Including combustion engines and propellers, and cutting-edge technology such as microfluidic ‘labs-on-a-chip’ or artificial organs, fluidic devices use fluids to perform functions such as generating power or transporting water.
Because they are so complex, fluidic devices are typically developed by experienced engineers who manually design, prototype, and test each apparatus through an iterative process that the researchers described as “expensive, time-consuming, and labour-intensive”.
Recently, computational tools have been developed to simplify the manual design process, but the researchers said they have limitations. Some require a designer to specify the device’s shape in advance, while others represent shapes using 3D cubes, known as voxels, that result in “boxy, ineffective designs”.
With the new system, users only need to specify the location and speeds at which fluid enters and exits a device. The computational ‘pipeline’ then automatically generates an optimal design that achieves those objectives.
The device’s shape automatically evolves during the optimisation with smooth, rather than blocky, inexact boundaries. This enables their system to create more complex shapes than other methods.
“Now you can do all these steps seamlessly in a computational pipeline. And with our system, you could potentially create better devices because you can explore new designs that have never been investigated using manual methods. Maybe there are some shapes that haven’t been explored by experts yet,” said Yifei Li, an electrical engineering and computer science graduate student who is lead author of a paper detailing the system.
The optimisation pipeline begins with a blank, three-dimensional region that has been divided into a grid of tiny cubes. Each of these voxels can be used to form part of the shape of a fluidic device.
One thing that separates their system from other optimisation methods is how it represents, or ‘parameterises’, the tiny voxels. The voxels are parameterised as anisotropic materials, which give different responses depending on the direction in which force is applied to them. Wood is much weaker to forces that are applied perpendicular to the grain, for example.
The team used this anisotropic material model to parameterise voxels as entirely solid (like the outside of a device), entirely liquid (the fluid within the device), and voxels that exist at the solid-fluid interface, which have properties of both solid and liquid material.
Their computational pipeline also thinks about voxels differently. Instead of only using voxels as 3D building blocks, the system can angle the surface of each voxel and change its shape in precise ways. Voxels can then be formed into smooth curves that enable intricate designs.
Once their system has formed a shape using voxels, it simulates how fluid flows through that design and compares it to the user-defined objectives. Then it adjusts the design to better meet the objectives, repeating this pattern until it finds the optimal shape. Designs could then be 3D-printed by the user.
The researchers used the technique to create intricate structures to transport liquid, and a propeller-shaped device for a ‘fluid twister’.
Li plans to enhance the system by using a more complex fluid simulation model. This would enable the pipeline to be used in more complex flow environments, and more complicated applications.
This research was supported by the US National Science Foundation and the Defence Advanced Research Projects Agency (Darpa).
Co-authors included Tao Du, a former postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) who is now an assistant professor at Tsinghua University, senior author Wojciech Matusik, professor of electrical engineering and computer science at CSAIL, and others from the University of Wisconsin at Madison, LightSpeed Studios, and Dartmouth College.
The research will be presented at ACM SIGGRAPH Asia 2022.
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Content published by Professional Engineering does not necessarily represent the views of the Institution of Mechanical Engineers.