Knowledge Guide : Health Physics Instrumentation Awareness for RAP Team Scientists, Revision 00
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Enghauser, Michael (ORCID:0009000470534902)↗
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Knowledge Guide : Environmental In-Situ Gamma Spectroscopy Overview for RAP Team Scientists, Revision 00
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Enghauser, Michael (ORCID:0009000470534902)↗
Knowledge Guide : SpecFIDLER Overview for RAP Team Scientists, Revision 00
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Enghauser, Michael (ORCID:0009000470534902)↗
DECOVALEX-2023: Task C Final Report
The Full-scale Emplacement (FE) heater experiment at the Mont Terri Underground Rock Laboratory (URL) was designed and conducted by Nagra to replicate an emplacement tunnel of Nagra’s reference repository design at 1:1 scale. Alongside testing the technical feasibility of constructing disposal tunnels, emplacing waste containers in the tunnels and then backfilling them, the main goals of the FE experiment are (1) to obtain a better understanding of the coupled effects of induced thermo-hydro-mechanical (THM) processes that may occur and (2) to validate existing coupled THM models (Müller et al., 2017). A key aspect of ensuring safety for repositories located in low-permeability rock involves minimizing any damage to the rock itself, thereby preserving its integrity and promoting a stable environment Amongst a number of processes that could damage the rock is the increase in pore pressure due to thermal loading caused by heat emitted from the waste. To reduce the potential damage of the rock, it is important to analyse the evolution of heat over time due to the heat load of the containers and assess possible consequences by coupled THM models. The aim of Task C of DECOVALEX-2023 was to build 3D numerical models of the FE experiment, focussing in particular on the heating induced pore pressure change in the Opalinus Clay. Data from a large number of sensors were available from the FE experiment for model comparison. These sensors measured temperature and relative humidity in the bentonite around the heaters, and temperature, pressure and displacement/strain in the surrounding Opalinus clay. Data were available from the start of excavation (April 2012) up to August 2020 for most sensors (more than 5 years from the start of heating in December 2014). To fulfil the overall aim of the task, the work was broken down into a number of steps, starting with simpler models to build confidence in each team’s approach and then moving to more complex models that better represent the FE experiment. Step 0 consisted of 2D benchmark models, gradually increasing the number of processes that are represented from thermal (T) only models in Step 0a, to coupled thermal hydraulic (TH) models in Step 0b with a representation of changing porosity, to coupled thermo-hydro-mechanical (THM) models in Step 0c, where porosity changes are calculated by the mechanical model. A detailed specification of processes, parameters, initial and boundary conditions was provided for this step, with the ambition that all teams would work towards close agreement in their model results, thus building confidence in the model implementations. vi It was not straightforward to achieve agreement between the teams, so additional steps (Step 0b2, 0b3, 0c2, 0c3) were added along with derivation of some analytical solutions against which the models could be compared. The reasons for the differences between teams were investigated and found to be caused primarily by different conceptual model assumptions (including temperature dependence of the thermal expansion of water), different model formulations (including porosity evolution) and differences in modelled domain sizes, boundary conditions and grid discretisation. This demonstrates that comparisons between multiple modelling teams and/or comparison with analytical results and experimental data are highly beneficial in providing an indication of uncertainty in model predictions. At the conclusion of Step 0, almost all teams had achieved a close agreement in model results and those that had not achieved an agreement knew the reason for this. Step 1 moved from 2D models to 3D models of the FE experiment without adding technical features like shotcrete or EDZ, and only considering the heating phase. Initially the 3D model was tightly specified to continue to build confidence in the model implementations (Step 1a). The results of Step 1a were compared to the data from the FE-experiment without the teams seeing the data. The teams were then provided with a sub-set of the data from the FE-experiment and invited to consider how best to use the large dataset for model comparison (Step 1b). Teams were then asked to use the data provided to calibrate their models, only changing material property values rather than adding features or processes to their models (Step 1c). In Step 1, teams were asked to only model the heating phase of the experiment, so pressure in the Opalinus Clay was reported as change in pressure since the initial conditions were specified rather than modelled. The change from 2D to 3D models was accompanied by an increase in the dispersion of results between the teams. Some of this was resolved during the task, but some remained and is potentially due to model discretisation. Calibration of parameters was useful in improving the fit of the models to the data but the remaining differences indicated that the models were missing features or processes. In Step 2, the teams were asked to update their models with additional features and processes as well as calibrating parameters to try and improve the fit of the models to the data. Teams were encouraged to represent ventilation of the open FE tunnel prior to backfilling with heaters and bentonite and in Step 2, the absolute pressure in the Opalinus Clay was compared between the teams. Teams took different approaches, but there was consideration of adding shotcrete and an EDZ into the model, representing stress change during excavation and different approaches to modelling ventilation of the FE tunnel. Overall, the documented results showed a very good agreement for temperature. The results for porewater pressure evolution showed a significant improvement for most teams compared to Step 1c with a good agreement to the measurements for several teams whereas some teams overpredicted the pressure increase and others overpredicted the drainage effect especially for the sensors close to the heater. Step 3 was an opportunity for teams to use the models developed in Step 1 and Step 2 to make predictions about the temperature and pressure changes that will be expected at the FE experiment over the next few years in light of the planned changes in thermal output of the heaters.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale
The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.
24 POWER TRANSMISSION AND DISTRIBUTION↗
An international study on THM modelling of the full-scale heater experiment at Mont Terri laboratory
We present results from an international model comparison study of the Full-Scale Emplacement (FE) experiment in Opalinus Clay at the Mont Terri Laboratory, Switzerland. Based on a provided parameter set the teams decided which parameters they adopted for their models, whether they considered the excavation and the ventilation phase in addition to the heating phase and if they included technical features like the shotcrete or the EDZ. The teams were able to reproduce the measured parameters temperature, relative humidity and pore pressure. The modelled results for temperature agree very closely between the teams especially in the sensors in Opalinus Clay. All teams were able to reproduce the redistribution of water in the bentonite backfill due to heating. The evolution of the relative humidity showed similar trends with differences in the intensity of the dry out effect. To model the pore pressure evolution is more complex because it comprises the full interaction of the coupled THM processes. The spread between the pore pressure modelled by the teams was larger, with some teams overestimating the pressure increase due to heating and some teams overestimating the extent of drainage. The agreement of modelled results with measurements improves with larger distance to the heater. We conclude that the EDZ and the shotcrete potentially influence the behaviour of the rock causing higher differences closer to the heater. Further research is needed to better implement those influences into the models. Based on the calibrated models, the future evolution of temperature, relative humidity and pore pressure was predicted over the next 10 years following a change of the heat power applied in 2023 and 2024. Again, the predicted temperatures agree very closely between the teams. Most teams do not expect an increase in relative humidity during the next 10 years after the initial dry-out.
58 GEOSCIENCES↗
DECOVALEX-2023: Task D Final Report
Task D of DECOVALEX-2023 is focused on the simulation of the coupled thermal hydraulic-mechanical (THM) behaviour in the full-scale engineered barrier system (EBS). The Horonobe EBS experiment is the demonstration of the full-scale EBS in the underground research laboratory (URL) (performed by JAEA in the Horonobe URL in Japan). Task D consisted of the three steps, a preliminary step (Step 0), simulation of the laboratory tests (Step 1) and simulation of the in-situ full-scale EBS experiment (Step 2). Since the Horonobe EBS experiment demonstrates the vertical emplacement option of the EBS, the experiment gallery is also backfilled with the backfill material. Therefore, interaction between the EBS and the backfill material can also be demonstrated, such as deformation (change of density) of the buffer material. The underground water in the Horonobe URL is saline. This fact adds chemical processes to THM behaviour. For example, mechanical properties (such as swelling pressure of the buffer material and backfill material) and hydraulic properties (such as permeability of the buffer material and backfill material) change depending on the water chemistry. Task D was therefore a challenging Task focused on not only the relatively simple THM behaviour but also complex THM behaviour including chemical processes. Six research teams (BGR, CAS, JAEA, KAERI, SNL and Taipower) participated the Task D. BGR, CAS, JAEA, KAERI and Taipower research teams selected a THM approach, while the SNL research team selected a TH approach. Step 1 involved the simulation of laboratory test results and was important to check the numerical codes developed by the research teams. Step 1 was divided into four sub steps. The simulation results through the Step 1 identified the parameters for simulation of the Step 2. Basic parameters of the materials (buffer material, backfill material, rock mass, concrete, sand) were provided by JAEA. Special parameters which research team needed were identified by back analysis of Step 1. Most notably the mechanical behaviour of swelling and displacement depended on the applied model (elastic model or elastoplastic model). Parameters such as Young’s modulus were found to need smaller values than characterised in the fundamental laboratory test results (Step 1-1, 1-2) for the elastic model. Although laboratory experiments are usually simple, test results contained some error. For example, if the saturation level is 100 % or higher, it should be considered an error. This situation was presented in the Step 1-3. A possible reason is that the buffer material is a mixture of bentonite and silica sand. When a specimen is cut to measure volume or weight, sand grains will affect the measurement data. In Step 2, boundary conditions such as temperature on the surface of the simulated overpack, heater power of the electrical heaters installed in the simulated overpack, injection pressure and inflow rate of the test water, were applied. The outer boundary conditions can be selected using measured data (injection pressure and inflow rate of the test water that is controlled by the injection systems installed in the sand layer around the buffer material and in the boundary between backfill material and concrete support). Since such measured data has some noise, research teams developed their own simplified boundary conditions. Inner boundary conditions can be selected using measured data as heater power and temperature on the surface of the simulated overpack. These data also contain some noise, so research teams developed their own simplified developed boundary conditions. Task D validated various approaches thorough the simulation of the in-situ full scale EBS system including backfill of the gallery: variations in the coupling processes (THM or THC), analysis codes, and boundary conditions. Temperature distribution in the buffer material was simulated well by all research teams. This means thermal behaviour is not sensitive to the simulation approaches. Although the water content distribution on the outside of the buffer material was well simulated by all research teams, the simulation results differ from the measured values inside the buffer material (at the centre and inside, near the simulated overpack). The buffer material is made from tap water, but in the in-situ experiment, saline groundwater infiltrates the buffer material. Therefore, the selection of the hydraulic parameters of the buffer material greatly affects the simulation results of the re saturation behaviour of the buffer material. In the Horonobe EBS experiment, measured values suitable for validating the simulation results were not obtained near the simulated overpack. When simulating the pressure and deformation of the buffer material, the measurement data is easily affected by the installation conditions of the measurement sensors, so verifying the measurement data itself remains an issue. Mechanical simulation results differ depending on whether they are considered as elastic or elastoplastic phenomena. The accuracy of measured in-situ data can be assessed by detailed analysis comparing sampling specimen analysis and measured data. The Horonobe EBS experiment is scheduled to be dismantled in the future (FY2026 and 2027). This detailed dismantling investigation will finally confirm the measured data.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Northwest Combined Heat and Power Technical Assistance Partnership: Final Scientific/Technical Report
During the years of 2018 through 2023, The Washington State University Energy Program (WSU EP) operated the Northwest Combined Heat and Power Technical Assistance Partnership under contract to the US Department of Energy’s Advanced Manufacturing Office (AMO), a part of DOE’s larger Office of Energy Efficiency and Renewable Energy. This contract provided direction and funding for providing technical outreach, information and technical/economic analysis services in support of expanding development of Combined Heat and Power (CHP) over a four state region in the Pacific Northwest. The states served included Alaska, Idaho, Oregon and Washington. During the duration of this contract, the WSU EP delivered performed specific assigned tasks and provided services of various generally prescribed types, with the overall goal of increasing adoption of CHP as an efficient heat and power supply in the region. Indications – though not formally evaluated to our knowledge – are that this goal was achieved. Over many years, the WSU team has provided support for CHP developments in these states under similar previous Department of Energy contracts, and many such projects have been built. While the early-phase outreach, education and techno-economic assessments that the NW CHP TAP provides rarely immediately result in project construction, there is no doubt that the WSU EP team has had substantial influence in the construction of a number of important CHP projects in the region. Examples of projects implemented directly under the current team’s guidance and services during this contract cycle include an 875 kilowatt biomass fueled CHP system installed at the University of Idaho, and 5 megawatt natural gas fueled CHP system at the University of Montana ( UM Breaks Ground on New Power Facility, Slashes Emissions 30% (umt.edu) ). Many other CHP systems recently installed are also either directly or at least partially the result of information and technical services provided by the team. Finally, though much more difficult to quantify and less glamorous, the team has helped countless organizations to understand CHP and determine whether and how CHP may be a fit for them, before they consider paying for engineering services. Whether the ultimate outcome has been to include CHP or not, the expert skills provided by the NW CHP TAP team have provided real value to these organizations considering this relatively complex technology. Over the duration of the contract our team provided 71 Initial Technical Assessments and 17 Advanced Technical Assessments; 53 End-User Engagements and 33 Stakeholder Engagements of various types; 42 Technical Profiles (Project Profiles, Program Profiles and Policy Profiles), and produced 14 technical articles for the Department of Energy. Unfortunately, these quantities provide no measure of the value that the team has provided. Only our clients can judge that, and we are confident that they are pleased with the benefits we have offered them.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Wholly Sustainable, Cost-Effective Carbon Fiber-Nylon Compounds CRADA 592 (Final Report)
Carbon fiber composites have attracted considerable attention due to the potential for substantial mass savings, with many examples now implemented in the low-volume luxury car market. However, migration to higher volume applications has been hindered by: (a) high material cost, (b) high processing times, and (c) perception of low Sustainability. This project will address all three of these barriers: (a) carbon fiber material to replace aluminum in structural components at a cost penalty of no more than $5/Kg-saved (aka weight buy), (b) fitting into high-rate processes for automotive production like injection molding, and (c) end-to-end Sustainable material – based on post industrial waste carbon fiber and nylon 66 and ability to recycle end-of-life auto parts. The opportunity lies in combining DowAksa capabilities in carbon fiber manufacturing, resin chemistry intermediate production with the unique testing capabilities inherent within PNNL. The teams from PNNL and DowAksa held several meetings virtually and in-person in Michigan and at PNNL, including a lab tour at PNNL. Throughout, the teams discussed DowAksa material sources, commercially available recycled base materials, and preliminary material properties. The teams also engaged in multiple discussions and evaluations of potential automotive applications based on the ideas suggested by PNNL. The teams discussed several potential automotive applications in which recycled carbon fiber and recycled PA resin can be used. The PNNL team identified 38 cast aluminum components that can potentially be assessed for redesign using the DowAksa materials system. The PNNL team also identified 27 polyamide components. The teams discussed the lists and narrowed it down to a handful of applications that are exterior and interior to common vehicle architectures. The team also considered semi-structural and structural components and short-listed the highest potential candidates, such as cross-car-beam. The cross-car beam was considered to be highly suitable and potentially viable demonstration applications based on the properties of the materials as well as the weight savings potentials and the reduction in embodied energy by utilizing wholly sustainable materials, since both materials, carbon fiber and resin, were derived from recycled materials. The next step was to reach out to potential OEMs and/or Tier1s who were interested in exploring such technology for future applications. However, the project was terminated, and no further discussions or exchange of information took place. No new data were generated, including no IP and no publications.
36 MATERIALS SCIENCE↗
Forecasting for ESCAPE: A Multi-Institution Hybrid Forecasting and Nowcasting Operation for Sea-Breeze Convection Supporting a Ground-Based and Airborne Field Campaign
The Experiment of Sea-Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field project deployed two aircraft and ground-based assets in the vicinity of Houston, Texas, between 27 May and 2 July 2022, examining how meteorological conditions, dynamics, and aerosols control the initiation, early growth stage, and evolution of coastal convective clouds. To ensure that airborne- and ground-based assets were deployed appropriately, a forecasting and nowcasting team was formed. Daily forecasts guided real-time decision-making by assessing synoptic weather conditions, environmental aerosol, and a variety of atmospheric modeling data to assign a probability for meeting specific ESCAPE campaign objectives. During the research flights, a small team of forecasters provided “nowcasting” support by analyzing radar, satellite, and new model data in real time. The nowcasting team proved invaluable to the campaign operation, as sometimes changing environmental conditions affected, for example, the timing of convective initiation. In addition to the success of the forecasting and nowcasting teams, the ESCAPE campaign offered a unique “testbed” opportunity where in-person and virtual support both contributed to campaign objectives. The forecasting and nowcasting teams were each composed of new and experienced forecasters alike, where new forecasters were given invaluable experience that would otherwise be difficult to attain. Both teams received training on forecast models, map analysis, Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), and thermodynamic sounding analysis before the beginning of the campaign. In this article, the ESCAPE forecasting and nowcasting teams reflect on these experiences, providing potentially useful advice for future field campaigns requiring forecasting and nowcasting support in a hybrid virtual/in-person framework.
54 ENVIRONMENTAL SCIENCES↗
Low-Cost, High-Performance Carbon Fiber for Compressed Natural Gas Storage Tanks (Final Technical Report – Down Select Report)
The aim of this project is to reduce the cost of Type IV, carbon fiber (CF) composite overwrap compressed gas storage tanks by reducing the cost of CF and CF composites. The project team worked to reduce the cost of CF by exploring and testing opportunities for a low-cost alternative precursor material for CF production to supplant market-dominant and costly polyacrylonitrile (PAN). Concurrently, the team aimed to reduce the cost of the tanks at the composite level by improving the interfacial adhesion between the fibers and the matrix resin through the incorporation of low-cost nanoparticles recycled from waste materials, which would reduce the volume of costly CF required to achieve the same tank performance. At the end of the first year, the project team selected mesophase pitch as the primary precursor candidate from a field of materials based on the superior mechanical performance and cost-saving potential. During the second year, the team produced CFs derived from mesophase pitch achieving an average tensile strength of 365.6 ksi and average tensile modulus of 40.74 Msi. Facility availability for spinning and converting these fibers at greater scale has hindered scale-up demonstration, but the team has identified opportunities to conduct this work in the near term. Cost modeling shows that these mesophase pitch-derived CFs can be up to 40% less expensive than PAN-derived CFs due to the lower cost of the feedstock material, higher throughput, greater conversion yield, and lower cost spinning method and compared to PAN. Additionally, the team has demonstrated at lab-scale that nanoparticle coating CFs can significantly increase the interfacial shear strength and load transfer efficiency of CFs in a matrix. Single filament pull-out testing showed a 27% average increase in max interfacial shear strength due to this coating. A continuous method of applying these coatings to a tow of CF has been developed for scale-up. 26 m tows of coated CFs were produced using this system and formed into composite ring samples for ASTM ring burst testing. Issues with the testing protocol have limited assessment of these results. A prototype Type IV tank was designed to meet ANSI HGV2 standards, and the design criteria set out by DOE, using the CF properties developed by the team paired with a proprietary resin matrix, a polyamide liner, and aluminum end bosses. The tank weighs 153.1 kg and with a total capacity of 5.8 kg H2 (5.6 kg usable), which yields a gravimetric capacity of 1.17 kWh/kg. Cost modeling predicts that the tank will have a projected cost of $15.73/kWh. Tank performance modeling does not include considerations for fiber-matrix load transfer efficiency improvements offered by nanoparticle coating method.
08 HYDROGEN↗
High-Fidelity, Large-Scale, Realistic Dataset Development
The final report summarizes the work performed for supporting the ARPA-E Grid Optimization Competition (Challenge 2 and Challenge 3) within the stated period. Challenge 2 For the challenge period, the main responsibility of the team is to investigate, gen- erate, and deliver parts of the data sets for the competition, based on the competition model for Challenge 2, existing data sets from Challenge 1, and data source supplied by other data set teams. Challenge 3 For the challenge period, the main responsibility of the team is to propose, create, deliver, and maintain the data format during the competition period. The data format will specify how the benchmark data will be represented and communicated to competitors. It will also specify how competitors should report back the solutions. The data format will be closely aligned with the problem formulation (maintained by the formulation team) and the solution validation process (maintained by the validation team). Our team is also responsible in investigating, generating, and delivering parts of the data sets for the competition. The data sets will be created based on the competition model for Challenge 3, existing data sets from Challenge 1 and Challenge 2, and data source supplied by other data set teams.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Laser Confocal Microscopy Uncertainty Quantification Study
At Los Alamos National Laboratory (LANL), the Storage Safety and Engineering (SSE) team completes annual surveillance on a subset of in-use interim nuclear material storage containers in fulfilment of requirements outlined in DOE Manual M 441.1-1. The containers are selected through several methods, such as subject matter expert judgement, random selection, and trending items. Following these selections, the SSE team has the capacity to complete surveillance on 15-20 containers each fiscal year, composed of a combination of SAVY-4000 and Hagan storage containers. Through previous work, the stainless-steel components of the containers have been identified as life limiting components, with an emphasis on the thin-walled bodies. The team is focused on understanding the extent of general and pitting corrosion, due to observations of extensive corrosion from stored contents and bag-out-bag degradation. Quantifying corrosion effects on the thin-walled stainless steel container bodies, and understanding potential impacts to the respective design release rates and design qualification release rates is paramount to the team. To date, destructive examination (DE) has proven to be the most insightful method for developing an understanding on the extent of corrosion on used containers. To standardize this process, the SSE team developed a destructive examination guide for analyzing stainless steel components of the containers. Corroded containers of interest are identified during surveillance activities and set aside for sectioning and characterization. Following sectioning, a major step in the DE workflow is the utilization of laser confocal microscopy for scanning corroded samples of interest and extracting data on pits, such as count, depth, and equivalent diameter. Adhering to the techniques outlined in the DE guide, analysis has been completed on two Hagans and one SAVY-4000 container, with the maximum pit depth recorded as 139.1 ± 22.82 μm on a 17.5 year old Hagan. The findings from the completed destructive examinations will be utilized to support lifetime extension efforts of the SAVY-4000 as the team can better estimate corrosion rates and effects over time based on stored contents and age. Due to the implications of observing extreme pit depths that approach the nominal container body thickness of .0299 inches (0.759 mm) or minimum container thickness of 0.236” (0.6 mm), high confidence in the LCM measurements is desired. Through testing outlined in, it was concluded that the total error ascribed to the 20x objective when conducting large image mapping on the Keyence VK-X3050 laser confocal microscope (LCM) relative to a 50x objective (reference) is 16.4% (± 8.73%). For shallow features on the order of pristine SAVY surface defects (i.e. 5 μm), this uncertainty is appropriate. However, this conservative estimate of total error poses a fundamental concern for pit depths that approach the thickness of the measured samples. That is, with the measurement uncertainty currently employed on all measurements, the LCM would be unable to resolve if a pit with a depth of 515 μm is through wall. Standard step height samples were procured and used in the present study to assess the resolution and repeatability of height measurements. Understanding the resolution and repeatability of height measurements was the first focus of the team as it relates directly to pit depth, which is of primary concern. Calibration gratings were procured to evaluate the resolution and repeatability of measurements in the X and Y axes of the LCM stage. The results of the depth uncertainty study were conducted first and presented in the subsequent sections. The planar uncertainty study is appended to the depth study with conclusions from both summarized at the end of the report.
36 MATERIALS SCIENCE↗
Confidence-weighted integration of human and machine judgments for superior decision-making
Large language models (LLMs) can surpass humans in certain forecasting tasks. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members’ confidence is well calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated its effectiveness in both image classification and neuroscience forecasting tasks. Combining human judgments with one or more machines consistently improved overall team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.
97 MATHEMATICS AND COMPUTING↗
Shadow of the Future: Developing Trust and Software within the Exascale Computing Project
Collaboration and team science are emerging areas of interest in software production. Historically, multi-institutional research collaborations are difficult to initiate and maintain, negatively impacting communication, negotiation, and dialogue between industry, government, and academic researchers. The Exascale Computing Project (ECP), a massive, multi-team, high-stakes initiative, facilitated broader research collaboration under a shared funding structure and extended timeline to support scientific discovery. Here, we conducted interviews with ECP teams, representing a variety of domain specialties, research institutions, and programming backgrounds. Using thematic analysis, we assessed how ECP’s structure created an environment of increased trust among projects and how software shared between teams facilitated sustained collaboration. We found that the expectation of future collaboration, i.e., the shadow of the future, greatly enhanced trust among teams and the quality of scientific software produced. Based on our findings within ECP projects, we connect to the existing literature on trust in software engineering and share recommendations for sustainable multi-institutional collaboration and shared best software practices.
Exascale computing project↗
NGEE Arctic Authorship Guidelines
Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.
Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗
Supporting ARPA-E Power Grid Optimization (Final Report)
Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.
24 POWER TRANSMISSION AND DISTRIBUTION↗
AMPP-4 Metric Dashboard
The AMPP-4 Metric Dashboard is an interactive tool used for visualizing key performance indicators and milestones for the various teams in the Material Recovery & Recycle (AMPP-4) group. This dashboard provides current group data for FY25 tracking purposes. The dashboard is organized by various chapters: 1) AMPP-4 overview, 2) Scheduling, and 3) individual chapters for each team. AMPP-4 supports 7 teams, where the day- and swing-shift aqueous demolition teams are treated as one team for the purpose of metric tracking. The dashboard is used internally to monitor the success and health of the AMPP-4 teams. Additionally, the dashboard can be used externally to support programmatic reporting to sponsors and aid in future milestone and work package planning.
97 MATHEMATICS AND COMPUTING↗