Results 121 to 130 of about 73,537 (247)
Determining the Power-Law Wind-Profile Exponent under Near-Neutral Stability Conditions at Sea [PDF]
S. A. Hsu+2 more
openalex +1 more source
Developing process parameters for the laser‐based Powder Bed Fusion of metals can be a tedious task. Based on melt pool depth, the process parameters are transferable to different laser scan speeds. For this, understanding the melt pool scaling behavior is essential, particularly for materials with high thermal diffusivity, as a change in scaling ...
Markus Döring+2 more
wiley +1 more source
Application study of wind power technology to the city of Hart, Michigan, 1977
J. Asmussen+3 more
openalex +2 more sources
Commercial Wind Power: Recent Experience in the United States [PDF]
G W Braun, Douglas R. Smith
openalex +1 more source
Wind power and the UK wind resource
The importance of wind power to the UK's renewable electricity supply is predicted to increase in the coming years. Together with this greater reliance on wind power there is a greater need for understanding the characteristics of the UK's wind resource.
openaire +2 more sources
Hybrid materials enable high‐performance components but are challenging to process. This study explores an inductive heating concept with spray cooling for steel–aluminum specimens in a two‐step process including friction welding and cup backward extrusion.
Armin Piwek+7 more
wiley +1 more source
Wind-powered hydrogen electric systems for farm and rural use. Final report, May--December 1975
R.R. Tison+7 more
openalex +2 more sources
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong+11 more
wiley +1 more source
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi+4 more
wiley +1 more source