Computational design of microvascular biomimetic materials

Biomimetic microvascular materials are increasingly considered for a variety of autonomic healing, cooling and sensing applications. The microvascular material of interest in this work consists of a network of hollow microchannels, with diameters as small as 10 μm, embedded in a polymeric matrix. Recent advances in the manufacturing of this new class of materials have allowed for the creation of very complex 2D and 3D structures. The computational design of such network structures, which is the focus of this work, involves a set of particular challenges, including a large number of design variables (e.g., topology of the network, number of diameters to consider and their sizes) that define the network, and a large number of multidisciplinary objective functions and constraints that drive the optimization process. The computational design tool to be developed must be capable of capturing the trade-off between the different objective and constraint functions, as, for example, networks designed for flow efficiency are likely to have a topology that is very different from those designed for structural integrity or thermal control. In this work, we propose to design these materials using Genetic Algorithms (GAs), the most common methodology within a broader category of Evolutionary Algorithms (EAs). GAs can be combined with a Pareto-selection mechanism to create Multi-Objective Genetic Algorithms (MOGAs), which enable the optimization of an arbitrary number of objective functions. As a result, a Pareto-optimal front is obtained, where all candidates are optimal solutions to the optimization problem. Adding a procedure to deal with constraints results in a powerful tool for multi-objective constrained optimization. The method allows the use of discrete variable problems and it does not require any a priori knowledge of the optimal solution. Furthermore, GAs search the entire decision space so the optimal solutions found are likely to be global. The MOGA optimization framework is also combined with a physical solver based on advanced finite element methods to study the thermal behavior of these materials. Because the MOGA requires a vast number of individual evaluations, emphasis is placed on the computational efficiency of the solver. Thus, a simplified formulation is used to take into account the cooling effect of the fluid, instead of solving the conjugate heat transfer problem for obtaining the temperature field in both solid and fluid domains. The Generalized Finite Element Method (GFEM) is adopted because accurate finite element approximations of the temperature field can be obtained on finite element meshes that are independent of the geometry of the embedded network. Numerical experiments of multi-physics optimization involving flow efficiency, void volume fraction and thermal control are presented. Results show that the trade-offs between conflicting objectives is well captured so that the optimal design is readily available to the analyst.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Civil Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Aragón, Alejandro M.

Contributors dc:contributor

• Geubelle, Philippe H.
• Duarte, C. Armando
• White, Scott R.
• Tortorelli, Daniel A.

• Microvascular materials
• Active Cooling
• Multi-physics optimization
• Multi-objective genetic algorithms
• Generalized Finite Element Method (GFEM)

Statement dc:rights

• Copyright 2010 Alejandro Marcos Aragón

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/18271

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/18271