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Computational instability analysis of inflated hyperelastic thin shells using subdivision surfaces

Zhaowei Liu, Andrew McBride, A. Ghosh, Luca Heltai, Weicheng Huang, Tiantang Yu, Paul Steinmann, Prashant Saxena

2023Computational Mechanics14 citationsDOIOpen Access PDF

Abstract

Abstract The inflation of hyperelastic thin shells is a highly nonlinear problem that arises in multiple important engineering applications. It is characterised by severe kinematic and constitutive nonlinearities and is subject to various forms of instabilities. To accurately simulate this challenging problem, we present an isogeometric approach to compute the inflation and associated large deformation of hyperelastic thin shells following the Kirchhoff–Love hypothesis. Both the geometry and the deformation field are discretized using Catmull–Clark subdivision bases which provide the required $$C^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>C</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> -continuous finite element approximation. To follow the complex nonlinear response exhibited by hyperelastic thin shells, inflation is simulated incrementally, and each incremental step is solved using the Newton–Raphson method enriched with arc-length control. An eigenvalue analysis of the linear system after each incremental step assesses the possibility of bifurcation to a lower energy mode upon loss of stability. The proposed method is first validated using benchmark problems and then applied to engineering applications, where the ability to simulate large deformation and associated complex instabilities is clearly demonstrated.

Topics & Concepts

Hyperelastic materialComputational Science and EngineeringInstabilitySubdivisionComputational mechanicsShell (structure)MechanicsMaterials scienceMathematicsStructural engineeringPhysicsFinite element methodApplied mathematicsComposite materialEngineeringCivil engineeringAdvanced Numerical Analysis TechniquesTribology and Lubrication EngineeringComposite Structure Analysis and Optimization