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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Optical droplet vaporization of nanoparticle-loaded stimuli-responsive microbubbles

A capillary co-flow focusing process is developed to generate stimuli-responsive microbubbles (SRMs) that comprise perfluorocarbon (PFC) suspension of silver nanoparticles (SNPs) in a lipid shell. Upon continuous laser irradiation at around their surface plasmon resonance band, the SNPs effectively absorb electromagnetic energy, induce heat accumulation in SRMs, trigger PFC vaporization, and eventually lead to thermal expansion and fragmentation of the SRMs. This optical droplet vaporization (ODV) process is further simulated by a theoretical model that combines heat generation of SNPs, phase change of PFC, and thermal expansion of SRMs. The model is validated by benchtop experiments, where the ODV process is monitored by microscopic imaging. The effects of primary process parameters on behaviors of ODV are predicted by the theoretical model, indicating the technical feasibility for process control and optimization in future drug delivery applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Including frameworks of public health ethics in computational modelling of infectious disease interventions

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

"Mathematical Biology"↗
DOE OSTI2024

Anomalous Hall crystals in rhombohedral multilayer graphene. II. General mechanism and a minimal model

Here we propose a minimal "three-patch model"for the anomalous Hall crystal (AHC), a topological electronic state that spontaneously breaks both time-reversal symmetry and continuous translation symmetry. The proposal for this state is inspired by the recently observed integer and fractional quantum Hall states in rhombohedral multilayer graphene at zero magnetic field. There, interaction effects appear to amplify the effects of a weak moiré potential, leading to the formation of stable, isolated Chern bands. It has been further shown that Chern bands are stabilized in mean-field calculations even without a moiré potential, enabling a realization of the AHC state. Our model is built on the dissection of the Brillouin zone into patches centered around high-symmetry points. Within this model, the wave functions at high-symmetry points fully determine the topology and energetics of the state. We extract two quantum geometrical phases of the noninteracting wave functions that control the stability of the topologically nontrivial AHC state. The model predicts that the AHC state wins over the topological trivial Wigner crystal in a wide range of parameters, and agrees very well with the results of full self-consistent Hartree-Fock calculations of the rhombohedral multilayer graphene Hamiltonian.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Expected individual benefit of prophylactic platelet transfusions in hemato‐oncology patients based on bleeding risks

Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.

Cornelissen, Loes L.↗
DOE OSTI2018

Catalyst Ammonia Storage Measurements Using Radio Frequency Sensing1

Motivated by increasingly strict nitrogen oxides (NOx) limits, engine manufacturers have adopted selective catalytic reduction (SCR) technology to reduce engine-out NOx. In the SCR process, NOx react with ammonia (NH3) to form nitrogen and water vapor. The reaction is influenced by several variables, including stored ammonia on the catalyst, exhaust gas composition, and catalyst temperature. Currently, measurements from NOx and/or NH3 sensors upstream and downstream of the SCR are used with predictive models to estimate ammonia storage levels on the catalyst and control urea dosing. This study investigated a radio frequency (RF)-based method to directly monitor the ammonia storage state of the SCR. This approach utilizes the catalyst as a cavity resonator, in which an RF antenna excites electromagnetic waves within the cavity to monitor changes in the catalyst state. Ammonia storage causes changes in the dielectric properties of the catalyst, which directly impacts the RF signal. Changes in the RF signal relative to stored ammonia (NH3) were evaluated over a wide range of frequencies, temperatures, and exhaust conditions. The RF response to NH3 storage, desorption, and oxidation on the SCR was well correlated with changes in the catalyst state. Calibrated RF measurements demonstrate the ability to monitor the adsorption state of the SCR to within 10% of the sensor full scale. The results indicate direct measurement of SCR ammonia storage levels, and resulting catalyst feedback control, via RF sensing to have significant potential for optimizing the SCR system to improve NOx conversion and decrease urea consumption.

Engineering↗
DOE OSTI2017

CATALYST AMMONIA STORAGE MEASUREMENTS USING RADIO FREQUENCY SENSING

Motivated by increasingly strict NO x limits, engine manufactures have adopted selective catalytic reduction (SCR) technology to reduce engine-out NO x below mandated levels. In the SCR process, nitrogen oxides (NO x ) react with ammonia (NH 3 ) to form nitrogen and water vapor. The reaction is influenced by several variables, including stored ammonia on the catalyst, exhaust gas composition, and catalyst temperature. Currently, measurements from NO x and/or NH 3 sensors upstream and downstream of the SCR are used with predictive models to estimate ammonia storage levels on the catalyst and control urea dosing. This study investigated a radio frequency (RF) -based method to directly monitor the ammonia storage state of the SCR catalyst. This approach utilizes the SCR catalyst as a cavity resonator, in which an RF antenna excites electromagnetic waves within the cavity to monitor changes in the catalyst state. Ammonia storage causes changes in the dielectric properties of the catalyst, which directly impacts the RF signal. Changes in the RF signal relative to stored ammonia (NH 3 ) were evaluated over a wide frequency range as well as temperature and exhaust conditions. The RF response to NH 3 storage, desorption, and oxidation on the SCR was observed to be well-correlated with changes in the catalyst state. Calibrated RF measurements demonstrate the ability to monitor the adsorption state of the SCR to within 10 % of the sensor full scale. The results indicate direct measurement of SCR ammonia storage levels, and resulting catalyst feedback control, via RF sensing to have significant potential for optimizing the SCR system to improve NO x conversion and decrease urea consumption.

Aguilar, Jonathan↗
DOE OSTI2016

Model Development for Current–Voltage and Transconductance Characteristics of Normally-off AlN/GaN MOSHEMT

In this paper, an AlN/GaN-based MOSHEMT is proposed, in accordance to this, a charge control model has been developed analytically and simulated with MATLAB to predict the characteristics of threshold voltage, drain currents and transconductance. The physics based models for 2DEG density, threshold voltage and quantum capacitance in the channel has been put forward. By using these developed models, the drain current for both linear and saturation models is derived. The predicted threshold voltage with the variation of barrier thickness has been plotted. A positive threshold voltage can be obtained by decreasing the barrier thickness which builds up the foundation for enhancement mode MOSHEMT devices. The predicted I{sub d}–V{sub gs}, I{sub d}–V{sub ds} and transconductance characteristics show an excellent agreement with the experimental results and hence validate the model.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Transcript Analysis for Internal Biodosimetry Using Peripheral Blood from Neuroblastoma Patients Treated with 131 I-mIBG, a Targeted Radionuclide

Calculating internal dose from therapeutic radionuclides currently relies on estimates made from multiple radiation exposure measurements, converted to absorbed dose in specific organs using the Medical Internal Radiation Dose (MIRD) schema. As an alternative biodosimetric approach, we utilized gene expression analysis of whole blood from patients receiving targeted radiotherapy. Collected blood from patients with relapsed or refractory neuroblastoma who received 131 I-labeled metaiodobenzylguanidine ( 131 I-mIBG) at the University of California San Francisco (UCSF) was used to compare calculated internal dose with the modulation of chosen gene expression. A total of 40 patients, median age 9 years, had blood drawn at baseline, 72 and 96 h after 131 I-mIBG infusion. Whole-body absorbed dose was calculated for each patient based on the cumulated activity determined from injected mIBG activity and patient-specific time-activity curves combined with 131 I whole-body S factors. We then assessed transcripts that were the most significant for describing the mixed therapeutic treatments over time using real-time polymerase chain reaction (RT-PCR). Modulation was evaluated statistically using multiple regression analysis for data at 0, 72 and 96 h. A total of 10 genes were analyzed across 40 patients: CDKN1A; FDXR; GADD45A; BCLXL; STAT5B; BAX; BCL2; DDB2; XPC; and MDM2. Six genes were significantly modulated upon exposure to 131 I-mIBG at 72 h, as well as at 96 h. Four genes varied significantly with absorbed dose when controlling for time. A gene expression biodosimetry model was developed to predict absorbed dose based on modulation of gene transcripts within whole blood. Three transcripts explained over 98% of the variance in the modulation of gene expression over the 96 h (CDKN1A, BAX and DDB2). To our knowledge, this is a novel study, which uses whole blood collected from patients treated with a radiopharmaceutical, to characterize biomarkers that may be useful for biodosimetry. Our data indicate that transcripts, which have been previously identified as biomarkers of external exposures in ex vivo whole blood and in vivo radiotherapy patients, are also good early indicators of internal exposure. However, for internal sources of radiation, the biokinetics and physical decay of the radionuclide strongly influence the gene expression.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Griffin Capability Improvements in Support of Ex-core Deep-Penetration Problems

Advanced reactor designs, especially portable reactors that are designed to be located closer to humans and operate autonomously, require the ability to accurately compute the ex-core neutron and gamma flux solutions in terms of shielding design optimization to reduce dose rates at the vessel boundary and detector signal prediction to drive the reactor control system. The Nuclear Energy Advanced Modeling and Simulation program has prioritized improvements to the Griffin discrete ordinates (SN) solver for deep-penetration problems in fiscal year 2025. Significant advancements have been made to the Griffin methodologies for solving ex-core deep-penetration problems for steady-state, fixed-source and transient calculations. This work presents the methodology improvements as well as a comprehensive demonstration with a Transient Test Reactor model and measurements.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2010

SLUDGE WASHING AND DEMONSTRATION OF THE DWPF FLOWSHEET IN THE SRNL SHIELDED CELLS FOR SLUDGE BATCH 6 QUALIFICATION

Prior to initiating a new sludge batch in the Defense Waste Processing Facility (DWPF), Savannah River National Laboratory (SRNL) is required to simulate this processing, including Chemical Process Cell (CPC) simulation, waste glass fabrication, and chemical durability testing. This report documents this simulation for the next sludge batch, Sludge Batch 6 (SB6). SB6 consists of Tank 12 material that has been transferred to Tank 51 and subjected to Low Temperature Aluminum Dissolution (LTAD), Tank 4 sludge, and H-Canyon Pu solutions. Following LTAD and the Tank 4 addition, Liquid Waste Operations (LWO) provided SRNL a 3 L sample of Tank 51 sludge for SB6 qualification. Pu solution from H Canyon was also received. SB6 qualification included washing the sample per LWO plans/projections (including the addition of Pu from H Canyon), DWPF CPC simulations, waste glass fabrication (vitrification), and waste glass characterization and chemical durability evaluation. The following are significant observations from this demonstration. Sludge settling improved slightly as the sludge was washed. SRNL recommended (and the Tank Farm implemented) one less wash based on evaluations of Tank 40 heel projections and projections of the glass composition following transfer of Tank 51 to Tank 40. Thorium was detected in significant quantities (>0.1 wt % of total solids) in the sludge. In past sludge batches, thorium has been determined by Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS), seen in small quantities, and reported with the radionuclides. As a result of the high thorium, SRNL-AD has added thorium to their suite of Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) elements. The acid stoichiometry for the DWPF Sludge Receipt and Adjustment Tank (SRAT) processing of 115%, or 1.3 mol acid per liter of SRAT receipt slurry, was adequate to accomplish some of the goals of SRAT processing: nitrite was destroyed to below 1,000 mg/kg and mercury was removed to below the DWPF target with 750 g of steam per g of mercury. However, rheological properties did not improve and were above the design basis. Hydrogen generation rates did not exceed DWPF limits during the SRAT and Slurry Mix Evaporator (SME) cycles. However, hydrogen generation during the SRAT cycle approached the DWPF limit. The glass fabricated with the Tank 51 SB6 SME product and Frit 418 was acceptable with respect to chemical durability as measured by the Product Consistency Test (PCT). The PCT response was also predictable by the current durability models of the DWPF Product Composition Control System (PCCS). It should be noted, however, that in the first attempt to make glass from the SME product, the contents of the fabrication crucible foamed over. This may be a result of the SME product's REDOX (Reduction/Oxidation - Fe{sup 2+}/{Sigma}Fe) of 0.08 (calculated from SME product analytical results). The following are recommendations drawn from this demonstration. In this demonstration, at the request of DWPF, SRNL caustic boiled the SRAT contents prior to acid addition to remove water (to increase solids concentration). During the nearly five hours of caustic boiling, 700 ppm of antifoam was required to control foaming. SRNL recommends that DWPF not caustic boil/concentrate SRAT receipt prior to acid addition until further studies can be performed to provide a better foaming control strategy or a new antifoam is developed for caustic boiling. Based on this set of runs and a recently completed demonstration with the SB6 Waste Acceptance Product Specifications (WAPS) sample, it is recommended that DWPF not add formic acid at the design addition rate of two gallons per minute for this sludge batch. A longer acid addition time appears to be helpful in allowing slower reaction of formic acid with the sludge and possibly decreases the chance of a foam over during acid addition.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Advancing Replicable Solutions for High-Performance Homes in the Southeast - JMC - Patrick Square

The work presented in this report advances the goals of the U.S. Department of Energy Building America program by improving the energy peformance of affordable and market-rate housing. Southface Energy Institute (Southface), part of the U.S. Department of Energy Building America research team Partnership for Home Innovation, worked with owners and builders with various market constraints and ultimate goals for three projects in three climate zones (CZs): Savannah Gardens in Savannah, Georgia (CZ 2); JMC Patrick Square in Clemson, South Carolina (CZ 3); and LaFayette in LaFayette, Georgia (CZ 4). This report documents the design process, computational energy modeling, construction, envelope performance metrics, long-term monitoring results, and successes and failures of the design and execution of these highperformance homes. The JMC Patrick Square project is a single floor with 1,828 ft2 of conditioned living space, three bedrooms, two bathrooms, and an attached two-car garage. This small-scale production builder wanted to increase its level of energy efficiency beyond its current green building practices, including bringing ducts into conditioned space. The team met this goal through a combination of upgrade measures and achieved many Zero Energy Ready Home program requirements. Monitoring the four ducted HPWHs in LaFayette and one in Savannah revealed that HPWH exhaust air impacts attic air during HPWH runtime only, and attic conditions return to previous levels shortly after the HPWH turns off. The HPWH did not appear to impact the loads on the heating and cooling systems, which were also placed in the attic. HPWHs should not be considered dehumidifiers if one is needed in an attic or basement/crawlspace. Ducting the HPWHs did not negatively impact performance compared to other published data of field performance. Changing duct configurations also did not alter the coefficient of performance. HPWHs in efficiency mode (heat pump only) could satisfy hot water demand for most residents. This mode maximizes energy efficiency. Adding .5 in. of insulated sheathing using the Huber ZIP System R Sheathing reduced peak summer temperatures and increased minimum winter temperatures inside the wall assemblies compared to the neighboring home. The neighboring home experienced significantly more risk of condensation and failed the American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Standard 160-2009: Criteria for Moisture-Control Design Analysis in Buildings. Despite the fact that energy modeling predicted only a 2% annual savings from the insulated sheathing, preliminary data indicate that reduced heating, ventilating, and air-conditioning runtimes and energy consumption attributed to this measure provide significantly greater savings. Additional research is necessary.

Array↗
DOE OSTI2020

Prediction for Pressure Differential Across HEPA Filter Media Based on Media Characteristics and Particle Size Distribution

A new method for predicting the pressure drop across High Efficiency Particulate Air (HEPA) filter media is proposed based upon mass deposited onto the filter and known physical characteristics of the filter media. Detailed are the methods used in conjunction with current filter loading models to predict the pressure drop, as well as tests conducted to validate the methods. The benefit of a prediction model for practical use lies in the manufacturing and service life of nuclear grade HEPA filters. HEPA filters for use in nuclear facilities have a prescribed expiration date and a maximum allowable operating pressure drop. Therefore, the ability to predict the pressure drop across a filter and relate it to an expected length of service time can enable a reduction in wasted filters. This will allow for more informed decisions to be made based upon the dictated life cycle of the filters. Also, understanding and predicting how the pressure drop of HEPA filter media behaves as a function of physical characteristics and loaded mass can assist in future design and manufacturing of filter media. Currently, most existing pressure drop models are either computationally based or analytical methods relying on data gathered during tests. Neither are practical for prediction; the computational methods are difficult to implement, and the current analytical methods are more useful as tools for analysis. The current analytical model developed by Bergman et al. is based upon the pressure drop from each media fiber and modeling the deposited particles as newly formed fibers. Using Bergman's model and the media properties, air properties, and known aerosol particle size distribution, a pressure drop curve as a function of loaded mass can be created. This curve implies initial loading in the depth of the filter media with a transition to pure surface loading on the filter face, and a sensitivity to an evolving particle size distribution as mass continues to load onto the filter. To implement the predictive model, knowledge of the mean filter fiber diameter and porosity of the filter media is required. Traditionally, the mean fiber diameter is calculated as an effective diameter from prior media testing, however, in this study a Scanning Electron Microscope (SEM) technique is used to acquire this variable. Validation of the predictive model is provided by flat sheet media tests under a controlled environment with a measured particle size distribution of the challenge aerosol and shows reasonable preliminary agreement.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles

Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs). In contrast, purely data-driven models, require a large number of observations and lack operational guarantees for safety-critical systems. Data-driven models leveraging available partially characterized dynamics have potential to provide reliable systems models in a typical data-limited scenario for high value complex systems, thereby avoiding months of expensive expert modeling time. In this work we explore this middle-ground between expert-modeled and pure data-driven modeling. We present control-oriented parametric models with varying levels of domain-awareness that exploit known system structure and prior physics knowledge to create constrained deep neural dynamical system models. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics. In addition, we explore a hybrid formulation that explicitly models the residual error related to imperfect graybox models. We compare the prediction performance of the learned models for different distributions of initial conditions and control inputs to assess their suitability for control.

Shaw Cortez, Wenceslao E.↗
DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗
DOE OSTI2020

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

17 WIND ENERGY↗
DOE OSTI2025

Ecovoltaic solar energy development can promote grassland bird communities

Ecologically informed photovoltaic (PV) developments that co-prioritize PV electricity generation with ecosystem function (‘ecovoltaics’) have emerged as a promising land sharing strategy to minimize ecological conflicts associated with PV solar energy development. While habitat-focused ecovoltaic designs can conceptually benefit biodiversity by offsetting or enhancing impacts of PV development, foundational field research is needed to examine how wildlife respond to these novel ecosystems. We conducted passive acoustic monitoring (PAM) in 2023 and 2024 at 13 solar facilities and paired control sites to investigate avian community responses to ecovoltaic facilities in the Midwestern United States. Compared to control sites (row crop agricultural fields), we found that ecovoltaic sites supported more grassland bird species throughout a 17-week monitoring period between May and September. Grassland bird communities on ecovoltaic sites were also more stable than on agricultural controls, as measured by the Jaccard dissimilarity index. We also used PAM-based weekly species occurrences in an occupancy-modelling framework to investigate the influence of PV development and other landscape variables on grassland bird occupancy. 10 out of 13 modelled grassland bird species had greater predicted occupancy probabilities (ψ) on PV sites than control sites. Synthesis and applications. Our findings suggest that properly sited and developed ecovoltaic solar facilities in human altered landscapes can improve habitat for birds and other wildlife, but further research is needed to understand which species may benefit most from these novel ecosystems.

14 SOLAR ENERGY↗
DOE OSTI2015

A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth system models, to the stability and variability of the AMOC in past climates.

54 ENVIRONMENTAL SCIENCES↗