Fast Contact Method for Speeding up Solving of Finite Element Problems involving Non-Linear Contact Behavior

Paper #:
  • 2017-01-2021

Published:
  • 2017-09-19
Citation:
Mazgaonkar, N. and Stankovich, A., "Fast Contact Method for Speeding up Solving of Finite Element Problems involving Non-Linear Contact Behavior," SAE Technical Paper 2017-01-2021, 2017.
Pages:
5
Abstract:
For large aerospace assemblies in finite element (FE) analysis problems, contact interaction between the surrounding bodies has to be established to simulate the load transferred between the components, like aircraft engine carrying bracket assemblies, spigots assemblies etc., and understand the effects of interaction between respective parts. In some cases, depending upon geometry of the assembly, the region of study may not be contact area but the stresses acting within the parts themselves. If there is no geometric or material non-linearity in such problems, a new contact formulation method known as Fast Contact can be used in these contact regions. In this method, contact non-linearity could be introduced to simulate the problem but friction between the contacting parts should not be present. Currently, there is a scope for applying this method for solving FE problems in the aerospace and rail industry. This paper focuses on this problem-solving method which has been developed by Altair’s OptiStruct to solve such FE problems accurately in short period of time. To show the advantages of time savings, this method has been applied to a real aerospace engine bracket model. Theoretical validation of this method is shown by comparing it with the Hertz contact theory. A model of the sphere-section in contact with the rectangular plate is used to prove the validation. Further, this method is also compared against the standard non-linear contact method. Since this method for contact analysis is faster to solve, it should be helpful to analyst who work with large assemblies, where multiple parts are in contact with each other. The method becomes even more helpful when performing an optimization or a design of experiments (DOE) with a large number of runs.
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