Water Sports: Hyper Hydration (2023)

Water Sports: Hyper Hydration is a foray into machine learning involving optimization algorithms. This project was developed as part of the Computational Explorations seminar at ITECH master's program in collaboration with Chris Kang, Markus Renner, and Cornelius Carl. The code and studies for this project can be found on GitHub.

The project's initial phase involved implementing various algorithms encompassing optimization techniques to efficiently optimize the shape of a water bottle.The primary objective was identifying optimal parameters for efficient water release, minimizing the time required for water to exit.

The single-objective optimization focused on maximizing water expulsion speed from the bottle.A Python script managed the process, leveraging algorithms to trigger simulation runs and initalizing the sequence of geometry generation, evaluation, and optimization.

The bottle's design utilized 5 circles with 6 control points each, totaling 30 parameters. Static bodies were consistently incorporated at both ends of the bottle, serving distinct purposes. The first housed water storage, while the final acted as the cap for controlled water release.

Initial observations from single-objective optimization were inconclusive regarding curvature patterns. Optimized single-objective designs aimed to enhance drainage efficiency through vortex creation, achieving an 11% improvement compared to a simple cylinder. Further analysis is needed to understand these improvements.

Single-Objective Convergence & Robustness

Results (in simulation steps)

None: 17,396	RBFOpt: 15,849	CMAE-S: 15,863	Random: 16,004

None: 17,396	RBFOpt: 15,849
CMAE-S: 15,863	Random: 16,004