Group Members: Georgia McSwain, Grace Chen, Sena Seojin Lee, Ziad Mosalam, Elise Vergos, Aijia Tao
Abstract:
Microplastics pose a significant threat to aquatic ecosystems, yet existing removal technologies struggle to capture these pollutants efficiently. This study applies Multidisciplinary Design Optimization (MDO) to the development of MOLLUSCA, an autonomous surface vehicle designed for microplastic recovery.
MOLLUSCA features an undulating plate mechanism to collect floating plastics while minimizing water disturbance, addressing key limitations in current removal methods.
The optimization aims to maximize plastic recovery, while minimizing both battery size and solar panel area. This dual goal balances environmental benefit against hardware mass and cost. The system is decomposed into five subsystems: geometry, stability, speed, plastic recovery, and power. A Design of Experiments (DOE) methodology explores the design space, generating an initial design vector that enhances computational efficiency for gradient-based optimization of plastic mass recovery using
Sequential Quadratic Programming (SQP). Sensitivity analysis of this single objective optimization shows that collection plate width, operating time, and RPM significantly impact recovery performance. Multiobjective optimization was performed using the Normal Boundary Intersection Method to balance the ASV’s environmental effectiveness with system affordability, and a final recommended design for the MOLLUSCA robot was produced from the results of this optimization.
30 April 2025
14 May 2025
Assignments that lead up to the final reports are listed below.
31 January 2025
6 March 2025
27 March 2025
24 April 2025