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162 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

3D CAD Modeling and Mechanical Integration at the Mu2e Experiment at Fermilab

This abstract encapsulates a transformative summer internship at Fermilab, focusing on the Mu2e Experiment within the Mechanical Integration and 3D CAD Modeling group. The internship was characterized by a two-phase approach, with the initial month dedicated to the design of an aluminum platform tailored for DS Trench Maintenance. This phase encompassed a comprehensive exploration of structural integrity, material selection, and precision engineering principles, demonstrating an adeptness in CAD modeling and a keen eye for detail. The subsequent month saw a seamless transition into a dynamic phase of the project, wherein the intern played a pivotal role in effecting numerous adjustments across diverse sections of the 3D experiment structure. This phase demanded a nuanced understanding of the intricacies of the Mu2e Experiment and a high level of proficiency in CAD modeling.

3D CAD Modeling↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

EVI-LOCATE User Manual

One of the longest stages in the deployment of electric vehicle supply equipment (EVSE) is the initial planning of the infrastructure itself. Engineers and fleet experts from the National Renewable Energy Laboratory (NREL) have supported dozens of charging infrastructure site plans over the past couple decades, including the generation of site schematics, determinations of electric capacity, and estimates for likely costs. As the market for electric vehicles (EVs) has matured, this approach should no longer require a time and personnel intensive process. In order to shorten the time taken to develop site plans and cost estimates, NREL developed a tool that fleet managers, facility managers, electricians, EVSE installers, and members of the public can use to develop initial schematics and ballpark pricing for charging station installations. The Electric Vehicle Infrastructure - Locally Optimized Charger Assessment Tool and Estimator (EVI-LOCATE) provides a structured and consistent way for users to enter information about their planned EVSE project in a relatively simple web-based format. EVI-LOCATE then calculates electrical equipment capacity, wiring runs, and project costs. It produces a site diagram optimized around surface characteristics with differential trenching costs for softscape such as grass compared to hardscape such as asphalt that can be adjusted by users in the tool. It also stores the resulting site plans and costs in a dashboard for access at a later date, including plan revisions if necessary. This document guides users through the EVI-LOCATE screens and associated questions. It contains tip text boxes throughout on how best to interface with the tool and find additional information or context. The appendices contain the assumptions and calculations underpinning the tool. Much of the information for EVI-LOCATE was gathered through industry engagements with EVSE installers, invoices from completed EVSE installations, Gordian's RS Means construction data, and the General Services Administration blanket purchase agreement for EVSE. For a visual tutorial of the tool, users can watch EVI-LOCATE Step-by-Step Video. The tool itself is available at https://evi-locate.nrel.gov.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

OES CO 2 Pipeline FEED Project Design Basis Memorandum

The OES CO₂ Pipeline project will move captured carbon dioxide from two ethanol facilities near Gibson City, Illinois, roughly 7.8 miles southeast to three injection wells outside Anchor, where it will be permanently stored underground. The system is designed to handle up to 4.5 million metric tonnes per year of dense-phase CO₂ at pressures up to 2,500 psig, using 16-inch mainline pipe and 10.750-inch laterals made from API 5L X-60 and X-65 steel. Wall thicknesses vary depending on location, with thinner pipe in open country, heavier wall at road crossings, and the heaviest where the pipe passes under highways or railroads via horizontal directional drill. The pipe gets a fusion-bonded epoxy coating, with an added abrasion-resistant layer wherever it's bored or drilled. Major water crossings will use HDD rather than open trenching. The pipeline will be cathodically protected, equipped with SCADA-compatible pressure and temperature instrumentation, and monitored for leaks using a computational pipeline monitoring system per API RP 1130. Hydrostatic testing will be performed at 1.25 times design pressure, and an ILI caliper run will follow to catch any construction defects. Several items, including fracture toughness requirements, specific NDE methods, and ILI tool selection, are left for the detailed design phase. The whole system falls under 49 CFR Part 195 and ASME B31.4, and Gulf Interstate Engineering prepared this document as the FEED-level design basis under the CarbonSAFE Phase III program.

09 BIOMASS FUELS↗
DOE OSTI2020

Post-Closure Report for Closed Resource Conservation and Recovery Act Corrective Action Units, Nevada National Security Site, Nevada, For Calendar Year 2019, Revision 0

This report serves as the combined annual report for post-closure activities in compliance with the requirements listed in Resource Conservation and Recovery Act Permit Number NEV HW0101 associated with the following closed corrective action units (CAUs): • CAU 90, Area 2 Bitcutter Containment • CAU 91, Area 3 U-3fi Injection Well • CAU 92, Area 6 Decon Pond Facility • CAU 110, Area 3 WMD U-3ax/bl Crater • CAU 111, Area 5 WMD Retired Mixed Waste Pits • CAU 112, Area 23 Hazardous Waste Trenches

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NIST: Soil Respiration, Moisture, Temperature, Chemistry; and Fine Root Measurements from a Transect Through a Forest Edge, Gaithersburg, Maryland, 2017-2021

This dataset contains soil respiration, moisture, temperature, and chemistry, as well as fine root measurements from the National Institute of Standards and Technology (NIST) Forested Optical Reference for Evaluating Sensor Technology (FOREST) research facility at Gaithersburg, Maryland. Measurements were taken at an existing transect array that begins in a grassy meadow, crosses a sharp forest edge, then a small stream, and finally extends upwards in the interior of the forest at the top of a ridge. There are 6 different landscape positions replicated across three transects in the array. Soil respiration was measured during growing seasons in 2017-2019 (2017-06-02 to 2020-02-27). Pedons (1 m3) were isolated from surrounding tree roots using trenching and a fabric to inhibit root ingrowth. Flux measurements inside the pedons were thus assumed to represent heterotrophic only respiration in 2019, and these fluxes were paired with nearby fluxes assumed to represent total respiration. Deep vertical probes measured volumetric moisture content and temperature at the same points in the array every 10 cm in depth to either 90 cm or 120 cm total depth, at 15 minute intervals, from 2019-2021 (2019-07-09 to 2021-09-10). Soil core samples were collected from each of the array points for three different months in early- to mid-2019 (2019-03-19 to 2019-07-10), at three depths each. Soils were analyzed for gravimetric moisture content; pH; total carbon, nitrogen, and phosphorus; texture; microbial biomass carbon, nitrogen, and phosphorus; extractable dissolved organic carbon, nitrogen, and phosphorus; extractable nitrate and ammonia; and extracellular hydrolytic enzyme activities. The fine roots were separated from the cores and segregated by plant functional type (grass or tree species) and if they were dead or alive. Fine roots were then measured for length, surface area, diameter, and dry mass. This dataset contains four data files in comma separated (*.csv) format. These data serve to deepen our understanding of root and soil processes at forest edges and in transitional zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

LGAD-Based Silicon Sensors for 4D Detectors

Low-Gain Avalanche Diodes (LGAD) are a class of silicon sensors developed for the fast detection of Minimum Ionizing Particles (MIPs). The development was motivated by the need of resolving piled-up tracks of charged particles emerging from several vertexes originating from the same bunch-crossing in High-Energy Physics (HEP) collider experiments, which, however, are separated not only in space but also in time by a few tens of picoseconds. Built on thin silicon substrates and featuring an internal moderate gain, they provide fast signals for excellent timing performance, which are therefore useful to distinguish the different tracks. Unfortunately, this comes at the price of poor spatial resolution. To overcome this limitation, other families of LGAD-based silicon sensors which can deliver in the same substrate both excellent timing and spatial information are under development. Such devices are, to name a few, capacitively coupled LGADs (AC-LGAD), deep-junction LGADs (DJ-LGAD) and trench-isolated LGADs (TI-LGADs). These devices can be fabricated by even small-scale research-focused clean rooms for faster development within the scientific community. However, to scale up production, efforts towards integrating these sensor concepts in CMOS substrates, with the obvious advantage of the possibility of integrating part of the read-out electronics in the same substrate, have begun.

spatial resolution↗
DOE OSTI2020

Printed assemblies of ultrathin, microscale inorganic light emitting diodes for deformable and semitransparent displays

Described herein are printable structures and methods for making, assembling and arranging electronic devices. A number of the methods described herein are useful for assembling electronic devices where one or more device components are embedded in a polymer which is patterned during the embedding process with trenches for electrical interconnects between device components. Some methods described herein are useful for assembling electronic devices by printing methods, such as by dry transfer contact printing methods. Also described herein are GaN light emitting diodes and methods for making and arranging GaN light emitting diodes, for example for display or lighting systems.

42 ENGINEERING↗
DOE OSTI2021

Removing or preventing dry etch-induced damage in Al/In/GaN films by photoelectrochemical etching

A method comprises providing a substrate comprising an n-type Al/In/GaN semiconductor material. A surface of the substrate is dry-etched to form a trench therein and cause dry-etch damage to remain on the surface. The surface of the substrate is immersed in an electrolyte solution and illuminated with above bandgap light having a wavelength that generates electron-hole pairs in the n-type Al/In/GaN semiconductor material, thereby photoelectrochemically etching the surface to remove at least a portion of the dry-etch damage.

Monavarian, Morteza↗
DOE OSTI2023

Coolant Pump Predictive Data Analytics from Signatures Generated by the Recursive Short Time Fast Fourier Transform

Although a nuclear reactor is a hostile environment for sensors and signal transmissions, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure installed at the Advanced Test Reactor (ATR) nozzle trench area records acoustic signals that can capture reactor operating states. The distinct states produce unique signatures that can be identified and tracked using data processing and data analytics. The infrastructure relies on acoustic transmission through ATR in-pile structural components, piping, and coolant that transmit acoustically modified signals generated by the coolant pumps. This paper will discuss results from using the Recursive Short Time Fast Fourier Transform (RSTFFT) technique used to process acoustic signals and provide signatures that are identified and monitored by analytics. The RSTFFT is applied to ATR data to understand the vibration levels and signatures for different operating regimes as displayed by the spectrogram. The combination of coolant pumps for normal and high-power operation generate unique signatures. These acoustic signatures are used to develop machine learning approaches to automatically classify operating regimes. Two machine-learning models, Support Vector Machines and Linear Discriminant Analysis, were developed to classify two event classes. Class 1 is a normal steady-state operation, and Class 2 is any event that is due to start up, shut down, or other actions. Both types of machine learning models had over a 96% prediction accuracy for the two classes. These results lay the foundation for predictive analytic frameworks that can be leveraged by ATR to optimize operations and maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Removing or preventing dry etch-induced damage in Al/In/GaN films by photoelectrochemical etching

A method comprises providing a substrate comprising an n-type Al/In/GaN semiconductor material. A surface of the substrate is dry-etched to form a trench therein and cause dry-etch damage to remain on the surface. The surface of the substrate is immersed in an electrolyte solution and illuminated with above bandgap light having a wavelength that generates electron-hole pairs in the n-type Al/In/GaN semiconductor material, thereby photoelectrochemically etching the surface to remove at least a portion of the dry-etch damage.

Monavarian, Morteza↗
DOE OSTI2020

Key Technical Issues for Greater-Than-Class-C (GTCC) Waste Disposal - 20164

Greater-than-Class C (GTCC) waste is low-level waste (LLW) that exceeds the Class C concentrations tabulated in Title 10, Code of Federal Regulations (CFR), Part 61. Disposal of GTCC waste in the near surface (i.e. upper 30 m of the earth's surface) is prohibited in the United States. Though GTCC waste disposal is generally prohibited, the Commission can approve disposal according to 10 CFR 61.55(a)(2)(iv) on a case-by-case basis. The waste classification tables, developed in the early 1980's, were based on model projections of dose to an inadvertent intruder in agricultural, construction, and discovery exposure scenarios. The assumptions and parameters used were documented in a series of public reports. Though different disposal facility designs and site conditions were considered, the waste classification tables were based on a disposal facility design located in a specific environment. The tables were based on shallow (i.e., top few meters) trench facility designs and did not consider deeper facilities. To determine the suitability of GTCC waste disposal in the near-surface, site-specific analyses must account for differences between GTCC waste and Class A, B, and C LLW (hereafter, traditional LLW). GTCC waste can have concentrations of radionuclides that are much higher than traditional LLW. Because of these higher concentrations, processes that are typically not significant for traditional LLW may be significant with respect to disposal of GTCC waste. These processes include, but are not limited to, heat generation, criticality, and radiolysis. The form of the waste as well as the barriers to release of the waste (e.g. waste package) could be substantially different than they are for traditional LLW. These barriers need to be considered when assessing the impacts of accidents during receipt and placement and in evaluating long-term performance. Some GTCC radioactivity is either embedded in stainless steel or contained in stainless steel barriers. Stainless steel can have very low corrosion rates under a variety of environmental conditions. Finally, and possibly most importantly, GTCC waste would likely need to be disposed deeper than traditional LLW to reduce the probability of disturbance. The waste classification table values of traditional LLW are based on an inadvertent intruder excavating into the waste and bringing some of the material to the land surface. If waste is deeper than approximately 5 m, the excavation scenario becomes very unlikely. Therefore, other intruder scenarios, such as drilling exposure scenarios, need to be evaluated. This paper summarizes key technical issues for the disposal of GTCC waste. Our previous study shows that certain GTCC waste may be suitable for near surface disposal whereas others may not. This study may support ongoing technical analyses assessing potential disposal of GTCC in a near surface disposal facility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Evaluating Remediation Options for a Perched-Water Unit at the Hanford Site - 20378

At the U.S. Department of Energy's Hanford Site, liquid-phase waste was discharged using surface disposal features such as ponds, trenches, and cribs. These practices distributed contaminants in the subsurface, some of which are currently in the vadose zone above the groundwater. At the 200-DV-1 operable unit (OU) at the Hanford Site, a key feature is a perched-water zone. This perched-water zone contains several million liters of water contaminated with high concentrations of uranium, technetium-99 (Tc-99), and nitrate that serves as a continuing source of contamination to the underlying aquifer. Currently, a pumping system is used to remove contaminant mass from the perched-water zone. However, water extraction will become more challenging as the perched unit dewaters. As a result, residual contaminants remaining after pumping is no longer effective needs to be addressed with post-extraction mitigation approaches, appropriate monitoring, and selection of suitable remediation endpoints. Thus, a remedy evaluation needs to include the unique conditions of this perched-water zone. As part of the initial evaluation, a set of technologies have been preliminarily identified as potentially applicable to the perched-water zone, including in situ remediation technologies to sequester uranium and Tc-99 and to degrade nitrate. While there is sufficient information available for direct consideration of some of the reviewed technologies in a feasibility study, five in situ remediation technologies were identified as needing additional information on their effectiveness under the geochemical and contaminant conditions of the perched-water zone. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Panel Session 46: Final Closure of Unlined LLW Disposal Facilities Worldwide (R3.5)

This panel discussed the on-going and emerging issues with the design and construction of final cover system of unlined disposal facilities (e.g., material disposal areas (MDAs), subsurface disposal areas (SDAs), burial grounds, and similar historical waste trenches, pits and vaults). The discussion focused on key technical and regulatory issues as well as lessons learned from years of engineering and construction in US and non-US locations. Panelists with presentations: Closure of Unlined LLW Disposal Sites (Craig Benson); Closure of LLW Facilities (Paul Pointon); Final Closure of Unlined LLW Disposal Facilities - Lessons Learned (Kevin Foye)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Hanford Immobilized Low-Activity Waste Transportation System Testing Results - 20043

The immobilized low-activity waste (ILAW) transportation system is a first-of-a-kind system that is being designed and tested to transport ILAW glass-filled containers from Hanford's Waste Treatment and Immobilization Plant (WTP) Low-Activity Waste (LAW) Facility to the Integrated Disposal Facility (IDF) burial trench for permanent disposal. A prototype design and fabrication of one transport trailer and four engineered ILAW pallets was completed in mid-2018. Prototype qualification testing to verify adequacy of the design was completed in 2019 by Washington River Protection Solutions (WRPS) and affiliate ATKINS SNC-Lavalin Group. Prototype testing was completed to advance design maturation across all operational and facility interfaces, and under adverse environmental and operating conditions, including thermal assessment of the system at peak projected temperatures. Test design, observation, and feedback was well represented and supported by all ILAW Transportation System interfacing organizations: WTP constructor Bechtel National, Inc.; IDF operator CH2M (CHPRC); transportation contractor Mission Support Alliance (MSA); the U. S. Department of Energy Richland Operations Office (DOE/RL) and Office of River Protection (DOE/ORP); and Washington State Department of Ecology. This paper presents the results of prototype qualification testing and opportunities for design improvements based on testing. Changes to the final design as a result of prototype testing include improvements in safety, ergonomics, operational flexibility, and As-Low-As-Reasonably-Achievable (ALARA) applications. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Modeling Efforts to Gain Insight into Historical Leak Events from the Single-Shell Tank A-105 in the Hanford 241-A Tank Farm - 20113

The 241-A Tank Farm is a single-shell tank (SST) farm constructed to store process waste from Hanford nuclear operations. Millions of gallons of nuclear waste were stored in the 241-A Tank Farm SSTs and some of the SSTs leaked in the past. In addition, spills and pipeline leaks during transfers and storage and intentional discharges to cribs and trenches resulted in releasing waste to the ground. Tank liner leaks are referred to as 'leaks' and all other discharges to the soil are referred to as 'releases.' Liquid waste that could be removed by pumping has been removed from all of the SSTs to reduce the potential for future leaks. The most significant historical leak event in 241-A Tank Farm occurred in 1965 when tank A-105 experienced a pressurized steam event. This event resulted in damage to the inner steel liner flooring of A-105, which was separated from the sidewalls over part of its circumference and buckled up to 2.5 m, vertically. During this event up to 7,570 L of contaminated liquid may have leaked to the adjacent soil. This leak occurred under extreme temperature and pressure conditions. Modeling efforts to better understand the nature and extent of historical leaks from subsurface SSTs that were used to store highly radioactive, self-boiling liquid wastes at 241-A Tank Farm have recently been undertaken. These efforts have compiled the available historical information to support a preliminary non-isothermal, multiphase flow and chemical transport modeling effort to re-create the conditions under which the leaks occurred and to formulate a conceptual model as to the extent and distribution of leaked radioactive contaminants in the adjacent soil material. The focus of this work was on developing an understanding of the key features, processes and bounding conditions related to tank A-105 leak events that occurred in the 1960's. The activities include estimation of leak composition, non-isothermal multiphase flow and transport modeling, and geochemical modeling. The STOMP{sup C} Water-Air-Energy modeling code was utilized to implement the three-dimensional representation of the subsurface tank and the surrounding flow field. Once constructed, the model was used to evaluate the conceptual understanding of leaks originating from different parts of the tank under the elevated temperature boundary conditions that were imposed by surrounding tanks at the time of the historical leaks. The chemical evolution of the liquid waste was also evaluated as it leaked from the tank under transient pressure and temperature gradients. The results of the preliminary evaluation suggested the presence of a heat-pipe effect beneath tank A-105 in which water vapor at an elevated temperature is driven away from the base of the tank to a position where water vapor cools, condenses and is then drawn back toward the tank by the strong capillary attraction of the dry soil. A non-sorbing contaminant (Tc-99) was introduced into this flow field to better understand the potential distribution patterns of leaked contaminants that may have occurred during the historical leak events. (authors)

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

High-Throughput Ion Irradiation and Microstructural Characterization of Multi-Principal-Element Alloys

Multi-principal-element alloys (MPEAs) have garnered interest in the nuclear community for their apparent resistance to microstructural damage under irradiation. While both experimentation and simulation have demonstrated reduced hardening and void swelling in MPEAs, the relationship between composition and irradiation response is still poorly understood since a rich experimental database for MPEAs does not yet exist. In this work, to accelerate the generation of experimental irradiation-response data for MPEAs, high-throughput synthesis, irradiation, and characterization techniques have been employed. Specifically, additive manufacturing has been used to produce arrays of various MPEAs (1-cm2 coupons), which have been subsequently irradiated using 4-MeV Ni2+ ions to a peak damage of 200 dpa at 500 °C. Hardening and void swelling have been assessed using nanoindentation and trenching via plasma focused ion beam (PFIB), respectively. Trends in the experimental data relating hardening and void swelling behavior to composition have been analyzed using different machine learning approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗