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At least 1,063 records · Page 59

Impact of Increased Latent Generations on Sensitivity Calculations with SCALE

Analyses of cross section sensitivity data from systems with fissile material allow analysts to associate an importance for each material, nuclide, reaction, and neutron energy by simulating real world criticality scenarios. Although criticality safety validation efforts can be guided by the cross-section sensitivity and uncertainty data generated for a particular system, these calculations can often be computationally expensive and sometimes cumbersome without proper guidance. The TSUNAMI suite within the SCALE code package has several methods for generating sensitivity data, including multigroup and continuous energy (CE) capabilities. The release of SCALE 6.3 has three different CE methods for generating cross section sensitivity data: (1) the Iterated Fission Probability (IFP) method with the KENO Monte Carlo transport solver, (2) the IFP method with the Shift Monte Carlo transport solver, and (3) the Contributon-Linked eigenvalue sensitivity/Uncertainty estimation via Tracklength importance CHaracterization (CLUTCH) method with the KENO Monte Carlo transport solver. Although the CLUTCH method has additional parameters for generating sensitivity data files relative to the IFP method, all three methods use latent generations, which are the generations between an event (i.e., fission) and the assessment of importance based on the asymptotic population of progeny neutrons. Increasing the number of latent generations in a calculation leads to increased discrimination of the sensitivity coefficients but at the cost of the increased uncertainty associated with those generated values. Analysts must balance the accuracy of the sensitivity calculations and its uncertainty with the associated computational cost involved in generating the values. This paper discusses the impact of adjusting the latent generation parameter for a range of sensitivity values and how these changes compare with the direct perturbation values obtained from a change of ±0.5% Δ k in both benchmark and safety application models. Two benchmarks from the International Handbook of Evaluated Criticality Safety Benchmark Experiments and the MPC-32 dual purpose canister for spent nuclear fuel are used for analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Evolving Competitive Markets in SAPP: Leveraging Competitive Wholesale Electricity Markets to Drive Renewable Generation Capacity in the Southern African Power Pool (SAPP)

The SADC region has significant natural resource potential to increase renewable energy generation, improve electricity reliability, and support economic development. This research finds an apparent lack of confidence from electricity infrastructure investors in SAPP wholesale electricity markets, which increases risk perception and lowers the likelihood of capital deployment. With respect to free market fundamentals, competitive market obstacles and renewable energy development obstacles are characterized. Stakeholders identified the top obstacles to well-functioning competitive markets as insufficient transmission infrastructure for interconnection and regional movement of electricity, dominance of national single-buyer markets, and lack of or weak nation-state regulatory frameworks. Stakeholders prioritized the top three obstacles for renewable energy development as a lack of viable commercial arrangements for variable renewable energy (VRE) balancing, lack of functional and consistent nation-level regulations, and higher project costs related to reliance on imported equipment. With respect to potential solution options, stakeholders prioritized the development of new cost allocation and finance methods to facilitate new transmission expansion, training to educate new or potential new market entrants on SAPP processes, as well as modeling and analysis of regional SAPP participation benefits disaggregated to the nation-state level. From these perspectives, this research identified strategy options for consideration including transitioning SAPP to a regional transmission operator (RTO) for operation and planning of cross-border transmission facilities and market administration, shifting operations of SAPP member transmission systems to Independent System Operators (ISOs), establishing a regional regulatory authority and enhancing market data transparency. Implementing these reforms is expected to be challenging, but not insurmountable, given the domestic political, legal, and jurisdictional complexities of the SADC region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Overview of the Geoanalytical Economic Evaluation of Saline Storage (GEESS) System

This report provides an overview on the Geoanalytical Economic Evaluation of Saline Storage (GEESS) system which characterized 57 geologic saline formations targeted for geologic carbon storage across the lower-48 U.S. states using publicly available resources. The GEESS system consists of high spatial resolution datasets (up to 5 km grid spacing) that provide key geologic parameters (e.g., depth, thickness, permeability), and estimates of CO 2 plume size and CO 2 first-year break-even price that were determined by exercising GEESS data through the FECM/NETL CO 2 Saline Storage Cost Model (CO2_S_COM). The GEESS geodatabase is available on NETL’s EDX at GEESS Geodatabase.

54 ENVIRONMENTAL SCIENCES↗

Structure property correlations in wet-spun textile PAN fibers for cost-effective carbon fibers applicable in H 2 storage tank manufacturing

The economic viability of high-strength carbon fibers is pivotal for their integration into Type IV compressed hydrogen storage tanks. Oak Ridge National Laboratory is advancing this objective by developing cost-effective, textile-grade polyacrylonitrile (PAN) precursor fibers through an optimized wet spinning-process to garner more microstructural control over the precursor fibers. Here, this study examines the microstructural characteristics of PAN fibers, particularly the crystalline orientation factor, and their impact on mechanical properties in both precursor and thermally converted carbon fibers (CFs). Three wet-spun PAN fibers, all having diameters of 12-13 µm but varying orientation factors were assessed. Comprehensive characterization using X-ray scattering and microscopy revealed that fibers with higher orientation factors exhibited enhanced crystallinity and optimized crystallite sizes. Mechanical testing demonstrated that the PAN fiber with the highest orientation factor achieved the greatest tensile strength (98 ksi) and modulus (1.60 Msi). Additionally optical and scanning electron microscopy confirmed smooth and defect-free surfaces across all samples. These findings indicate that manipulating spinning parameters to maximize orientation factor is more impactful in achieving high strength and modulus over factors attributed to crystallinity and crystallite sizes. Here, highest performing CFs, thermally processed in lab from textile-grade precursor averaged 558 ksi, a 10% increase over previous results.

36 MATERIALS SCIENCE↗

Underwater Noise Measurements around a Tidal Turbine in a Busy Port Setting

Acoustic emissions from current energy converters remain an environmental concern for regulators because of their potential effects on marine life and uncertainties about their effects stemming from a lack of sufficient observational data. Several recent opportunities to characterize tidal turbine sound emissions have begun to fill knowledge gaps and provide a context for future device deployments. In July 2021, a commercial-off-the-shelf hydrophone was deployed in a free-drifting configuration to measure underwater acoustic emissions and characterize a 25 kW-rated tidal turbine at the University of New Hampshire’s Living Bridge Project in Portsmouth, New Hampshire. Sampling methods and analysis were performed in alignment with the recently published IEC 62600-40 Technical Specification for acoustic characterization of marine energy converters. Results from this study indicate acoustic emissions from the turbine were below ambient sound levels and therefore did not have a significant impact on the underwater noise levels of the project site. As a component of Pacific Northwest National Laboratory’s Triton Field Trials (TFiT) described in this Special Issue, this effort provides a valuable use case for the IEC 62600-40 Technical Specification framework and further recommendations for cost-effective technologies and methods for measuring underwater noise at future current energy converter project sites.

tidal turbine↗

Atom Probe Tomography for understanding microstructural and chemical changes in nuclear materials

Atom Probe Tomography (APT) is routinely employed to determine 3D microstructure and chemistry changes in structural materials used in reactor to understand irradiation effect. These findings help to correlate changes to mechanical and physical properties of nuclear materials which are subjected to extreme reactor conditions. With safety and economics of a reactor in mind, it is essential to be able to predict the changes in the fuel composition and microstructure as a function of burnup. Also, there is current need to develop advanced fuels with improved properties and performance which requires the experimental data on changes in microstructure, physical and chemical properties after irradiation for feedback process. A novel approach is presented to determine the local burnup in irradiated fuels using isotopic quantification obtained by Atom Probe Tomography (APT). Considering the volume of sample used (<100?µm3) for APT experiments using the lift-out process in a scanning electron microscope equipped with a Focused Ion Beam (FIB), the presented method determines the local burnup from a nuclear fuel, where a minimal amount of waste is produced. In this work, three samples were analyzed with different burnup conditions to quantify the isotopes of 235U, 236U, and 238U for burnup calculation in the irradiated metallic U–7Mo dispersion fuel. The calculated values were found in excellent agreement with MCNP calculations. With regard to structural materials, one of the primary factors limiting the utilization of fuel rods of Zr alloys is hydrogen pick up during oxidation. Past experimental studies have shown that the hydrogen pickup in Zirconium alloys is influenced by the alloy composition as well by irradiation temperature and neutron dose. Oftentimes, it is not feasible to carry out comprehensive studies on understanding the microstructural evolution in neutron irradiated materials since in reactor irradiation programs are extremely costly, lengthy and logistically complicated. The use of ion irradiation as a substitute has grown in recent years for several reasons including less radioactivity, faster irradiations and lower costs. In the framework of the MUZIC 3 project, samples of Zircaloy-4 have been proton and Zr2+ irradiated at the Michigan Ion Beam Lab (MIBL) facility with aim to create a microstructure that is similar to that observed after in-reactor service. The temperature of the environment is adjusted to compensate for the higher dose rate, as has been done previously. As a first step in this project the irradiated microstructures have been characterized using Transmission Electron Microscopy and Atom Probe Tomography to elucidate the microstructural changes, including precipitate amorphization and dissolution and the formation of dislocation loops. This presentation plans to highlight initial finding of this project.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Bioconversion of Thermochemical Intermediates

Thermochemical (TC) biofuels production via both pyrolysis and hydrothermal liquefaction produces aqueous waste streams, typically with organic compounds at concentrations of -50-100 g/L. These streams represent a wastewater treatment cost and carbon loss for the TC biorefinery, but the concentration range for these compounds is ideal for bioconversion. To that end, the Bioconversion of Thermochemical Intermediates (BTI) project is developing advanced analytics and engineered microbes to convert these waste streams to co-products, with the overall aim of improving the economics and carbon conversion efficiency of TC biorefining. To date, we have primarily focused on development of advanced analytical chemistry approaches to fully characterize TC aqueous streams and engineering of Pseudomonas putida for conversion of non-conventional substrates, including methylated phenolics, cyclic ketones, furans, and C1-C3 light oxygenates, into atom-efficient products. Two primary challenges are the rapid deployment of aqueous-compatible analytics to changing upstream conditions and dealing with the toxicity of the feed streams to engineered microbes. The project efforts have resulted in engineered strains of P. putida able to consume 90% of the organic compounds in aqueous waste streams from catalytic fast pyrolysis, more than 300-fold toxicity tolerance improvements in P. putida, and carbon closures exceeding 90% for TC wastewater streams across multiple processing technologies.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗

An assessment of power flexibility from commercial building cooling systems in the United States

Understanding varying characteristics and aggregate potential of power flexibility from different building types considering regional diversity is critically important to actively engaging building resources in future eco-friendly, low-cost, and sustainable power systems. This paper presents a comprehensive characteristics analysis and potential assessment of the power flexibility from heating, ventilation, and air conditioning (HVAC) loads in commercial buildings in the U.S. using a simulation-based method. In this method, commercial buildings are first grouped by building types and climate regions. The U.S. Department of Energy Commercial Prototype Building Models are used to represent an average building in each group and are simulated to characterize corresponding power flexibility. Based on building survey data, the number of commercial buildings in each group is estimated and used to calculate aggregate power flexibility. It is found that HVAC loads in commercial buildings offer more flexibility for increasing power consumption than for decreasing it. The power consumption of commercial buildings in the U.S. can be increased by 46 GW and decreased by 40 GW on peak summer days. Among all commercial building types, standalone retail buildings provide the most absolute flexibility while the medium office buildings have the most flexibility as a percentage of the rated power consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Development of Chromium and Sulfur Getter for Solid Oxide Fuel Cell (SOFC) System

Under the “real world” SOFC operating conditions, gaseous Cr and S species coexist in air stream. The combined Cr and S effect on the electrochemical poisoning of candidate LSM and LSCF electrodes has been investigated. Electrochemical and structural analysis revealed that the combined Cr and S interactions with the LSCF electrode remains significantly different from those of the individual Cr and S interactions. S reacts with the surface SrO accompanied by morphological changes whereas the hexavalent gaseous Cr species mainly deposit at the LSCF/GDC interface as is the case with Cr-only poisoning. For LSM electrode, the combined interactions of Cr and S remains similar to the sum of individual Cr and S effects as SO 2 reacts with Sr-rich regions of the bulk LSM, while Cr deposits at the electrochemically active sites. The use of getter, consisting of alkaline earth and transition metal oxides, has been proposed as a cost-effective approach to mitigate electrode poisoning. SrMnO 3 (SMO) is suggested as a robust getter material for the co-capture of airborne gaseous S and Cr species entering high-temperature electrochemical systems. The honeycomb getter forms, covered with SMO nanoparticles, were fabricated using dip coating. The SMO getter successfully maintained the electrochemical activity of LSM under the presence of gaseous Cr and S species, validating the efficacy of the getter. Post-test characterization revealed that the absorption of S and Cr contaminants led to the elongation of granular SMO particles, forming SrO nanorods and SrCrO 4 whiskers leaving Mn oxides underneath where inward-migrating Cr reside, indicating the high mutual affinity of Sr and Mn. The growth of reaction products during the long term exposure favors continued absorption of incoming S and Cr contaminants. The Mn 2 O 9 dimers, existing on the surface, are considered to help absorb S and Cr impurities, along with the Sr-terminated surface. The SMO getter also displays robust stability in humid environments at high temperatures without phase transformation or hydrolysis, fulfilling the requirement for operation in high-temperature electrochemical systems.

30 DIRECT ENERGY CONVERSION↗

LEED: A Lightwave Energy-Efficient Datacenter

The Lightwave Energy-Efficient Datacenter (LEED) program is a disruptive “green-field” approach that provides a quantum leap in the energy efficiency of datacenters. LEED’s fundamental value proposition is that a novel and re-architected optical network—RotorNet— can deliver “more bandwidth per buck” as well as unique system-level attributes that significantly improve overall datacenter energy efficiency and performance. LEED has developed three system-level testbeds. The first testbed uses calibrated hardware and software power measurements to determine server energy efficiency as a function of network bandwidth and workload. These measurements have shown that increasing network communications bandwidth dramatically increases server energy efficiency providing a realistic path to the overall ENLITENED program goal of doubling the number of transactions per joule. The second testbed demonstrates key hardware: a prototype low-loss, high-port count optical “selector switch”. This switch was fabricated, racked, and tested. Measured switch characteristics include loss, bandwidth, crosstalk, switch time, system-level switch time (including the transceivers), and bit error rate. The third testbed demonstrates a fully working and manufactured pinwheel design which dramatically lowers the cost of design, while delivering high switch radix and low reconfiguration times. The LEED project has tied these three novel photonic switch prototypes together with production servers and software through the development of a novel FPGA-based NIC platform called Corundum. Corundum ensures that the packet-switched protocols supported by commodity operating systems and devices can interface with the Rotor switch design. The LEED group has used this combined hardware and software prototype to characterize applications running at a commercially relevant scale. The project has used a combination of enhanced optical modulation amplitude (OMA) modulators, broadband multiplexers and demultiplexers, avalanche photodiodes, and a novel burst-mode receivers to enable the insertion of LEED-developed optical switches without the need for expensive optical amplification. Our modeling has shown that measured LEED-developed device characteristics can achieve link characteristics of 2 pJ/bit including both transceivers and the Rotor switch. In summary, the LEED program has demonstrated a credible and practical path, through novel hardware and software, to realize the program objectives of ENLITENED. The net result will ensure that the United States maintains its strength in the crucial sector of Information Technology, which is vital to both our economic security and our national security.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Assessment and Non-Destructive Evaluation of the Influence of Residual Solvent on a Two-Part Epoxy-Based Adhesive Using Ultrasonics

Polymers are increasingly being used in higher demanding applications due to their ability to tailor the properties of structures while allowing for a weight and cost reduction. Solvents play an important role in the manufacture of polymeric structures since they allow for a reduction in the polymer’s viscosity or assist with the dispersion of fillers into the polymer matrix. However, the incorrect removal of the solvent affects both the physical and chemical properties of polymeric materials. The presence of residual solvent can also negatively affect the curing kinetics and the final quality of polymers. Destructive testing is mainly performed to characterize the properties of these materials. However, this type of testing involves using lab-type equipment that cannot be taken in-field to perform in situ testing and requires a specific sample preparation. Here, a method is presented to non-destructively evaluate the curing process and final viscoelastic properties of polymeric materials using ultrasonics. In this study, changes in longitudinal sound speed were detected during the curing of an aerospace epoxy adhesive as a result of variations in polymer chemistry. To simulate the presence of residual solvent, samples containing different weight percentages of isopropyl alcohol were manufactured and tested using ultrasonics. Thermogravimetric analysis was used to show changes in the decomposition of the adhesive due to the presence of IPA within the polymer structure. Adding 2, 4, and 6 wt.% of IPA decreased the adhesive’s lap shear strength by 40, 58, and 71%, respectively. Ultrasonics were used to show how the solvent influenced the curing process and the final sound speed of the adhesive. Young’s modulus and Poisson’s ratio were determined using both the longitudinal and shear sound speeds of the adhesive. Using ultrasonics has the potential to non-invasively characterize the quality of polymers in both an in-field and manufacturing settings, ensuring their reliability during use in demanding applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE-BU-26096-4

The infiltration of nanoparticle electrocatalysts into solid oxide fuel cell (SOFC) electrodes has been proven to produce a high density of electrochemically active sites, and reduce charge transfer polarization losses in SOFC electrodes. This is crucial for intermediate temperature operation, as these losses increase greatly at lower temperatures. Nickel-yttria stabilized zirconia (Ni-YSZ) cermets are low-cost, and exhibit excellent stability, but their main disadvantage stems from nickel coarsening and performance loss over their operational lifetimes. Infiltration of electrocatalyst nanoparticles has been shown to mitigate nickel coarsening and the consequent anode degradation. In this work, the effects of these infiltrants have been observed in a standard Ni-YSZ electrode. In addition to nickel, mixed ionic and electronic conducting (MIEC) phases have been infiltrated into Ni-YSZ scaffolds and their performance characterized using electrochemical impedance spectroscopy (EIS). Cross-sectional microscopy of fractured cells has been used to compare electrode microstructure and particle statistics. A model has been proposed to explain the origin of anode performance enhancement from nanoscale electrocatalysts.

Mo, Boshan↗

Stakeholder-Engaged Structured Decision Making for the Los Alamos Legacy Cleanup Mission - 20501

The Los Alamos National Laboratory (LANL) environmental legacy cleanup program requires decisions to be made for environmental remediation, decommissioning and disposal or management of radioactive waste. This legacy cleanup program was established to address groundwater contamination, material disposal areas (MDAs) that have been used to dispose of radioactive and other waste material, and 'aggregate areas' that might produce radioactive or other chemical waste as a result of remediation activities. The LANL site is regulated for environmental concerns under the Resource Conservation and Recovery Act (RCRA). However, some parts of LANL, such as material disposal area G (MDA G), have disposed of radioactive waste under DOE Order 435.1, and are subject to other regulations. For example, decommissioning the remote-handled TRU material stored in 33 shafts at MDA G falls under EPA's 40 CFR 191. Collectively, the regulations are all aimed in the same direction of finding the best solution, either through constructs such as 'as low as reasonably achievable' (ALARA), considering balancing factors as opposed to only cost and human health risk, and, under EPA regulations such as RCRA and NEPA, evaluating impact from all chemicals and both human health and ecological endpoints. Despite the basic goals and objectives of the regulations or their guidance, the main challenge is in their implementation. Arguably perhaps, but really in principle, all of these (and similar) regulations are asking for a decision analysis to be performed. Implementation challenges encountered have included lack of understanding of decision analysis in the industry, lack of effective stakeholder engagement in the decision analysis process, and lack of appreciation of the need to separate value judgments from science, the latter leading to developing conservative, or protective, science-based models. Conservative models lead to poor solutions, lack of effective stakeholder engagement leads to long drawn out protracted approaches to finding a solution (which also might never be found with this approach), and lack of understanding of decision analysis and Bayesian statistics causes poor models to be built, which creates unfortunate situations of 'garbage in, garbage out' becoming the basis for decision making. Stakeholder-engaged structured decision making (SDM) is an approach to solving problems that relies on the theory of decision science to involve stakeholders in the decision-making process. This approach incorporates stakeholder values using a scientifically rigorous methodology that separates value judgments from science in a way that helps avoid the pitfalls of biased, protective, or conservative modeling. This approach has its foundation in Keeney's 1992 treatise on value-focused thinking [1]. Keeney advocated a paradigm shift in decision making based on the idea that the standard way of thinking about decisions is backwards. The standard approach of focusing first on identifying alternatives rather than on articulating values results in a reactive approach with the emphasis on mechanics and fixed choices instead of the core values that have meaning to stakeholders. This paradigm shift effectively engages all stakeholders in the decision-making process while using a values focused thinking approach that can lead to the identification of decision opportunities and the creation of better alternatives. The intent is to be proactive and generate solutions that are related directly to values and objectives as identified by stakeholders. There are, perhaps, two overarching reasons why SDM can be used to benefit LANL's environmental legacy cleanup. Some of LANL's remaining waste management and environmental management problems are challenging and complex (for example, the Cr and RDX plumes, and MDAs) and while the traditional approach has, arguably, worked well for relatively simple risk-based problems, it cannot, or should not, be applied to more complex problems if the most effective and efficient solutions are desired. The second reason is cost. This has perhaps become more critical since publication of the Government Accountability Office (GAO) reports that DoE's environmental liability is considered a high-risk concern for the nation [2]. The focus of SDM is on structuring solutions to decision risk problems by first addressing stakeholder and decision maker values and subsequently developing decision objectives and ways to measure those objectives, preference weighting across objectives, identifying decision alternatives that best achieve those values, and characterizing uncertainty in predictions of the measures. Because a complete decision model is created using SDM, it can be evaluated to find the main elements of the model that drive, or predict, the best solution. This approach creates complete decision models that are transparent, traceable, reproducible and technically defensible. The science behind SDM, or decision analysis, is well founded, yet it is not unusual to see ad hoc approaches to decision making implemented under various environmental regulations that are pertinent to the LANL site, including NEPA, RCRA and DOE Order 435.1. Such ad hoc approaches are often not transparent or traceable, and lack reproducibility and technical defensibility. The LANL legacy cleanup program has embarked on using SDM to address the complex problems that remain. Stakeholder meetings have been held, and a prototype version of the stakeholder value system has been developed. Further meetings are expected in the future to address specific project needs. This is a long-term endeavor considering the complex environmental problems faced by DOE EM in Los Alamos (EM-LA), and careful planning, consideration of stakeholder value systems, and engagement with stakeholders throughout the SDM process is expected to lead to a successful endpoint. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Exploring the Functional Performance of a Commercial High-Temperature Photopolymer Resin for Vat Photopolymerized Injection Molding Tools

Additive manufacturing (AM) presents a promising approach for rapid tooling, offering significant reductions in production costs and lead times compared to that of conventional mold&#x2;making processes. This work examines the fabrication of molds for injection molding through vat photopolymerization (VPP) AM, using a commercially available photopolymer resin intended for high temperature applications (High Temp V2 resin). The photorheological behavior of the resin is examined to evaluate its curing kinetics and mechanical development during polymerization, ensuring suitability for mold fabrication. The thermal and mechanical properties of the printed photopolymer resin are characterized, alongside assessments of dimensional accuracy and surface finish, which are critical factors influencing the performance and quality of injection molded parts. Additionally, specimens produced using VPP-fabricated molds are evaluated and benchmarked against those molded with traditional metal molds.

Jones, Haley W. [Savannah River National Laborator↗

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗

Turbulence-parameter estimation for current-energy converters using surrogate model optimization

Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Additionally, parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.

16 TIDAL AND WAVE POWER↗

Entropy and crystal-facet modulation of P2-type layered cathodes for long-lasting sodium-based batteries

Abstract P2-type sodium manganese-rich layered oxides are promising cathode candidates for sodium-based batteries because of their appealing cost-effective and capacity features. However, the structural distortion and cationic rearrangement induced by irreversible phase transition and anionic redox reaction at high cell voltage (i.e., >4.0 V) cause sluggish Na-ion kinetics and severe capacity decay. To circumvent these issues, here, we report a strategy to develop P2-type layered cathodes via configurational entropy and ion-diffusion structural tuning. In situ synchrotron X-ray diffraction combined with electrochemical kinetic tests and microstructural characterizations reveal that the entropy-tuned Na 0.62 Mn 0.67 Ni 0.23 Cu 0.05 Mg 0.07 Ti 0.01 O 2 (CuMgTi-571) cathode possesses more {010} active facet, improved structural and thermal stability and faster anionic redox kinetics compared to Na 0.62 Mn 0.67 Ni 0.37 O 2 . When tested in combination with a Na metal anode and a non-aqueous NaClO 4 -based electrolyte solution in coin cell configuration, the CuMgTi-571-based positive electrode enables an 87% capacity retention after 500 cycles at 120 mA g −1 and about 75% capacity retention after 2000 cycles at 1.2 A g −1 .

25 ENERGY STORAGE↗

A Combined Computer Vision and Deep Learning Approach for Rapid Drone-Based Optical Characterization of Parabolic Troughs

Optical accuracy is a primary driver of parabolic trough concentrating solar power (CSP) plant performance, but can be damaged by wind loads, gravity, error during installation, and regular plant operation. Collecting and analyzing optical measurements over an entire operating parabolic trough plant is difficult, given the large scale of typical installations. Distant Observer, a software tool developed at the National Renewable Energy Laboratory, uses images of the absorber tube reflected in the collector mirror to measure both surface slope in the parabolic mirror and offset of the absorber tube from the ideal focal point. This technology has been adapted for fast data collection using low-cost commercial drones, but until recently still required substantial human labor to process large amounts of data. A new method leveraging advanced deep learning and computer vision tools can drastically reduce the time required to process images. This new method addresses the primary analysis bottleneck, identifying featureless, reflective mirror corner points to a high degree of accuracy. Recent work has shown promising results using computer vision methods. The combined deep learning and computer vision approach presented here proved highly effective and has the potential to further automate data collection and analysis, making the tool more robust. The method presented in this paper automatically identified 74.3% of mirror corners within 2 pixels of their manually marked counterparts and 91.9% within 3 pixels. This level of accuracy is sufficient for practical Distant Observer analysis within a target uncertainty. A commercial drone collected video of over 100 parabolic trough modules at an operating CSP plant to demonstrate the deep learning and computer vision method's usefulness in processing large amounts of data. These troughs were successfully analyzed using Distant Observer, paired with the new deep learning and computer vision algorithm, and can provide plant operators and trough designers with valuable insight about plant performance, operating strategies, and plant-wide optical error trends.

computer vision↗