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At least 217 records · Page 12

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DOE OSTI2021

In situ/operando soft x-ray spectroscopy of chemical interfaces in gas and liquid environments

Many energy storage and energy conversion systems are based on the complexity of material architecture, chemistry, and interfacial interactions. To understand and thus ultimately control the energy applications calls for in situ/operando characterization tools. Over the years, in situ/operando soft x-ray spectroscopy has been developed for the studies of gas molecules, molecular liquids, catalytic, and electrochemical reactions. Soft x-ray spectroscopy offers electronic structure characterization of materials in energy conversion, energy storage, and catalysis regarding functionality, complexity of material architecture, and chemical interactions. It has been shown how to use the powerful in situ/operando soft x-ray spectroscopy characterization techniques of interfacial phenomena and how to reveal the mechanism of charge transfer and chemical transformation of solid/gas and solid/liquid interfaces of energy storage and catalytic materials in a realistic environment. It has been demonstrated how to overcome the challenge that soft x-rays cannot easily peek into the high-pressure catalytic or liquid electrochemical reactions. Finally, the unique design of in situ/operando soft x-ray spectroscopy instrumentation and fabrication principle and examples of experiments are presented.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Elucidating the Role of Electric Fields in Fe Oxidation via an Environmental Atom Probe

We quantify the effects of intensely applied electric fields on the Fe oxidation mechanism. The specimen are pristine Fe single crystals exposing a variety of surface structures identified by field ion microscopy. These crystals are simultaneously exposed to low pressures of pure oxygen gas, on the order of 10 −7 mbar, while applying intense electric fields on their surface of several tens of volts per nanometer. The local composition of the different surface structures is probed directly and in real time using an Environmental Atom Probe and successfully compared with first principles-based models. We found that rough Fe{244} and Fe{112} facets are more reactive toward oxygen than compact Fe{024} and Fe{011} facets. Results demonstrate that the influence of an electric field on the oxidation kinetics depends on the timescales that are involved as the system evolves toward equilibrium. The initial oxidation kinetics show that strong increases in electric fields facilitate the formation of an oxide. However, as one approaches equilibrium, high field values mitigate this formation. Ultimately, this study elucidates how high externally applied electric fields can be used to dynamically exploit reaction dynamics at the nanoscale towards desired products in a catalytic reaction at mild reaction conditions.

09 BIOMASS FUELS↗

DOE OSTI2023

Image Characterization of Reactions Generated by an Aeroengine Micromixing Injector for Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blend

An aeroengine micromixing injector, originally designed for lean direct injection of jet fuel, was adapted to work with hydrogen, natural gas, and any blend in between. The ultimate goal of the design was to achieve low NOx emissions when operated on pure hydrogen; to better characterize its performance, flame imaging diagnostics was implemented. Three types of cameras mounted parallel to the injector base and pointing towards the quartz combustor cylinder with the same angle were operated simultaneously: Nikon D90, Dynacolor FB-N9-U, and Phantom v7.1, to capture the visible spectrum, OH* chemiluminescence, and dynamic behavior of the flames, respectively. The current work also includes the overlapping of visible spectrum and OH* chemiluminescence, as an effort to qualitatively define the heat release over the flame area, i.e., UV over visible domain. Additionally, a z-type schlieren configuration was employed to reinforce the presence of some small-scale details occurring near the injector outlet ports. Using the airbox pressure drop, preheat temperature, fuel composition and flame temperature as the factors of study, a Box Behnken model was designed yielding into a 16-points matrix for the operability region, at atmospheric pressure. The main responses were extracted from the OH* images, because of its correlation with the flame heat release, these being the flame area and its average brightness, the heat release area and its center of gravity and leading edge. To assess the importance of the main factors and their interactions, an analysis of variance (ANOVA) was performed for each response, considering as significant each contributor with a p-value below 0.05. For these five responses it was found that all four main factors need to be included into the model (most of them because its own significance, others to ensure the model hierarchy), as well as some common interactions as the preheat and flame temperatures or pressure drop and fuel composition, and quadratic terms from fuel composition or flame temperature. Three of the five models presented R2 and coefficient of variance (C.V.) indicators around 0.97 and 5%, respectively; the leading edge ANOVA yielded into R2=0.78 and C.V.=18.88%; and the heat release area showed R2=0.91 and C.V.=24.13%. Lastly, the confinement ratio effect of the combustor over the flame structure was quantified, for three different conditions: the original 80 mm inner diameter and 200 mm length cylinder was compared against a shorter 150 mm tube (same inner diameter) and a narrower 47 mm inner diameter one (same 200 mm length). The length had a smaller effect than the inner diameter, even though both factors were not strong enough to make a significant deviation on most of the flame parameters: the difference was within the repeatability margin of error for the brightness, flame area, and center of gravity and leading edge of the heat release area.

Imaging diagnostics, flame characterization, OH* c↗

DOE OSTI2025

Macro-micro multiscale modeling to assist the design of HPDC Al castings microstructure and alloys for EV super-large body structures (Phase 1)

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Macro-micro multiscale modeling to assist the design of HPDC Al castings microstructure and alloys for EV super-large body structures (Phase 1)

Implementation of High Pressure Die Casting (HPDC) Aluminum (Al) body structures for high volume electrified vehicles (EV) to improve electric efficiency remains a key strategy within many original equipment manufacturer (OEM)s. In addition to high strength for safety requirements, superior Self-Piercing Riveting (SPR) performance is demanded for HPDC Al alloys to be compatible with high volume SPR joining. In this work, it is proposed to extend and validate an existing Contractor finite element multiscale macro-micro modeling approach to quantify the influence of the microstructure of HPDC alloys on the fracture strain/displacement under 3-point bend and clinch testing. The success of this work will allow to replace solution treatment stage with low energy consumption heat treatment (HT) processes, or to design new non heat treatable (NHT) HPDC Al alloys to eliminate HT requirements. Ultimately, this project will facilitate the application of HPDC Al alloys for super-large vehicle structures to significantly reduce vehicle weight, and thus improving energy efficiency. The purpose of this project is to extend and validate an existing finite element code, which is based on the Contractor developed macro-micro multi-scale modeling approach, to numerically simulate the three-point bending and clinch test and study the influences of material microstructural characteristics and phase properties on the rivetability. The macro-micro modeling approach begins with a sample scale model and identify the location, which is mostly prone to failure, the deformation history of the boundaries of that location calculated will be used to drive a microstructure-based sub-models where the material microstructure and microscale properties are considered. Using this approach, the wrap-bending failure for two Al alloys are correctly predicted for the first time. This will start with phase I effort of building a framework of macro-micro three point bending test and clinch test of Al10SiMgMn HPDC alloy in the as-cast and T7 heat treated conditions. Those results will then be validated with experimental test results. The phase II effort will involve the utilization of the knowledge learned in phase I to establish the quantitative correlation between the microstructure characteristics and the riveting performance, which will be further used to guide the optimization of HPDC Al alloy microstructure using heat treatment process to achieve sufficient rivetability to join large thin-wall HPDC alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Operando XPS in reactive plasmas: The importance of the wall reactions

In this article, advancements in differential pumping and electron optics over the past few decades have enabled x-ray photoelectron spectroscopy (XPS) measurements at (near-)ambient pressures, bridging the pressure gap for characterizing realistic sample chemistries. Recently, we have demonstrated the capabilities of an ambient pressure XPS setup for in situ plasma environment measurements, allowing plasma-surface interactions to be studied in operando rather than using the traditional before-and-after analysis approach. This new “plasma-XPS” technique facilitates the identification of reaction intermediates critical for understanding plasma-assisted surface processes relevant to semiconductor nanomanufacturing, such as physical vapor deposition, etching, atomic layer deposition, and many other plasma applications. In this paper, we apply the plasma-XPS approach to monitor real-time surface chemical changes on a model Ag(111) single crystal exposed to oxidizing and reducing plasmas. We correlate surface-sensitive data with concurrent gas-phase XPS measurements and residual gas mass-spectrum analysis of species generated during plasma exposure, highlighting the significant role of plasma-induced chamber wall reactions. Ultimately, we demonstrate that plasma-XPS provides comprehensive insights into both surface and gas-phase chemistry, establishing it as a versatile and dynamic characterization tool with broad applications in microelectronics research. Finally, we outline potential enhancements and future metrology directions to advance plasma-XPS investigations further.

36 MATERIALS SCIENCE↗

DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗

DOE OSTI2025

Control of core–shell nanoparticles properties through plasma synthesis: a computational study

The improved properties of core–shell nanoparticles (CSNPs) over homogeneous nanoparticles (NPs) have expanded and diversified the applications of these nanomaterials. However, controlling the properties of CSNPs can be a challenging task. Low temperature plasmas have proven to be an effective method of producing NPs with uniform size and morphology, and high yield. That said, NP transport and growth dynamics are sensitive to LTP properties. We report on a computational investigation of the evolution of Ge–Si CSNP properties as a function of operating conditions through the modeling of a flowing, two-zone inductively coupled plasma (ICP) reactor. Ar/GeH 4 and Ar/SiH 4 gas mixtures were supplied to separate plasma zones at a pressure of 1 Torr to promote growth of Ge cores and Si shells. The negatively charged CSNPs are trapped electrostatically in the vicinity of the antennas where the plasma is generated and where the majority of particle growth occurs. Particles that grow to a critical size are then de-trapped by fluid drag due to neutral gas flow. A two-dimensional hybrid plasma model coupled with a three-dimensional kinetic NP transport model were utilized to resolve plasma chemistry and NP growth processes that take place on distinct timescales. The trends in CSNP properties and trapping mechanisms associated with flow rate, applied ICP power and inlet precursor fraction are discussed. While the spatial distribution of plasma produced radical species can have significant impact on the NP growth process, the NP transport dynamics are what ultimately dictates the growth environment that is unique to each particle and so determines their final dimension and composition. The key to optimizing reactor conditions involves controlling the spatial density of growth species and plasma profile as a means to tailor particle trapping dynamics suitable to produce CSNPs for a specific application.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Benchmarking a 9Cr-2WVTa Reduced Activation Ferritic Martensitic Steel Fabricated via Additive Manufacturing

Reduced activation ferritic (RAF) martensitic steels are promising candidates for the first wall of fusion reactors. However, current manufacturing capabilities call for these components to be made by welding wrought plates. This limits design freedom and necessitates the use of post-weld heat treatments (PWHT) in accordance with the boiler and pressure vessel code. Additive manufacturing (AM) can offer a unique solution to solve this challenge by leveraging the layer-wise deposition strategy to come up with temper bead deposition techniques to eliminate post-processing heat treatments (PPHT). However, it is necessary to benchmark the properties of RAF steels fabricated by AM with their wrought counterparts to identify the process-structure-property correlation, which is the goal of this study. The study demonstrates that while tensile properties at room temperature and high temperatures are satisfactory, the as fabricated and samples after PPHT have significant heterogeneity in tensile elongation. This has been attributed to the presence of discontinuities in the build. The as-fabricated samples have an average tensile strength of 1190 + 12 MPa and an average elongation of 15 + 5% at room temperature and 658 ± 20 MPa ultimate tensile strength (UTS) and 14 ± 7% at 600 °C. After the post-weld heat treatment, mechanical properties decrease to around 600–650 MPa and an elongation between 20–25% at room temperature to 300 MPa UTS and 25–28% elongation at 600 °C. The characterization of microstructures at various length scales demonstrates that the as-fabricated structure has a significant fraction of delta ferrite in a lath martensitic matrix. No precipitates could be identified in the as-fabricated structure. PPHT led to a decrease in the area fraction of delta ferrite and precipitation of M 23 C 6 and MX. Detailed characterization clearly demonstrates that the lack of precipitates in the as-fabricated structure could be due to the slow tempering response of the alloy. Finally, the needs to develop new alloys to achieve the objectives stated above are articulated.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Oxidized Sulfur Species in Slab Fluids as a Source of Enriched Sulfur Isotope Signatures in Arcs

Recycling of oxidized sulfur from subducting slabs to the mantle wedge provides simultaneous explanations for the elevated oxygen fugacity (fO 2 ) in subduction zones, their high hydrothermal and magmatic sulfur outputs, and the enriched sulfur isotopic signatures (i.e., δ 34 S > 0‰) of these outputs. However, a quantitative understanding of the abundance and speciation of sulfur in slab fluids consistent with high pressure experiments is lacking. Here we analyze published experimental data for anhydrite solubility in H 2 O-NaCl solutions to calibrate a high-pressure aqueous speciation model of sulfur within the framework of the deep earth water model. We characterize aqueous complexes, required to account for the high experimental anhydrite solubilities. We then use this framework to predict the speciation and solubility of sulfur in chemically complex fluids in equilibrium with model subducting mafic and ultramafic lithologies, from 2 to 3 GPa and 400 to 800°C at log fO 2 from FMQ-2 to FMQ+4. We show that sulfate complexes of calcium and sodium markedly enhance the stability of sulfate in moderately oxidized fluids in equilibrium with pyrite at fO 2 conditions of FMQ+1 to +2, causing large sulfur isotope fractionations up to 10‰ in the fluid relative to the slab. Such fluids could impart oxidized, sulfur-rich and high δ 34 S signatures to the mantle wedge that are ultimately transferred to arc magmas, without the need to invoke 34 S-rich subducted lithologies.

58 GEOSCIENCES↗

DOE OSTI2023

Machine Learning Prediction of Fracture Toughness in Hydrogen-charged Stainless Steels

Austenitic stainless steels are structural materials utilized in tritium gas pressure boundaries since they are resistant to hydrogen isotope embrittlement [1-3]. However, exposure to tritium over long periods of time leads to tritium uptake which decays to result in helium ingrowth. This helium ingrowth results in further embrittlement effects which are synergistic with that from the hydrogen isotope [4]. Therefore, it is important for tritium facilities to understand the material limitations of stainless steel in this environment. The Savannah River National Laboratory (SRNL) has available a large experimental data set of austenitic stainless steels which have been exposed to tritium environments for various lengths of time. With the availability of this data set, machine learning (ML) algorithms provide an opportunity to model the embrittlement of stainless steel due to the algorithm’s ability to identify patterns in data sets that are difficult and costly to identify in other manners [5]. Ultimately, the amount and quality of the available data is one defining force in the ability of a ML model to accurately predict the desired outputs. The models developed herein will illustrate the ability for the various algorithms to predict the change in fracture toughness in stainless steels due to hydrogen-isotope embrittlement.

Hoar, Eric T.↗

DOE OSTI2025

Enabling Low-Temperature (LTP) Ignition Technologies for Multi-Mode Engines through the Development of a Validated High-Fidelity LTP Model for Predicative Simulations Tools

The goal of multi-mode engine architectures is to extend current lean-burn dilution limits with renewable fuels, which requires spark plugs to deposit high energies (hundreds of mJ) in order to initiate ignition and complete combustion. At elevated energy deposition rates, spark plugs experience increased electrode erosion and thermal losses, which ultimately shortens the spark-plug lifetime and lowers ignition efficiency. As such, in order to safeguard the efficiency gains of multi-mode concepts, new and improved ignition technologies are required. Recently, non-equilibrium low-temperature plasmas (LTP) have been shown to promote energy-efficient ignition via quenching and transport of electronically excited atoms and molecules, selective radical production and fast heating of hydrocarbon/air mixtures [1-2]. Thus, LTP is seen as a technology that can potentially improve the energy extraction efficiency of fuels, while enabling kinetically controlled combustion modes towards fuel leaner conditions to realize current DOE VTO goals of improving the sustainability of future mobility [3]. Although many previous studies have demonstrated the efficacy of plasma-assisted ignition to enhance combustion, the detailed enhancement mechanisms remain largely unknown, especially for oxygenated fuels and at elevated pressures that are most relevant to practical engine conditions. These barriers hinder the development of accurate and comprehensive numerical models that seek to describe LTP-based ignition in existing engine design software tools and methods. Current state-of-the-art simulation capabilities for LTP ignition systems are in need of improvements since they deliver qualitative results only due to important limitations of existing approaches. Firstly, validated kinetic models with elementary steps for plasma discharges in oxygenated fuel/air mixtures of relevance to the transportation sector are required. Such kinetic models do not exist at present and will be developed and validated within this project. Secondly, plasma discharges and reactive mixture ignition are multi-scale, unsteady processes requiring high-performance numerical methods and software that execute efficiently on DOE supercomputers. Such software does not exist at present and will be developed and applied to practical LTP ignition scenarios as part of this project. Thirdly, experimental databases that are tailored to serve as benchmark in support of the development of predictive computational models of LTP ignition do not exist and will be part of this project.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Machine learning assisted phase and size-controlled synthesis of iron oxide particles

Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. Furthermore, this study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Discretization limits of lattice‐Boltzmann methods for studying immiscible two‐phase flow in porous media

Summary Digital images of porous media often include features approaching the image resolution length scale. The behavior of numerical methods at low resolution is therefore important even for well‐resolved systems. We study the behavior of the Shan‐Chen (SC) and Rothman‐Keller (RK) multicomponent lattice‐Boltzmann models in situations where the fluid‐fluid interfacial radius of curvature and/or the feature size of the medium approaches the discrete unit size of the computational grid. Various simple, small‐scale test geometries are considered, and a drainage test is also performed in a Bentheimer sandstone sample. We find that both RK and SC models show very high ultimate limits: in ideal conditions the models can simulate static fluid configuration with acceptable accuracy in tubes as small as three lattice units across for RK model (six lattice units for SC model) and with an interfacial radius of curvature of two lattice units for RK and SC models. However, the stability of the models is affected when operating in these extreme discrete limits: in certain circumstances the models exhibit behaviors ranging from loss of accuracy to numerical instability. We discuss the circumstances where these behaviors occur and the ramifications for larger‐scale fluid displacement simulations in porous media, along with strategies to mitigate the most severe effects. Overall we find that the RK model, with modern enhancements, exhibits fewer instabilities and is more suitable for systems of low fluid‐fluid miscibility. The shortcomings of the SC model seem to arise predominantly from the high, strongly pressure‐dependent miscibility of the two fluid components.

Li, Zhe↗

DOE OSTI2021

Bed form-induced hyporheic exchange and geochemical hotspots

Small-scale bed form topographies control hyporheic exchange and biogeochemical processes within aquatic sediments, which ultimately affect water quality and nutrient cycling at the watershed scale. The impact of three-dimensional and small-scale bed form topographies on hyporheic exchange and solute mixing is investigated in the present work. The effect of bed form morphologies on the development of zones of enhanced reaction rates (i.e., hotspots) is also studied. A computational fluid dynamics model to simulate river flow over bed forms is combined with a subsurface flow and multicomponent reactive solute transport model. A wide variety of bed form topographies are generated using geometric models by varying parameters controlling curvature as well as bed form wavelength and amplitude. The results in this research suggest that out-of-phase bed forms generate more complex hyporheic flow patterns which reduce the efficiency of solute transformations. Higher phase shifts in bed form shapes result in overall higher average velocity, larger zones of enhanced pressure and reaction rates, and higher amounts of solute exchange. Moreover, the bed form shapes control the reaction process for a wide range of sediment conductivities. Here this study advances the understanding of the effects of complex and small scale morphological features on hyporheic exchange processes including the rate and spatio-temporal distribution of reaction hotspots.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Slot-die-coating operability windows for polymer electrolyte membrane fuel cell cathode catalyst layers

Roll-to-roll (R2R) slot-die coating of polymer electrolyte membrane fuel cell (PEMFC) catalyst layers represents a scalable deposition method for producing 10–20 m 2 ·min -1 of catalyst-coated gas diffusion layers (GDLs). This high-throughput production technique will help lower the cost of PEMFC catalyst layers. The uniformity of the wet layer applied by slot die deposition is affected by process parameters such as substrate speed, vacuum pressure applied at the upstream meniscus, gap between the slot die lips and substrate, ink rheology, and other ink and substrate properties. The set of conditions for producing a defect-free coating with a dilute ink typically requires little to no upstream vacuum pressure, so suitable operating conditions can be found easily through trial and error and operator intuition. However, the higher viscosity of more concentrated inks dramatically shifts the range of settings that result in a homogeneous coating to higher vacuum levels, which are harder to find through hit or miss. A predictive model showing the range of operable conditions decreases material wastage inherent in experimentally searching for suitable parameters. In this study, the defect-free coating parameter window is explored experimentally and theoretically for two concentrations of PEFC cathode inks. Both a full capillary hydrodynamic model and a computationally cheaper viscocapillary model successfully predict the experimentally determined coating window within the experimental and model uncertainty limits for inks with 5.3 wt% and 12.0 wt% solids ink while maintaining the 0.1 mg Pt ·cm -2 Pt areal loading target. Here, this paper demonstrates a viable pathway for meeting the $30/kW net ultimate cost target of the United States Department of Energy (U.S. DOE) Hydrogen Fuel Cells Technologies Office (HFTO). The concentrated ink lowers the thermal energy and capital expenditure (CapEx) budget of the coating process by decreasing the amount of time, energy, and floorspace required for drying the coating.

25 ENERGY STORAGE↗

DOE OSTI2023

Testing of Microchannels and Lab-Grown Stress Corrosion Cracks for Quantification of Aerosol Transmission

The formation of a stress corrosion crack (SCC) in the canister wall of a dry cask storage system (DCSS) has been identified as a potential issue for the long-term storage of spent nuclear fuel. The presence of an SCC in a storage system could represent a through-wall flow path from the canister interior to the environment. Modern, vertical DCSSs are of particular interest due to the commercial practice of using higher backfill pressures in the canister, up to approximately 800 kPa, compared to their horizontal counterparts. This pressure differential offers a relatively high driving potential for blowdown of any particulates that might be present in the canister. In this study, the rates of gas flow and aerosol transmission of a spent fuel surrogate through an engineered microchannel with dimensions representative of an SCC were evaluated experimentally using coupled mass flow and aerosol analyzers. The microchannel was formed by mating two gage blocks with a linearly tapering slot orifice nominally 13 μm (0.005 in.) tall on the upstream side and 25 μm (0.0010 in.) tall on the downstream side. The orifice is 12.7 mm (0.500 in.) wide by 8.86 mm (0.349 in.) long (flow length). Surrogate aerosols of cerium oxide, CeO 2 , were seeded and mixed with either helium or air inside a pressurized tank. The aerosol characteristics were measured immediately upstream and downstream of the simulated SCC at elevated and ambient pressures, respectively. These data sets are intended to add to previous testing that characterized SCCs under well-controlled boundary conditions through the inclusion of testing improvements that establish initial conditions in a more consistent way. While the engineered microchannel has dimensions similar to actual SCCs, it does not reproduce the tortuous path the aerosol laden flow would have to traverse for eventual transmission. SCCs can be rapidly grown in a laboratory setting given the right conditions, and initial characterization and clean-flow testing has begun on lab grown crack samples provided to Sandia National Laboratories (SNL). Many such samples are required to produce statistically relevant transmission results, and SNL is developing a procedure to produce samples in welded steel plates. These ongoing testing efforts are focused on understanding the evolution in both size and quantity of a hypothetical release of aerosolized spent fuel particles from failed fuel to the canister interior and ultimately through an SCC.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Wellbore Fracture Imaging Using Inflow Detection Measurements

One of the most striking measurements taken during DOE’s EGS Collab project at the 4850-foot depth location was the so-called ‘sewer cam’, which enabled direct visualization of the flow of water into the production well through fractures during the stimulation. The ability to see directly which fractures were flowing and (roughly) how much was a breakthrough in understanding the topology of the created fracture network. Achieving this kind of fracture flow imaging at FORGE would be more challenging because of the 225°C temperature, but equally or even more valuable if it could be achieved. In 2017, a joint project between Sandia and Stanford developed a downhole tool concept to measure the enthalpy of multiphase fluid entering a geothermal well from individual fractures (Gao et al., 2017). For the FORGE project, measuring enthalpy is of less interest because the fluid is expected to be single-phase liquid water. However, the foundation of the device was the measurement of chloride ion concentration, which could form the basis for a direct measurement of inflow from fractures. During the 2017 project, this novel chloride sensing system was implemented into a laboratory test instrument, and we confirmed the capability of the system to measure the ion concentration of fluid entering a model wellbore through a small entry port. The wellbore was a 6-inch diameter model well, and the port was approximately 0.08 inch (2mm) in diameter. The device could measure the chloride concentration accurately even when the well was flowing in a bubbly flow. Given its accuracy, the tool should be able to identify locations of water entering the wellbore even if the ion concentration differs only slightly from that of the water in the well. It is likely that different fractures may flow slightly different chloride concentrations, which would make it feasible to detect individual fractures as well as to estimate the volume of their flow. Ultimately, we could also recognize different fractures flowing back significantly different ion concentrations after fracturing in the FORGE wells. This could be realized by adding different ions in the fracturing fluids in different fractures created at different stages of stimulation (and modifying the tool to include different ion specificity). Sandia’s tool was shown during the study to have the capability to withstand the 225°C temperature, and the electrochemical sensing elements were tested in the laboratory to 225°C at 1500 psia for 24 hours. An early implementation of the fully integrated downhole electrochemical tool, including high-temperature electronics, robust housing, and wireline truck interface, had previously been constructed and tested successfully at Sandia; thus, hardware development tasks focused on advancing the technology readiness level (TRL) of this promising technology for FORGE deployment, rather than on developing a new scientific basis for its operation. The data collection electronics in this tool allowed for several other sensors (pressure, temperature, flow spinner) to be implemented in parallel as well. The research was a new collaboration between Stanford and Sandia to modify and refine the tool for FORGE deployment, to make the downhole measurements, and to characterize the evolving fractures.

15 GEOTHERMAL ENERGY↗