In 2001 White et al. introduced the concept of self-healing composite materials, where a polymeric matrix contains embedded microcapsules that encapsulate a healing agent. The agent is released and polymerizes with a catalyst, also embedded in the polymeric matrix, after being in contact with a crack. This system suffered from the inability to provide continuous healing over time, since the healing agent contained in the microcapsules is depleted after a few healing cycles.
In an effort to overcome this problem, microvascular materials were considered for a variety of autonomic healing and active cooling applications. The microvascular material consists of a network of hollow microchannels (with diameters as small as 10 μm) embedded in a polymeric matrix. Advances in 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 involves a set of interesting challenges in terms of the large number of design variables (e.g., topology of the network, number of diameters to consider and their sizes) that define the network, and of the large number of multidisciplinary objective and constraint functions that drive the optimization process. The computational design developed are capable of capturing the trade-offs between the different objective and constraint functions since, 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.
The objective of this research is to design these microvascular materials using Genetic Algorithms (GAs), the most common methodology within a broader category of Evolutionary Algorithms (EAs). GAs are 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 optimal solutions. Furthermore, GAs search a wider region of the design space so optima found are likely to be global. The MOGA optimization framework is also combined with an advanced finite element solver to study thermal behavior. Because the MOGA requires a vast number of individual evaluations, emphasis is placed on computational efficiency. 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. Numerical experiments are conducted of multi-physics optimization involving flow efficiency, void volume fraction, and thermal control. Results show the trade-offs between conflicting objectives is well captured so that the optimal design is readily available to the analyst.