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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Development and Field Testing Novel Natural Gas Surface Process Equipment for Replacement of Water as Primary Hydraulic Fracturing Fluid (Final Report)

Southwest Research Institute® (SwRI ® ), Schlumberger Technology Corporation (SLB), and Chevron Corporation ® (Chevron) developed a novel, optimized, and lightweight process for natural gas (NG) to replace water as a low-cost fracturing medium with a low environmental impact. Hydraulic fracturing is used to increase oil and NG production by injecting high-pressure fluid, primarily water, into a rock formation, which fractures the rock and releases trapped oil and NG. This method was developed to increase yield and make feasible production areas that would not otherwise be viable for large-scale oil and NG extraction using traditional drilling technologies. Since the fracturing fluid is composed of approximately 90% water, one of the principal drawbacks to hydraulic fracturing is its excessive water use and associated large environmental footprint. According to recent data collected at fracture sites within the United States, fracturing applications in North America can consume as much as 9 million gallons of water per well. During the fracturing process, some of the fracturing fluid is permanently lost and the portion that is recovered is contaminated by both fracturing chemicals and dissolved solids from the formation. The recovered water or flow-back represents a significant environmental challenge, as it must be treated before it can be reintroduced into the natural water system. Although there is some recycling for future fracturing, the majority of the flow-back water is hauled from the well site to a treatment facility or to an injection well for permanent underground disposal. To mitigate these issues, an optimized, lightweight and modular surface process using NG to replace a majority of the water was developed as a cost-effective and environmentally clean fracturing fluid. Using NG will result in significantly less consumption since the gas that is injected as a fracturing fluid will be mixed with the formation gas and extracted as if it were from the formation itself. This process will minimize the collection, waste, and treatment of large amounts of water and will reduce the environmental impact of transporting and storing the fracturing fluid. There are two major steps involved in utilizing NG as the primary fracturing medium: (1) increasing the supply pressure of NG to wellhead pressures suitable for fracturing and (2) mixing the required chemicals and proppant needed for the fracturing process at these elevated pressures. The second step (NG-proppant mixing at elevated pressures) still requires demonstration; but very similar processes have been demonstrated in the field with other gases such as nitrogen (N 2 ) and carbon dioxide (CO 2 ). However, the first step (a compact, on-site unit for generating high-pressure NG at costs feasible for fracturing) has not been developed and is currently not commercially available. Due to the inherent compressibility of NG, more energy is required to compress the gas than what is required to pump water (or other incompressible liquids) to the very high pressure required for downhole injection. This project aimed to develop a novel, hybrid method to overcome this challenge. The project accomplishments and findings are discussed in this report. Ultimately, the research and development efforts described herein demonstrate that fracturing with NG foam is a feasible alternative to using water.

02 PETROLEUM↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2025

Reducing module soiling with scalable and robust photocatalytic coatings

The air-glass interface at the front of a photovoltaic (PV) module reflects approximately 4% of incident light, decreasing the potential power output of the module by the same amount. Today’s modules reduce this loss by adding a low-refractive-index (1.25-1.30) SiO2 coating to the sunward side of the module glass; this antireflection coating recovers approximately 3% of the 4% light that would otherwise be lost. While such antireflection coatings work very well on clean, new modules, they do not inhibit soiling—the accumulation of soilants such as dust, pollen, soot, or other foreign material—on the module glass, and soilants reflect and scatter incident light. An improved coating would serve provide not only an antireflection effect, but also an anti-soiling effect. The goal of this project was to develop such a coating and provide a path for it to be manufactured in the U.S. The project successfully designed and fabricated coatings that provided >3% transmittance gain compared to bare glass (matching the performance of commercial antireflection coatings) and displayed anti-soiling behavior in standard laboratory soiling effects. This was achieved by using a Swift Coat proprietary coating deposition technique, aerosol impact-driven assembly (AIDA), to control the porosity and thus refractive index of coatings of photocatalytic materials—such as TiO2—that would otherwise increase (instead of decrease) reflection. These combined antireflection/anti-soiling coatings passed PV industry standard module reliability tests as well as coating-specific abrasion tests, showing that they have the durability needed for decades in the field. Swift Coat scaled the AIDA hardware and deposition process to make mini-modules that were monitored for nearly two years during field tests administered by a third party, as well as demonstrated scaling to the widths of full-sized modules. The fielded mini-modules outperformed reference modules (with commercial antireflection coatings) in two locations, providing a 1% absolute average performance boost and larger increases during periods of heavier soiling. Swift Coat’s cost analysis indicated a coating manufacturing cost below the sales price of today’s antireflection coatings. More than five module manufacturers sampled and assessed the coatings, and three provided letters of support. The coating developed in this project increases the energy output of PV modules, thereby decreasing the cost per kilowatt-hour of solar energy generated. Cheaper solar electricity benefits the public by accelerating the transition to a stable, affordable, carbon-free energy economy. In addition, for select applications in which PV modules are highly visible—such as on residential rooftops—the coating provides an aesthetic benefit because it stays cleaner than today’s modules. Finally, Swift Coat and its prospective customers are U.S. companies, and successful commercialization of this technology will provide U.S. jobs and a secure solar supply chain.

14 SOLAR ENERGY↗
DOE OSTI2024

First Measurement of Deeply Virtual Compton Scattering on the Neutron with Detection of the Active Neutron

Measuring deeply virtual Compton scattering (DVCS) on the neutron is one of the necessary steps to understand the structure of the nucleon in terms of generalized parton distributions (GPDs). Neutron targets play a complementary role to transversely polarized proton targets in the determination of the GPD E . This poorly known and poorly constrained GPD is essential to obtain the contribution of the quarks’ angular momentum to the spin of the nucleon. DVCS on the neutron was measured for the first time selecting the exclusive final state by detecting the neutron, using the Jefferson Lab longitudinally polarized electron beam, with energies up to 10.6 GeV, and the CLAS12 detector. The extracted beam-spin asymmetries, combined with DVCS observables measured on the proton, allow a clean quark-flavor separation of the imaginary parts of the Compton form factors H and E . Published by the American Physical Society 2024

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI1975

Nuclear Safety [Vol. 16, No. 2, March-April 1975]

Nuclear Safety covers significant developments in the field of nuclear safety. The scope is limited to topics relevant to the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, safety considerations in regard to the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 127 Quality Assurance in the Construction of Nuclear Power Plants by Sidney A. Bernsen, 141 1974 ANS Topical Meeting on Fast Reactor Safety by M. H. Fontana; CONTROL AND INSTRUMENTATION: 150 GBR-4 Protection Systems: Failures and Their Consequences by Peter Burgsmüller, J. J. Dekais, Albert Krähe, Raffaello Pignatelli, and Gottfried Vieider, 162 Standby Emergency Power Systems. Part 2—The Later Plants by E. W. Hagen; PLANT SAFETY FEATURES: 180 Radiotoxic Hazard Measure for Buried Solid Radioactive Waste by J. Hamstra, 190 The Thirteenth AEC Air-Cleaning Conference by D. W. Moeller, D. W. Underhill, and M. W. First, 203 Book Review: Nuclear Criticality Safety; CONSEQUENCES OF EFFLUENT RELEASE: 204 Environmental Radiation Effects of Nuclear Facilities in New York State by M. S. Terpilak and B. L. Jorgensen, 222 Book Review: Thermal Ecology; OPERATING EXPERIENCES: 223 Set-Point Drift in Nuclear Power-Plant Safety-Related Instrumentation Adapted by the Nuclear Safety Staff, 224 Diesel-Generator Operating Experience at Nuclear Power Plants, 227 Summary of Operating U. S. Power Reactors as of Jan. 1, 1975, 232 Selected Safety-Related Occurrences Reported in November and December 1974 Compiled by William R. Casto, 235 Recent Occurrences at Nuclear Reactors and Their Causes Compiled by William R. Casto; CURRENT EVENTS: 243 General Administrative Activities Compiled by Wm. B. Cottrell, 251 Action on Power-Reactor Projects Undergoing Regulatory Review or Consideration Compiled by Wm. B. Cottrell, 266 Action on Nonreactor Projects Undergoing Regulatory Review or Consideration Compiled by Wm. B Cottrell, 268 Proposed Rule Changes as of Jan. 1, 1975; MISCELLANY: 149 Course in Italy on High-Energy Radiation Dosimetry and Protection (Announcement), 250 Course at Northwestern on Safety of Light-Water-Cooled Nuclear Power Plants (Announcement), 266 Symposium of the Combined Effects on the Environment of Radioactive, Chemical, and Thermal Releases from the Nuclear Industry (Announcement), 271 Short Course on Engineering for Extreme Winds and Tornadoes (Announcement), 272 Three 1-Week Courses at MIT on Nuclear Power-Reactor Safety (Announcement), 272 Harvard University Short Courses (Announcement).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Sparsely Dispersed CeO x ‑Stabilized Pt Nanoparticles Overcome Pt Loading–Durability Trade-Off for Highly Durable Heavy-Duty Fuel Cells

Proton-exchange-membrane fuel cells (PEMFCs) are clean and sustainable mobile power sources for transportation. Recently, their deployment in heavy-duty vehicles (HDVs) has attracted growing interest owing to their high energy scalability and lower infrastructure requirements. However, to meet the stringent requirements for efficiency and long-term durability for HDV applications, PEMFCs typically employ a relatively high platinum group metal (PGM) loading (>0.2 mg PGM /cm 2 ). This elevated PGM loading significantly increases the stack and system costs, surpassing the U.S. Department of Energy (DOE) target of $\$ 60$/kW for commercial viability. Reducing PGM loading while maintaining performance and durability remains a central challenge for HDV fuel cells. Here we exploit metal oxide–Pt interactions and utilize the strong CeO x –Pt interaction to design a CeO x @Pt catalyst structure with exceptional durability. At a low total PGM loading (0.1 mg PGM /cm 2 ), the CeO x @Pt/C catalyst demonstrates high fuel cell performance (8.8 kW/g PGM ) and stability (power retention >90%) after the challenging HDV durability testing (90,000 accelerated-stress-test cycles). With the CeO x @Pt/C catalyst, we showcase over 70% reduction in Pt cost from the M2FCT target (to $\$ 9$/kW), highlighting its promising potential for enabling stable and cost-effective fuel cell systems for heavy-duty applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

FENIX: Towards a Fully Integrated Multiphysics Framework for Plasma Facing Component Modeling

Computational tools have a crucial role to play in accelerating the deployment of fusion as a clean, reliable, abundant, and sustainable energy source. Multiphysics, high-fidelity simulation capabilities can help model, study, and predict intricate interactions between materials performance, plasma exposure, neutron irradiation, and engineering processes. As such, they can assist in the resolution of scientific and engineering challenges underpinning design, construction, and commission of fusion power plants. To address these needs, ongoing efforts are leveraging the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework and delivering new computational tools for the fusion community. These tools inherit crucial attributes from MOOSE. They are open-source, modular, integrated with nuclear industry-standard software quality assurance processes, and enable multiphysics, multi-fidelity, fully integrated, zero- to three-dimensional, and massively parallel simulations. After a short overview of these capabilities, we will present the development of Fusion ENergy Integrated multiphys-X (FENIX), a MOOSE-based application designed to enable plasma facing component design and performance evaluation. Throughout their lifetime, plasma facing components are exposed to extreme thermal loads, repeated thermal shocks, and irradiation by plasma ions, neutral particles, and high-energy neutrons. Consequently, designing a plasma facing component with acceptable lifetime degradation is extremely challenging. FENIX aims to model the multiphysics environment in which plasma facing components evolve to accelerate their design studies. To that end, FENIX couples existing MOOSE capabilities such as heat transfer, thermomechanics, and thermal hydraulics, with tritium transport via the MOOSE-based Tritium Migration Analysis Program, Version 8 (TMAP8), with neutronics via the MOOSE-based high-fidelity neutron-photon transport and fluid dynamics code Cardinal, and finally with Particle-in-Cell plasma simulation capabilities being developed in this project. In this study, we present the current FENIX capabilities and preliminary results of its application to model the Tritium Plasma Experiment set up at Idaho National Laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Electromagnetic Energy-Assisted Thermal Conversion of Fossil-Based Hydrocarbons to Low-Cost Hydrogen

The goal of this project was to develop and optimize catalysts for methane decomposition, particularly focusing on regeneration via an electromagnetic energy-assisted mechanism, to produce hydrogen more cost-effectively compared to electrolysis routes. To achieve this goal, the project pursued several key objectives. The project began with the preparation and testing of various catalysts. A nickel-silica based catalyst was identified as the most promising material for the pyrolysis of methane into carbon and hydrogen. Kinetic parameters for methane decomposition were determined, aiding in computational modeling efforts. Structured catalysts were investigated, highlighting the need for frequent cleaning or regeneration to maintain performance, with methane conversion rates exceeding 70% in tube furnace tests. Computational fluid dynamics modeling was employed to optimize reactor designs and electrode angles, leading the project team to propose a multi-compartment thermal conversion system for larger setups. This modeling work was important in understanding reaction characteristics, carbon deposition rates, and temperature profiles under various conditions. A bench-scale reactor system was assembled to evaluate catalyst regeneration using electromagnetic energy-assisted mechanisms. Experiments demonstrated the potential for carbon removal, though further optimization is needed. The carbon produced from the methane conversion process was evaluated for potential use in lithium-ion battery electrodes. The carbon exhibited properties similar to commercially available high-purity multi-walled carbon nanotubes and nanofibers, with a carbon content greater than 95%. Coin cell batteries assembled with this carbon showed that lower replacement levels (10% to 33%) outperformed the control group, improving specific capacity density and stability. However, higher replacement levels (100%) demonstrated poorer performance, suggesting that excessive carbon substitution negatively impacts battery performance. These findings indicate the potential marketability of the produced carbon as a component in lithium-ion batteries, though further testing is necessary to confirm long-term advantages and disadvantages associated with the use of the carbon product. These results however justified further technoeconomic assessments to determine if the process can provide low-cost hydrogen. The economic feasibility and technical performance of methane decomposition for hydrogen production were assessed, focusing on three plant configurations: 100E (electrically heated), 100C (combustion heated using produced hydrogen), and PE-Hybrid (a combination of pyrolysis (indicating decomposition) and electrolysis). The Levelized Cost of Hydrogen (LCOH) for the pyrolysis configurations was found to be approximately 25% lower than that of electrolysis. The 100E configuration had the lowest LCOH at $\$$3.12/kg. Including carbon product sales significantly improved the economics, with the 100C configuration achieving a negative LCOH of -$\$$0.35/kg. The PE-Hybrid configuration was not economically advantageous compared to pure pyrolysis plants due to its complexity and additional equipment requirements. Ultimately, methane pyrolysis presents a viable method for near carbon dioxide-free hydrogen production, with significant economic advantages over electrolysis, especially when considering the sale of carbon byproducts. The 100E and 100C configurations showed the most promise, with the choice between them ultimately depending on the prices of power and natural gas. In conclusion, this technology has the potential to lower hydrogen production costs by leveraging the methane decomposition process with the sale of valuable carbon byproducts. By optimizing catalyst performance and integrating electromagnetic energy-assisted regeneration, the process can achieve higher efficiency and economic viability, making it a competitive alternative to traditional hydrogen production methods.

08 HYDROGEN↗
DOE OSTI2020

Dynamic Behavior of Natural Seep Vents: Analysis of Field and Laboratory Observations and Modeling (Final Scientific/Technical Report)

In this project, we have analyzed data collected by the U.S. Department of Energy (DOE), National Energy Technology Laboratory (NETL) in a high pressure water tunnel (HPWT) and data from two research cruises to natural seeps in the Gulf of Mexico to adapt and validate a numerical model to predict the dynamics of natural seeps in the deep oceans. The HPWT data include video observations of the shrinkage rate of individual methane and natural gas bubbles under simulated deep-water conditions. Field data were collected during two cruises by the Gulf Integrated Spill Research (GISR) Consortium led by Texas A&M University and funded by the Gulf of Mexico Research Initiative (GoMRI). These data included in situ observations from a remotely operated vehicle (ROV) of gas bubbles at two natural seep sites in the Gulf and acoustic observations of the natural seep bubble flares in the ocean water column. The acoustic data were from multibeam echosounders, one mounted in a forward-looking orientation on the ROV and another mounted down-looking in the haul of the ship. All of these laboratory and field data were focused on the dynamics of natural gas bubbles at temperatures and pressures favorable for clathrate hydrate formation between the gas and water. Our analyses of this data focused on understanding the mechanisms responsible for gas bubble dissolution within the hydrate stability zone (HSZ) of the oceans. Ice-like hydrate shells may form on the bubble-water interface under these conditions, and it was unknown how this might affect the mass transfer of gas into the ocean. We were able to extract bubble shrinkage rates from the HPWT datasets. Using this data we determined that mass transfer coefficients with and without a hydrate shell match empirical values for bubbles in contaminated systems (so-called dirty bubbles contaminated by naturally occurring surfactants). We also showed that free gas, and not gas hydrate, is the dominant dissolving phase when the hydrate sub-cooling is below 11 degree Celsius (temperature difference between hydrate the hydrate formation temperature and ambient temperator) or the pressure is reducing as bubbles rise through the ocean water column. Using this mass transfer model, our numerical model of bubble dissolution matched the over 200 HPWT experiments with an average error of 10% for predicting the bubble size at the end of an experiment. From field data in the literature, we also observed that gas bubbles dissolve faster when they are initially released, following mass transfer coefficients for so-called clean-bubbles (those not yet contaminated by surfactants). Shortly after release within the HSZ, a hydrate shell forms on the bubble-water interface, and the mass transfer reduces to rates matching those of dirty bubbles. We correlated this transition time from clean to dirty bubble behavior with the initial bubble surface area and the hydrate sub-cooling. With this model for hydrate formation time and using the mass transfer coefficients deduced from the HPWT data, we validated our numerical model for predicting the rise heights of natural seep flares in the oceans. Flare heights are commonly observed in haul-mounted acoustic multibeam data. The numerical model predicts bubbles to rise high in the ocean water column owing to the slower mass transfer rates for dirty bubbles that accompany the majority of their rise time. We found that the numerical model predictions matched the observed flare heights within 5% to 10% accuracy when we compared the rise heights of the largest bubbles released from the seafloor with the bubbles acoustically visible in the multibeam data. Bubbles become acoustically transparent as they shrink to sizes of order 1 mm in diameter for the multibeam frequencies used in the field. The forward-looking multibeam on the ROV also provided data on the lateral spreading of bubbles in natural seep flares. Our analysis of this data showed that spreading follows a diffusion process, with the effective diffusivity correlating with the wobbling length scale of these ellipsoidal bubbles. When we apply this diffusivity in a random displacement model of bubble spreading, our numerical simulations match closely the lateral spread observed by the M3 in the ocean water column. Finally, we compared the seep model predictions for the acoustic properties of these natural seep plumes with that observed by the acoustic instruments in the field. The M3 and EM 302 observations were converted to relative values of target strength using a calibration we obtained in the laboratory for the M3 and using an algorithm from the manufacturer for the EM 302. Comparing the numerical seep model to these data, we obtain good agreement over the whole height of rise of these bubble flares. This further validates the numerical model. Overall, our validated seep model captures the key dynamics of gas bubbles released from natural seeps in the oceans and helps to predict the fate of methane in the water column.

03 NATURAL GAS↗
DOE OSTI2021

Assessment of Electrode Contamination Mitigation at 0.5 MA Scale

The Z Machine at Sandia National Laboratories uses current pulses with peaks up to 27 MA to drive target implosions and generate high energy density conditions of interest for stockpile stewardship programs pertinent to the NNSA program portfolio . Physical processes in the region near the Z Machine target create electrode plasmas which seed parasitic current loss that reduce the performance and output of a Z experiment. Electrode surface contaminants (hydrogen, water, hydrocarbons) are thought to be the primary constituent of electrode plasmas which contribute to loss mechanisms. The Sandia team explore d in situ heating and plasma discharge techniques by integrating requisite infrastructure into Sandia's Mykonos LTD accelerator, addressing potential impacts to accelerator operation, and reporting on the impact of these techniques on electrode plasma formation and shot performance. The in situ discharge cleaning utilizes the electrodes of the accelerator to excite an argon-oxygen plasma to sputter and chemically react contaminants from electrode surfaces. Insulating breaks are required to isolate the plasma in electrode regions where loss processes are most likely to occur. The shots on Mykonos validate that these breaks do not perturb experiment performance, reducing the uncertainty on the largest unknown about the in situ cleaning system. Preliminary observations with electrical and optical diagnostics suggest that electrode plasma formation is delayed, and overall inventory has been substantively reduced. In situ heating embeds cartridge heaters into accelerator electrodes and employs a thermal bakeout to rapidly desorb contaminants from electrode surfaces. For the first time, additively manufactured (AM) electrode assemblies were used on a low impedance accelerator to integrate cooling channels and manage thermal gradients. Challenges with poor supplier fabrication to specifications, load alignment, thermal expansion and hardware movement and warpage appears to have introduced large variability in observed loss, though, preventing strong assertions of loss reduction via in situ heating. At this time, an in situ discharge cleaning process offers the lowest risk path to reduce electrode contaminant inventories on Z, though we recommend continuing to develop both approaches. Additional engineering and testing are required to improve the implementation of both systems. .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design

Access to reliable, clean energy sources is a major concern for national security. Much research is focused on the “grand challenge” of producing energy via controlled fusion reactions in a laboratory setting. For fusion experiments, specifically inertial confinement fusion (ICF), to produce sufficient energy, the fusion reactions in the ICF fuel need to become self-sustaining and burn deuterium-tritium (DT) fuel efficiently. The recent record-breaking NIF ignition shot was able to achieve this goal as well as produce more energy than used to drive the experiment. This achievement brings self-sustaining fusion-based power systems closer than ever before, capable of providing humans with access to secure, renewable energy. In order to further progress toward the actualization of such power systems, more ICF experiments need to be conducted at large laser facilities such as the United States's National Ignition Facility (NIF) or France's Laser Mega-Joule. The high cost per shot and limited number of shots that are possible per year make it prohibitive to perform large numbers of experiments. As such, experimental design relies heavily on complex predictive physics simulations for high-fidelity “preshot” analysis. These multidimensional, multi-physics, high-fidelity simulations have to account for a variety of input parameters as well as modeling the extreme conditions (pressures and densities) present at ignition. Such simulations (especially in 3D) can become computationally prohibitive to turn around for each ICF experiment. In this work, we explore using Bayesian optimization with Gaussian processes (GPs) to find optimal designs for ICF double shell targets, while keeping computational costs to manageable levels. These double shell targets have an inner shell that grades from beryllium on the outer surface to the higher Z material molybdenum, as opposed to the nominally used tungsten, on the inside in order to trade off between the high performance associated with high density inner shells and capsule stability. We describe our results for “capsule-only” xRAGE simulations to study the physics between different capsule designs, inner shell materials, and potential for future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Data Curation for Machine Learning Applied to Geothermal Power Plant Operational Data for GOOML: Geothermal Operational Optimization with Machine Learning: Preprint

Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.

access↗
DOE OSTI2023

Defining a compact dry cooler design to reduce LCOE contribution in a CSP facility

Concentrating solar power (CSP), when coupled with a supercritical carbon dioxide (sCO 2 ) power cycle and sensible heat storage, presents a renewable and clean alternative for utility-scale power generation. However, in order to be competitive in the current and future markets, CSP facilities must limit their levelized cost of electricity (LCOE) by minimizing capital costs and reducing operating costs over the lifetime of the plant. Targeting this goal, this study investigates the LCOE impact of the power cycle pre-cooler. This study considers a compact dry cooler with micro-channel technology on the CO 2 side and formed fin geometry on the air side, using directly-coupled centrifugal fans and a transition duct to improve air distribution across the fins as well as protect the fins from contaminants which may cause blockage, soiling, fouling, and damage. In an effort to better understand the dry cooler impact on LCOE, a sensitivity study was conducted using various combinations of end-to-end approach temperatures, air-side pressure drop values, CO 2 -side pressure drop values, fan types, cooler turndown control schemes, cooler module sizes, and design-point ambient temperatures. Furthemore, off-design cycle performance data was calculated for each dry cooler design using NPSS simulation software; cycle performance data were then input to System Advisor Model (SAM) along with the associated capital costs for LCOE prediction of a 100 MW system over a 30 year plant lifetime. Results of this study show the LCOE is most sensitive to air-side performance, followed by heat transfer effectiveness and capital cost. It was found that a power cycle with a mid- to high-performance dry cooler will produce the most competitive power-production costs. Designing at the extreme ends for approach temperature (or effectiveness), design-point ambient temperature, and compactness (footprint) produce higher LCOE values; mid-range values for these parameters balance performance, operating costs, and associated capital cost to optimize LCOE.

14 SOLAR ENERGY↗
DOE OSTI2025

EMPOWERED Distributed Energy Resources Permit Accelerator Pilot

This report summarizes the EMPOWERED Distributed Energy Resources (DER) Permit Accelerator Pilot project. Its primary objective was to streamline the design, installation, permitting, and inspections of targeted Distributed Energy Resource (DER) solutions. Streamlining these processes is critical to strengthening and accelerating the adoption of DER solutions nationwide. Moreover, streamlined processes help expedite the clean energy transition and expand energy resilience by improving understanding and capability among key stakeholders like property owners, code officials, and the general workforce. The project team accomplished this by developing and deploying a set of design and permitting guides for simple DER solutions, which they rolled out via a permitting pilot program. The project team developed nine permitting and inspection guides for simple DER solutions, as follows: 1. Single-Family and Duplex: Electric Vehicle Service Equipment (EVSE), and Storage, Solar + Storage (each in two code cycles) 2. Multi-family and Office: EVSE, Storage, Solar + Storage (each in two code cycles) 3. A permitting process guide to support the implementation of the permitting guides. The guides cover key code requirements via plan review and field inspection checklists for code officials and building owners to follow to streamline the permitting and inspections process for DER solutions. Following the development of the initial set of guides, the project team formed two cohorts, each made up of four jurisdictions, to participate in the Permit Accelerator Pilot. Each cohort received training and technical assistance to support them in incorporating the guides into use. The project successfully engaged jurisdictions in two disparate US regions: Chelsea, Somerville, Natick, and Norwood in metro Boston; and Pima County, Town of Gilbert, Flagstaff, and Sedona in Arizona. Feedback gathered from the project was valuable in refining the guides to better address jurisdictional needs. After the pilot period, the project team developed final versions of the guides based on extensive feedback from the cohorts and from external technical peer review. Feedback included needs and barriers related to implementation, as well as detailed technical improvements from external peer review. The final guides were presented in a series of webinars with a national reach, with 192 attendees from 35 states in the live session of the final webinar, and the final guides are posted online and available for use nationwide. Key findings: • The most receptive jurisdictions were those adjacent to other jurisdictions that had already adopted similar initiatives. The least receptive jurisdictions were those in the midst of adopting new code cycles. • The most common barrier to jurisdictional adoption of new guidelines were internal administrative and process delays, more so than technical barriers. • The main technical barrier to adoption proved to be the concern of fire hazard risks posed by energy storage systems. There is some apprehension in the code enforcement community about the safety of the batteries. Education coordinated with fire and safety services will help alleviate concerns and increase confidence in the acceptance of innovative technologies. • Opportunities to further leverage and advance the guides and related resources include outreach, education, and jurisdictional support; updates to the resources to align with code cycle changes and local requirements; and advancements supporting emerging technologies.

14 SOLAR ENERGY↗
DOE OSTI2026

Hot-Carrier Injection and Millisecond Charge Separation from a Robust Heteroleptic Iron(II) Chromophore Immobilized on TiO 2

The synthesis, spectroscopic characterization, computational analysis, and photoelectrochemical behavior of a new iron-based chromophore, [(Cpy) 2 Fe- (deeb)](PF 6 ) 2 (Fe(Cpy) 2 (deeb)), where Cpy is 1-methyl-3-(2-pyridyl)imidazole and deeb is 4,4’-(CO 2 CH 2 CH 3 ) 2 -2,2’-bipyridine, is reported. Electrochemically reversible waves assigned to a metal-centered E o (Fe III/II ) = +0.48 and a ligand-centered E o (Fe 2+/+ ) = −1.47 V vs Fc +/0 reduction were evident in cyclic voltammetry measurements. The combination of a strong σ-donor and a π-acceptor lowered the energy of the metal-toligand charge-transfer (MLCT) excited state relative to the metal-centered state. Two MLCT transitions appear in the visible region at 424 and 580 nm. TDDFT calculations revealed that the lower-energy band was well formulated as Fe(II)→deeb, and the higher-energy transition was charge transfer to both the deeb and Cpy ligands. Resonance Raman spectroscopy supports these findings showing enhanced deeb vibrational modes with 532 nm excitation, both deeb and Cpy modes with 473 nm excitation, and exclusively Cpy with 405 nm excitation. Ultrafast spectroscopy reveals a short-lived (∼2 ps) MLCT excited state and a longer-lived (∼20 ps) metal-centered state. Efficient methods to deprotect the ester groups and anchor the complex to mesoporous TiO 2 (anatase) thin films in high surface coverages, Fe(Cpy) 2 (dcb)|TiO 2 σ = 3 × 10 −8 mol/cm 2 , were established. Pulsed light excitation of Fe(Cpy) 2 (dcb)|TiO 2 resulted in rapid excited state injection (k inj > 10 8 s −1 ) and formation of a charge-separated state, Fe III (Cpy) 2 (dcb)|TiO 2 (e), which persists on the millisecond time scale before returning cleanly to the ground state with secondorder kinetics. Injection yields measured 50 ns after light excitation were found to double from Φ = 0.15 with green (532 nm) light to 0.30 with blue (457 nm) light excitation. Incident photon-to-current efficiency (% IPCE) measurements as a function of excitation wavelength in a 0.5 M LiI/I 2 /CH 3 CN electrolyte provide clear evidence for band-selective “hot carrier” injection from the remote Cpy-localized excited state. Collectively, the spectroscopic and photoelectrochemical data indicate that a semiconductor can intercept hot electrons from iron chromophores even when the excited-state dipole is oriented away from the surface-anchoring ligand.

Charge transfer↗
DOE OSTI2024

MR13A-3183: Microbial and Geochemical Characterization of Groundwater: Implications for Underground Hydrogen Storage Leakage

Underground hydrogen storage (UHS) in geological formations is a key element of the clean energy transition as it enables the decarbonization of the transportation and industrial sectors by decoupling hydrogen production and storage. UHS has many benefits, including low cost, much wider availability, large storage capacity, well-established infrastructure, and increased safety because of geological sealing capabilities. However, the impact of hydrogen (H2) biogeochemical interactions in the presence of subsurface microorganisms is largely neglected from UHS perspectives. These interactions might affect the effectiveness of storage and can even cause H2 to leak into the shallow aquifers. Leakage of H2 into groundwater can change the geochemistry and induce several microbial-driven processes. Microorganisms, such as sulfate-reducers, are naturally abundant in groundwater and consume H2 to produce hydrogen sulfide (H2S), which can contaminate the freshwater drinking groundwater and cause damage to infrastructure. Hydrogen leakage can also trigger microbial reactions responsible for metal mobility, which can impact the water quality. However, the kinetics of these reactions and the temporal impact of hydrogen leakage in groundwater are still unknown. Therefore, a time series hydrogen-groundwater interaction experiment was conducted, and the changes in fluid chemistry and headspace gas composition will be analyzed along with DNA sequencing results to understand the extent and kinetics of biogeochemical reactions that occur if hydrogen leaks into groundwater. In the experiments, Ultra High Purity (UHP) hydrogen gas will be injected into glass vials with groundwater samples, for a designated time period. For each glass-sealed vial, 16S rRNA gene sequencing, IC, ICP-MS, and GC-TCD will be performed. The experiments provide insights into plausible impacts of hydrogen leakage into shallow drinking water aquifers.

Clark, Allison [West Virginia University (WVU)]↗
DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗