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High-fidelity wind farm simulation methodology with experimental validation [electronic resource]
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States National Nuclear Security Administration Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
Bộ sưu tập: Metadata
ddc:  628.3
 
Large-Eddy Simulation Sensitivities to Variations of Configuration and Forcing Parameters in Canonical Boundary-Layer Fl...
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2018
Bộ sưu tập: Metadata
ddc:  621.5
 
Simulating effects of a wind-turbine array using LES and RANS [electronic resource]
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2016
Bộ sưu tập: Metadata
ddc:  621.44
 
GE Renewable Energy [electronic resource] : Perdigao wind dataset from Large Eddy Simulation and ERA5
Tác giả:
Xuất bản: Oak Ridge Tenn Oak Ridge Tenn: Oak Ridge National Laboratory Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2022
Bộ sưu tập: Metadata
ddc:  621.45
 
Gaseous jet through an outward opening injector [electronic resource] : Details of mixing characteristic and turbulence ...
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
Bộ sưu tập: Metadata
ddc:  629.28
 
Large eddy simulation of a coaxial jet with a synthetic turbulent inlet [electronic resource]
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States National Nuclear Security Administration Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2014
Bộ sưu tập: Metadata
ddc:  629.225
 
Deep learning-based model for progress variable dissipation rate in turbulent premixed flames [electronic resource]
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States Office of the Assistant Secretary of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2020
Bộ sưu tập: Metadata
ddc:  629.2
 
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