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NASA NTRS2008

Famine Early Warning Systems and Remote Sensing Data

This book describes the interdisciplinary work of USAID's Famine Early Warning System Network (FEWS NET) and its influence on how food security crises are identified, documented and the kind of responses that result. The book describes FEWS NET's systems and methods for using satellite remote sensing to identify and describe how biophysical hazards impact the lives and livelihoods of the population where they occur. It presents several illustrative case studies that will demonstrate the integration of both physical and social science disciplines in its work. FEWS NET s operational needs have driven science in biophysical remote sensing applications through its collaboration with the US Geological Survey, the National Aeronautics and Space Administration, National Oceanographic and Atmospheric Administration, and US Department of Agriculture, as well as methodologies in the social science domain through its support of the US Agency for International Development, UNWorld Food Program and numerous international non-governmental organizations such as Save the Children, Oxfam and others. Because FEWS NET is an organization that must provide a global picture of food insecurity to decision makers, the information it relies on are by necessity observable and able to be documented. Thus many aspects of traditional livelihood analysis, for example, cannot be used by FEWS NET as they rely upon relationships, and ways of expressing power and knowledge at the local scale that cannot be easily scaled up to express variations in access to food at a community level. The book focuses on the ways that remote sensing information is transformed into an understanding of the actions that must be taken in order to ensure that lives and livelihoods are protected, including describing the remote sensing observations and models needed to identify hazards and the information gathering requirements and analytical frameworks needed to understand their impact. Its focus is primarily analysis conducted in Africa, but also touches upon FEWS NET s work in Central America, Haiti and Afghanistan. As an organization that seeks to integrate social and physical science methodologies and strategies into its work on a daily basis, it is a fascinating and rich example of interdisciplinary knowledge generation and innovation.

Brown, Molly E.↗

DOE OSTI2025

Synthesis of nanodiamonds encapsulated by zeolitic imidazole framework-8 for quantum sensing applications

Nitrogen vacancy (NV)-containing nanodiamonds are widely used in quantum sensing applications due to their high sensitivity to magnetic fields, relatively low cost, and ability to be initialized, manipulated, and read out at room temperature. Quantum sensing techniques such as optically detected magnetic resonance (ODMR) and spin relaxometry have exploited the sensitivity of the NV nanodiamonds to magnetic fields to detect a range of analytes, such as pH, metal ions, and biomolecules. However, diversifying the sensing targets accessible by NV diamond quantum sensors typically requires careful engineering of the diamond surface chemistry with stimuli-responsive functional groups. Here, a simple protocol for coating NV nanodiamonds with the zeolitic imidazole framework 8 (ZIF-8), a widely used metal-organic framework, is presented. ZIF-8 is a highly porous material that has been used as a selective sensor for gasses, metal ions, and other analytes. The material is well-characterized by x-ray diffraction, transmission electron microscopy, scanning electron microscopy, x-ray photoelectron spectroscopy, and luminescence spectroscopy. Encapsulation of NV nanodiamonds with a porous scaffold such as ZIF-8 provides a promising method for improving the selectivity for the quantum sensing of various analytes. Importantly, the ZIF-8 coating does not impact the luminescence properties of the NV diamond, which is a key readout in ODMR and spin relaxometry sensing approaches. Indeed, the ODMR spectra with and without the ZIF-8 shell is nearly identical. Moreover, the ZIF-8 coating increases the longitudinal spin relaxation time of the NV nanodiamond by a factor of 4 relative to aggregated diamond, a desirable outcome for spin relaxation-based quantum sensing. Metal-organic framework composites with nanodiamonds thus are an exciting strategy for enhancing NV nanodiamond performance in applications such as quantum sensing and quantum-enhanced nuclear magnetic resonance spectroscopy.

nitrogen vacancy nanodiamond↗

DOE OSTI2026

Electrochemical Modeling of PID Leakage Current of PV Modules: Steady-State Current, Transient Current, and RC-Equivalent Circuit

Potential-induced degradation (PID) remains a significant reliability concern for photovoltaic (PV) modules, arising when a voltage difference between the module frame and the solar cells drives unintended leakage current through the glass-encapsulant stack. Although PID ultimately manifests as PID-s, PID-p, or PID-c, the underlying behavior of the leakage current-its magnitude and time dependence-requires clearer electrochemical interpretation. Traditional explanations attribute the initial transient current to bulk capacitive elements of the glass, encapsulant, and antireflection coatings, and the steady-state current according to their effective ohmic resistance. More recent studies, however, indicate that electrochemical charge-transfer processes at the encapsulant-metallization interface can play a dominant role in defining the leakage-current path. This paper develops a unified electrochemical framework for modeling PID leakage current. First, an RC-equivalent circuit is formulated by combining conventional RC elements with a Randles-type interface to capture transient leakage current through double-layer capacitance and faradaic processes at ionic-electronic boundaries. Second, the steady-state current-voltage behavior is explained using a linearized Butler-Volmer relationship, showing that the measured ohmic response corresponds to the low-overpotential limit of charge-transfer kinetics. Analytical results demonstrate that, for typical module materials-3.2-mm soda-lime glass and 0.45-mm encapsulant-the dominant modulators to PID leakage current are the glass surface resistance (under dry-surface conditions), the glass bulk capacitance, and the encapsulant resistance (under wet-surface conditions), with soda lime glass surface and EVA/POE encapsulant resistances primarily governing steady-state current. The proposed electrochemical model is validated against measured leakage-current data, showing good agreement in both the magnitude and the time-dependent evolution of PID leakage current.

14 SOLAR ENERGY↗

DOE OSTI2025

Analyzing the Impact of Future Weather Data on Energy Consumption in Weatherization Assistant

This study supports the mission of the U.S. Department of Energy’s Weatherization Assistance Program (WAP), which aims to increase the energy efficiency of dwellings and reduce their total residential expenditures. Specifically, we examine how projected future climate conditions may affect residential building energy performance by integrating future weather data into the National Energy Audit Tool (NEAT). Since WAP evaluates the cost-effectiveness of retrofit measures over lifespans of up to 30 years, accounting for evolving climate conditions is increasingly important. To reflect future household energy demands, this study replaces historically based Typical Meteorological Year (TMY3) weather inputs with Future Typical Meteorological Year (fTMY) datasets derived from global climate model (GCM) projections. A simulation-based framework was established to enable NEAT analysis under future weather conditions. This workflow involves converting EPW-format weather files into JSON inputs compatible with NEAT and generating degree-hour metrics needed for load calculations. The fTMY dataset used in this study was developed by Oak Ridge National Laboratory through downscaling of six GCMs under different emission scenarios and covers the period from 2020 to 2100. In contrast, the TMY3 dataset is based on historical weather data from 1961 to 1990. Simulations were conducted for benchmark single-family prototype buildings across ASHRAE climate zones 1–7, which cover all regions of the U.S. except the subarctic Zone 8 in northern Alaska, evaluating both heating and cooling loads under TMY3 and fTMY conditions. Four foundation types were tested, while heating systems were standardized, as NEAT does not differentiate thermal energy load by HVAC system type in its load calculations. Results show that fTMY weather input consistently yield lower heating loads and higher cooling loads across most locations, aligning with expected climate warming trends. Notably, colder regions such as zones 6A, 6B, and 7 experience marked reductions in heating load, while warmer and transitional zones, such as 2A (Lufkin, TX) and 3C (San Francisco, CA), have substantial increases in cooling loads. Although this study does not directly assess the performance of retrofit measures under future climate conditions, it provides a critical foundation for doing so. By quantifying shifts in baseline (i.e., pre-retrofit case) energy loads between historical and future weather files, the study highlights the importance of integrating climate-responsive data into audit tools. These findings will inform future efforts to evaluate the long-term effectiveness and cost-effectiveness of weatherization measures under changing climate conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Coordinated Natural Gas and Electric Planning: Case Studies of Current Approaches and Practices

This paper examines how natural gas and electric utilities across eight U.S. states and two Canadian provinces are beginning to coordinate historically separate planning processes in response to growing system interdependencies, policy mandates, aging infrastructure, and changing customer energy choices. Electricity planning has long relied on robust integrated resource planning frameworks that weigh numerous objectives, risks, and costs. Natural gas planning, on the other hand, is typically less transparent and more narrowly focused on safety and system integrity. As economic, reliability, and policy drivers place new and shared demands on both systems, jurisdictions and utilities are experimenting with approaches such as coordinated forecasting, non pipeline alternatives, and combined planning pilots. A few have issued regulatory or statutory directives for greater data sharing and methodological alignment. Case studies from British Columbia, California, Colorado, Illinois, Massachusetts, Minnesota, New York, Québec, Rhode Island, and Washington illustrate a wide range of approaches to rethinking siloed planning. The paper identifies several common themes across the jurisdictions examined. It provides observations on the methods, processes, and organizational steps that may be needed in the future to address the challenges being faced by states and utilities. Lastly, it identifies some initial steps that states and utilities can consider if they would like to pursue more integrated, cost-effective, policy-aligned energy system planning.

03 NATURAL GAS↗

DOE OSTI2025

Postdoctoral insights on mentoring excellence: a framework for best practices at Sandia National labs

The Sandia National Laboratories Strategic Plan FY24-FY27, updated for FY25, outlines Sandia’s two Big Labs-wide Goals, Accelerate Innovation and Lead in Modern Engineering. The goal of Accelerate Innovation is that “by FY27, Sandia will be a leader in scientific, engineering and operational innovation and an employer of choice for highly innovative and creative talent.” Sandia’s postdocs are leaders in innovation, well-versed in emerging techniques and cutting-edge methods, and capable of acting as a highly agile technical force across domains at the lab. As a federally funded research and development center (FFRDC), Sandia National Laboratories attracts top doctoral talent by offering a unique opportunity for postdoctoral researchers to develop at the crossroads of government, academia, and industry, working in multi-disciplinary teams and performing cutting-edge, mission-specific research that responds to immediate needs of national interest. However, this creates unique opportunities and demands of both postdoctoral appointees and the Sandia staff who act as their mentors, making mentorship key to attract talent. Since 2007 the Sandia Postdoctoral Development (SPD) Board, originally Postdoc To Professional (PD2P), a networking group at Sandia composed of a voluntary board of current postdocs and two staff liaisons, has advocated for postdoctoral development within Sandia National Labs. In this white paper, SPD board members and the Sandia Postdoctoral Development Office, organized in 2019, have come together to develop a comprehensive overview of the postdoctoral mentoring landscape at Sandia National Labs as we currently know it. By scouring various forms of data from efforts since 2018, we’ve compiled a community-derived perspective on what makes postdoctoral mentorship at Sandia unique. First, we analyze working sessions held between mentors and mentees to develop a comprehensive map of who is involved in postdoctoral mentorship at the lab and how the responsibilities are divided amongst mentors and mentees. We then combine multiple forms of data, including exit surveys, annual surveys, and community workshops, to identify the specific challenges that mentors and mentees encounter at the national lab. Finally, we use text mining and sentiment analysis to analyze mentoring award data to develop an idea of what postdocs are self-identifying as excellent mentorship within the lab. It is our goal that this white paper act as an ongoing resource to the postdoc and postdoc mentoring communities and provide a firm foundation for further conversations on the future of postdoctoral mentorship at Sandia National Labs.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Emulation With Uncertainty Quantification of Regional Sea‐Level Change Caused by the Antarctic Ice Sheet

Abstract Projecting regional sea‐level change under various climate‐change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice‐mass change, which requires substantial computational cost if applied to probabilistic frameworks requiring thousands to millions of samples. Here we build emulators of regional sea‐level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are evaluated against a numerical sea‐level model applied to an ensemble of ice‐sheet model simulations of the Antarctic Ice Sheet through 2100. We build a physics‐based emulator using a recent sensitivity kernel approach and compare it to machine learning based emulators (neural network and conditional variational autoencoder methods). In order to quantify uncertainty, we derive well‐calibrated prediction intervals for regional sea‐level change via split‐conformal inference and linear regression, and show that Monte Carlo dropout does not yield well‐calibrated uncertainties in this instance. We also demonstrate substantial gains in computational efficiency using both the physics‐based emulator and neural networks in comparison to the numerical model for the complete regional sea‐level solution. Overall, we find the physics‐based emulator modestly outperforms the machine learning emulators for this problem.

58 GEOSCIENCES↗

DOE OSTI2026

Numerical simulation of vortex-induced vibration response of a single IEA 10-MW wind turbine blade

Three-dimensional simulation of vortex-induced vibration (VIV) of a single International Energy Agency (IEA) 10-MW reference wind turbine blade with a length of 97.325 m is performed using the ExaWind stack, an open-source suite of codes. This study aims to illustrate the spanwise VIV response characteristics and cross-validate the results with an existing commercial framework. Five near-body meshes and three time steps are selected for the convergence study. To improve computational efficiency, several VIV triggering methods are also compared to shorten the VIV development period. The ExaWind-based VIV simulation strategy for a single IEA 10-MW blade is determined. First, the modal shape is validated against published results. Then, spanwise VIV responses of four blade configurations under a fixed and varied incoming flow velocity are analyzed. Results show that the VIV response is dominated by the first edgewise (second overall) mode. Little first-mode contributions appear near the second-mode node, producing a pi phase jump, and a higher harmonics response occurs near the blade root. Rotational degrees of freedom are minor compared with translational motion. The response versus reduced velocity is analyzed, showing a two-branch behavior similar to that of VIV for a bluff cylinder. Across all tested cases, the dominant frequency remains locked to the natural frequency of the second mode with no observed desynchronization. A mild deviation is observed for the case of 90-degree pitch and 310-degree azimuth rotation near a reduced velocity of 6, which will be examined with additional cases in future work. These findings indicate that severe VIV responses can arise under specific configurations and flow conditions, thereby increasing the potential for VIV fatigue damage and requiring greater attention during operation.

17 WIND ENERGY↗

DOE OSTI2025

Tracking seasonal variability in plant traits from spaceborne PRISMA and NEON AOP across forest types and ecoregions

Plant traits serve as critical indicators of how plants adapt to environmental changes and influence ecosystem functions. While airborne hyperspectral remote sensing effectively maps plant traits through detailed reflectance properties, it is limited by cost and scale, making large-scale and temporal studies challenging. The recently launched spaceborne hyperspectral imager, PRecursore IperSpettrale della Missione Applicativa (PRISMA), offers frequent, large scale and high-fidelity observations on a spatial resolution of 30 m and a revisit time of around 29 days, making it suitable for large-scale seasonal trait mapping. However, their potential remains largely unexplored. This study developed a multi-stage framework by leveraging the PRISMA spaceborne hyperspectral data and National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) hyperspectral data to investigate the seasonal dynamics of four key plant traits — chlorophyll content, carotenoid content, equivalent water thickness, and nitrogen content — across eleven NEON sites representing diverse forest types and ecoregions in the contiguous U.S. Our results demonstrated that PRISMA hyperspectral data can reliably track seasonal variability in plant traits, achieving overall R 2 values ranging from 0.78 to 0.88 and normalized root mean square error (NRMSE) values ranging from 5.4% to 8.4% for the four traits. Seasonal patterns revealed bell-shaped trajectories for chlorophyll and carotenoids, while equivalent water thickness decreased steadily across most sites, driven by structural changes during leaf maturation and senescence. Nitrogen content exhibited less pronounced seasonal variation but followed expected nutrient resorption patterns. Analysis of environmental drivers showed that seasonal variability is primarily controlled by solar radiation and day length in northern sites, vapor pressure in semi-arid regions, and temperature in mid-southeastern sites. Spatial variability, meanwhile, was primarily driven by soil properties, particularly during the peak growing season. However, the influence of soil variables slightly declines toward the end of the season at several sites, as climatic factors become more prominent. This study highlights the capability of PRISMA, and potentially other similar spaceborne hyperspectral data for large-scale, time-series plant trait mapping and provides valuable insights into the interactions between plant traits and environmental factors. In conclusion, these findings contribute to advancing our understanding of plant functional ecology and improving predictions of ecosystem responses to environmental changes.

Environmental drivers↗

DOE OSTI2021

Review of Intrusion Detection Methods and Tools for Distributed Energy Resources

Recent trends in the growth of distributed energy resources (DER) in the electric grid and newfound malware frameworks that target internet of things (IoT) devices is driving an urgent need for more reliable and effective methods for intrusion detection and prevention. Cybersecurity intrusion detection systems (IDSs) are responsible for detecting threats by monitoring and analyzing network data, which can originate either from networking equipment or end-devices. Creating intrusion detection systems for PV/DER networks is a challenging undertaking because of the diversity of the attack types and intermittency and variability in the data. Distinguishing malicious events from other sources of anomalies or system faults is particularly difficult. New approaches are needed that not only sense anomalies in the power system but also determine causational factors for the detected events. In this report, a range of IDS approaches were summarized along with their pros and cons. Using the review of IDS approaches and subsequent gap analysis for application to DER systems, a preliminary hybrid IDS approach to protect PV/DER communications is formed in the conclusion of this report to inform ongoing and future research regarding the cybersecurity and resilience enhancement of DER systems.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

Multivariate environmental and trait-based controls of transpiration in the Central Amazon Rainforest

Tropical forest tree mortality is increasing due to more severe droughts, yet our understanding of how tree traits and life strategies are linked to drought stress has been limited by measurement scarcity. The BIONTE (BIOmass and NuTrient Experiment) near Manaus, Brazil hosts one of the world’s largest sap flow installations, with sensors in 90 canopy trees across a wood density gradient monitored since June 2022. The 2023 El Niño drought provided a unique opportunity to evaluate how water availability impacts tree transpiration. An interpretable machine learning framework was used to study the complex interactions between transpiration and multiple environmental variables such as soil water availability and vapor pressure deficit (VPD), and how these interactions vary with wood density and individual trees. We found varying responses of transpiration from different trees during the El Niño drought. Transpiration generally increased with temperature, with stronger effects in wetter areas and in trees with low to medium wood density. However, this response was modulated by stomatal sensitivity to VPD, which constrained transpiration under high atmospheric demand, particularly in intermediate-moisture area. The inflection in transpiration rate at high temperatures (>32°C) underscores the role of stomatal and hydraulic regulation in limiting water loss and protecting trees from excessive evaporative demand. Analysis of soil water contribution to transpiration revealed unimodal patterns in wetter area, with peak contributions near 0.45 cm 3 cm -3 of surface soil water and declining or flat responses beyond that threshold, suggesting a shift from water- to energy-limited transpiration. In contrast, drier areas exhibited limited transpiration sensitivity to soil water conditions and minimal trait-based variation in VPD responses, indicating supply-limited conditions. Despite higher wood density trees being generally more resilient, this study shows diverse tree drought resilience, prompting further investigation into the specific traits and dynamics between environmental variables in regulating transpiration and other physiological processes in trees.

Drought↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Challenges in Tracking Waste Reduction Performance Improvement in Manufacturing Plants

The recently released Circularity Gap Report 2023 by the Circle Economy Foundation states that the circularity score for the global economy is declining. The US Environmental Protection Agency (EPA) tracked municipal solid waste from 1960 to 2018 and found that 50% of the waste was destined for landfills. EPA estimates that US industry is responsible for 2.7 Gt of solid nonhazardous waste annually in the US mostly linear economy model. The circular economy framework aims to decouple economic value generation from the extraction of virgin materials from nature. The linear model of material extraction and disposal at the end of life is highly unsustainable. Manufacturing companies are adopting ambitious waste reduction targets to achieve sustainability. Through the Better Plants program, US DOE has established the Waste Reduction Network, which offers technical assistance to partners to achieve their ambitious waste reduction goals. Basic requirements of establishing a target include identifying a baseline, quantifying waste performance, and measuring progress over time. One problem faced by industry is unstandardized metrics for quantifying waste performance that may not be well suited to demonstrate progress. This paper studies traditional methods used to measure waste performance and highlights advantages, disadvantages, and limitations of each method. It also examines the suitability of applying the methods in different manufacturing circumstances. Finally, the paper presents a case study of a large manufacturer that faced inconsistencies in its tracked measurement metric. A solution was proposed and implemented to alter the methodology to enable more accurate waste performance tracking against a baseline.

circular economy↗

DOE OSTI2025

Reconstruction framework advancements to support streaming for the ePIC detector at the EIC

The ePIC collaboration adopted the JANA2 framework to manage its reconstruction algorithms. This framework has since evolved substantially in response to ePIC’s needs. There have been three main design drivers: integrating cleanly with the Podio-based data models and other layers of the key4hep stack, enabling external configuration of existing components, and supporting timeframe splitting for streaming readout. The result is a unified component model featuring a new declarative interface for specifying inputs, outputs, parameters, services, and resources. This interface enables the user to instantiate, configure, and wire components via an external file. One critical new addition to the component model is a hierarchical decomposition of data boundaries into levels such as Run, Timeframe, PhysicsEvent, and Subevent. Two new component abstractions, Folder and Unfolder, are introduced in order to traverse this hierarchy, e.g. by splitting or merging. The pre-existing components can now operate at different event levels, and JANA2 will automatically construct the corresponding parallel processing topology. This means that a user may write an algorithm once, and configure it at runtime to operate on timeframes or on physics events. Overall, these changes mean that the user requires less knowledge about the framework internals, obtains greater flexibility with configuration, and gains the ability to reuse the existing abstractions in new streaming contexts.

Brei, Nathan [Thomas Jefferson National Accelerato↗

DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗

DOE OSTI2025

Winds of fortune? Understanding the geographic, sociodemographic, and temporal distribution of benefit mechanisms from land-based wind projects in the United States

Despite decades of research on factors shaping local responses to wind development, there is relatively little known about benefit mechanisms (e.g., agreements, funds, donations) used by developers in the U.S. land-based wind sector. To address this gap, we collected benefit mechanism data across all current utility-scale land-based wind projects installed between 1982 and 2024 (n = 1047), finding that just under one-third of projects had a benefit mechanism attached to them. We find the use of benefit mechanisms has become more common over time, is associated with larger projects, and varies by region. In terms of host community characteristics, the use of benefit mechanisms is associated with characteristics like higher education level, higher percent white, higher percent Republican, higher decision-making capacity, lower unemployment rate, and higher poverty rate. Building on a theoretical framework of purposes, we discuss what these findings could suggest about the motivations driving developers' use of these mechanisms, such as increasing local acceptance of a wind project or supporting distributive fairness. This first-of-its-kind study builds a comprehensive understanding of how benefit mechanisms have been used in the U.S. wind industry throughout its history, which can inform future approaches to benefit-sharing across sectors.

17 WIND ENERGY↗