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At least 1,693 records · Page 94

The establisment of an achievement test for determination of primary teachers’ knowledge level of earthquake

In this study it was aimed to improve an academic achievement test to establish the students’ knowledge about the earthquake and the ways of protection from earthquakes. In the method of this study, the steps that Webb (1994) was created to improve an academic achievement test for a unit were followed. In the developmental process of multiple choice test having 25 questions, was prepared to measure the pre-service teachers’ knowledge levels about the earthquake and the ways of protection from earthquakes. The multiple choice test was presented to view of six academics (one of them was from geographic field and five of them were science educator) and two expert teachers in science Prepared test was applied to 93 pre-service teachers studying in elementary education department in 2014-2015 academic years. As a result of validity and reliability of the study, the test was composed of 20 items. As a result of these applications, Pearson Moments Multiplication half-reliability coefficient was found to be 0.94. When this value is adjusted according to Spearman Brown reliability coefficient the reliability coefficient was set at 0.97.

42 ENGINEERING↗

Architecture for interoperable software in biology

Understanding biological complexity demands a combination of high-throughput data and interdisciplinary skills. One way to bring to bear the necessary combination of data types and expertise is by encapsulating domain knowledge in software and composing that software to create a customized data analysis environment. To this end, simple flexible strategies are needed for interconnecting heterogeneous software tools and enabling data exchange between them. Drawing on our own work and that of others, we present several strategies for interoperability and their consequences, in particular, a set of simple data structures—list, matrix, network, table and tuple—that have proven sufficient to achieve a high degree of interoperability. We provide a few guidelines for the development of future software that will function as part of an interoperable community of software tools for biological data analysis and visualization.

59 BASIC BIOLOGICAL SCIENCES↗

Distribution Integrity Management Plan (DIMP): Los Alamos National Laboratory Natural Gas Distribution System

This document is the distribution integrity management plan (Plan) for the Los Alamos National Laboratory (LANL) Natural Gas Distribution System. This Plan meets the requirements of 49 CFR Part 192, Subpart P Distribution Integrity Management Programs (DIMP) for the LANL Natural Gas Distribution System. This Plan was developed by reviewing records and interviewing LANL personnel. The records consist of the design, construction, operation and maintenance for the LANL Natural Gas Distribution System. The records system for the LANL Natural Gas Distribution System is limited, so the majority of information is based on the judgment of LANL employees; the maintenance crew, the Corrosion Specialist and the Utilities and Infrastructure (UI) Civil Team Leader. The records used in this report are: Pipeline and Hazardous Materials Safety Administration (PHMSA) 7100.1-1, Report of Main and Service Line Inspection, Natural Gas Leak Survey, Gas Leak Response Report, Gas Leak and Repair Report, and Pipe-to-Soil Recordings. The specific elements of knowledge of the infrastructure used to evaluate each threat and prioritize risks are listed in Sections 6 and 7, Threat Evaluation and Risk Prioritization respectively. This Plan addresses additional information needed and a method for gaining that data over time through normal activities. The processes used for the initial assessment of Threat Evaluation and Risk Prioritization are the methods found in the Simple, Handy Risk-based Integrity Management Plan (SHRIMP™) software package developed by the American Pipeline and Gas Agency (APGA) Security and Integrity Foundation (SIF). SHRIMP™ uses an index model developed by the consultants and advisors of the SIF. Threat assessment is performed using questions developed by the Gas Piping Technology Company (GPTC) as modified and added to by the SHRIMP™ advisors. This Plan is required to be reviewed every 5 years to be continually refined and improved. Records for all piping system installed after the effective date of this Plan will be captured and retained in the UI records documentation system. Primary Utility Asbuilts are maintained by Utilities Mapping (UMAP) and additional records are maintained on the N drive. Engineering Change Notices (ECNs) are stored on the N drive under configuration management and kept up by Utilities and Infrastructure Division Office (UI-DO). Records include, at a minimum, the location where new piping and appurtenances are installed and the material of which they are constructed.

03 NATURAL GAS↗

Differentiable Multiphysics Codes: A Breakthrough Technology for Simulation and Computing

This document summarizes the findings of a strategic planning exercise commissioned by the Weapons Simulation and Computing, Computational Physics (WSC/CP) program at the Lawrence Livermore National Laboratory (LLNL) in FY24. During the year, the committee met with multiple stakeholder communities to gather input, opinions, suggestions and concerns which have been incorporated throughout this document. The key findings from this exercise are summarized: • The development of multiphysics modelling and simulation (mod/sim) codes and software technologies, their deployment on exascale compute platforms, and their broad adoption across the NNSA is a major success of the Advanced Simulation and Computing (ASC) program and the Exascale Computing Project (ECP). Sustained investment in these core technologies is essential. • Today’s state of the art involves running ensembles of O(100K) simulations to perform uncertainty quantification (UQ) and design studies using multiple statistical methods such as Bayesian optimization to understand sensitivities of our models and explore parameterized design spaces. Even with exascale computing, we are practically limited to O(10) parameters in these studies since the number of simulations required to sample the space scales exponentially with the number of design parameters. • The data from these simulation ensembles is increasingly being used to train machine learned (ML) surrogates (or reduced order models, ROMs) which can then be used for optimization or real time design exploration. However, the trained surrogates are still limited in the number of parameters they can represent due to the sampling limitations previously noted. • Augmenting our suite of integrated multiphysics simulation codes, both current and emerging, with the ability to compute gradients (solution derivatives) of arbitrary simulation outputs with respect to (some or all) simulation inputs would be a breakthrough technology, opening the door to a new era of efficient and automated inverse design based on verified and validated mod/sim capabilities. • This capability, which we refer to as differentiable multiphysics codes (DMCs), would revolutionize both UQ and optimization studies by breaking the curse of dimensionality that presently limits our “gradient-free” ensemble based computing approach. A similar breakthrough occurred in the AI/ML community once the ability to compute gradients of arbitrary loss functions using back-propagation became commonplace. Gradient information from the multiphysics codes can also be used to dramatically improve the efficiency and scale of training of ML/ROM surrogates for rapid assessments. • Achieving this in our suite of codes will be a grand challenge, similar to the amount of effort that was required to transition from CPU to GPU computing. It will require buy-in from the entire WSC/CP program and beyond, including all integrated codes, physics and engineering models, third-party library dependencies and performance portability abstractions. It will also require investment in research and development of numerical methods for computing adjoints of coupled physics across multiple adaptively refined moving meshes and of stochastic (Monte Carlo) and mesh free (SPH) methods. • New software and numerical techniques, largely pioneered by the AI/ML community, make this feasible. Chief among these is automatic differentiation (AD), the ability to employ AD at point-wise locations in a physics calculation (instead of traditional black-box approaches) and the ability to perform “back-propagation in time” (or reverse mode AD) for non-linear partial differential equations (PDEs). Fundamentally, the conclusion of this strategic planning exercise is that the time is right to undertake a large scale effort in WSC, centered on the existing integrated codes, to continue the natural evolution of mod/sim in the age of AI/ML. Instead of attempting to replace mod/sim with purely data driven AI/ML models, we believe the key to success is to integrate AI/ML by building on top of the decades of hard-won knowledge and the verified/validated multiphysics modelling capability that is the hallmark of the ASC program.

97 MATHEMATICS AND COMPUTING↗

Library of Advanced Materials for Engineering (LAMÉ) (V.5.24)

Accurate and efficient constitutive modeling remains a cornerstone issue for solid mechanics analysis. Over the years, the LAMÉ advanced material model library has grown to address this challenge by implementing models capable of describing material systems spanning soft polymers to stiff ceramics including both isotropic and anisotropic responses. Inelastic behaviors including (visco)plasticity, damage, and fracture have all incorporated for use in various analyses. This multitude of options an

36 MATERIALS SCIENCE↗

Microbial Ecology of Permafrost Soils: Populations, Processes, and Perspectives

Permafrost microbial research has flourished in the past decades, due in part to improvements in sampling and molecular techniques, but also the increased focus on the permafrost greenhouse gas feedback to climate change and other ecological processes in high latitude and alpine permafrost soils. Permafrost microorganisms are adapted to these extreme environments and remain active at low temperatures and when resources are limited. They are also an important component of global elemental cycles as they regulate organic matter turnover and greenhouse gas production, particularly as permafrost thaws. Here we review the permafrost microbiology literature coupled with an exploration of its historical aspects, with a particular focus on a new understanding advanced by molecular biology techniques. We further identify knowledge gaps and ways forward to improve our understanding of microbial contributions to ecosystem biogeochemistry of permafrost-affected systems.

54 ENVIRONMENTAL SCIENCES↗

Progress in the Physisorption Characterization of Nanoporous Gas Storage Materials

Assessing the adsorption properties of nanoporous materials and determining their structural characterization is critical for progressing the use of such materials for many applications, including gas storage. Gas adsorption can be used for this characterization because it assesses a broad range of pore sizes, from micropore to mesopore. In the past 20 years, key developments have been achieved both in the knowledge of the adsorption and phase behavior of fluids in ordered nanoporous materials and in the creation and advancement of state-of-the-art approaches based on statistical mechanics, such as molecular simulation and density functional theory. Together with high-resolution experimental procedures for the adsorption of subcritical and supercritical fluids, this has led to significant advances in physical adsorption textural characterization. In this short, selective review paper, we discuss a few important and central features of the underlying adsorption mechanisms of fluids in a variety of nanoporous materials with well-defined pore structure. In conclusion, the significance of these features for advancing physical adsorption characterization and gas storage applications is also discussed.

42 ENGINEERING↗

Advances in rapid compression machine studies of low- and intermediate-temperature autoignition phenomena

Rapid compression machines (RCMs) are widely-used to acquire experimental insights into fuel autoignition and pollutant formation chemistry, especially at conditions relevant to current and future combustion technologies. RCM studies emphasize important experimental regimes, characterized by low- to intermediate-temperatures (600–1200 K) and moderate to high pressures (5–80 bar). At these conditions, which are directly relevant to modern combustion schemes including low temperature combustion (LTC) for internal combustion engines and dry low emissions (DLE) for gas turbine engines, combustion chemistry exhibits complex and experimentally challenging behaviors such as the chemistry attributed to cool flame behavior and the negative temperature coefficient regime. Challenges for studying this regime include that experimental observations can be more sensitive to coupled physical-chemical processes leading to phenomena such as mixed deflagrative/autoignitive combustion. Experimental strategies which leverage the strengths of RCMs have been developed in recent years to make RCMs particularly well suited for elucidating LTC and DLE chemistry, as well as convolved physical-chemical processes. Specifically, this work presents a review of experimental and computational efforts applying RCMs to study autoignition phenomena, and the insights gained through these efforts. A brief history of RCM development is presented towards the steady improvement in design, characterization, instrumentation and data analysis. Novel experimental approaches and measurement techniques, coordinated with computational methods are described which have expanded the utility of RCMs beyond empirical studies of explosion limits to increasingly detailed understanding of autoignition chemistry and the role of physical-chemical interactions. Fundamental insight into the autoignition chemistry of specific fuels is described, demonstrating the extent of knowledge of low-temperature chemistry derived from RCM studies, from simple hydrocarbons to multi-component blends and full-boiling range fuels. In conclusion, emerging needs and further opportunities are suggested, including investigations of under-explored fuels and the implementation of increasingly higher fidelity diagnostics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Process-dependent anisotropic thermal conductivity of laser powder bed fusion AlSi10Mg: impact of microstructure and aluminum-silicon interfaces

Purpose AlSi10Mg alloy is commonly used in laser powder bed fusion due to its printability, relatively high thermal conductivity, low density and good mechanical properties. However, the thermal conductivity of as-built materials as a function of processing (energy density, laser power, laser scanning speed, support structure) and build orientation, are not well explored in the literature. This study aims to elucidate the relationship between processing, microstructure, and thermal conductivity. Design/methodology/approach The thermal conductivity of laser powder bed fusion (L-PBF) AlSi10Mg samples are investigated by the flash diffusivity and frequency domain thermoreflectance (FDTR) techniques. Thermal conductivities are linked to the microstructure of L-PBF AlSi10Mg, which changes with processing conditions. The through-plane exceeded the in-plane thermal conductivity for all energy densities. A co-located thermal conductivity map by frequency domain thermoreflectance (FDTR) and crystallographic grain orientation map by electron backscattered diffraction (EBSD) was used to investigate the effect of microstructure on thermal conductivity. Findings The highest through-plane thermal conductivity (136 ± 2 W/m-K) was achieved at 59 J/mm 3 and exceeded the values reported previously. The in-plane thermal conductivity peaked at 117 ± 2 W/m-K at 50 J/mm 3 . The trend of thermal conductivity reducing with energy density at similar porosity was primarily due to the reduced grain size producing more Al-Si interfaces that pose thermal resistance. At these interfaces, thermal energy must convert from electrons in the aluminum to phonons in the silicon. The co-located thermal conductivity and crystallographic grain orientation maps confirmed that larger colonies of columnar grains have higher thermal conductivity compared to smaller columnar grains. Practical implications The thermal properties of AlSi10Mg are crucial to heat transfer applications including additively manufactured heatsinks, cold plates, vapor chambers, heat pipes, enclosures and heat exchangers. Additionally, thermal-based nondestructive testing methods require these properties for applications such as defect detection and simulation of L-PBF processes. Industrial standards for L-PBF processes and components can use the data for thermal applications. Originality/value To the best of the authors’ knowledge, this paper is the first to make coupled thermal conductivity maps that were matched to microstructure for L-PBF AlSi10Mg aluminum alloy. This was achieved by a unique in-house thermal conductivity mapping setup and relating the data to local SEM EBSD maps. This provides the first conclusive proof that larger grain sizes can achieve higher thermal conductivity for this processing method and material system. This study also shows that control of the solidification can result in higher thermal conductivity. It was also the first to find that the build substrate (with or without support) has a large effect on thermal conductivity.

Engineering↗

GaAsP/Si Tandem Solar Cells: Pathway to Low-Cost, High-Efficiency Photovoltaics

Si is the dominant PV technology, now and for the foreseeable future, due to its extensive manufacturing infrastructure, supply chain, feedstock availability, and highly optimized degree of fabrication processes, which altogether has produced an economic scenario where PV electricity generation is often cheaper than conventional fossil based generation. In many places, the overarching goal of grid parity has been achieved, but further improvement in performance-cost metrics are still needed to sustain the continued LCOE reductions needed to not only compete with conventional generation, but displace it on a global scale; a matter of critical importance if we stand any hope of slowing climate change. Nevertheless, single-junction Si PV is already nearing its physical limit, both in performance and cost, and is thus cannot meet these long-term goals alone. To this end, we are working on the development of monolithic III-V/Si tandem solar cells, which improve upon the performance of pure Si by providing enhanced utilization (reduced thermalization) of high-energy photons. This architecture nominally combines the substantial existing knowledge base, manufacturing infrastructure, and low cost of Si PV with the high efficiencies afforded by the well-established multijunction approach — the only proven way to break the single-junction limit. Although the metal-halide perovskite/Si tandem architecture has garnered substantial attention in recent years, serious questions regarding reliability and service lifetime remain, whereas III-V PV has a proven track record, including in the harsh concentrator and space environments. Additionally, there are multiple fabrication approaches to producing III-V/Si tandem cells, but we are focused on monolithic epitaxial integration as it is the most likely to yield the lowest ultimate LCOE in a fully mature, scaled technology. In this work we have produced multiple generations of GaAsP/Si tandem solar cells, demonstrating a more than 10% absolute AM1.5G efficiency improvement within the time frame of the project, including two verified world records. We have done this using industry-standard fabrication methods, showing that this platform can ultimately be manufactured at scale using existing or only slightly upgraded Si and III-V tooling. Our scientific and engineering advances across a range of fundamental and applied areas – III-V/Si heteroepitaxial integration, defect control in metamorphic III-V epitaxy, fundamental materials-oriented solar cell design and modeling methodology, and more – have created clear pathways for continued advances toward the goal of >30% AM1.5G cell efficiency (and >25% module) and will serve to inform the broader research community for well beyond this immediate application. Techno-economic modeling indicates that our approach can indeed meet SunShot/SETO LCOE targets, but as with any “post-Si” technology there are difficult, but not insurmountable barriers, requiring continued focused research and development efforts.

14 SOLAR ENERGY↗

FY2021 Status Report on the Computing Systems for the Yucca Mountain Project TSPA-LA Models and Testing of Selected Process Models

Sandia National Laboratories continued evaluation of the total system performance assessment (TSPA) for License Application (LA) computing systems for the previously considered Yucca Mountain Project (YMP). This was done to maintain the operational readiness of the computing infrastructure (computer hardware and software) and knowledge capability for total system performance assessment) type analysis, as directed by the National Nuclear Security Administration (NNSA), DOE 2010. The FY21 task included continued operation of the cluster; maintenance of the TSPA-LA models (with GoldSim 9.60.300); continued assessment of the status of the Infiltration Model; (a process model that feeds the TSP -LA) and preliminary assessments of the Unsaturated Zone Flow Model and the Saturated Zone Flow and Transport Model Abstraction (process models that feed the TSPA-LA). The 2014 cluster and supporting software systems are currently fully operational to support TSPA-LA type analyses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

How Accurate Can Crystal Structure Predictions Be for High-Energy Molecular Crystals?

Molecular crystals have shallow potential energy landscapes, with multiple local minima separated by very small differences in total energy. Predicting molecular packing and molecular conformation in the crystal generally requires ab initio methods of high accuracy, especially when polymorphs are involved. We used dispersion-corrected density functional theory (DFT-D) to assess the capabilities of an evolutionary algorithm (EA) for the crystal structure prediction (CSP) of well-known but challenging high-energy molecular crystals (HMX, RDX, CL-20, and FOX-7). While providing the EA with the experimental conformation of the molecule quickly re-discovers the experimental packing, it is more realistic to start instead from a naïve, flat, or neutral initial conformation, which reflects the limited experimental knowledge we generally have in the computational design of molecular crystals. By doing so, and using fully flexible molecules in fully variable unit cells, we show that the experimental structures can be predicted in fewer than 20 generations. Nonetheless, one must be aware that some molecular crystals have naturally hindered evolutions, requiring as many attempts as there are space groups of interest to predict their structures, and some may require the accuracy of all-electron calculations to discriminate between closely ranked structures. To save resources in this computationally demanding process, we showed that a hybrid xTB/DFT-D approach could be considered in a subsequent study to push the limits of CSP beyond 200+ atoms and for cocrystals.

42 ENGINEERING↗

Detailed assessment of diesel spray atomization models using visible and X-ray extinction measurements

The physical mechanisms characterizing the breakup of a diesel spray into droplets are still unknown. This gap in knowledge has largely been due to the challenges of directly imaging this process or quantitatively measuring the outcomes of spray breakup, such as droplet size. Recent x-ray measurements by Argonne National Laboratory, utilized in this work, provide needed information about the spatial evolution of droplet sizes in selected regions of the spray under a range of injection pressures (50–150 MPa) and ambient densities (7.6–22.8 kg/m3) relevant for diesel operating conditions. Ultra-small angle x-ray scattering (USAXS) measurements performed at the Advanced Photon Source are presented, which quantify Sauter mean diameters (SMD) within optically thick regions of the spray that are inaccessible by conventional droplet sizing measurement techniques, namely in the near-nozzle region, along the spray centerline, and within the core of the spray. To quantify droplet sizes along the periphery of the spray, a complementary technique is proposed and introduced, which leverages the ratio of path-integrated x-ray and visible laser extinction (SAMR) measurements to quantify SMD. The SAMR and USAXS measurements are then utilized to evaluate current spray models used for engine computational fluid dynamic (CFD) simulations. We explore the ability of a carefully calibrated spray model, premised on aerodynamic wave growth theory, to capture the experimentally observed trends of SMD throughout the spray. The spray structure is best predicted with an aerodynamic primary and secondary breakup process that is represented with a slower time constant and larger formed droplet size than conventionally recommended for diesel spray models. Additionally, spray model predictions suggest that droplet collisions may not influence the resultant droplet size distribution along the spray centerline in downstream regions of the spray.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

SUBTASK 2.19 – OPERATIONAL FLEXIBILITY OF CO2 TRANSPORT AND STORAGE

Carbon dioxide (CO2) is produced in large quantities during electricity generation and by industrial processes. These CO2 streams vary in terms of both composition and mass flow rate, sometimes substantially. The impact of a varying CO2 stream on pipeline and storage operation is not fully understood in terms of either operability or infrastructure robustness. This study was performed to summarize basic background from the literature on the topic of operational flexibility of CO2 transport and storage, but the primary focus was on compiling real-world lessons learned about flexible operation of CO2 pipelines and storage from both large-scale field demonstrations and commercial operating experience. Modeling and pilot-scale results of research in this area were included to illustrate some of the questions that exist relative to operation of carbon capture and storage (CCS) projects with variable CO2 streams. It is hoped that this report’s real-world findings provide readers with useful information on the topic of transport and storage of variable CO2 streams. The real-world results were obtained from two sources. The first source consisted of five full-scale, commercial transport–storage projects: Sleipner, Snøhvit, In Salah, Weyburn, and Illinois Basin–Decatur. These scenarios were reviewed to determine the information that is available about CO2 stream variability/intermittency on these demonstration-scale projects. The five projects all experienced mass flow variability or an interruption in flow. In each case, pipeline and/or injection engineers were able to accommodate any issues that arose. Significant variability in composition has not been an issue at these five sites. The second source of real- world results was telephone interviews conducted with experts in CO2 pipeline transport, injection, and storage during which commercial anecdotal information was acquired to augment that found during the literature search of the five full-scale projects. The experts represented a range of disciplines and hailed from North America and Europe. Major findings of the study are that compression and transport of CO2 for enhanced oil recovery (EOR) purposes in the United States has shown that impurities are not likely to cause transport problems if CO2 stream composition standards are maintained and pressures are kept at 10.3 MPa or higher. Cyclic, or otherwise intermittent, CO2 supplies historically have not impacted in-field distribution pipeline networks, wellbore integrity, or reservoir conditions. The U.S. EOR industry has demonstrated that it is possible to adapt to variability and intermittency in CO2 supply through flexible operation of the pipeline and geologic storage facility. This CO2 transport and injection experience represents knowledge that can be applied in future CCS projects. A number of gaps in knowledge were identified that may benefit from future research and development, further enhancing the possibility for widespread application of CCS. This project was funded through the Energy & Environmental Research Center–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FC26-08NT43291. Nonfederal funding was provided by the IEA Greenhouse Gas R&D Programme.

Jensen, Melanie↗

Use Case-Informed Framework for Utility Cloud Migration

This white paper presents a comprehensive methodology for assessing utilities’ cloud postures and frameworks. It aims to produce a roadmap and strategy for a cloud-enabled grid future, providing guidance for integrators, asset owners, and operators. Instead of offering a yes or no answer for cloud implementation, this framework offers strategic guidance on responsibly preparing for and deploying cloud solutions. This paper delves into cloud-service models pertinent to the electric sector, dissecting the shared responsibility model and elucidating what on-premise infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) entail. A pivotal consideration within the context of the shared responsibility model is the allocation of responsibility for foundational security aspects—a decision that will be informed by a comprehensive risk assessment. The ensuing discussion will present a checklist of certifications necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and with a strategic roadmap. Furthermore, the paper outlines gaps in understanding the U.S. government’s role in shaping technology development and responsible use. Its purpose is to aid decision-making by offering support for risk-informed solutions that benefit those managing assets and operating in the cloud environment. The primary objective is to enhance the resilience and future readiness of a decarbonized electric grid, with cloud solutions as one viable option. The paper synthesizes information on current and future grid architectures and applications, considering both conservative and progressive energy transitions, along with scalable and distributed computing considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Data-driven analysis of ultrasonic pressure tube inspection data - 220

Pressure tubes are critical components of the CANDU reactors and other pressurized heavy water type reactors, as they contain the nuclear fuel and the coolant. Manufacturing flaws, as well as defects developed during the in-service operation, can lead to coolant leakage and can potentially damage the reactor. The current inspection process of these flaws is based on manually analyzing ultrasonic data received from multiple probes during planned, statutory outages. Recent advances on ultrasonic inspection tools enable the provision of high resolution data of significantly large volumes. This is highlighting the need for an efficient autonomous signal analysis process. Typically, the automation of ultrasonic inspection data analysis is approached by knowledge-based or supervised data-driven methods. This work proposes an unsupervised data-driven framework that requires no explicit rules, nor individually labeled signals. The framework follows a two-stage clustering procedure that utilizes the DBSCAN density-based clustering algorithm and aims to provide decision support for the assessment of potential defects in a robust and consistent way. Nevertheless, verified defect dimensions are essential in order to assess the results and train the framework for unseen defects. Initial results of the implementation are presented and discussed, with the method showing promise as a means of assessing ultrasonic inspection data. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗