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Airfoil Computational Fluid Dynamics 9k shapes, 2 AoA's
This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 8,996 airfoil shapes, computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes ...
Ramos, D. et al National Renewable Energy Laboratory (NREL)
Feb 10, 2023
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
Airfoil Computational Fluid Dynamics 2k shapes, 25 AoA's, 3 Re numbers
This dataset contains aerodynamic quantities including flow field values (momentum, energy, and vorticity) and summary values (coefficients of lift, drag, and momentum) for 1,830 airfoil shapes computed using the HAM2D CFD (computational fluid dynamics) model. The airfoil shapes w...
Ramos, D. et al National Renewable Energy Laboratory (NREL)
Feb 10, 2023
3 Resources
0 Stars
Publicly accessible
3 Resources
0 Stars
Publicly accessible
INTEGRATE Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design techno...
Vijayakumar, G. et al National Renewable Energy Laboratory (NREL)
May 04, 2021
8 Resources
0 Stars
Publicly accessible
8 Resources
0 Stars
Publicly accessible
BUTTER Empirical Deep Learning Dataset
The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four le...
Tripp, C. et al National Renewable Energy Laboratory
May 20, 2022
4 Resources
0 Stars
Publicly accessible
4 Resources
0 Stars
Publicly accessible
ARPA-E Grid Optimization (GO) Competition Challenge 2
The ARPA-E Grid Optimization (GO) Competition Challenge 2, from 2020 to 2021, expanded upon the problem posed in Challenge 1 by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and l...
Elbert, S. et al Pacific Northwest National Laboratory
Sep 20, 2024
29 Resources
0 Stars
Curated
29 Resources
0 Stars
Curated