Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques
Hydrodynamic thrust bearings are machine elements used in many rotating machinery in order to support axial loads. The investigation of the lubrication in such mechanisms using numerical analysis methods has been the major subject of many studies over the years. Furthermore, the evolution of technol...
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Format: | Article |
Language: | English |
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EDP Sciences
2021-01-01
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Series: | MATEC Web of Conferences |
Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_03003.pdf |
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author | Katsaros Konstantinos P. Nikolakopoulos Pantelis G. |
author_facet | Katsaros Konstantinos P. Nikolakopoulos Pantelis G. |
author_sort | Katsaros Konstantinos P. |
collection | DOAJ |
description | Hydrodynamic thrust bearings are machine elements used in many rotating machinery in order to support axial loads. The investigation of the lubrication in such mechanisms using numerical analysis methods has been the major subject of many studies over the years. Furthermore, the evolution of technology in the last decade brought the concept of industry 4.0 and machine learning techniques have started to play important role in the operational optimization of such assemblies. The aim of this study is to examine optimal designs of tilting pad thrust bearings by combining numerical analysis and machine learning techniques. |
first_indexed | 2024-12-17T12:49:38Z |
format | Article |
id | doaj.art-ec6cd0c6fb8047f2bf1fa7ae890d8132 |
institution | Directory Open Access Journal |
issn | 2261-236X |
language | English |
last_indexed | 2024-12-17T12:49:38Z |
publishDate | 2021-01-01 |
publisher | EDP Sciences |
record_format | Article |
series | MATEC Web of Conferences |
spelling | doaj.art-ec6cd0c6fb8047f2bf1fa7ae890d81322022-12-21T21:47:39ZengEDP SciencesMATEC Web of Conferences2261-236X2021-01-013490300310.1051/matecconf/202134903003matecconf_iceaf2021_03003Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniquesKatsaros Konstantinos P.0Nikolakopoulos Pantelis G.1Machine Design Laboratory, Department of Mechanical Engineering and AeronauticsMachine Design Laboratory, Department of Mechanical Engineering and AeronauticsHydrodynamic thrust bearings are machine elements used in many rotating machinery in order to support axial loads. The investigation of the lubrication in such mechanisms using numerical analysis methods has been the major subject of many studies over the years. Furthermore, the evolution of technology in the last decade brought the concept of industry 4.0 and machine learning techniques have started to play important role in the operational optimization of such assemblies. The aim of this study is to examine optimal designs of tilting pad thrust bearings by combining numerical analysis and machine learning techniques.https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_03003.pdf |
spellingShingle | Katsaros Konstantinos P. Nikolakopoulos Pantelis G. Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques MATEC Web of Conferences |
title | Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques |
title_full | Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques |
title_fullStr | Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques |
title_full_unstemmed | Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques |
title_short | Optimal Designs of Tilting-Pad Thrust Bearing operation with the combination of numerical and machine learning techniques |
title_sort | optimal designs of tilting pad thrust bearing operation with the combination of numerical and machine learning techniques |
url | https://www.matec-conferences.org/articles/matecconf/pdf/2021/18/matecconf_iceaf2021_03003.pdf |
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