Tools and techniques for building models for isogeometric analysis. (January 2019)

• Record Type: Journal Article
• Authors: Perduta, Anna; Putanowicz, Roman
• Is Part Of: Advances in engineering software. Volume 127(2019)
• Page Start: 70
• Page End: 81
• Publication Date: 2019-01
• Subjects: Isogeometric analysis -- IGA preprocessing -- Geometric modelling; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2018.10.008
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Special features of geometric models required by Isogeometric Analysis are discussed. Selection of popular tools for preparing IGA models is presented. Design principles for I/O facilities are resented. Preprocessing technique based on Rhino and Python is detailed. Building IGA models with Blender and Asymptote is discussed in details and advocated as flexible preprocessing solution. Abstract: Isogeometric Analysis (IGA) is a new computer method that has gained much attention in recent years. Its distinguishing feature is the use of parametric representation of curves, surfaces and solids, based on Non-Uniform Rational B-Splines, which are also used as shape functions in Galerkin approximation schemes. NURBS are commonly used in CAD modelling packages, thus among other advantages IGA facilitates use of CAD models as geometric domains for solving Boundary Value Problems. IGA is a relatively young method and despite its popularity there still is a small number of tools available, especially in comparison with Finite Element Method. This is notably nagging issue in case of tools for preparing geometric models for IGA. In this paper we discuss several popular tools and tricks that can be used in place of dedicated IGA preprocessor. The presented techniques can be particularly useful for researchers interested in starting work in IGA field, as they can speed up generation of comprehensive models and are incentive for data sharing, an important point from research reproducibility perspective.