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Gas-lubricated journal bearings. --- Journal bearings. --- Gas-lubricated bearings. --- Bearings (Machinery)
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Journal bearings. --- Gas-turbines --- Loads (Mechanics) --- Bearings. --- Mathematical models.
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Journal bearings --- Aerodynamic load --- Fluid-film bearings. --- Rayleigh quotient. --- Testing. --- Testing.
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Hydrodynamic Lubrication is the culmination of over 20 years close, collaborative work by the five authors and discusses the practical use of the formalization of low pressure lubrication. The work concentrates on the developments to journal and thrust bearings and includes subjects such as: the dynamic behaviour of plain and tilting-pads the thermal aspects the positive and negative effects of non-cyclindricity and shape defects resulting from manufacturing or operation the effects of inertia the appearance of Taylor's vortices and of turbulence and their reper
Fluid-film bearings. --- Journal bearings. --- Tribology. --- Friction --- Surfaces (Technology) --- Bearings (Machinery) --- Bearings, Fluid-film --- Hydrostatic bearings --- Fluid dynamics --- Lubrication and lubricants
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Turbomachines --- Journal bearings --- Fluid-film bearings --- Lubrication and lubricants --- Paliers fluides --- Lubrifiants --- Bearings --- -Turbines --- Grease --- Lubricants --- Tribology --- Bearings (Machinery) --- Lubrication systems --- Oils and fats --- Bearings, Fluid-film --- Hydrostatic bearings --- Fluid dynamics --- -Bearings
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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.
artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- n/a
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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.
Technology: general issues --- History of engineering & technology --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound
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This brief details non-circular journal bearing configurations. The author describes the mathematical and experimental studies that pertain to non-circular journal bearing profiles and how they can be applied to other types of bearing profiles with some modifications. He also examines non-circular journal bearing classifications, the methodology needed to carry out mathematical modeling, and the experimental procedures used to determine oil-film temperature and pressures.
Materials Science --- Mechanical Engineering - General --- Chemical & Materials Engineering --- Mechanical Engineering --- Engineering & Applied Sciences --- Journal bearings. --- Bearings (Machinery) --- Chemistry, inorganic. --- Engineering. --- Thermodynamics. --- Tribology, Corrosion and Coatings. --- Engineering Thermodynamics, Heat and Mass Transfer. --- Chemistry, Physical and theoretical --- Dynamics --- Mechanics --- Physics --- Heat --- Heat-engines --- Quantum theory --- Construction --- Industrial arts --- Technology --- Inorganic chemistry --- Chemistry --- Inorganic compounds --- Tribology. --- Corrosion and anti-corrosives. --- Coatings. --- Heat engineering. --- Heat transfer. --- Mass transfer. --- Mass transport (Physics) --- Thermodynamics --- Transport theory --- Heat transfer --- Thermal transfer --- Transmission of heat --- Energy transfer --- Surface coatings --- Materials --- Surfaces (Technology) --- Coating processes --- Thin films --- Anti-corrosive paint --- Atmospheric corrosion --- Metal corrosion --- Metals --- Rust --- Rustless coatings --- Chemical inhibitors --- Chemistry, Technical --- Fouling --- Weathering --- Paint --- Protective coatings --- Waterproofing --- Friction --- Mechanical engineering --- Corrosion --- Deterioration --- Surfaces
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