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    Development of a Machine Learning Model for Elastohydrodynamic Pressure Prediction in Journal Bearings1 

    Source: Journal of Tribology:;2022:;volume( 144 ):;issue: 008:;page 81603-1
    Author(s): Hess, Nathan; Shang, Lizhi
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper presents a machine learning neural network capable of approximating pressure as the distributive result of elastohydrodynamic (EHD) effects for a journal bearing at steady state. Design of efficient, reliable ...
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    Effects of Chevron Micro-Textures on Tribological and Lubricating Performance of Cylinder Block/Valve Plate Interface in Axial Piston Pumps 

    Source: Journal of Tribology:;2022:;volume( 145 ):;issue: 003:;page 32201-1
    Author(s): Chen, Luanxia; Shang, Lizhi; Liu, Zhanqiang; Mukherjee, Swarnava; Cai, Yukui; Wang, Bing
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The cylinder block/valve plate interface is one of the major power loss sources and main failure points in axial piston pumps and motors. Surface micro-texture has been proven to be an effective approach to reduce friction ...
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