspective, a foundation for a software platform with an intuitive Graphical User Interface (GUI) was developed to access and analyze data from a decommissioned nuclear production complex operated by the U.S. Department of Energy (DOE, Richland, USA). Exploratory data analysis (EDA), involving classical non-parametric statistics, and machine learning (ML) techniques, were used to develop statistical summaries and learn characteristic features of key weather patterns and signatures. The new approach and GUI provide key insights into using big data and ML to assist site operation related to safety management strategies for extreme weather events. Specifically, this work offers a practical guide to analyzing long-term meteorological data and highlights the integration of ML and classical statistics to applied risk and decision science.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Field Testing of an Affordable Zero-Liquid-Discharge Arsenic-Removal Technology for a Small-Community Drinking Water System in Rural California

Arsenic contamination in groundwater threatens public health, particularly in small, low-income communities lacking affordable treatment solutions. This study investigated the field implementation of novel air cathode assisted iron electrocoagulation (ACAIE) technology for arsenic removal in Allensworth, California, where groundwater arsenic concentrations exceeded 250 µg/L. Over four months, a pilot-scale ACAIE system, operating at 600 L/h, consistently reduced arsenic levels to below the EPA’s maximum contaminant level of 10 µg/L. Laboratory experiments informed the optimization of charge dosage and flow rates, which were validated during field testing of the ACAIE 600 L/h system. The in-situ generation of hydrogen peroxide at the cathode speeded up the reaction kinetics, ensuring high arsenic removal efficiency while allowing high throughput, even with a compact reactor size. An economic analysis demonstrated a treatment cost of USD 0.02/L excluding labor, highlighting the system’s affordability compared to conventional methods. Adding labor costs increased the treatment cost to USD 0.09/L. The regeneration of air cathodes extended their operational life, addressing a key maintenance challenge, thus reducing the costs slightly. Intermittent challenges were encountered with filtration and secondary contaminant removal; these issues highlight opportunities for further operational improvements. Despite these challenges, ACAIE’s low operational complexity, scalability, and cost-effectiveness make it a promising solution for underserved small communities. These findings provide critical insights into deploying sustainable arsenic remediation technologies that are tailored to the needs of rural, low-resource communities.

Bandaru, Siva R S↗