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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In-Situ Blade Strain Measurements of a Crossflow Turbine Operating in a Tidal Flow

This data was collected between October 25 and December 12 of 2022 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). The goal was to collect blade strain data from a crossflow turbine operating in a tidal flow. A table in ('Deployment Schedule.PNG') outlines the various dates when each instrument was operational, and more details can be found via literature listed in 'Related Publications'.txt. This dataset includes zipped folders for each instrument containing data in .csv files for the relevant duration specific to each instrument, along with separate README file for each measurement. Some instrument files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. All data contained in this submission is unfiltered/unprocessed data unless otherwise noted in the README file. Blade strain was measured using 8 foil-based strain gauges along the span of a single turbine blade. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) both upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal flow with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Chancey 2019. The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. A single blade of this turbine was further modified to accommodate 8 full-bridge strain gauges (Bharath et al 2023, Bichanich et al 2024). For power performance and other relevant details on the turbine and its characteristics, see O'Byrne 2022.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at $\sqrt{s}$ = 13.6 TeV

“Soft” muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously suppressing backgrounds from long-lived light-flavor hadron decays, is therefore important for the success of the CMS physics program. Multivariate techniques have been shown to deliver better muon identification performance than traditional selection techniques. To take full advantage of the large data set currently being collected during Run 3 of the CERN LHC, a new multivariate classifier based on a gradient-boosted decision tree has been developed. It offers a significantly improved separation of signal and background muons compared to a similar classifier used for the analysis of the Run 2 data. The performance of the new classifier is evaluated on a data set collected with the CMS detector in 2022 and 2023, corresponding to an integrated luminosity of 62 fb -1 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Direct Visualization of Magnetic Correlations in Frustrated Spinel ZnFe 2 O 4

Magnetic materials with the spinel structure (A 2+ B 3+ 2 O 4 ) form the core of numerous magnetic devices, and ZnFe 2 O 4 constitutes a peculiar example where the nature of the magnetism is still unresolved. Susceptibility measurements revealed a cusp around T c = 13 K resembling an antiferromagnetic transition, despite the positive Curie–Weiss temperature determined to be Θ CW = 102.8(1) K. Bifurcation of field-cooled and zero-field-cooled data below T c in conjunction with a frequency dependence of the peak position and a non-zero imaginary component below T c shows it is in fact associated with a spin-glass transition. Highly structured magnetic diffuse neutron scattering from single crystals develops between 50 K and 25 K revealing the presence of magnetic disorder which is correlated in nature. Here, the 3D-mΔPDF method is used to visualize the local magnetic ordering preferences, and ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor correlations are shown to be dominant. Their temperature dependence is extraordinary with some flipping in sign and a strongly varying correlation length. The correlations can be explained by orbital interaction mechanisms for the magnetic pathways and a preferred spin cluster. This study demonstrates the power of the 3D-mΔPDF method in visualizing complex quantum phenomena thereby providing a way to obtain an atomic-scale understanding of magnetic frustration.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Recommendations for developing, documenting, and distributing data products derived from NEON data

The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly-generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cover crops and poultry litter impact on soil structural stability in dryland soybean production in southeastern United States

Abstract This study explored the efficacy of soil aggregate indices in quantifying soil structural development, utilizing 5‐year field experiment data from the Southeastern United States. The experiment utilized a split‐plot design with cover crops (native vegetation as control, cereal rye (Secale cerealeL.), winter wheat (Triticum aestivum), hairy vetch (Vicia villosa), and mustard (Brassica rapa) plus cereal rye as the main factor and fertilizer source (no fertilizer as control, inorganic fertilizer with phosphorus, potassium, and elemental sulfur, and poultry litter) as the secondary factor. Aggregate size fractions were determined using the wet‐sieving method, and aggregate stability index (ASI), mean weight diameter (MWD), geometric mean diameter (GMD), and fractal dimension (FD) were calculated to assess soil structural stability. Main effects results indicated that cereal rye (55.11%) and poultry litter (50.97%) exhibited the highest ASI values. The highest MWD, GMD, and FD were observed under mustard plus cereal rye (1.187 mm), cereal rye (0.462 mm), and hairy vetch (2.573), respectively. Principal component analysis revealed that cover crops significantly improved soil aggregate structure and stability, overcoming limitations of sole fertilization practices. Regression analysis suggested that ASI, MWD, and GWD positively correlated with soil organic carbon, whereas FD negatively correlated with MWD, GMD, and ASI. Principal component analysis exhibited that FD decreased with increasing soil organic carbon, ASI, MWD, and GMD, demonstrating that lower FD values indicate enhanced soil aggregation and structure. Assessed indices, FD included, effectively gauged soil structural stability. These metrics should be prioritized in managerial decisions to support soil productivity and health in agricultural systems.

Agriculture↗
DOE OSTI2021

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Crack faults in rotating machines can cause machine shutdown or scrapping, endangering the normal operation and safety of nuclear power plants. Intelligent diagnostic techniques based on machine learning have the potential to diagnose crack faults. However, problems such as scarcity of field fault data and high noise of plant measurements pose challenges to the application of machine learning. Here this study proposes an ensemble learning approach to mitigate the negative impacts of the problems. Ensemble learning is a strategy for combining multiple machine learning models into a composite model. The basic idea of ensemble learning is that even if one model makes a mistake, other models can correct it. Case studies based on bearing and gear system fault experiments show that the proposed ensemble learning models have better diagnostic results than the single model in the presence of noise and small data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Energy performance of an operational government building retrofitted with ceiling phase change material tiles in a mixed-humid climate

The aging U.S. building stock requires various retrofit measures to enhance their energy efficiency. Here, this study explores the integration of thermal energy storage and advanced building controls as viable retrofit solutions for load flexibility and peak demand response while maintaining the occupants' comfort. A detailed assessment is conducted on the energy use of an administrative building in Sumner County, Kansas, focusing on the implementation of phase change materials (PCMs) in the ceiling of occupied zones. First, a time-resolved, whole-building energy model is developed in EnergyPlus, incorporating complex thermal behaviors such as air exchange between the plenum space and occupied zones, envelope leakage, and operational schedules. The model is then validated using experimental field test data, and subsequently a parametric assessment of key PCM properties and application strategies is performed to evaluate cooling electricity demand benefits. The parametric study shows that the optimal retrofit strategy, comprising a PCM with 23°C peak melting temperature, 0.125 in. (3.17 mm) thickness, and 150 kJ/kg latent heat, combined with active controls that include 8 h of precooling, forced convection under the ceiling, and a 2°C thermostat setback during peak hours, can result in a maximum load shift during the peak period of 99.6 % and the total electricity savings during the peak period of 98.9 % for the optimum case and thus provide significant cost savings under time-of-use pricing scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Statistics-Based Threshold for the RMS-Energy Oscillation Detector

System operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a detection threshold for the widely deployed root-mean-square (RMS)-energy detector. This expression provides a theoretical basis for the detector's configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide events. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of grid disturbances while maintaining a low probability of false alarm.

Follum, James D.↗
DOE OSTI2023

Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning

Active reservoir management (ARM) through brine extraction can reduce pressure buildup during large-scale implementation of carbon capture and storage (CCS) projects. This study used machine learning (ML)-assisted approaches to analyze bottomhole pressure (BHP) responses to various brine injection and extraction scenarios. Field monitoring data were collected over a 2-year operation period at two injection wells and one extraction well (about 400 meters away) as part of a Brine Extraction and Storage Test (BEST) in the North Dakota portion of the Williston Basin. Injection activities increased the BHPs at the injection wells by around 0.70 MPa (~100 psi) during the operation period. Extraction activities demonstrated the capability to decrease the BHPs at the injection wells by approximately 0.21–0.34 MPa (30–50 psi) depending on the ratio of the extraction and injection well flow rates (the “extraction ratio” – a normalization procedure used in the analysis). The pressure reduction provided by the extraction well equated to 30%–50% of the pressure buildup at the injection well. Furthermore, this work shows how ML analytics can play a key role in estimating reservoir pressure responses for complex injection and extraction activities during pressure management practices of CCS projects.

58 GEOSCIENCES↗
DOE OSTI2022

Floating solar panels on reservoirs impact phytoplankton populations: A modelling experiment

Floating solar photovoltaic (FPV) deployments are increasing globally as the switch to renewable energy intensifies, representing a considerable water surface transformation. FPV installations can potentially impact aquatic ecosystem function, either positively or negatively. However, these impacts are poorly resolved given the challenges of collecting empirical data for field or modelling experiments. In particular, there is limited evidence on the response of phytoplankton to changes in water body thermal dynamics and light climate with FPV. Given the importance of understanding phytoplankton biomass and species composition for managing ecosystem services, we use an uncertainty estimation approach to simulate the effect of FPV coverage and array siting location on a UK reservoir. FPV coverage was modified in 10% increments from a baseline with 0% coverage to 100% coverage for three different FPV array siting locations based on reservoir circulation patterns. Results showed that FPV coverage significantly impacted thermal properties, resulting in highly variable impacts on phytoplankton biomass and species composition. The impacts on phytoplankton were often dependent on array siting location as well as surface coverage. Changes to phytoplankton species composition were offset by the decrease in phytoplankton biomass associated with increasing FPV coverage. We identified that similar phytoplankton biomass reductions could be achieved with less FPV coverage by deploying the FPV array on the water body's faster-flowing area than the central or slower flowing areas. The difference in response dependent on siting location could be used to tailor phytoplankton management in water bodies. Simulation of water body-FPV interactions efficiently using an uncertainty approach is an essential tool to rapidly develop understanding and ultimately inform FPV developers and water body managers looking to minimise negative impacts and maximise co-benefits.

14 SOLAR ENERGY↗
DOE OSTI2021

Assessment of the driving factors of CO 2 mitigation costs of household biogas systems in China: A LMDI decomposition with cost analysis model

China has been placing a substantial focus on biogas for reducing energy consumption and carbon dioxide (CO 2 ) emissions. The operation mode of biogas systems may make the CO 2 reduction target over-optimistic. There is limited research to investigate the influential factors that may be causing the gap between the actual and theoretical CO 2 reduction costs of biogas systems in China. In this research, by using field survey data of 209 biogas users and 489 non-biogas users from 19 villages in 2015, the gap between actual and theoretical unit CO 2 reduction cost is quantified at approximately 156 USD/t CO 2 . By employing the Logarithmic Mean Divisia Index I (LMDI) model, it is found that both the cost effect (48%) and the reduction effect (52%) contribute to the unit CO 2 reduction cost gap. Furthermore, four influential factors–household labor, accessibility to the energy resource, acceptance of biogas technology, and subsidy–significantly narrow the gap between actual and theoretical CO 2 reduction costs, while the levelized subsidy contributes to widening the gap. On average, biogas systems should be operated for at least four years and the substitution rate should be more than 67% in order to keep the gap between actual and theoretical CO 2 reduction costs under 50%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Chemical Differences between Phenolic Secondary Organic Aerosol Formed through Gas-Phase and Aqueous-Phase Reactions

Phenolic compounds, which are significant emissions from biomass burning (BB), undergo rapid photochemical reactions in both gas and aqueous phases to form secondary organic aerosol, namely, gasSOA and aqSOA, respectively. The formation of gasSOA and aqSOA involves different reaction mechanisms, leading to different product distributions. In this study, we investigate the gaseous and aqueous reactions of guaiacol a representative BB phenol to elucidate the compositional differences between phenolic aqSOA and gasSOA. Aqueous-phase reactions of guaiacol produce higher SOA yields than gas-phase reactions (e.g., roughly 60 vs 30% at one half-life of guaiacol). These aqueous reactions involve more complex reaction mechanisms and exhibit a more gradual SOA evolution than their gaseous counterparts. Initially, gasSOA forms with high oxidation levels (O/C > 0.82), while aqSOA starts with lower O/C (0.55–0.75). However, prolonged aqueous-phase reactions substantially increase the oxidation state of aqSOA, making its bulk chemical composition closer to that of gasSOA. Additionally, aqueous reactions form a greater abundance of oligomers and high-molecular-weight compounds, alongside a more sustained production of carboxylic acids. AMS spectral signatures representative of phenolic gasSOA have been identified, which, together with tracer ions of aqSOA, can aid in the interpretation of field observation data on aerosol aging within BB smoke. The notable chemical differences between phenolic gasSOA and aqSOA highlighted in this study also underscore the importance of accurately representing both pathways in atmospheric models to better predict the aerosol properties and their environmental impacts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Interpreting the Diurnal Cycle of Clouds and Precipitation in the ARM GoAmazon Observations: Shallow to Deep Convection Transition

The Green Ocean Amazon (GoAmazon) 2014/5 field campaign data are used to study the diurnal cycle of clouds and precipitation. Through a careful classification of days with shallow cumulus, congestus and deep convection, we investigate the major differences among locally generated convection regimes and the most important environmental factors governing the shallow-to-deep convection transition. On shallow cumulus days, a greater sensible heat flux drives deeper boundary layer growth, which entrains drier free-tropospheric air and lowers the relative humidity, thus leading to a significantly higher cloud base than those on days with deeper convection. Congestus and deep convection regimes exhibit distinct cloud top height distributions with noticeable differences in the vertical wind shear in the mid-troposphere, suggesting an important role of wind shear in limiting the vertical extent of convection. On deep convection days, with preexisting nocturnal convection or cold-pools from external disturbances, the timing of peak surface precipitation (12:00–13:00 LST) tends to be in-phase with the diurnal variation in surface fluxes. However, it takes longer for local deep convection to develop without these disturbances. A plume model with thermodynamic and dynamical constraints is developed to explore the relative importance of various convection-controlling factors. Initial cloud-base vertical velocity and buoyancy are important in helping parcels ascend to the level of free convection (LFC). After parcels reach the LFC, entrainment of environmental air and lower free troposphere humidity become crucial in determining cloud top. Entrainment rate differentiates among convection regimes, which may be tied to the cloud size distribution at cloud base.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Global Simulations Suggest Biomass Burning Aerosol Emissions From Grassland Fires Could Be Important Ice Nucleating Particles

Ice nucleating particles (INP) capable of nucleating ice crystals via immersion freezing at temperatures above approximately −35°C may strongly influence cloud glaciation, with implications for global precipitation and climate feedback. In addition to mineral dust, soil dust, and marine organics, laboratory and field measurements suggest biomass burning aerosols (BBA) can act as immersion-mode INP between around −30°C and −15°C. However, the contribution of BBA to the global INP budget remains poorly understood due to poor knowledge of which fuels yield INPs, uncertainties in global coverage of those fuels, and unknown size distributions of the INPs in the BBA. Nonetheless, with some understanding of these uncertainties from sensitivity studies, the relative importance of ice nucleation activity of BBA compared to other INP sources can be quantified. In this work, we investigate the potential global importance of BBA as INP using a global aerosol-climate model, specifically the UK Met Office Unified Model (UM). We evaluate the model using field campaign data sets. We examine potential uncertainties in fuel types and particle sizes on BBA-based INP concentrations. Averaged over June–September between 15°S and 50°S, BBA is a more important INP than dust and marine INP about 30% of the time at altitudes with temperatures between −30°C and −20°C. Our simulations therefore suggest BBA INPs may be at least as important as mineral dust and marine INP over the atmospheric regions and seasons where grassland fires are frequent.

Gohil, Kanishk [Carnegie Mellon University, Pittsb↗
DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗
DOE OSTI2022

Estimating mass-absorption cross-section of ambient black carbon aerosols: theoretical, empirical, and machine learning models

The mass-absorption cross-section of black carbon (MAC BC ) is an essential parameter to link the atmospheric concentration of black carbon (BC) with its radiative forcing. When a direct calculation of MAC BC based on observations of aerosol light absorption and BC mass concentration is impossible, we rely on modeling and simulations to estimate MAC BC , but currently, there is no consensus model that can be relied on for accurate predictions across all atmospheric environments when BC particles have different coating thicknesses. Here, we applied five MAC BC prediction models (including three light scattering theories, an empirical model based on observations of particle mass concentrations, and a machine learning model developed in our previous work) to aerosols from three Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) field campaigns. While many studies have found that increasing the complexity of the models helps to constrain biases of the estimated MAC BC , our effort is to evaluate the models based on the criteria of simplicity and accuracy. We find that our machine learning model (support vector machine for regression, SVM) generally performs well across all DOE ARM field campaign data, while the accuracy of core-shell Mie theory depends on the bias correction algorithm applied to filter-based light absorption data. Generally, the empirical model for internally-mixed particles that we considered tends to over-predict MAC BC , while Mie theory for externally-mixed particles tends to under-predict MAC BC . An examination of the influence of coating material on BC cores suggests that the performance of our current SVM model is degraded when the BC is thickly-coated (e.g., it has undergone aging and mixing with other materials in the atmosphere).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Electromagnetic Analysis of ITER Electron Cyclotron Emission Components and Support Structures for the Preliminary Design Review

The ITER electron cyclotron emission (ECE) diagnostic system is located at Design Shield Module (DSM) 2, Equatorial Port (EP) 9 to measure electron temperature profile and electron temperature fluctuations and also to assess nonthermal electron distributions via the oblique view. Therefore, ECE has both radial and oblique views with two couples of mirrors for two different optical views. This ECE diagnostic system shall be exposed to significant power due to unabsorbed electron cyclotron heating power in the plasma. It shall also receive large electromagnetic (EM) loads up to 100 MN/m 3 force density due to the eddy currents generated by the short 16-ms transient plasma disruption. The global EM models of EP12 and EP11 with the worst case of plasma disruption Major Disruption Downward Exponential 16 ms Category III (MD_DW_EXP16MS_CATIII) have been provided by ITER. However, the support structure for these critical components, including four mirrors, is still under the design and development phase, especially during the period of preliminary design reviews (PDRs). For those small in-vessel components that are not modeled explicitly for the PDRs, we can extract the magnetic field (B) data and flux variations (dB/dt) at those locations from the global model and then use the B and dB/dt method to calculate the eddy currents, EM forces, and torques on those components in the local submodels. In conclusion, this paper will present the detailed PDR EM analysis results of the ECE components and support structures.

Electromagnetic↗