We are developing software which automates much of the design work for engineering problems in structural mechanics, heat transfer, and fluid dynamics. Computational topology design and optimization allows engineers to offload work to a computer, boosting their productivity. These techniques produce nonobvious designs which outperform those created manually. Widespread adoption of this technology will allow major advances in fields such as transportation, power generation, and aerospace.
Despite the power of topology optimization, it has very little adoption in industry. We believe that this is due to large barriers to entry, in the form large capital overhead from very expensive software licenses and the cost of HPC hardware. This is where Mechanical Mind comes in.
We plan to bundle our topology optimization software with compute services offered through AWS in a simple pay-as-you-go format. Users will be able to parameterize their design problems via a CAD plugin, then simply upload the compute job to our servers. Upon completion, the result is returned and the user is billed.
Typically, most topology optimization is based on a continuous approach, however we tackle this problem with a new discrete combinatoric method. This simplifies our solution search space and allows us to infer information about what sorts of solutions exist where. Also, as quantum computers mature, this solution approach directly maps to qubits, allowing near instantaneous solving by quantum methods.
Here's a simple proof of concept demo where a set of optimal cooling fins are designed to minimize the core temperature of a thermal body. Note that since this was run on a consumer laptop, the breadth of the search was limited for performance reasons. With more computational power, the quality of the solutions will improve.
https://imgur.com/a/T08nA
Any feedback or questions are appreciated! I'll be dropping in and out respond to them as they come.
