Wave Energy Converter

Ocean wave energy, capable of generating 34% of the US's electricity needs with current technologies, stands as a significant but untapped resource for achieving our nation's carbon neutrality goal. It is at least five times more energy dense than wind, can be harnessed on both coasts, Hawaii, and Alaska, and is consistent throughout the day unlike its wind and solar counterparts, eliminating the need for costly energy storage. However, the complex marine environment has driven the wave power industry toward large, immobile, and material-intensive designs whose high costs, unfortunately, render them economically non-competitive

Out of the Theoretical & Applied Fluid Dynamics Lab, our team aimed to build a prototype wave energy converter (WEC) that leveraged inflatables and a streamlined power take-off system to develop a cost-effective method in harnessing energy from our oceans. I led the team of five engineers in developing the prototype by fostering collaboration in task orchestration, role delineation, and strategic planning.

We began the first phase of our project by breaking down the functions of a WEC to its most lean components and conducting literature review to identify the WEC design that strikes a balance between simplicity and power output. By then detailing our core requirements and goal to portably charge a mobile device, we established a framework for our prototype's development. 

Recognizing that the feasibility of harnessing wave energy hinges significantly on its cost-effectiveness, our team aggressively committed to incorporating affordable materials, embracing a straightforward design, and demonstrating scalability. Consequently, it informed us to focus on these key performance metrics: 1) maximize power output and efficiency, and 2) minimize cost.

Through our research, we concluded on the structure of our WEC system and identified the various design parameters that would impact its performance. After constructing a table-top model to confirm functionality, we proceeded with designing an experiment to optimize the system's configuration across a range of simulated wave profiles and tunable parameters for maximum power efficiency and output.

I engineered the data acquisition architecture and developed data analysis software to automate and streamline the data processing for system performance optimization. The software utilized MATLAB computer vision and Python tools to synchronize time-varying data, isolate measurement streams, and integrate them to output performance metrics. For fun, I also constructed a Neural Network machine learning model to aid us in understanding the characteristics of the system and how it can be further improved.

In final, we completed our most ambitious goal of building a functioning wave energy converter prototype that was capable of charging a mobile device. Additionally, its inflatable and lean design allows for easy deployment, serviceability, and portability, providing a possible approach for pain points of current wave power solutions. During simulated runs and at current size, we measured efficiencies of around 60% with peak power outputs over 50W. However, we estimate that with a few configuration tweaks, we can achieve power of over 75W at this size.