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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 OSTI2019

NiMo-ceria-zirconia-based anode for solid oxide fuel cells operating on gasoline surrogate

A multi-functional NiMo-ceria-zirconia composite was developed as an anode for a solid oxide fuel cell (SOFC) running on isooctane that is a commonly used model fuel for gasoline. The anode layer was fabricated on an electrolyte-supported single cell using yttria-stabilized zirconia (YSZ) as the electrolyte and La0.6Sr0.4Co0.2Fe0.8O3-d as the cathode. Our results indicated that the single NiMo-ceria-zirconia layer possessed a dual-functionality: the first function was to internally convert complex hydrocarbons into a synthesis gas and the second function was to electrochemically oxidize the synthesis gas mixture for electric power generation. The addition of Mo to the Ni-ceria-zirconia allowed direct conversion of isooctane in SOFC. It significantly suppressed carbon deposition in the anode and improved the lifetime of a single cell. Additionally, the presence of Mo in the Ni-ceria-zirconia anode appeared to facilitate a higher degree of sintering, which increased the electrical conductivity and maximum power density of the single cell. When operating on isooctane at 800 °C, single cell with the 5 wt.% Mo in the Ni-ceria-zirconia-based anode displayed a maximum power density of 212 mW cm-2 at 0.48V which was higher than that of a single cell with the Ni-YSZ-based anode without the Mo promoter (49 mW cm-2 at 0.48 V).

NiMo-Ceria-Zirconia composite, Single layer multif↗
DOE OSTI2021

A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power

Power curve, the functional relationship that governs the process of converting a set of weather variables experienced by a wind turbine into electric power, is widely used in the wind industry to estimate power output for planning and operational purposes. Existing methods for power curve estimation have three main limitations: (i) they mostly rely on wind speed as the sole input, thus ignoring the secondary, yet possibly significant effects of other environmental factors, (ii) they largely overlook the complex marine environment in which offshore turbines operate, potentially compromising their value in offshore wind energy applications, and (ii) they solely focus on the first-order properties of wind power, with little (or null) information about the variation around the mean behavior, which is important for ensuring reliable grid integration, asset health monitoring, and energy storage, among others. In light of that, this study investigates the impact of several wind-and wave-related factors on offshore wind power variability, with the ultimate goal of accurately predicting its first two moments. Further, our approach couples OpenFAST—a multi-physics wind turbine simulator—with Gaussian Process (GP) regression to reveal the underlying relationships governing offshore weather-to-power conversion. We first find that a multi-input power curve which captures the combined impact of wind speed, direction, and air density, can provide double-digit improvements, in terms of prediction accuracy, relative to univariate methods which rely on wind speed as the sole explanatory variable (e.g. the standard method of bins). Wave-related variables are found not important for predicting the average power output, but interestingly, appear to be extremely relevant in describing the fluctuation of the offshore power around its mean. Tested on real-world data collected at the New York/New Jersey bight, our proposed multi-input models demonstrate a high explanatory power in predicting the first two moments of offshore wind generation, testifying their potential value to the offshore wind industry.

17 WIND ENERGY↗
DOE OSTI2013

Cell diameter measurements obtained with a handheld cell counter could be used as a surrogate marker of G2/M arrest and apoptosis in colon cancer cell lines exposed to SN-38

Highlights: •Chemo-sensitivity to SN-38 was assayed by the automated cell counter. •Colon cancer cell line, HCT116 cells were more sensitive to SN-38 than HT29 cells. •Increase of cell size reflects G2/M arrest. •Appearance of small particles indicates cell apoptosis. -- Abstract: In vitro assessment of chemosensitivity are important for experiments evaluating cancer therapies. The Scepter 2.0 cell counter, an automated handheld device based on the Coulter principle of impedance-based particle detection, enables the accurate discrimination of cell populations according to cell size and volume. In this study, the effects of SN-38, the active metabolite of irinotecan, on the colon cancer cell lines HCT116 and HT29 were evaluated using this device. The cell count data obtained with the Scepter counter were compared with those obtained with the {sup 3}H-thymidine uptake assay, which has been used to measure cell proliferation in many previous studies. In addition, we examined whether the changes in the size distributions of these cells reflected alterations in the frequency of cell cycle arrest and/or apoptosis induced by SN-38 treatment. In our experiments using the Scepter 2.0 cell counter, the cell counts were demonstrated to be accurate and reproducible measure and alterations of cell diameter reflected G2/M cell cycle arrest and apoptosis. Our data show that easy-to-use cell counting tools can be utilized to evaluate the cell-killing effects of novel treatments on cancer cells in vitro.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Towards robust surrogate models: Benchmarking machine learning approaches to expediting phase field simulations of brittle fracture

Data-driven approaches have the potential to make modeling complex, nonlinear physical phenomena significantly more computationally tractable. For example, computational modeling of fracture is a core challenge where machine learning techniques have the potential to provide a much needed speedup that would enable progress in areas such as multi-scale modeling and uncertainty quantification. Currently, phase field modeling (PFM) of fracture is one such approach that offers a convenient variational formulation to model crack nucleation, branching and propagation. To date, machine learning techniques have shown promise in approximating PFM simulations. While standard fracture benchmarks represent realistic scenarios frequently observed in practice, they typically do not provide sufficiently challenging tests for data-driven methods. Here, to address this gap, we introduce a challenging dataset based on PFM simulations designed to benchmark and advance ML methods for fracture modeling. This dataset includes three energy decomposition methods, two boundary conditions, and 1000 random initial crack configurations for a total of 6000 simulations. Each sample contains 100 time steps capturing the temporal evolution of the crack field. Alongside this dataset, we also implement and evaluate Physics Informed Neural Networks (PINN), Fourier Neural Operators (FNO), and UNet models as baselines, and explore the impact of ensembling strategies on prediction accuracy. With this combination of our dataset and baseline models drawn from the literature we aim to provide a standardized and challenging benchmark for evaluating machine learning approaches to solid mechanics. Our results highlight both the promise and limitations of popular current models, and demonstrate the utility of this dataset as a testbed for advancing machine learning in fracture mechanics research.

Benchmark dataset↗