Isogeometric modeling and analysis for complex science and engineering applications

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2021-08
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Johnson, Emily Lyder
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Hsu, Ming-Chen
Ganapathysubramanian, Baskar
Hu, Chao
Krishnamurthy, Adarsh
Passalacqua, Alberto
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Mechanical Engineering
Abstract
Despite numerous technological developments over the last few decades, considerable obstacles remain in the practical design and scientific analysis of complex engineering problems. Additionally, for many applications, real-world engineering structures can be difficult or prohibitive to study experimentally due to the scale, cost, and accessibility of the systems. Two globally relevant areas of interest that continue to pose engineering challenges are healthcare and renewable energy production, in which cardiac health and wind energy systems introduce numerous complications for experimental approaches. With such complex engineering problems, computational methods provide a suitable alternative for scientific investigation. One challenge associated with computational modeling and simulation arises from converting computer-aided design models into a suitable format for traditional analysis methods. When isogeometric analysis (IGA) was originally proposed, it addressed this disconnect by incorporating the same representation of the geometry model for both design and analysis. While IGA has been proven as an efficient method to facilitate design-to-analysis, additional strategies are often necessary when simulating more complicated structures using IGA. The presented computational methods address some of the fundamental challenges of isogeometric modeling and analysis for complex science and engineering applications. This research demonstrates the effectiveness of the proposed simulation approaches for numerous science and engineering problems, including wind turbine blades and multi-component heart valves. Innovative solutions are also presented for the design and parameterization of engineering structures, which can facilitate data-oriented research, including design optimization, sensitivity analysis, uncertainty quantification, and machine learning.
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