Combustion engines, propellers, and hydraulic pumps are examples of fluidic devices – instruments that use fluids to perform certain functions, such as generating power or transporting water.
Because fluidic devices are so complex, they are typically developed by experienced engineers who design, prototype, and manually test each device through an iterative process that is expensive, time-consuming, and laborious. But with a new system, the user only has to specify the locations and velocities at which the fluid enters and exits the device – the computation pipeline then automatically generates an optimal design that achieves these goals.
The system could make it faster and cheaper to design fluidic devices for all sorts of applications, such as microfluidic labs on a chip that can diagnose diseases from a few drops of blood or artificial hearts that could save people’s lives. transplant patients.
Recently, computer tools have been developed to simplify the manual design process, but these techniques have had limitations. Some required a designer to specify the shape of the device in advance, while others represented shapes using 3D cubes, called voxels, which resulted in boxy and inefficient designs.
The computational technique developed by researchers at MIT and elsewhere overcomes these pitfalls. Their design optimization framework does not require a user to make assumptions about what a device should look like. Additionally, the shape of the device automatically evolves during optimization with smooth, rather than blocky, inaccurate boundaries. This allows their system to create more complex shapes than other methods.
“Now you can perform all of these steps seamlessly in a compute pipeline. And with our system, you could potentially create better devices because you can explore new designs that have never been explored using manual methods. There may be shapes that have not yet been explored by experts,” says Yifei Li, a graduate student in electrical engineering and computer science who is the lead author of a paper detailing this system.
Co-authors include Tao Du, a former post-doctoral fellow at the Computer Science and Artificial Intelligence Laboratory (CSAIL) who is now an assistant professor at Tsinghua University; and lead author Wojciech Matusik, professor of electrical engineering and computer science, who leads the computer design and manufacturing group at CSAIL; along with others at the University of Wisconsin at Madison, LightSpeed Studios, and Dartmouth College. The research will be presented at ACM SIGGRAPH Asia 2022.
Shaping a fluidic device
The researchers’ optimization pipeline begins with a blank three-dimensional region that has been divided into a grid of tiny cubes. Each of these 3D cubes, or voxels, can be used to form part of the shape of a fluidic device.
One thing that separates their system from other optimization methods is how it represents or “parameterizes” these tiny voxels. Voxels are parameterized as anisotropic materials, that is, materials that give different responses depending on the direction in which force is applied to them. For example, wood is much weaker to forces applied perpendicular to the grain.
They use this model of anisotropic material to parameterize voxels as fully solid (as would be found outside the device), fully liquid (the fluid inside the device), and voxels that exist inside the device. solid-fluid interface, which have solid and liquid material properties.
“When you go in the solid direction, you want to model the material properties of solids. But when you go in the fluid direction, you want to model the behavior of fluids. That’s what inspired us to use anisotropic materials to represent the solid-fluid interface. And this allows us to model the behavior of this region very precisely,” Li explains.
Their compute pipeline also thinks about voxels differently. Instead of just using voxels as 3D building blocks, the system can tilt the surface of each voxel and change its shape very precisely. Voxels can then be formed into smooth curves that allow for complex designs.
Once their system forms a shape using voxels, it simulates how fluid flows through that design and compares it to user-defined goals. Then he adjusts the design to better meet the goals, repeating this pattern until he finds the optimal shape.
With this design in hand, the user could use 3D printing technology to fabricate the device.
Demonstration of designs
Once the researchers created this design pipeline, they tested it against state-of-the-art methods known as parametric optimization frameworks. These frameworks require designers to specify in advance what they think the shape of the device should be.
“Once you make that assumption, all you’ll get are variations of the shape within a shape family,” Li says. “But our framework doesn’t need you to make such variations. assumptions because we have such a high degree of design freedom representing this domain with many tiny voxels, each of which can vary in shape.”
In every test, their framework outperformed the baselines by creating smooth shapes with intricate structures that would likely have been too complex for an expert to specify in advance. For example, he automatically created a tree-shaped fluidic diffuser that transports liquid from a large inlet to 16 smaller outlets while bypassing an obstacle in the middle of the device.
The pipeline also generated a propeller-shaped device to create a twisting flow of liquid. To achieve this complex shape, their system automatically optimized nearly 4 million variables.
“I was really excited to see that our pipeline was able to automatically develop a fan-shaped device for this fluid twister. This form would lead to a very powerful device. If you model that lens with a parametric shape frame, because it can’t develop such a complex shape, the final device wouldn’t perform as well,” she says.
Although she was impressed with the variety of shapes it could automatically generate, Li plans to improve the system by using a more complex fluid simulation model. This would allow the pipeline to be used in more complex stream environments, which would allow it to be used in more complex applications.
“This work contributes to the important problem of automating and optimizing the design of fluidic devices, which are found almost everywhere,” says Karl Willis, senior research director at Autodesk Research, who was not involved. to this study. “This brings us closer to generative design tools that can both reduce the number of human design cycles needed and generate new, optimized and more efficient designs.”
This research was supported, in part, by the National Science Foundation and the Defense Advanced Research Projects Agency.
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