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Efficient Fatigue Modelling For Large Finite Element Models

Sharma, Harshita (2024) Efficient Fatigue Modelling For Large Finite Element Models. Master's, Ruhr-Universität Bochum.

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Abstract

For a multi-material component subjected to vibrations fatigue analyses have been performed. Complexity arises from the different materials used in the component, which results in multiple resonance points. The presence of numerous resonance points makes the process to evaluate both cumbersome and time consuming. Additionally, the presence of multiaxial vibrational loading necessitates the computation of the most suitable equivalent stress. This involves determining distinct equivalent stress values for different materials, further adding complexity to the fatigue analysis. In the present work to simplify these issues, an efficient failure criteria has been developed utilizing a Python script that automatically selects the specified equivalent stress for different materials to predict the failure of the component.

Item URL in elib:https://elib.dlr.de/208492/
Document Type:Thesis (Master's)
Title:Efficient Fatigue Modelling For Large Finite Element Models
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sharma, HarshitaRuhr-Universität Bochum, Interdisciplinary Center for Advanced Materials SimulationUNSPECIFIEDUNSPECIFIED
Date:2024
Open Access:No
Number of Pages:87
Status:Published
Keywords:Vibrations, resonance, fatigue, metals, short fiber reinforced plastics
Institution:Ruhr-Universität Bochum
Department:Interdisciplinary Center for Advanced Materials Simulation - ICAMS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Clean Propulsion
DLR - Research area:Aeronautics
DLR - Program:L CP - Clean Propulsion
DLR - Research theme (Project):L - Advanced Materials and New Manufacturing Technologies
Location: Köln-Porz
Institutes and Institutions:Institute of Materials Research > Experimental and Numerical Methods
Deposited By: Bartsch, Dr.-Ing. Marion
Deposited On:13 Nov 2024 08:58
Last Modified:13 Nov 2024 08:58

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