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

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impact of a dynamic grid mix and climate on operational carbon emissions modeling for different building typologies and climate zones

Calculating operational carbon emissions through a building’s lifecycle is complex due to the dynamic nature of influencing factors such as climate and energy grid mix. This paper introduces a novel methodology for modeling 30-year operational carbon impacts of buildings and applies this method to mid-rise office and residential typologies across various US climate zones. The method accounts for these temporal variabilities using new and scarcely cited data sources. Key findings indicate that future changes in the climate, while impactful, play a relatively modest role in operational carbon emissions compared to significant reductions with modeling scenarios using the projected decarbonization of the electricity grid. Here, the study also finds that using annual, month-hourly, or hourly grid emission factors have a minimal impact on carbon accounting, except in certain climates and program types where emission patterns do not align with a building’s energy consumption. Warmer climates like Miami, Florida and Tucson, Arizona, which rely heavily on cooling, demonstrate larger variations in carbon emissions when using higher temporal resolution emission factors. Ultimately, this study underscores the critical role of grid decarbonization in reducing long-term emissions and the importance of incorporating this variable in life cycle assessment (LCA) modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Characterization of Temporal Changes of Interfacial Air Voids During Adhesive Curing and Bonding for Adhesively-Bonded Structures

This paper studied the evolution of air voids at the substrate-adhesive interface during the curing and bonding process of an epoxy-based adhesive between two glass substrates due to the sequence of adhesive placement, external pressure, and plasma treatment on substrates for bonding. The experimental results showed that a higher quantity of interfacial voids were generated during adhesive curing when the substrate was attached to the adhesive compared to when the adhesive was applied and distributed on the substrate. Such interfacial voids can be efficiently removed or reduced by applying slight pressure on the bonded structure during adhesive curing, or by leveraging plasma-treated substrates, as less reported in the literature. This work provided practical suggestions for preparing adhesive joints with minimized interfacial air voids to enhance bonding performance, particularly for dissimilar joints such as metal-CFRTP (carbon-fiber-reinforced thermoplastic polymer), as well as combinations with different types of polymer composites.

Qiao, Yao [BATTELLE (PACIFIC NW LAB)] (ORCID:00000↗
DOE OSTI2025

The temporal onset of associations of cortical proteins with cognitive resilience vary during late life

Background: Cortical proteins associated with cognitive resilience have been identified but their temporal onset in older adults is unknown. We present a multistage approach to first identify cortical proteins associated with cognitive resilience and then examine their associated temporal onset. Methods: We used data from a subset of 1088 decedents from two cohort-studies who had selected reaction monitoring proteomics from the dorsolateral prefrontal cortex, and at least 3 cognitive assessments. Cognition was assessed using a composite derived from 19 tests. We first used linear mixed-effects models to identify cortical proteins associated with cognitive resilience. We then used functional mixed-effects models to examine non-linear associations between proteins and cognitive resilience to identify their temporal onset. Results: Mean age at death was 90 years (SD = 6.4); 69 % were female. On average, cognition started to decline at around 15 years before death, with accelerated decline in the last 7 years. We identified 40 proteins associated with cognitive resilience, of which 17 proteins also showed non-linear associations. Non-linear associations indicated that higher levels of 10 proteins were associated with slower cognitive decline between 23 and 4 years before death. In contrast, higher levels of 7 proteins were associated with faster decline only within the last 7 years before death. Conclusions: Cognitive resilience proteins are differentially related to late-life cognitive aging; the onset of proteins that maintain cognition may begin many years before the onset of proteins that hasten cognitive decline. The temporal onset of cognitive resilience proteins may be crucial for timing efficacious interventions.

Zammit, Andrea↗
DOE OSTI2025

Non-stationary precipitation design standards for stormwater infrastructure modernization at USAF installations

The resilience of defense infrastructure systems to a changing climate is critical for national security. Climate induced recurrent flooding is already impacting over 20 U.S. Air Force installations, underscoring the urgency of revisiting precipitation standards and stormwater infrastructure design. Despite growing scientific knowledge and an expanding set of tools for updating outdated precipitation standards based on the assumption of climate stationarity, the adoption of climate informed analyses remain limited in practice. This study utilizes an existing framework to update Intensity (or Depth)-Duration-Frequency (DDF) curves using an ensemble of future climate projections. Change factors in precipitation estimates are derived and applied to six USAF installations across the U.S. The analysis is further extended to evaluate the implications of climate-informed DDFs on stormwater infrastructure performance and flood analysis at Tyndall AFB. Results indicate that the current design precipitation estimates are likely to become obsolete in all six USAF bases by the end of the century. The wide range of change factors across 32 GCM ensembles highlights the need to integrate uncertainty and evolving scientific data into infrastructure planning. The study also finds that the impacts of a changing climate vary spatially and temporally, emphasizing the value of localized analysis for infrastructure decision-making. The work advances ongoing DoD and societal efforts to implement adaptation strategies aimed at enhancing infrastructure resilience.

Intensity-duration-frequency curves↗
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

UV-Induced Reaction Pathways in Bromoform Probed with Ultrafast Electron Diffraction

For many chemical reactions, it remains notoriously difficult to predict and experimentally determine the rates and branching ratios between different reaction channels. This is particularly the case for reactions involving short-lived intermediates, whose observation requires ultrafast methods. The UV photochemistry of bromoform (CHBr 3 ) is among the most intensely studied photoreactions. Yet, a detailed understanding of the chemical pathways leading to the production of atomic Br and molecular Br 2 fragments has proven challenging. In particular, the role of isomerization and/or roaming and their competition with direct C–Br bond scission has been a matter of continued debate. Here, in this work, gas-phase ultrafast megaelectronvolt electron diffraction (MeV-UED) is used to directly study structural dynamics in bromoform after single 267 nm photon excitation with femtosecond temporal resolution. The results show unambiguously that isomerization contributes significantly to the early stages of the UV photochemistry of bromoform. In addition to direct C–Br bond breaking within <200 fs, formation of iso-CHBr 3 (Br-CH-Br-Br) is observed on the same time scale and with an isomer lifetime of >1.1 ps. The branching ratio between direct dissociation and isomerization is determined to be 0.4 ± 0.2:0.6 ± 0.2, i.e., approximately 60% of molecules undergo isomerization within the first few hundred femtoseconds after UV excitation. The structure and time of formation of iso-CHBr 3 compare favorably with the results of an ab initio molecular dynamics simulation. The lifetime and interatomic distances of the isomer are consistent with the involvement of a roaming reaction mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Contrasting age-dependent leaf acclimation strategies drive vegetation greening across deciduous broadleaf forests in mid- to high latitudes

Increasing leaf area and extending vegetation growing seasons are two primary drivers of global greening, which has emerged as one of the most significant responses to climate change. However, it remains unclear how these two leaf acclimation strategies would vary across forests at a large spatial scale. Here, in this study, using multiple satellite-based datasets and field measurements, we analysed the temporal changes (Δ) in maximal leaf area index (LAI max ) and length of the growing season (LOS) from 2002 to 2021 across deciduous broadleaf forests (DBFs) in the middle to high latitudes. Contrary to the widely held assumption of coordination, our results revealed a negative correlation between ΔLAI max and ΔLOS. Notably, the trade-offs between ΔLAImax and ΔLOS were strongly explained by stand age. Younger DBFs, with lower baseline LAI max , predominantly located in eastern Asia, displayed an increase in LAI max with small changes in LOS. This acquisitive strategy facilitated younger DBFs to grow more photosynthetically efficient leaves with low leaf mass per area, enhancing their light use efficiency. Conversely, older DBFs with a higher baseline LAI max , primarily located in North America and Europe, extended their LOS by increasing leaf mass per area. This conservative strategy facilitated older DBFs to produce thicker, but less photosynthetically efficient leaves, resulting in decreased light use efficiency. Our findings offer new insights into the contrasting changes in leaf area and growing season length and highlight their divergent impacts on ecosystem functioning.

Wang, Fangyi [Sun Yat-Sen Univ., Zhuhai (China)]↗
DOE OSTI2025

Structured light approaches in laser-based plasma diagnostics

There is a growing demand for plasma diagnostics suitable for industrial plasma reactors employed in semiconductor nanofabrication, especially relevant to microelectronics and quantum information systems. Such reactors typically have limited optical access and pose considerable diagnostic challenges, including intense background emission, significant thermal loads, and contamination of optical viewports. In this study, we outline research into structured light techniques (laser beams with tailored spatial, temporal, or phase characteristics) that effectively overcome these issues using laser-induced fluorescence (LIF) as an example. The focus of presented diagnostics is on ion kinetics analysis within an industrial plasma source, although this approach is broadly applicable to other plasma systems and diagnostic contexts. We present a confocal LIF implementation using an axicon-generated Bessel annular beam, achieving spatial resolutions of approximately 5 mm at a focal distance of 300 mm, with potential improvements to about 1 mm. This approach matches conventional orthogonal LIF performance but requires only one optical port. Wavelength-modulation LIF employs nonlinear laser wavelength tuning to measure spectral line derivatives, suppressing background emission and enhancing details of spectral line shape. Additionally, we present new results on applying vortex beams (laser beams carrying orbital angular momentum, OAM) for LIF measurements in an industrial plasma device. These measurements enable simultaneous axial and tangential velocity determination using a single laser beam and have been tested with xenon ion transition. Initial quantification of results was performed. Together, these structured-light approaches provide robust, background-resilient, multi-dimensional diagnostics for complex plasma environments.

Romadanov, Ivan [Princeton Plasma Physics Laborato↗
DOE OSTI2025

Canopy Structure Exhibits Linear and Nonlinear Links to Biome‐Level Maximum Light Use Efficiency

Maximum light use efficiency (ε max ) represents a plant's capacity to convert light into carbon during photosynthesis. Although prior studies have explored ε max variations between sunlit and shaded leaves or its temporal ties to canopy structure, the spatial relationship between biome-level ε max (ε biome ) and biome structure remains poorly understood. We analysed data from 320 eddy covariance sites (~855 site-years) with satellite-derived near-infrared reflectance of vegetation (NIRv) and leaf area index (LAI). We introduced NIRvN (NIRv/LAI) to isolate architectural effects from leaf quantity. Site-level ε max was calculated and aggregated by biome to derive ε biome . Results show ε biome rises nonlinearly with NIRv and LAI, saturating at high LAI, with crops and tropical evergreen forests deviating from this trend. Conversely, ε biome decreases linearly with increasing NIRvN, indicating that biomes with greater NIR scattering efficiency exhibit lower ε biome . These results enhance understanding of structural influences on carbon uptake across global biomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Expanding Activity Allocation Models to Daily Activities: Tracking Simulated Agent Trips to Exercise Locations in Clarksville, TN

Exercise facilities have been proven to have numerous physical, mental, and psychological benefits, yet exercise facilities are still inaccessible to a large portion of the population. This study serves to explore the accessibility of fitness centres through geographical, demographic, and temporal lenses through an expansion of the UrbanPop framework that seeks to allocate simulated agents to fitness centres in the Clarksville metro to explore the effects of travel distances on different demographics throughout the week. Findings indicate that senior and retired demographics consistently travel longer distances to exercise in the larger Clarksville area, likely due to tendencies to live further from the center of the metropolitan area. Furthermore, all demographics tend to travel further distances to exercise on the weekends rather than the weekdays, indicating that travel distance can affect likelihood of agents to travel, especially on weekdays when many agents are in the workforce or participating in schooling.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment

An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures included Tau PET, Aβ PET, and MRI. Positive results on tau PET (temporal meta–region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Operando Depth-Resolved Measurement of Solvation Entropy, Interfacial Transport, and Charge-Transfer Kinetics in Lithium-Ion Batteries

Understanding and improving the performance and longevity of lithium-ion batteries critically depends on insight into the dynamic processes occurring at buried electrode-electrolyte interfaces. However, direct, depth-resolved, and operando diagnosis of these interfaces remains a longstanding challenge due to their inaccessibility beneath bulk materials, the limitations of conventional surface- and bulk-sensitive characterization tools, and the difficulty of maintaining realistic cell environments during measurement. These challenges have made it nearly impossible to uniquely resolve important interfacial properties such as charge transfer resistance, SEI (solid electrolyte interphase) resistance, and solvation entropy at the individual electrode interfaces within a working cell, information that is essential for mechanistic insight and accelerated battery design. Here, in this study, we report the development of multiharmonic electro-thermal spectroscopy (METS), an operando technique that enables depth-resolved measurement of solvation entropy, interfacial transport resistance, charge-transfer resistance, and SEI resistance at individual electrode-electrolyte interfaces within practical lithium-ion batteries. By leveraging frequency-dependent, thermal-wave sensing and interface-specific modeling, METS uniquely attributes interfacial properties to specific electrodes, as validated by comparison with traditional electrochemical impedance spectroscopy (EIS). The ability to spatially and temporally resolve interfacial processes in real time provides new diagnostic capabilities that are crucial for mechanistic studies of battery degradation and for the rapid development of next-generation energy storage systems.

Chalise, Divya [University of California, Berkeley↗
DOE OSTI2026

Discovery of nanopore filling by gypsum in wellbore cement exposed to 17 MPa CO 2 under geologic carbon storage conditions

Here, this study investigates the pore structure evolution of the reaction zones in wellbore cement samples exposed to a CO 2 -rich solution in equilibrium with 17 MPa supercritical CO 2 over 14 days. Through advanced characterization methods of field emission SEM, Quantitative Evaluation of Minerals by Scanning Electron Microscopy (QEMSCAN), and micro-CT, a new mechanism of CO 2 -cement reaction involving filling of nanopores in the interior of cement by gypsum was revealed. Gypsum was formed by the liberation of SO 4 2− from ettringite (AFt) and monosulfate (AFm) caused by a decrease in pH. Based on these experimental observations, a new CO 2 -cement reaction model that incorporates four distinct reaction zones is developed. This model provides a comprehensive framework for understanding the spatial and temporal distribution of minerals in cement due to high pressure CO 2 —cement reactions. This study demonstrates that the major damage induced by high pressure CO 2 alteration occurs in the most exterior region of the cement. The interior region of the cement maintains its integrity due to nanopore filling by gypsum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Vapor-cavity-QED system for quantum computation and communication

In this work, we propose performing key operations in quantum computation and communication using room-temperature atoms moving across a grid of high-quality-factor, small-mode-volume cavities. These cavities enable high-cooperativity interactions with single atoms to be achieved with a characteristic timescale much shorter than the atomic transit time, allowing multiple coherent operations to take place. We study scenarios where we can drive a Raman transition to generate photons with specific temporal shapes and to absorb, and hence detect, single photons. The strong atom-cavity interaction can also be used to implement the atom-photon controlled-phase gate, which can then be used to construct photon-photon gates, create photonic cluster states, and perform nondemolition detection of single photons. We provide numerics validating our methods and discuss the implications of our results for several applications.

Austin, Sharoon [National Institute of Standards a↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2025

Characterizing Stress Roughness at Utah FORGE Through Simulation of Hydraulic Fracture Growth During 16A Stage 3 Stimulation

In most geologic formations, the vertical gradient of the minimum horizontal stress (𝜎 h ) exceeds the hydrostatic gradient, often driving hydraulic fractures to propagate upward. However, observations indicate that the upward growth of real hydraulic fractures is less pronounced than theoretical predictions based on smoothly varying 𝜎 h fields. In fact, the layered structure of sedimentary rocks hinders fracture propagation across layers, partially explaining the limited height growth observed in practice (Zoback et al., 2022). While crystalline rocks lack the pervasive layered fabric of sedimentary formations, they still possess structural fabric formed over their geologic history, resulting in inherently "rough" in situ stress fields. Recent studies have identified another key factor: the "roughness" of in situ stress, characterized by temporal, relatively short-wavelength fluctuations superimposed on the overall stress gradient. This stress roughness leads to apparent toughness anisotropy, where the vertical toughness appears significantly larger than the horizontal toughness (P. Fu et al., 2019; Dontsov & Suarez-Rivera, 2021). Neglecting the effects of rock fabric and stress roughness can yield inaccurate predictions of hydraulic fracture geometry and growth rates at larger length scales.

58 GEOSCIENCES↗
DOE OSTI2025

Validating Greater Sage-Grouse Individual-based Model (IBM) Tool (Final Report)

The project focused on validating the previously developed Greater Sage-Grouse Individual-based Model (GrSG IBM; LaGory et al. 2012, 2021). The objective was to transform this predictive, spatially and temporally explicit model into a portable resource to assist siting/resource managers in proactively assessing the cumulative impacts of wind energy development on the greater sage-grouse. Utilizing a bottom-up, individual-based approach, the GrSG IBM accounts for landscape context and species behavior, aiming to reduce uncertainty in estimating development impacts and support ecologically mindful land-based wind energy development. The validation effort covered approximately 6,540 km 2 near the Seven Mile Hill Wind Project in Wyoming. The GrSG IBM tool, built on the NetLogo platform (Tisue and Wilensky 2004), was executed over a 50-year period, with the analysis focusing on years following a 10-year initialization phase. Key results demonstrated the tool’s biological soundness across five key biological metrics: non-chick age class distribution (older than 10 weeks), adult sex ratio, life expectancy, population size, and overall population growth. For instance, the tool estimated that 58.6% of the non-chick population was reproductively immature, while the reference ranges from 51.4% to 57.8% (Patterson 1952, Rogers 1964). Experts confirmed the tool’s estimate was within a reasonable range for the species. The tool estimated average life expectancy of 1.43 years, while the reference ranges from 0.9 years to 1.1 years (Ammann 1957, Hamerstrom 1949). Experts also supported the model’s life-expectancy estimate as ecologically sound for the species in the study area. In terms of population change, the model estimated an annual shift between a 0.6% decline and a 1.0% increase over 50 years. While the reference suggests 2.9% annual decline in range-wide populations (Cortes et al. 2023), that includes many at-risk populations in South Dakota and Washington, for example. Our study area—in the northeastern part of Carbon County and western-edge of Albany County, Wyoming—is one of the remaining greater sage-grouse habitats supporting some of the most stable populations. Experts confirmed that the range of the annual population change spanning from a 0.6% decline to a 1.0% increase estimated by the tool was reasonable for our study area for this reason and confirmed that aligned with population estimates from existing studies on the greater sage-grouse and wind energy development in the study area (LeBeau et al. 2017a, Smith et al. 2024). Furthermore, the project showed that temporally explicit biological metrics generated by the GrSG IBM tool can complement the USGS’ Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET; Duchardt et al. 2021) by incorporating habitat restoration strategies into seasonal habitat suitability models to visualize population responses over time.

17 WIND ENERGY↗