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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Attached Jet Velocity Profiles in Mixing Tanks

Free jets have been studied in detail over much of the last century, but the theory for offset and attached jets remains incomplete. Attached jets differ from free jets in that they lose momentum to nearby surfaces, attenuating their velocities. The velocity profiles of free circular jets are nearly Gaussian, with quantitative mathematical descriptions derived from first principles by Goertler and Tollmien (Rajaratnam, 1976). In contrast, mathematical descriptions of three-dimensional attached jets from circular nozzles remain much less mature. Agelin-Chaab and Tachie (2011) used particle imaging velocimetry of a three-dimensional attached jet to show that the scaled velocity decays with scaled distance from the nozzle with a power law exponent between -1.15 and -1.21, which is larger in magnitude than that of a free jet. However, quantitative analytical expressions for the velocity profiles of attached jets similar to those of free jets remain elusive. This paper addresses this critical gap. Here we evaluate the velocity profiles of three-dimensional offset jets emerging from circular nozzles that that become attached jets. These jets lose momentum due to interactions with nearby surfaces and are important to evaluating flows in mixing vessels and to suspending solids and trapped gases in radioactive waste tanks. Despite the importance of attached jets, prior insight has been purely experimental, limited to overly simplistic analytical models, or restricted to computationally expensive computational fluid dynamics case studies. We compare the expression of Verhoff (1963) to experimental results to find reasonable quantitative agreement. As stated by Agelin-Chaab and Tachie (2011), “detailed velocity measurements of 3D offset jets are rare.” Such remains the case. This study adds to the literature by providing information at two additional Reynolds numbers (1.43 106 and 1.87 106) and evaluating simple but accurate expressions for velocity profiles. These Reynolds numbers are an order of magnitude higher than prior reports. The semi-empirical stream wise velocity profile perpendicular to the surface proposed by Verhoff (1963) is in approximate agreement with these velocity profiles, which is surprising because these attached jets are three-dimensional instead of two-dimensional as evaluated by Verhoff. However, additional work is necessary to fully describe these profiles quantitatively.

jet, attached jet, offset jet↗
DOE OSTI2024

Characterizing the microstructures of mammalian enamel by synchrotron phase contrast microCT

The enamel of mammalian teeth is a highly mineralized tissue that must endure a lifetime of cyclic contact and is inspiring the development of next-generation engineering materials. Attempts to implement enamel-inspired structures in synthetic materials have had limited success, largely due to the absence of a detailed understanding of its microstructure. Here, the present work used synchrotron phase-contrast microCT imaging to evaluate the three-dimensional microstructure of enamel from four mammals including Lion, Gray Wolf, Snow Leopard, and Black Bear. Quantitative results of image analysis revealed that the decussation pattern of enamel consists of discrete diazone (D) and parazone (P) bands of rods organized with stacking arrangement of D+/P/D-/P in all mammals evaluated; the D+ and D- refer to distinct diazone bands with juxtaposed rod orientations from the reference plane. Furthermore, the rod orientations in the bands can be described in terms of two principal angles, defined here as the pitch and yaw. While the pitch angle increases from the outer enamel to a maximum (up to ≈ 40°) near the dentin enamel junction, minimal spatial variations are observed in yaw across the enamel thickness. There are clear differences in the decussation parameters of enamel across species that are interpreted here with respect to the structural demands placed on their teeth. The rod pitch and band width of enamel are identified as important design parameters and appear to be correlated with the bite force quotient of the four mammals evaluated.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Pyroelectric Heat Detection for Calibrated Measurement of Atomic Layer Deposition Reaction Heat

Atomic layer deposition (ALD) precursors and processes have been identified that enable the deposition of materials that span the periodic table. However, the detailed mechanisms, kinetics, thermodynamics, and complexities of even the most well-studied ALD half-reactions remain insufficiently resolved experimentally to test present computational hypotheses. In this work, a pyroelectric calorimeter is designed, implemented, calibrated, and electrically modeled to enable quantitative ALD reaction heat detection. The operation and response of three ALD calorimeter designs were evaluated for use as absolute ALD half-reaction heat detectors. The most effective design was calibrated in situ before being utilized to measure the heat generated by the reaction of trimethylaluminum with a hydroxylated alumina surface to produce 96 μJ/cm 2 over a reaction time of ~30 ms. With thermal and temporal resolutions down to 0.1 μJ/cm 2 and 50 ns, respectively, this sensitive and ultrafast probe is poised to improve our fundamental understanding of numerous ALD reaction mechanisms through quantitative comparison with first-principles computation predictions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Integrating risk assessment methods for carbon storage: A case study for the quest carbon capture and storage facility

This paper documents how tools developed by the National Risk Assessment Partnership (NRAP) complement the Bowtie risk assessment approach through the demonstration of a risk-based Area of Review (AoR) determination for the Quest Carbon Capture and Storage (CCS) facility near Edmonton, Alberta with the NRAP Open-Source Integrated Assessment Model (NRAP Open-IAM). The Bowtie risk assessment for the Quest CCS site was developed by Shell Canada Ltd., who operates the asset on behalf of the Athabasca Oil Sand Project — a joint venture, owned by Canadian Natural Resources Ltd, Chevron Canada and Shell Canada Ltd. The Quest Bowtie treats site risk comprehensively – considering a large set of technical and project risks (e.g., operational risks, above ground infrastructure, public perception, etc.). NRAP tools, methods, and analyses are focused on quantitative assessment of subsurface technical risks. NRAP-Open-IAM was used to evaluate potential groundwater impacts at the Quest site, i.e., a sufficient change above background groundwater quality levels to be measurable, should CO 2 and/or brine leak from the reservoir via a faulty and/or compromised wellbore. Our results show there is a low potential for groundwater impact and support a further reduction (from ~460 to 100 km 2 ) to the established AoR for the Quest site. Integrating both approaches early in the site characterization period could significantly benefit commercial-scale projects by drastically reducing the region over which leakage risks (e.g., legacy wells) need to be evaluated, i.e., the AoR. This is especially true for brownfield sites, which could contain hundreds to thousands of legacy wells. Further, basing monitoring network design on those regions where impacts are most likely to occur has the potential to facilitate more efficient deployment of monitoring technologies and improve the likelihood of detecting a leak should one occur.

58 GEOSCIENCES↗
DOE OSTI2021

Methane generation from low-maturity coals and shale source rocks at low temperatures (80–120 °C) over 14–38 months

This study contributes long-term (14 to 38 months) experimental evidence for geocatalytically mediated methanogenesis in immature to early mature shale and coal source rocks at temperatures from 80 to 120 °C. Borosilicate glass tubes with pre-outgassed coal and shale source rock chips and water were sealed under vacuum, sterilized, heated isothermally, and finally opened in connection with a vacuum line where headspace gases methane (CH 4 ) and carbon dioxide (CO 2 ) were collected. The possibility of admitting pre-existing CH 4 from closed pores into product gases during heating experiments was evaluated by comparative ball milling of original and heated rock chips, followed by quantitation of released gases with a novel closed-circuit laser-based SARAD RTM2200 gas detector system with sub-µmol sensitivity. The yields of produced CH 4 from individual source rocks were corrected by subtracting the amounts of pre-existing CH 4 from formerly closed pores in original source rocks that had leaked during long-term heating. Different shales and coals express contrasting propensities to geocatalytically generate CH 4 and CO 2 , with CH 4 yields ranging from 0.1 to 5.5 µmol g-1 total organic carbon (TOC). CH 4 yields from two petrographically different samples of Springfield Coal with comparable thermal maturity suggest that liptinite expresses a far higher propensity for methanogenesis, but liberates less CO 2 than vitrinite. Shale from the Second White Specks Formation generated approximately 10 times more CH 4 than New Albany Shale per g of TOC, further suggesting complex controls on CH 4 generation during catalytic methanogenesis. Higher temperature can enhance the activities of catalytic methanogenesis. Here, the extrapolation of laboratory-based reaction rates to natural conditions in organic-rich buried sediments suggests that geocatalytic methanogenesis can be fast enough in some source rocks to generate economically sizeable gas plays from immature to early mature source rocks over geologic time.

03 NATURAL GAS↗
DOE OSTI2021

Strain fields in twisted bilayer graphene

Van der Waals heteroepitaxy allows deterministic control over lattice mismatch or azimuthal orientation between atomic layers to produce long-wavelength superlattices. The resulting electronic phases depend critically on the superlattice periodicity and localized structural deformations that introduce disorder and strain. In this study we used Bragg interferometry to capture atomic displacement fields in twisted bilayer graphene with twist angles < 2°. Nanoscale spatial fluctuations in twist angle and uniaxial heterostrain were statistically evaluated, revealing the prevalence of short-range disorder in moiré heterostructures. By quantitatively mapping strain tensor fields, we uncovered two regimes of structural relaxation and disentangled the electronic contributions of constituent rotation modes. Further, we found that applied heterostrain accumulates anisotropically in saddle-point regions, generating distinctive striped strain phases. Our results establish the reconstruction mechanics underpinning the twist-angle-dependent electronic behaviour of twisted bilayer graphene and provide a framework for directly visualizing structural relaxation, disorder and strain in moiré materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Developing an Automated Uncertainty Quantification Tool to Improve Watershed-Scale Predictions of Water and Nutrient Cycling

Managing the flow of water, nutrients, and contaminants in watersheds is vital to addressing pressing issues related to water scarcity, access to clean drinking water, energy production, resilience to natural and anthropogenic perturbations, and ecological restoration. Decisions about the management of watersheds critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted by computer models. Prediction uncertainty can be reduced by matching the model to data, which are collected in the field at great expense. The contribution of watershed characterization data to reducing uncertainty of relevant model predictions can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives. To achieve this goal, we developed a software package that implements the data-worth analysis approach for use with state-of-the-art watershed models. The purpose of the proposed data-worth analysis is to help decision-makers allocate resources for watershed characterization such that the uncertainty in model predictions can be significantly reduced, which leads to better, more effective management decisions. At the same time, watershed characterization costs can be reduced. The specific technical objectives of this SBIR/STTR Phase II project were to develop a framework and associated software toolsets that implement the uncertainty quantification and data-worth analysis approach for use with state-of-the-art watershed models. This goal was achieved by (A) developing a user-friendly, robust software package that is accessible to a wide audience, including watershed managers, policy-makers, and public stakeholders; (B) by demonstrating application of the prototype on several use cases that are representative of complex watershed management challenges spanning a range of scales and that consider different open-source, DOE-based codes and other modeling platforms; and (C) by gathering information about the needs and requirements from potential users to help guide future developments, ensuring that the final product will be commercially viable. The developed software consists of a graphical user interface that guides the user through a sequence of analysis steps, supported by toolsets that leverage state-of-the-art computational simulation-optimization capabilities. A prototype of the software runs on multiple platforms (PC, Mac, multi-processor Linux environment), is linked to diverse watershed simulators (e.g., ECOSYS, TOUGH2, TOUGHREACT, Amanzi-ATS), performs multiple analysis tasks (predictive simulations, sensitivity analysis, uncertainty analysis, automatic parameter estimation, and data-worth analysis, multicomponent geothermometry), and is readily extensible to include external simulators and analysis tools. The software is being commercialized and will be continually updated to address user needs.

58 GEOSCIENCES↗
DOE OSTI2021

Predicting the Dissolution Rate of Borosilicate Glasses using QSPR analysis based on molecular dynamics simulations

Quantitative Structure Property Relationship (QSPR) analysis based on molecular dynamics (MD) simulations is a promising approach for establishing the composition-property relationships of glass and other materials with complex structures. A series of 20 borosilicate and boroaluminosilicate glasses have been modeled by using MD simulations with recently developed effective potentials. Short- and medium-range structures of these glasses were analyzed and, based on these structural information, QSPR analysis of the initial dissolution rate (r0) was made and compared with measured r0 at 90°C and pH 9 using various structural descriptors such as percentage of bridging oxygen species, network connectivity and average ring size. The structural descriptors, Fnet, containing energetic information such as single bond strength and other structural information were also used. It was found that overall network connectivity, average ring size and Fnet give reasonable predictions of the r0 of studied glasses, given the conditions that the glasses are homogeneous and dissolve congruently. Modifying glass compositions to account preferential release of modifiers gives a better prediction for incongruently dissolving glasses. The results were compared with our recent work of predicting glass dissolution behavior from compositions using the topological-constraints-based models.

Du, Jincheng↗
DOE OSTI2023

Quantitative approaches for including equity in risk and resilience infrastructure planning analyses

Risk and resilience assessments for critical infrastructure focus on myriad objectives, from natural hazard evaluations to optimizing investments. Although research has started to characterize externalities associated with current or possible future states, incorporation of equity priorities at project inception is increasingly being recognized as critical for planning related activities. However, there is no standard methodology that guides development of equity-informed quantitative approaches for infrastructure planning activities. Here, to address this gap, we introduce a logic model that can be tailored to capture nuances about specific geographies and community priorities, effectively incorporating them into different mathematical approaches for quantitative risk assessments. Specifically, the logic model uses a graded, iterative approach to clarify specific equity objectives as well as inform the development of equations being used to support analysis. We demonstrate the utility of this framework using case studies spanning aviation fuel, produced water, and microgrid electricity infrastructures. For each case study, the use of the logic model helps clarify the ways that local priorities and infrastructure needs are used to drive the types of data and quantitative methodologies used in the respective analyses. The explicit consideration of methodological limitations (e.g., data mismatches) and stakeholder engagements serves to increase the transparency of the associated findings as well as effectively integrate community nuances (e.g., ownership of assets) into infrastructure assessments. Such integration will become increasingly important to ensure that planning activities (which occur throughout the lifecycle of the infrastructure projects) lead to long-lasting solutions to meet both energy and sustainable development goals for communities.

community-based↗
DOE OSTI2020

Kinetic Isotope Effects: Interpretation and Prediction Using Degrees of Rate Control

Kinetic isotope effects (KIEs) have been used for decades in catalysis research as a tool for clarifying reaction mechanisms. Significant primary kinetic isotope effects have usually been interpreted as being a result of isotope substitution at a site of bond breaking (or forming) in the rate-determining step in the reaction mechanism. However, quantitative analysis of the magnitude of the KIE in complex multistep reaction mechanisms is seldom reported. We prove here that the logarithm of the rate ratio for two isotopes is the weighted average over all species in the mechanism of their standard-state free energy difference between the two isotopes, divided by RT. The weighting factor is the degree of rate control (DRC) for that species (e.g., transition state, intermediate, reactant) when the rate is measured separately for each isotope. It is instead the degree of selectivity ratio control (DSRC) when the KIE is measured as the product selectivity in a parallel competition between two isotopes within the same reactant molecule. Since only a few species have non-zero DRCs (or DSRCs) for most reactions, only these few contribute to this weighted average and the KIE. We show that this provides a simple way to interpret and quantitatively predict kinetic isotope effects that is powerful in the insights it provides, allowing one to evaluate directly which species contribute most to the KIE. By applying it to H/D KIEs in several example mechanisms, we further show that the traditional way of interpreting KIEs that focuses only on the rate-determining step can easily lead to misunderstanding of KIE and the reaction mechanism. Finally, this highlights the importance to consider the effect of isotope substitution on the energies of all species with large DRCs (i.e, those whose energies are kinetically relevant). This method also offers opportunities for quantitative validation of mechanism-based microkinetic models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations

Abstract. Ice and mixed-phase clouds play a key role in our climate system because of their strong controls on global precipitation and radiation budget. Their microphysical properties have been characterized commonly by polarimetric radar measurements. However, there remains a lack of robust estimates of microphysical properties of concurrent pristine ice and aggregates because larger snow aggregates often dominate the radar signal and mask contributions of smaller pristine ice crystals. This paper presents a new method that separates the scattering signals of pristine ice embedded in snow aggregates in scanning polarimetric radar observations and retrieves their respective abundances and sizes for the first time. This method, dubbed ENCORE-ice, is built on an iterative stochastic ensemble retrieval framework. It provides the number concentration, ice water content, and effective mean diameter of pristine ice and snow aggregates with uncertainty estimates. Evaluations against synthetic observations show that the overall retrieval biases in the combined total microphysical properties are within 5 % and that the errors with respect to the truth are well within the retrieval uncertainty. The partitioning between pristine ice and snow aggregates also agrees well with the truth. Additional evaluations against in situ cloud probe measurements from a recent campaign for a stratiform cloud system are promising. Our median retrievals have a bias of 98 % in the total ice number concentration and 44 % in the total ice water content. This performance is generally better than the retrieval from empirical relationships. The ability to separate signals of different ice species and to provide their quantitative microphysical properties will open up many research opportunities, such as secondary ice production studies and model evaluations for ice microphysical processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Prediction and Validation of Flow Properties in Porous Lattice Structures

High-porosity metal foams have been extensively studied as an attractive candidate for efficient and compact heat exchanger design. With the advancements in additive manufacturing, such foams can be manufactured with controlled topology to yield highly tailorable mechanical and transport properties. In this study, a lattice Boltzmann method (LBM)-based pore-scale model is implemented to simulate the fluid flow in additively manufactured (AM) metal foams with unit cell topologies of Cube, Face Diagonal (FD)-Cube, Tetrakaidecahedron (TKD), and Octet lattices. The pressure gradient versus average velocity profiles predicted by the LBM model were validated against in-house measurements on the AM lattice samples with the same unit cell topologies. Based on the simulation results, a novel hybrid model is proposed to accurately predict the volume averaged flow properties (permeability and inertial coefficients) of the four structures. Specifically, the linear LBM (neglecting inertial forces) is first implemented to obtain the intrinsic permeability, and then the standard LBM is applied to obtain the inertial coefficient. Convenient correlations for those flow properties as a function of porosity and fiber diameter are constructed. The effects of the AM print qualities on the flow properties are also discussed. The advantages of the hybrid model compared to the polynomial fitting approach for determining flow properties are discussed and compared quantitatively. The hybrid model and presented results are valuable for flow and thermal transport evaluation when designing new metal foams for specific applications and with different materials and topologies. Finally, the presented correlations based on pore-scale simulations can also be conveniently used in volume-averaged models to predict the macroscale flow behavior in such complex structures.

42 ENGINEERING↗
DOE OSTI2022

Levelized Cost of Electricity (LCOE) Content Model for Design of High Deflection Foils for MHK Applications

The LCOE Content Model provides data submitters with an easy and consistent means of uploading data that can be used to calculate the levelized cost of energy for MHK devices. These data are important to DOE and will be used to develop data products that provide quantitative information to guide and support programmatic decisions. Data will also be used to evaluate MHK products and determine costs and can help data users compare the combination of capital costs and operations and maintenance over time. The ultimate goal is to use these data to perform research and tailor programs to best benefit the industry.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Feedback from International Decommissioning Planning Projects - 20269

Decommissioning of Nuclear Power Plants is an increasing reality in many countries with nuclear power. Already many years before final shutdown, planning of the decommissioning work is usually started in order to understand for example the costs, waste volume capacity and licensing work needed. In many countries it is required to have a final decommissioning plan before any decommissioning work is allowed to start. Westinghouse has been involved in international decommissioning planning for more than 30 years, where the first 20 years was mostly funding and preliminary plans due to that decommissioning was many years away. However, as many more plants are now moving into decommissioning, the focus has shifted to final decommissioning plans with budgets instead of funding plans, strategic decisions instead of choices, waste volume specifications instead of estimates and a defined approach instead of what ifs. Westinghouse has developed a methodology based on the planning and execution of many decommissioning projects in Europe, America and Asia. That international experience and understanding helps the customers to adopt a systematic planning process where strategy development is done with the end state in mind, where the goals are clearly outlined and the starting point described. The process and steps to reach the desired end state need to be clearly defined and all options/alternatives evaluated from a cost/benefit point of view. This is followed by a thorough and quantitative risk analysis in order to give solid recommendations and credible conclusions. One important feedback has been that real decommissioning experience from reference projects is invaluable for an efficient and timely progress in order not to spend unnecessary time and effort on understanding the barriers and links between different activities in decommissioning. Skilled project managers or engineers can drive the project forward, but without decommissioning experience previously gained, valuable lessons learned are not used and the same mistakes can be made again. Many utilities have invested in knowledge transfer from other decommissioning projects through site visits, exchange programs and supplier meetings, which is a valuable asset and way of implementing lessons learned from performed decommissioning projects into the planning process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Measuring Sustainability of Solar Modules for Energy Transition: Mass, Energy, and Circularity

Transition to a carbon-free energy system is crucial for global decarbonization and underpins Circular Economy (CE) goals. Photovoltaic (PV) technology is required for Energy Transition, but manufacturing and circular pathways can be material, energy, and carbon-intensive. Therefore, we need a prioritization of sustainability strategies for PV evolution and lifecycle management in the context of Energy Transition. This study employs a suite of quantitative metrics to compare different proposed sustainability strategies for PV modules on their ability to achieve Energy Transition. Proposals for sustainable PV range from high-yield, high-efficiency paradigms, to short-lived and fully recyclable, to long-lasting, indestructible modules. We leverage a global decarbonization deployment schedule through 2100 with the open-source PV in Circular Economy (PV ICE) tool to quantify the impacts of different evolving module design scenarios covering the range of proposed sustainability strategies. First, modules are compared on effective capacity and required replacements to meet and maintain decarbonization capacity targets through 2100. We demonstrate the effects of lifetime, degradation, and reliability on effective capacity. Next, we quantify and compare virgin material demands and lifecycle wastes, examining the impacts of lifetime and recycling rates. Finally, and critically for renewable energy technologies, we quantify the energy demands required to achieve the decarbonization capacity targets and calculate energy balance metrics (net energy, energy return on investment). These results are then summarized into a metric matrix, demonstrating tradeoffs and the importance of longevity. Our suite of mass and energy metrics provides stakeholders and decision-makers with quantitative data on circular economy choices for PV in the energy transition, enabling informed evaluation of tradeoffs of different PV module designs and CE pathways.

circular economy↗
DOE OSTI2025

Combining Deep Learning and scatterControl for High-Throughput X-ray CT Based Non-Destructive Characterization of Large-Scale Casted Metallic Components

X-ray computed tomography (XCT) is essential for nondestructive evaluation and quality control of large-scale metal components. XCT imaging, however, faces significant challenges from metal artifacts, particularly those caused by Compton scattering, which degrade image quality and obscure critical details. Hardware-based solutions (e.g. scatterControl) offer advancements by intercepting scattered photons and reducing artifacts, but they can be time-consuming and require additional processing. Here, we propose modifying and leveraging a novel deep learning (DL) framework, Simurgh, to enhance and accelerate scatter correction in XCT. By combining scatterControl with DL-based artifact removal, we demonstrate significant reduction in scan time while producing high-quality reconstructions. Through extensive evaluation on industrial XCT data, we show that our methods reduce scan time by up to more than 10 x while preserving flaw detectability. Quantitative analysis across multiple segmentation techniques confirms that Simurgh-based reconstructions consistently outperform traditional Feldkamp-Davis-Kress, model-based iterative reconstruction, and commercial DL models in both pixel-level and task-specific evaluations, enabling scalable, high-throughput XCT workflows for characterization of large scale components in applications such as casting and metal additive manufacturing.

Complex metal parts↗