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NSCOR for Evaluating Risk Factors and Biomarkers for Adaptation and Resilience to Spaceflight: Emotional Valence and Social Processes in ICC/ICE Environments

Space exploration class missions, such as a mission to Mars, will require optimization of human performance, adaptability, and resilience. This NASA Specialized Center of Research (NSCOR) utilizes the NIMH Research Domain Criteria (RDoC) framework to identify biological and behavioral markers of individual social adaptation and emotional resilience (as well as vulnerability) to spaceflight-relevant stressors such as living in extended isolation. The overarching goal of this NSCOR is to obtain novel information to help identify biomarkers of individuals who are resilient and/or adaptable to the stressors of isolated, confined, and controlled (ICC) and isolated, confined, and extreme (ICE) environments.A total of N=90 healthy adult astronaut surrogates are being studied in three spaceflight-analog environments: (1) n=40 healthy adults in the Isolation and Confinement Analog Research Unit (ICARUS), an ICC at the University of Pennsylvania, during 7-day missions, for a target total of 280 subject days; (2) n=32 healthy adult astronaut surrogates studied in NASA’s Human Exploration Research Analog (HERA), an ICC at Johnson Space Center during 45-day missions, for a target total of 2,112 subject days; and (3) n=18 healthy adults in the Alfred-Wegener-Institute’s Neumayer Station III, an ICE in Antarctica, during 14-month missions, for a target total of 7,560 subject days. Dr. Nindl’s Laboratory at the University of Pittsburgh is analyzing a priori selected protein biomarkers in blood, saliva, and urine. Complementary rodent models of exposure to early life stressors, confinement, and isolation are being evaluated at Dr. Hensch’s Laboratory to further validate the neurobehavioral and biological findings from the human studies.Given the inconsistency and varied definition of resilience/adaptation in the scientific literature, the NSCOR team developed a composite resilience/adaptation measure that reflects the most relevant outcomes to resilience/adaptation across psychosocial and neurobehavioral functions, as well as neurocognitive and spaceflight-relevant operational performance. To achieve this, group consensus was attained from subject matter experts to produce a rank-order of importance for each input variable. The final resilience/adaptation score included 36 variables that were collected across spaceflight analogs. As of 10/1/2021, the NSCOR project acquired data on n=27 subjects at ICARUS, n=16 at HERA, and n=18 at Neumayer. The COVID-19 pandemic delayed data acquisition at ICARUS and HERA.Among subjects studied to date, 99% of neural and neurobehavioral data (e.g., neuroimaging for structure and function, behavioral measures) as well as blood, saliva, and urine for biochemical assays have been acquired. For rodent models, Dr. Hensch’s laboratory has established biochemical and behavioral parameters reflecting confinement stress in social networks of mice for comparison to stress responses in the human spaceflight analog environments.Group social behaviors were measured with a Social Network Analysis (SNA) approach to define objective parameters associated with sociability and its plasticity by sex. This analytic approach may help identify a network of individuals who are more effective teammates or more likely to generate new social relationships. Data acquisition, biomarker assessment, and data quality control will continue through September 2022.

D F Dinges↗

DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Manuscript model outputs, model source code, and figure scripts: The role of geomorphology in mediating biomass allocation impacts on salt-marsh resilience and carbon accumulation

This data package provides model source code (C++), model outputs, and figure generation scripts (R) needed to reproduce the following manuscript: Bruns, Nicholas E., Genevieve L. Noyce, and Matthew L. Kirwan. "The Role of Geomorphology in Mediating Biomass Allocation Impacts on Salt-Marsh Resilience and Carbon Accumulation." Estuarine, Coastal and Shelf Science 327 (December 2025): 109549. https://doi.org/10.1016/j.ecss.2025.109549. This manuscript investigates how geomorphology mediates the impact of biomass allocation shifts on salt marsh persistence and carbon (C) sequestration under sea level rise. We use a 1-D soil-column model (Kirwan and Mudd 2012) to perform experiments across a range of static root:shoot ratios (RSR = 1-4) spanning observed values. The model explicitly simulates interactions between tidal inundation, productivity, inorganic sediment deposition, and organic matter accumulation. Experiments determine whether geomorphic feedbacks amplify, dampen, or leave unchanged the ecosystem response to biomass allocation shifts. A first experiment uses constant sea level rise (2.5 mm/yr) to examine equilibrium dynamics and their influence on carbon accumulation rates across different suspended sediment concentrations (SSC). A second set of experiments calculates threshold sea level rise rates for marsh drowning across RSR and SSC combinations. Final experiments apply accelerating sea level rise scenarios (2000-2200) derived from NOAA projections (Sweet et al. 2022) to generate an envelope of expected responses, quantifying the importance of biomass allocation shifts on marsh carbon accumulation and persistence. All experiments are repeated across SSC ranging from 5-50 mg/L to investigate how these interactions vary in micro-tidal marshes with different sediment supplies. Package contents: * README.txt with detailed description of package contents and instructions for reproducing manuscript figures and model outputs * R scripts for generating all manuscript figures * C++ baseline model code from Kirwan and Mudd (2012) * C++ source code for 4 experimental model variants used in the manuscript, extending above baseline code * Model input files (.csv, .txt) including sea level rise scenarios * Model output files (.txt) used in manuscript analyses Temporal coverage: Model simulations span years 1900-2200, with accelerating sea level rise scenarios for 2000-2200. Key variables: Root:shoot ratio, suspended sediment concentration, carbon accumulation rate, vertical accretion rate, marsh elevation, inundation depth, threshold sea level rise rate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

First Annual Report on Development of Microwave Resonant Cavity Transducer for Fluid Flow Sensing: Development of Sensor Performance Model of Microwave Cavity Flow Meter for Advanced Reactor High Temperature Fluids

We are investigating a microwave cavity-based transducer for in-core high-temperature fluid flow sensing in molten salt cooled reactors (MSCR) and sodium fast reactors (SFR). This sensor is a hollow metallic cylindrical cavity, which can be fabricated from stainless steel, and as such is expected to be resilient to radiation, high temperature and corrosive environment of MSCR and SFR. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. Feasibility of the sensor was initially investigated with analytical derivations and with COMSOL RF Module computer simulations of resonant frequency spectral shift due to uniform load. We also investigated the mechanical integrity of the flowmeter’s membrane through analytical modelling and COMSOL Structural Mechanics Module computer simulations. Both the analytic model and COMSOL model showed that maximum stresses on the plate, which are at the radial boundary of the plate, are three orders of magnitude smaller than the material’s yield strength and ultimate tensile strength. This indicates that the sensor is at a low risk of mechanical failure. Using results from models, we have developed an initial design for a microwave K-band sensor. A cylindrical resonator prototype was fabricated from brass for the initial tests. The external dimensions of the cavity are matched to the flange of a standard WR-42 waveguide. Microwave field is coupled into the resonant cavity through a subwavelength-size aperture. A test article was developed consisting of a piping Tee with a bulkhead WR-42 microwave waveguide installed in a leak-proof assembly. A microwave waveguide circulator was installed in the setup to suppress the effect of reflections at the cavity entrance by increasing the isolation between the input and the output port. Preliminary spectral characterization of cavity spectral response was performed with a portable PXIe chassis microwave VNA with a custom GUI. Preliminary dry tests of the transducer response were conducted with a set of calibrated weights. Transducer frequency shift was shown to be monotonically increasing with increasing pressure. The next steps will involve investigation of the transducer performance for water flow sensing.

42 ENGINEERING↗

DOE OSTI2022

Soil resistance and recovery during neotropical forest succession

The recovery of soil conditions is crucial for successful ecosystem restoration and, hence, for achieving the goals of the UN Decade on Ecosystem Restoration. Here, we assess how soils resist forest conversion and agricultural land use, and how soils recover during subsequent tropical forest succession on abandoned agricultural fields. Our overarching question is how soil resistance and recovery depend on local conditions such as climate, soil type and land-use history. For 300 plots in 21 sites across the Neotropics, we used a chronosequence approach in which we sampled soils from two depths in old-growth forests, agricultural fields (i.e. crop fields and pastures), and secondary forests that differ in age (1–95 years) since abandonment. We measured six soil properties using a standardized sampling design and laboratory analyses. Soil resistance strongly depended on local conditions. Croplands and sites on high-activity clay (i.e. high fertility) show strong increases in bulk density and decreases in pH, carbon (C) and nitrogen (N) during deforestation and subsequent agricultural use. Resistance is lower in such sites probably because of a sharp decline in fine root biomass in croplands in the upper soil layers, and a decline in litter input from formerly productive old-growth forest (on high-activity clays). Soil recovery also strongly depended on local conditions. During forest succession, high-activity clays and croplands decreased most strongly in bulk density and increased in C and N, possibly because of strongly compacted soils with low C and N after cropland abandonment, and because of rapid vegetation recovery in high-activity clays leading to greater fine root growth and litter input. Furthermore, sites at low precipitation decreased in pH, whereas sites at high precipitation increased in N and decreased in C : N ratio. Extractable phosphorus (P) did not recover during succession, suggesting increased P limitation as forests age. These results indicate that no single solution exists for effective soil restoration and that local site conditions should determine the restoration strategies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

To What Extent Will Decarbonization Deepen the Conversation Between Industry and the Grid?

Decarbonization - the transition away from un-mitigated fossil fuel combustion throughout the economy - requires big changes from both power and process systems. On the power system side, those changes are expected to include large increases in variable generation, e.g., from wind and solar, which has near-zero marginal costs and at large shares can produce infrequent but consequential energy droughts. On the process systems side, industries are investigating their options for direct and indirect electrification, the latter exemplified by replacing fossil fuel inputs with zero-carbon, energy-carrying chemicals like hydrogen and ammonia produced via electrochemical processes. The economic features of these changes within the larger context of power and process systems suggest that their realization could be accompanied by a paradigm shift in how industrial facilities interact with the grid. For example, the dominant type of demand participation in power markets could change from today's focus on load reductions at peak times to a new focus on shifting electricity use, enabled in part by large-scale product storage, to take advantage of renewable energy that would otherwise be curtailed and to avoid consumption during high-price energy droughts. This talk will describe these and other possible design and operational approaches from grid and industrial economic perspectives, culminating in an enumeration of open problems that lie at the interface of today and tomorrow's power and process systems.

co-design↗

DOE OSTI2026

Data and Scripts Associated with "Modeling Ecohydrological Responses of Vegetation to Urban Microclimates Using the E3SM Land Model"

This dataset supports the study of vegetation ecohydrological responses to urban microclimates using the land component of the Energy Exascale Earth System Model (ELM) at four urban sites in Knoxville, Tennessee, USA. It includes the model inputs, simulation outputs, and associated scripts for running ELM simulations and analyzing the resulting data. The Model_Inputs folder includes static surface data, satellite-derived phenology (i.e., leaf area index), and atmospheric forcing data used to drive ELM simulations. Detailed descriptions of these datasets are provided in Section 2.3.2 of the associated manuscript. The Model_Outputs folder contains simulation results for the baseline, treatment, and ensemble experiments. Outputs from the baseline and treatment simulations are provided as raw ELM NetCDF files. Because the raw outputs from the 4,000-member ensemble are prohibitively large, the ensemble results are provided as summarized CSV files, which also serve as the source data for Figure 5 of the associated manuscript. The Scripts folder contains three components: E3SM, the core codebase of the Energy Exascale Earth System Model (E3SM); elm-olmt, the Offline Land Model Testbed (OLMT) used to perform the simulations; and knoxville_elm, which contains the analysis scripts used to process model outputs and generate the figures and results presented in the associated manuscript. Additional information is provided in Scripts_readme.txt within the Scripts directory.

Lu, Xiaoman [ORNL] (ORCID:0000000306698780)↗

DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗

DOE OSTI2025

Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale

The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Nitrogen Deposition Weakens Soil Carbon Control of Nitrogen Dynamics Across the Contiguous United States

ABSTRACT Anthropogenic nitrogen (N) deposition is unequally distributed across space and time, with inputs to terrestrial ecosystems impacted by industry regulations and variations in human activity. Soil carbon (C) content normally controls the fraction of mineralized N that is nitrified ( ƒ nitrified ), affecting N bioavailability for plants and microbes. However, it is unknown whether N deposition has modified the relationships among soil C, net N mineralization, and net nitrification. To test whether N deposition alters the relationship between soil C and net N transformations, we collected soils from coniferous and deciduous forests, grasslands, and residential yards in 14 regions across the contiguous United States that vary in N deposition rates. We quantified rates of net nitrification and N mineralization, soil chemistry (soil C, N, and pH), and microbial biomass and function (as beta‐glucosidase (BG) and N ‐acetylglucosaminidase (NAG) activity) across these regions. Following expectations, soil C was a driver of ƒ nitrified across regions, whereby increasing soil C resulted in a decline in net nitrification and ƒ nitrified . The ƒ nitrified value increased with lower microbial enzymatic investment in N acquisition (increasing BG:NAG ratio) and lower active microbial biomass, providing some evidence that heterotrophic microbial N demand controls the ammonium pool for nitrifiers. However, higher total N deposition increased ƒ nitrified , including for high soil C sites predicted to have low ƒ nitrified , which decreased the role of soil C as a predictor of ƒ nitrified . Notably, the drop in contemporary atmospheric N deposition rates during the 2020 COVID‐19 pandemic did not weaken the effect of N deposition on relationships between soil C and ƒ nitrified . Our results suggest that N deposition can disrupt the relationship between soil C and net N transformations, with this change potentially explained by weaker microbial competition for N. Therefore, past N inputs and soil C should be used together to predict N dynamics across terrestrial ecosystems.

Nieland, Matthew A. [Stockbridge School of Agricul↗

DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Coal-dependent Communities in Transition: Identifying Best Practices to Ensure Equitable Outcomes

The U.S. coal industry is experiencing a sharp increase in the numbers of retired and/or decommissioned coal-fired power plants across the U.S. In the years between 2010-2019, around 102 gigawatts (GW) of coal-fired generating capacity has been announced to be decommissioned, representing more than 546 coal-fired power plant units, and an additional 17 GW is planned to be decommissioned by 2025. This change in the energy production landscape presents an impact on the social, environmental, and economic prospects of coal-dependent communities. This report examined the role of communities in the coal power plant decommissioning process and provided community-identified best practices to ensure an equitable process. The experiences of four coal-dependent communities—Wise County, VA, Muskegon, MI, Anderson County, TN, and Becker, MN—are presented as case studies to understand the impacts of the decommissioning process, and associated best practices, from the communities’ perspective. The report results highlight the need to recognize that the decommissioning decision-making process must be community-based to be equitable. Each community’s input is key to the transition away from coal power because there is no one-size-fits-all development plan. In other words, each community’s trajectory through the decommissioning process—from the retirement decision-making stage to the final site redevelopment phase—is unique because each community has distinct needs and wants from the energy transition. What is best for one community may not be suited for another. Ultimately, the framework for site development and community revitalization post-decommissioning cannot be universal because each community’s profile—from a social, cultural, and economic perspective—is different. Community impacts of power plant decommissioning are not limited to job and revenue losses. Communities are likely to be impacted culturally, socially, environmentally, and have long-term health-based impacts that should be acknowledged and addressed in post-retirement plans. Commonly identified decommissioning best practices include: Early and continued engagement throughout, with a number of mediums for communication and feedback (e.g., in-person sessions, virtual meetings, written comment opportunities); Early planning of post-decommissioning projects to replace lost jobs, revenue, and economic activity; Recognition (and mitigation, if possible) of social impacts on the community due to plant closure; Transparency throughout the process, with trusted information being provided about the decommissioning process and timeline; potential impacts on the workforce, economy, and environment; and the feasibility of alternative site uses; Identification of funding sources, technical experts, and/or strategic partnerships to support decommissioning and the affected communities upfront; and, Acknowledgment of communities as stakeholders who have a role in the conversation and right to determine their futures. Three key areas for assisting coal-dependent communities affected by the energy transition: Technical assistance: assessment of site feasibility for alternative uses or to repower with new technologies; Cross-partnership engagement and collaboration: facilitate knowledge-sharing of “lessons learned” about the decommissioning process between communities and provide guidance for decision-making processes; and, Financial assistance: access to grant and/or loan programs to assist with redevelopment survey, bolster community economic security through job creation, and cover environmental clean-up costs. Technical assistance, cross-partnership engagement, and financial aid can be mobilized to help communities throughout various stages of the decommissioning process, including the retirement decision, the site reclamation phase, and eventual revitalization of the site and surrounding community.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2025

Field Validation of Thermoelectric Generation System at Holcim Cement Plant in Alpena, Michigan

Executive Summary Project Background The Industrial Technology Validation (ITV) program aims to identify and demonstrate the performance of new, emerging, and underutilized energy-saving technologies in the industrial sector to help inform decisions to help accelerate their commercialization and deployment, as well as to help make industries more competitive. This ITV demonstration evaluated a thermoelectric generation (TEG) technology at a cement plant, aiming to reduce energy demand in the cement industry. A median cement plant consumes 5.73 million British thermal units per ton of clinker production (resulting in 0.838 metric tons of carbon dioxide [CO₂] emissions per ton of clinker) (Boyd and Zhang 2011, EPA 2021), equivalent to approximately 6.9 trillion British thermal units (TBtu) per year in energy consumption at a cement plant producing 3,300 tons of clinker per day.¹ Collaborating with Holcim, Advanced Thermovoltaic Systems (ATS) developed and deployed a pilot-scale thermoelectric power system to efficiently capture and convert waste heat to electricity. The system leverages the Seebeck effect to convert temperature differences on two sides of semiconductor cartridges into electrical power (ScienceDirect, n.d.). This generation is realized with minimal moving parts compared to existing waste-heat-to-generation solutions and allows capture from heat sources with temperatures as low as 150°C. This project aimed to validate a scalable solution applicable for capturing medium-temperature waste heat, including ambient losses from other high-temperature processes, and high-temperature sources less suitable for other waste-heat-to-power solutions. By recovering this otherwise wasted heat, this project intends to validate improvements to overall process efficiency through reduction in purchased electricity, thereby reducing operational costs while enhancing resiliency and competitiveness. Description and Scope This study evaluated the performance of a TEG system from ATS as a solution to convert waste heat into useful power at a Holcim cement plant in Alpena, Michigan. This plant is a fully integrated cement plant that has been operating since 1907. The facility operates continuously (24/7/365) with approximately 250 employees and five long dry kilns, yielding a total production capacity of 7,852 tons of cement per day (EPA 2023). Currently, the Alpena plant uses waste heat boilers to convert waste heat from the exhaust of each kiln into steam, which drives steam turbine generators. The ATS TEG is being evaluated for its potential to supplement the steam turbines by capturing the remaining lower grade heat. This technology is also being considered for other Holcim plants where steam turbines are not a viable option. ATS installed a pilot-scale TEG unit with an array of 582 individual thermoelectric semiconductor cartridges, of which 573 were operational. The cartridges are sandwiched between 48 hot plates and 49 cold plates. Each cartridge is designed to generate 20 watts (W) of gross power at a hot-side temperature of 240°C and cold-side temperature of 20°C. As such, the total gross generation capacity of the installed system is 11.5 kilowatts (kW) at design conditions. The system configuration for the evaluation was designed to prioritize convenience of installation and minimize disruption to production at the site, while ensuring that the heat required can be obtained for evaluating the TEG system at various operational conditions. To accomplish this, a portion of the steam supplied to Alpena’s steam turbine generation system was diverted to be used as the heat source for the TEG system, while water was supplied to the cold side of the system from nearby Lake Huron. This configuration was designed for the evaluation of the pilot-scale system to assess the performance at different conditions. A commercial-scale system will likely vary from the pilot system depending on typical configurations, including both scale and application. Future commercial applications of the ATS system would involve integrating the system into the exhaust from kiln preheaters, clinker coolers, or radiant heat capture from kiln shells for the heat source. For the cold source, a range of cooling solutions can be considered, including a mechanical cooling system, depending on the location and the application. To increase the generation capacity for commercial applications, the technology provider is working toward developing a commercial-scale TEG system, which would combine multiple TEG units (each similar in design to the pilot system) together. The scope of this evaluation includes the pilot-scale TEG system and all impacted equipment including pumps, controllers, and power handling equipment. Study Objectives The evaluation's goal was to assess the potential of the ATS TEG system to generate useful electrical power by capturing waste heat from cement production kilns. The objectives of this study are to evaluate and verify the following claims made by ATS regarding the pilot-scale system installed at the Holcim Alpena plant. The following design parameters and claims are also outlined in Table ES- 1 and Table ES- 2: • Gross Power: The thermoelectric system converts heat into power to create gross power, the total measured power generated by the system. The 573 active cartridge pilot-scale system is expected to generate 11.5 kW of gross power at the designed hot-side temperature of 240°C and cold-side temperature of 20°C. Power production is dependent on the temperature difference between the heat source (ultimately from the waste heat) and cold temperature supply source. • Net Power: The net power is the total usable power provided to the site by the TEG system after deducting parasitic power loads from the gross generated power. Supplementary equipment is required to operate the TEG system including pumps, controllers, and, in certain anticipated applications, mechanical cooling, which introduce parasitic loads to system operation. After deducting the parasitic loads from the gross power generation, ATS anticipates achieving a net power generation of 7.5 kW from the pilot-scale system. • Thermal Efficiency: The thermal efficiency is the percent of the total heat transferred to the TEG system that is converted to gross power. Historically, TEGs have a thermal efficiency of 2%–5% (DOE 2008). Prior industrial-scale TEG systems, such as the E1 TEG offered by Alphabet Energy, operated at an efficiency of 2.5% (Lamonica, 2014). ATS anticipates achieving an average efficiency of 4.8% or higher in converting heat energy to usable electricity. • Cartridge Performance: The TEG system comprises 573 active individual semiconductor cartridges, each of which generates a portion of the total power. Cartridge optimization and selection is an important design consideration for potential future TEG system design performance. Therefore, understanding the distribution of gross power and efficiency within the pilot system is vital to understanding what is achievable. At a design hot-side temperature of 240°C and cold-side temperature of 20°C, ATS anticipates a cartridge performance of 20 W of gross power per cartridge at an efficiency of 4.8% per cartridge. In addition to evaluating the claimed performance of the TEG pilot-scale unit, the study estimated the potential annual impacts of a scaled-up commercial system used to capture kiln waste heat over annual operations. The evaluation estimated the gross and net annual electric generation achievable by capturing heat from the two proposed tap-in points: the kiln exhaust and the clinker cooler exhaust; see Section 2.1 for details. Two use cases were examined: • Holcim Alpena: The Holcim Alpena site consists of long dry kilns with superheater boilers, which differs from the rest of Holcim’s cement plant portfolio and results in lower waste heat temperatures. The study estimates gross and net annual generation using the superheater boiler exhaust and clinker cooler exhaust, based on 2023 operational data. • Typical Installation: Common cement plants have preheater kilns with higher exhaust temperatures than Holcim Alpena across a range of production rates. The study estimates gross and net annual generation using the preheater exhaust and clinker cooler exhaust, with a sensitivity analysis to account for the typical range of preheater exhaust temperatures, clinker cooler exhaust temperatures, and clinker production rates. Methodology The evaluation methodology followed a measurement and verification (M&V) strategy based on the International Performance Measurement and Verification Protocol Option B through comprehensive measurements and analyses of the affected systems. Evaluation data was collected from March 9 to March 11, 2024, the test period of the pilot TEG system. During the test period, in coordination with the ITV team, the ATS team adjusted system operations to capture the range of variability expected for each of the variables pertinent to performance of the system. The methodology consisted of two parts: evaluating the performance of the pilot unit's TEG system and estimating the annual TEG impact in terms of gross and net power based on a given waste heat profile. First, the evaluation of the thermoelectric generation performance of the pilot unit relative to the claims was performed by analyzing the collected test data. Gross power of the pilot TEG system was directly measured. Net power was determined by deducting the measured parasitic power from the gross power. The gross power generation was compared to heat transferred to the system by the working fluid (which was heated by steam generated from the kiln waste heat) to calculate the thermal efficiency achieved by the system. Performance of individual semiconductor cartridges within the pilot array was also assessed in terms of measured gross cartridge power and calculated cartridge thermal efficiency. The second part of the evaluation estimated the annual TEG impacts in terms of gross power and net power (calculated from the difference between gross power and parasitic power). This analysis comprised development of mathematical regression models for gross power and parasitic power, with assessment of each model’s goodness-of-fit characteristics to ensure satisfaction of statistical requirements. The models predicted the gross power generation, the parasitic load based on the temperature difference between the hot working fluid and the cold-side fluid (cold water from Lake Huron) entering the system, the volumetric flow rate of the cold-side fluid at the inlet, and the volumetric flow rate of the hot working fluid at the inlet. The annual impact analysis considered a theoretical commercial-scale system sized to capture the available waste heat at a cement plant, consisting of linked pilot-scale units that receive heat from a theoretical gas-to-working-fluid heat exchanger. To estimate annual impacts at the Alpena plant, the gross power and parasitic power regression models were applied to the arrays in the theoretical commercial-scale system. The heat supplied to the unit was calculated based on the kiln run time, annual production, kiln exhaust waste heat, and clinker cooler waste heat derived from 2023 Holcim Alpena kiln operational data. Net power impacts were calculated by deducting the resulting parasitic power from the estimated gross power. Inputs for the model were generated from a combination of hourly data, assumed design considerations for TEG system scale-up from the pilot-scale unit, and assumptions regarding TEG system operations. This analysis was then used as the basis for estimating annual impacts of typical TEG installation at cement plants, by applying sensitivity analyses to key kiln operational characteristics including kiln preheater exhaust temperatures, cooler clinker exhaust temperatures, and plant daily production rates across a range of expected values. Project Results/Findings Table ES- 2 and Table ES- 2 provide a summary of the operating conditions and evaluation results compared to the stated claims from the technology provider. Key takeaways include: • Gross Power: The peak gross power achieved during the testing period was 10.0 kW, compared to the 11.5 kW expected for 573 active cartridges. The claimed gross power was associated with a target hot side of 240°C; however, the system only received a maximum hot-side mean plate temperature of 212°C during the testing period. • Net Power: The pilot-scale unit exceeded the claims for net power, achieving a peak of 7.7 kW net compared to a claim of 7.5 kW. One factor contributing to the higher achieved net power is the relatively high water pressure available through Lake Huron. The pilot TEG system did not require cold-side pumps during the test, whereas most installations would. This reduced the parasitic loads on the system, ultimately contributing to higher net power relative to the gross power. • Thermal Efficiency: The pilot-scale unit outperformed the claimed efficiency, achieving a peak system efficiency of 5.0% thermal efficiency compared to the stated 4.8%. • Cartridge Performance: To compare cartridge performance against claims, the study focused on the third day of testing, which aimed for conditions closest to the design specifications, with a hot side of 240°C and cold-side exit temperature of 6.4°–30°C. On this day, the mean gross power observed in the cartridges within the TEG array was 18.1 W/cartridge, and the peak performance was 34.7 W/cartridge. The estimated mean cartridge efficiency was 5.2%, and the estimated efficiency at peak gross cartridge power was 10%. The regression models developed for gross power generation and parasitic loads were used to estimate the generation impact for given heat input to the TEG from the working fluid (captured from the waste heat) and from the cold loop (Lake Huron) on an hourly basis for a year of operation. Based on this analysis, installation of a commercial-scale TEG system at the Holcim cement plant in Alpena, Michigan, with a waste heat exchanger of 0.85 effectiveness, would generate up to 391 kW of net power, translating to between 920,000 and 1,800,000 kilowatt-hours (kWh) in net electricity per year. Based on typical grid emissions for Alpena, this would avoid estimated net emissions by 752 metric tons of CO₂ annually.² The sensitivity analysis estimated that typical TEG system installations at cement plants could generate an average of 56–1,040 kW of net power, or between 488,000 and 9,110,000 kWh of net energy. This generation potential is most significantly affected by plant production rates and also influenced by preheater and clinker cooler exhaust temperatures. Applying the national average emission rate, typical commercial-scale installations at Holcim plants are projected to avoid between 182 and 3,401 metric tons of CO₂ annually per site. Table ES- 3 shows a summary of the estimated annual impacts.³ While parasitic loads are significant and vary by application, this analysis assumed the use of heating loop pumps and access to Lake Huron as a cold sink. This setup assumed no need for cooling loop pumps due to the available water pressure at the test site. Applications that require cooling towers or additional equipment are likely to experience higher parasitic loads. Therefore, the study’s estimates are most applicable to scenarios with similar parasitic load configurations—namely, access to a high-pressure cold sink. Applicability to other locations may be limited, as differing conditions could necessitate additional pumps and cooling systems, potentially impacting performance significantly.

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