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Center for Frontiers of Subsurface Energy Security (Final Report)

For the past two decades a slowly growing consensus has been that rising temperature is being caused by increases in carbon dioxide (CO 2 ) in the Earth’s atmosphere. What is less consensual is what to do about it. Solutions range from reducing emissions to various ways to capture CO 2 (from effluents of power plants, as plants, etc.) and store it away from the atmosphere. The problem is of such magnitude that it is likely that we will need many methods. Also some methods would cause draconian harm to global economies. This work is about the storage part of the reducing of CO 2 in the atmosphere. There have been many methods proposed for storing CO 2 : in vegetation, at the bottom of oceans, in land farms, and in subsurface formations. The latter is particularly prominent given the large estimated volume in porous formation in the Earth, and the 50-year old experience in injecting CO 2 for enhanced oil recovery. This report is about the science behind subsurface storage, called here geological carbon storage or GCS. Effective GCS requires that billions of metric tons of CO 2 be injected into storage reservoirs annually. GCS feasibility requires balancing the opposing impacts of large emplacement (injection) rates without compromising caprock seal integrity during injection. Once injected, the challenge is to assure the injected CO 2 remains permanently stored. Under most conditions, CO 2 is in a supercritical state, or scCO 2 . scCO 2 is immiscible with the subsurface brine and has a factor of ten smaller viscosity, leading to the development of two-phase fluid flow and scCO 2 bypassing. After the injection of large volumes of scCO 2 , the pore pressure, state of stress, chemical, thermal, and biological steady-state or equilibrium conditions in the subsurface are disrupted, which may lead to unpredictable behavior because the responses to these changes are often non-linear. Current challenges for efficient and reliable scCO 2 storage are: (1) sustaining large injection rates; (2) using available pore space efficiently, and (3) controlling undesired or unexpected behavior, such as scCO 2 leakage. Various aspects of these challenges are addressed by collaborative projects within Center for Frontiers in Subsurface Energy Security (CFSES). Challenge 1 – Sustaining large storage rates. To offset the annual CO 2 emissions of in the United States, ~7 billion metric tons of CO 2 must be injected into the storage reservoirs annually. Injected CO 2 forms a plume that starts spreading, displacing originally resident brine, though inefficiently so. Because of its low viscosity, and buoyant forces, a CO 2 plume rises to the reservoir rock-caprock interface. Injecting large volumes of scCO 2 at sufficiently high rates into subsurface storage formations leads to increases in pore pressures, potential expansion of the reservoir rock, and may result in fracturing of the reservoir rock and/or caprock. CFSES research uses existing and new experimental and modeling approaches to identify chemical-physical controls on the permeability and pore pressure dynamics, anticipating geomechanical (fracturing) events, predicting multi-phase flow patterns and trapping of CO 2 . Challenge 2 – Using pore space with unprecedented efficiency. Since large volumes of scCO 2 are to be stored, the volume available in storage reservoirs must be used efficiently. Storage efficiency is defined as a fraction of pore space occupied by carbon dioxide. Current estimates are that less than 5 percent of available pore volume is available for storage because scCO 2 , having a smaller density and viscosity than reservoir brine, tends to form viscous fingers, and bypass available pore space. This flow regime results in a CO 2 plume spreading over large areas in subsurface. The research goal in Challenge 2 is to advance the fundamental understanding of two-phase flow through porous media, including molecular-to meter- scale experimental and modeling approaches. This work leads to novel strategies to ensure that injected CO 2 occupies more than 5% of the pore space, ideally up to 50%. Challenge 3 – Controlling undesired or unexpected behavior. Following the injection of scCO 2 , the subsurface reservoir re-equilibrates to different physical and chemical states. This re-equilibration in space and time could lead to unexpected and/or undesired behavior: fracturing of reservoir rock and/or caprock, development of preferential flow paths, CO 2 leakage (to the surface or overlaying fresh water reservoirs), induced seismicity, and activation of faults. One of the main research goals of CFSES was to advance the fundamental understanding of how individual chemical and physical processes are coupled. We particularly focused on the chemical-mechanical coupling, and flow-mechanical coupling. We collected experimental and modeling data to determine the temporal and spatial scales of the coupled processes, which may lead to pore collapse caused by creep, activation of fractures and faults, chemically-induced fracture propagation, and self-focusing of scCO 2 flow.

54 ENVIRONMENTAL SCIENCES↗

Effect of Current Sheets on the Solar Wind Magnetic Field Power Spectrum from the Ulysses Observation: From Kraichnan to Kolmogorov Scaling

The MHD turbulence theory developed by Iroshnikov and Kraichnan predicts a k{sup -1.5} power spectrum. Solar wind observations, however, often show a k{sup -5/3} Kolmogorov scaling. Based on 3 years worth of Ulysses magnetic field data where over 28 000 current sheets are identified, we propose that the current sheet is the cause of the Kolmogorov scaling. We show that for 5 longest current-sheet-free periods the magnetic field power spectra are all described by the Iroshnikov-Kraichnan scaling. In comparison, for 5 periods that have the most number of current sheets, the power spectra all exhibit Kolmogorov scaling. The implication of our results is discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Increasing the Rate and Extent of Microbial Coal to Methane Conversion through Optimization of Microbial Activity, Thermodynamics, and Reactive Transport (Final Scientific/Technical Report)

Currently, coal bed methane (CBM) wells have a limited lifetime since the rate of methane removal via the installed wells is much faster than the in situ methane production rates. Along with water issues created by large amounts of CBM production water, the short life span of CBM wells is a huge deterrent to the environmental and economic feasibility of CBM production. The process of biogenic methanogenesis can be enhanced via the stimulation of the associated microbial communities that can convert the organic fractions of coal to methane. This process is termed Microbially-Enhanced Coal Bed Methane (MECBM). However, the rates of methane production are still limited and long incubation times are necessary. We hypothesized that the elucidation of chemical and biological parameters that limited MECBM together with thermodynamic considerations would inform strategies to optimize the process under flow conditions. We incorporated microbiological, physicochemical, and engineering processes to develop a more sustainable CBM production scheme with native coal and native microorganisms. The proposed combination of microbial ecology and physiology as well as optimized engineering principles minimized key constraints that impact microbial coal conversion to methane under environmentally relevant conditions. The combined approach for bench-scale tests resulted in more effective and less environmentally burdensome coal-dependent methane production with the potential for H 2 O and CO 2 management.

01 COAL, LIGNITE, AND PEAT↗

Deep Borehole Field Test Specifications

This report documents conceptual design development for the Deep Borehole Field Test (DBFT), mainly the test packages (not containing waste) and the system for demonstrating emplacement and retrieval of those packages in the Field Test Borehole (FTB). For the DBFT to have demonstration value, it must be based on conceptualization of a deep borehole disposal (DBD) system for specific waste forms. This document therefore describes a current reference DBD concept, and analyzes key design options for disposal, to guide selection of options for the DBFT. The most important of these options is the emplacement mode, i.e. whether packages are emplaced using a wireline or a string of drill pipe (with a drill rig). This choice is analyzed using cost and risk models, in Sections 5 and 6. Other emplacement mode options are also identified and discussed. The reference DBD concept and the analysis of waste packaging and emplacement options, are used to develop requirements and assumptions for the DBFT and to recommend DBFT specifications. Design issues are identified, and priorities are developed for further conceptual design development and additional engineering analysis, anticipating future design activities. Conceptual design development is part of a process that proceeds in three stages: 1) conceptual design including feasibility studies; 2) preliminary design that includes technical and cost information necessary for final design; and 3) final design sufficient for fabrication or construction. The DBFT engineering demonstration will follow such an evolution. Whereas design evolution typically begins with bench-scale and pilot-scale investigations proceeding to conceptual, preliminary, and final designs, the DBFT can proceed directly to design because of extensive previous work and published literature on scientific drilling, characterization methods, waste packaging and handling, industrial deep-hole drilling and construction, and downhole operations. Hence it is anticipated that this report will lead to completion of conceptual design, then preliminary and final design, fabrication and testing, and demonstration of waste emplacement in a deep borehole.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗

Highly dense atomic Fe–Ni dual metal sites for efficient CO 2 to CO electrolyzers at industrial current densities

Carbon-supported, atomically dispersed, nitrogen-coordinated metal sites (e.g., Fe and Ni) are arguably the most promising catalysts for the electrochemical reduction of CO 2 to CO due to their unique catalytic properties and the use of earth-abundant elements. However, conventional single metal sites are constrained by their structural simplicity, causing either too weak or too strong absorption/desorption of multiple critical intermediates (e.g., *COOH and *CO). Current catalysts also suffer from ultra-low loadings (<1.0 wt%) of atomic metal active sites in catalysts, leading to inadequate performance for CO 2 -to-CO conversion. Here, we develop dual Ni/Fe metal site catalysts with significantly increased atomically dispersed metal loadings (up to 4.8 wt%). A gas-phase chemical vapor deposition (CVD) approach to introducing single Ni sites was integrated with Fe 2 O 3 /ZIF-8 precursors, followed by an optimal thermal activation. The optimized CVD-Ni/Fe–N–C catalyst exhibited remarkable electrocatalytic performance for the CO 2 reduction to CO in a continuous membrane-electrode-assembly electrolyzer, achieving a maximum CO faradaic efficiency (FE CO ) of 96% at a current density of 700 mA cm −2 in a near-neutral electrolyte. Furthermore, a desirable but challenging acidic flow-cell electrolyzer was designed using this dual metal site catalyst to improve CO 2 utilization, accomplishing a FE CO of up to 95% at a CO partial current density close to 600 mA cm −2 . Density functional theory (DFT) calculations suggest a synergetic effect between Fe–Ni pairs facilitating *COOH intermediate formation and *CO desorption simultaneously during CO 2 to CO conversion. This is key to breaking the linear scaling relationship of conventional single-metal site catalysts during the CO 2 reduction reaction.

36 MATERIALS SCIENCE↗

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗

Predictive characterization of aging and degradation of reactor materials in extreme environments. Final report, December 20, 2013 - September 20, 2017

Understanding of reactor material behavior in extreme environments is vital not only to the development of new materials for the next generation nuclear reactors, but also to the extension of the operating lifetimes of the current fleet of nuclear reactors. To this end, this project conducted a suite of unique experimental techniques, augmented by a mesoscale computational framework, to understand and predict the long-term effects of irradiation, temperature, and stress on material microstructures and their macroscopic behavior. The experimental techniques and computational tools were demonstrated on two distinctive types of reactor materials, namely, Zr alloys and high-Cr martensitic steels. These materials are chosen as the test beds because they are the archetypes of high-performance reactor materials (cladding, wrappers, ducts, pressure vessel, piping, etc.). To fill the knowledge gaps, and to meet the technology needs, a suite of innovative in situ transmission electron microscopy (TEM) characterization techniques (heating, heavy ion irradiation, He implantation, quantitative small-scale mechanical testing, and various combinations thereof) were developed and used to elucidate and map the fundamental mechanisms of microstructure evolution in both Zr and Cr alloys for a wide range environmental boundary conditions in the thermal-mechanical-irradiation input space. Knowledge gained from the experimental observations of the active mechanisms and the role of local microstructural defects on the response of the material has been incorporated into a mathematically rigorous and comprehensive three-dimensional mesoscale framework capable of accounting for the compositional variation, microstructural evolution and localized deformation (radiation damage) to predict aging and degradation of key reactor materials operating in extreme environments. Predictions from this mesoscale framework were compared with the in situ TEM observations to validate the model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗

Chapter 9.12 - Weather, Climatic and Ecological Impacts of Onshore Wind Farms

Wind power is an environmentally sustainable technology that is likely to be part of the solution to the climate change, air pollution, and energy security problems. Despite many positive benefits, the rapid development of wind power has raised concerns about some potential adverse environmental impacts. While converting wind?s kinetic energy into electricity, wind turbines (WTs) modify properties of the atmospheric boundary layer (ABL) including the vertical profiles and surface-atmosphere exchanges of energy, momentum, mass, moisture, and trace gases. Given the current installed capacity and the projected installation worldwide, wind farms (WFs) are likely becoming a major driver of manmade land use change on Earth. Hence, understanding WT-atmosphere-surface interactions and assessing potential environmental impacts of WFs are of significant scientific, societal and economic importance. Here we review our progress in assessing potential impacts of onshore wind power on weather, climate and vegetation activity. A consensus is emerging based on observations and modeling studies that WFs cause a local to regional warming effect, particularly at nighttime, while the impacts on precipitation, wind patterns, crop yields and vegetation activity are uncertain. The warming effect results simply from vertical heat redistribution within the ABL due to turbine-enhanced vertical turbulent mixing in the wakes. At the global scale, with a substantial installation of WFs, mesoscale and climate models predicted large regional changes but small global impacts on temperature, while the impacts on precipitation, clouds, wind patterns and large-scale circulation have large uncertainties and are region specific and scale dependent. Despite increasing number of research efforts, our assessment of potential WF impacts is still very limited. Although the WF impacts are mostly local and limited to the near-surface ABL, this is the layer where we live and plants grow. Hence, more studies are needed to improve our understanding of WT-atmosphere-surface interactions and our capability to model and project the weather, climatic and ecological impacts of large WFs.

atmospheric boundary layer↗

DETECTING FIRE WITH MACHINE LEARNING-ENABLED VISUAL MONITORING FOR NUCLEAR POWER PLANT ENVIRONMENTS

Nuclear power plants are experiencing significant cost challenges to remain competitive with other energy-generation utilities. Unlike other industries, the cost of operation and maintenance activities is mostly attributed to workforce costs. To mitigate this, nuclear power plant stakeholders are increasingly interested in the development and deployment of machine learning methods to potentially automate or augment manually intensive tasks to reduce costs, especially for monitoring activities. One monitoring function that is visually demanding and that can occur frequently to meet the requirements of a fire protection program is visually monitoring an area for fire occurrence. Currently, fire watch activities consist of a worker physically stationed at a given location with the sole responsibility of observing a given area to ensure a fire is detected and mitigated promptly. This effort focused on the development and evaluation of a suitable deep convolutional neural network to classify individual video frames at a sub-second frequency for the occurrence of “fire” and “no fire” in varying industrial environments similar to nuclear power plants. It is believed that a trained neural network model could be integrated with existing facility video surveillance camera feeds to generate alerts when fire inferences occur in individual frames captured at sub-second temporal resolutions. Extensive effort was dedicated to identifying and curating suitable imagery training data representing varying environments and scene settings with and without flame features to maximize generalization in nuclear power plant environments. The data collection effort resulted in the aggregation of a large, labeled image library exceeding 12,000 images to support model training for diverse industrial environments. A deep neural network model incorporating parallel multi-scale capabilities was developed and trained to support accurate image-based detection of flame incidents of varying sizes and spectral feature properties within heterogeneous scenes. Analysis results show that the trained model can achieve high inference accuracy despite heterogeneous scene environments and components. Testing accuracy exceeded 95.0 percent with very low false positive and false negative inferences.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Combined Experimental and Computational Approach to Predict the Glass-Water Reaction

The use of mineral and glass dissolution rates measured in laboratory experiments to predict the weathering of primary minerals and volcanic and nuclear waste glasses in field studies requires the construction of rate models that accurately describe the weathering process over geologic timescales. Additionally, the need to model the long-term behavior of nuclear waste glass for the purpose of estimating radionuclide release rates requires that rate models be validated with long-term experiments. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test B, and pressurized unsaturated flow (PUF)], thereby reducing the duration required to evaluate long-term performance. Currently, the PUF test is the only method that mimics the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitors the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior. A one-dimensional reactive chemical transport simulation of glass dissolution and secondary phase formation during a 1.5-year-long PUF experiment was conducted with the Subsurface Transport Over Reactive Multiphases (STORM) code. Results show that parameterization of the computer model by combining direct bench scale laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over the length of the experiment. Over the 1.5-year-long test duration, the rate decreased from 0.2 to 0.01 g/(m2 day) based on B release for low-activity waste glass LAWA44. The observed decrease is approximately two orders of magnitude higher than the decrease observed under static conditions with the SON68 glass (estimated to be a decrease by four orders of magnitude) and suggests that the gel-layer properties are less protective under these dynamic conditions.

aluminoborosilicate nuclear waste glass↗

Review of Wave Energy Converter Power Take-Off Systems, Testing Practices, and Evaluation Metrics: Preprint

While the field of wave energy has been the subject of numerical simulation, scale model testing, and precommercial project testing for decades, wave energy technologies remain in the early stages of development and must continuing proving themselves as a promising modern renewable energy field. One of the difficulties that wave energy systems have been struggling to overcome is the design of highly efficient energy conversion systems that can convert the mechanical power, derived from the oscillation of wave activated bodies, into another useful product. Often the power take-off (PTO) is defined as the single unit responsible for converting mechanical power into another usable form such as electricity, pressurized fluid, compressed air, and others. The PTO, and the entire power conversion chain (PCC), is of great importance as it affects not only how efficient wave power is converted into electricity, but also contributes to the mass, size, structural dynamics, and levelized cost of energy (LCOE) of the wave energy converter (WEC). Unlike wind and solar, there is no industrial standard device, or devices, for wave energy conversion and this diversity is transferred to the PTO system. The majority of current WEC PTO systems incorporate a mechanical or hydraulic drive train, power generator, and an electrical control system. The challenge of WEC PTO designs is designing a mechanical-to-electrical component that can efficiently convert irregular, bi-directional, low frequency and low alternating velocity wave motions. While gross average power levels can be predicted in advance, the variable wave elevation input has to be converted into smooth electrical output and hence usually necessitates some type of energy storage system, such as battery storage, accumulator super capacitors, etc., or other means of compensation such as an array of devices. One of the primary challenges for wave energy converter systems is the fluctuating nature of wave resources, which require WEC components to be designed to handle loads (i.e. torques, forces, and powers) that are many times greater than the average load. This approach requires a much greater PTO capacity than the average power output and indicates a higher cost. In addition, supporting mechanical coupling and or gearing can be added to the PCC to help alleviate the difficulties with transmission and control of fluctuating large loads with low frequencies (indicative of wave forcing) into smaller loads at higher frequencies (optimum for conventional electrical machine design) can quickly increase the complexity of the PCC which could result in a greater number of failure modes and increased maintenance costs. All of the previous points demonstrate how the PTO influences WEC dynamics, reliability, performance and cost which are critical design factors. This paper further explores these topics by providing a review of the state-of-the-art PTO systems currently under development, how these novel PTO systems are tested and derisked prior to precommercial deployment, and the evaluation metrics historically used to differentiate between PTO designs and how they can be improved to support control co-design focused development of wave energy systems.

laboratory testing↗

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗

Unlocking Larger Scales and Aspect Ratios in 3D Printed Glass: Coupling Active Mixing and UV Curing for Advanced Printability and Crack Resistance

Recent developments in additive manufacturing (AM) of glass via silica-filled inks have facilitated fabrication of previously unattainable geometries and compositions. However, the maximum processable size of 15 mm limits the use of these prints in applications such as optics. A key limitation lies in the trade-off between material printability and green strength: increasing silica content in the feedstock improves crack resistance and reduces shrinkage but results in dramatic changes in viscoelastic properties that hinder flowability. Here, this paper presents a novel approach that offers expanded versatility in processable size, feedstock formulation, and printing. Described here is a direct ink writing (DIW) system coupled with an active high-shear micromixer and UV light source, capable of simultaneously printing multiple inks with a wide range of rheological properties. Choice of silica sourc, solvent, UV-curable binder, and dispersant is used to tune the ink rheology and improve printability and mechanical properties. Imparting high shear with the micromixer while UV-curing the extrudate allows for increased ink viscosities and reduced nozzle diameters, enabling printing finer feature sizes. With these advances, thin-walled high-aspect ratio structures and a crack-free glass disk measuring 44 mm in diameter are demonstrated, an increase of 3× in the greatest dimension compared to current state-of-the-art.

36 MATERIALS SCIENCE↗

Architectural development of an ST fusion device

A recent U.S. National Academy study recommended the development of a next step Sustained High Power Density (SHPD) facility within the U.S. as an intermediate step in designing and building a Pilot Plant device. Several papers have been written describing the physics, design and engineering scoping analysis performed in developing this machine design. This paper places emphasis on the continued evolution of the architectural development of past activities to meet the requirements of an ST fusion pilot plant and power plant. The current effort centers on meeting basic physics and component requirements in a machine design that improves the chance of achieving fission level availability (95%) within an arrangement that promotes design simplicity, offsite construction with on-site modular assembly. As a prelude to both the SHPD and Pilot Plant, a scoping design of a 500MWe Spherical Tokamak Advanced Reactor (STAR) has been defined to investigate design options and physics scenarios that can successfully meet physics performance, engineering requirements and economic conditions of an ST power plant. Following a successful physics/engineering assessment, the STAR Power Plant design will be downsized to meet the specifications established in the design of a near term ST Pilot Plant

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Discovery of Marburg virus neutralizing antibodies from virus-naïve human antibody repertoires using large-scale structural predictions

Marburg virus (MARV) disease is lethal, with fatality rates up to 90%. Neutralizing antibodies (Abs) are promising drug candidates to prevent or treat the disease. Current efforts are focused in part on vaccine development to induce such MARV-neutralizing Abs. We analyzed the antibody repertoire from healthy unexposed and previously MARV-infected individuals to assess if naïve repertoires contain suitable precursor antibodies that could become neutralizing with a limited set of somatic mutations. Here, we computationally searched the human Ab variable gene repertoire for predicted structural homologs of the neutralizing Ab MR78 that is specific to the receptor binding site (RBS) of MARV glycoprotein (GP). Eight Ab heavy-chain complementarity determining region 3 (HCDR3) loops from MARV-naïve individuals and one from a previously MARV-infected individual were selected for testing as HCDR3 loop chimeras on the MR78 Ab framework. Three of these chimerized antibodies bound to MARV GP. We then tested a full-length native Ab heavy chain encoding the same 17-residue-long HCDR3 loop that bound to the MARV GP the best among the chimeric Abs tested. Despite only 57% amino acid sequence identity, the Ab from a MARV-naïve donor recognized MARV GP and possessed neutralizing activity against the virus. Crystallization of both chimeric and full-length native heavy chain-containing Abs provided structural insights into the mechanism of binding for these types of Abs. Our work suggests that the MARV GP RBS is a promising candidate for epitope-focused vaccine design to induce neutralizing Abs against MARV.

59 BASIC BIOLOGICAL SCIENCES↗

The X-ray Power Density Spectrum of the Seyfert 2 Galaxy NGC 4945: Analysis and Application of the Method of Light Curve Simulations

The study of the power density spectrum (PDS) of fluctuations in the X-ray flux from active galactic nuclei (AGN) complements spectral studies in giving us a view into the processes operating in accreting compact objects. An important line of investigation is the comparison of the PDS from AGN with those from galactic black hole binaries; a related area of focus is the scaling relation between time scales for the variability and the black hole mass. The PDS of AGN is traditionally modeled using segments of power laws joined together at so-called break frequencies; associations of the break time scales, i.e., the inverses of the break frequencies, with time scales of physical processes thought to operate in these sources are then sought. I analyze the Method of Light Curve Simulations that is commonly used to characterize the PDS in AGN with a view to making the method as sensitive as possible to the shape of the PDS. I identify several weaknesses in the current implementation of the method and propose alternatives that can substitute for some of the key steps in the method. I focus on the complications introduced by uneven sampling in the light curve, the development of a fit statistic that is better matched to the distributions of power in the PDS, and the statistical evaluation of the fit between the observed data and the model for the PDS. Using archival data on one AGN, NGC 3516, I validate my changes against previously reported results. I also report new results on the PDS in NGC 4945, a Seyfert 2 galaxy with a well-determined black hole mass. This source provides an opportunity to investigate whether the PDS of Seyfert 1 and Seyfert 2 galaxies differ. It is also an attractive object for placement on the black hole mass-break time scale relation. Unfortunately, with the available data on NGC 4945, significant uncertainties on the break frequency in its PDS remain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗