DEVELOPMENT OF BOLTED FLANGE DESIGN TOOL BASED ON FINITE ELEMENT ANALYSIS AND ARTIFICIAL NEURAL NETWORK

Date

2015-11-19

Author

Yildirim, Alper
Kayran, Altan
Gulasik, Hasan
Çöker, Demirkan
Gürses, Ercan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

155
views

0
downloads

In bolted flange connections, commonly utilized in aircraft engine designs, structural integrity and minimization of the weight are achieved by the optimum combination of the design parameters utilizing the outcome of Many structural analyses. Bolt size, the number of bolts, bolt locations, casing thickness, flange thickness, bolt preload, and axial external force are some of the critical design parameters in bolted flange connections. Theoretical analysis and finite element analysis (FEA) are two main approaches to perform structural analysis of bolted flange connections. Theoretical approaches require the simplification of the geometry and are generally oversafe. In contrast, finite element analysis is more reliable but at the cost of high computational power. In this paper, a methodology is developed for iterative analyses of bolted flange that utilizes artificial neural network approximation of a database formed with more than ten thousand non-linear analyses with contact algorithm. In the design tool, a structural analysis database is created by taking permutations of the parametric variables. The number of intervals for each variable in the upper and lower range of the variables is determined with the parameters correlation study in which the significance of parameters are evaluated. The prediction of the ANN based design tool is then compared with FEA results and the theoretical approach of ESDU. The results show excellent agreement of the ANN based design tool with the actual non-linear finite element analysis results within the training limits of the ANN.

Subject Keywords

Bolted flange connection, Artificial neural network, Parameters correlation, FEA, Contact analysis

URI

https://hdl.handle.net/11511/55532

Collections

Department of Aerospace Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Development of bolted flange design tool based on finite element analysis and artificial neural network Yıldırım, Alper; Kayran, Altan; Department of Aerospace Engineering (2015) In bolted flange connections, commonly utilized in aircraft engine designs, structural integrity and minimization of the weight are achieved by the optimum combination of the design parameters utilizing the outcome of many structural analyses. Bolt size, number of bolts, bolt locations, casing thickness, flange thickness, bolt preload, and axial external force are some of the critical design parameters in bolted flange connections. Theoretical analysis and finite element analysis (FEA) are two main approach...
Development of structural optimization and neural network design tool for buckling behaviour of skin stringer structures under combined compression and shear loading Okul, Aydın; Gürses, Ercan; Department of Aerospace Engineering (2019) Stiffened panels are commonly used in aircraft structures in order to resist high compression and shear forces with minimum total weight. Minimization of the weight is obtained by combining the optimum design parameters. The skin panel dimensions, the stringer spacing and the stringer dimensions are some of the critical parameters which affect the global buckling behaviour of the stiffened panel. The aim of this thesis is to develop a neural network design tool and to carry out a geometric optimization for ...
Development of artificial neural network based design tool for aircraft engine bolted flange connection subject to combined axial and moment load Sanlı, Tahir Volkan; Kayran, Altan; Department of Aerospace Engineering (2018) In this thesis, a design tool using artificial neural network (ANN) is developed for the bolted flange connections, which enables the user to analyze typical aircraft engine connections subjected to combined axial and bending moment in a fast yet very accurate way. The neural network trained for the design tool uses the database generated by numerous finite element analyses for different combinations of parametric design variables of the bolted flange connection. The defined parameters are the number of bol...
Development of Structural Neural Network Design Tool for Buckling Behaviour of Skin-Stringer Structures Under Combined Compression and Shear Loading Okul, Aydın; Gürses, Ercan (2018-11-15) Stiffened panels are commonly used in aircraft structures in order to resist high compression and shear forces with minimum total weight. Minimization of the weight is obtained by combining the optimum design parameters. The panel length, the stringer spacing, the skin thickness, the stringer section type and the stringer dimensions are some of the critical parameters which affect the global buckling allowable of the stiffened panel. The aim of this study is to develop a design tool and carry out a geometri...
Development of Bolted Flange Design Tool Based on Artificial Neural Network Yıldırım, Alper; Akay, Ahmet Arda; Gülaşık, Hasan; Çöker, Demirkan; Gürses, Ercan; Kayran, Altan (ASME International, 2019-7-17) <jats:p>Finite element analysis (FEA) of bolted flange connections is the common methodology for the analysis of bolted flange connections. However, it requires high computational power for model preparation and nonlinear analysis due to contact definitions used between the mating parts. Design of an optimum bolted flange connection requires many costly finite element analyses to be performed to decide on the optimum bolt configuration and minimum flange and casing thicknesses. In this study, very fast resp...

Citation Formats

A. Yildirim, A. Kayran, H. Gulasik, D. Çöker, and E. Gürses, “DEVELOPMENT OF BOLTED FLANGE DESIGN TOOL BASED ON FINITE ELEMENT ANALYSIS AND ARTIFICIAL NEURAL NETWORK,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55532.