Team 5: Multi-objective design of a fuel tank
As the complexity of the problem grows, so does the amount of data generated during an optimization. This adds on the challenge of interpreting the data. We will investigate different methods for representing the data. Visualization methods will be considered in two groups- those suited to developing a greater understanding of the problem (for team members) and those suited to presenting results (for customers).
This project is intended to give a flavor of what an industrial mathematician does throughout the lifecycle of a given project. This project has 3 steps:
- Model the various components representing each discipline (some components will be provided) and link them together into a system.
- Implement solution to multi-objective optimization problem, including choosing which problem formulation to use and which optimization libraries to use.
- Explore various methods of visualizing data to best communicate differences between designs.
Prerequisites: All team members must have some computing skills (Matlab, Java, Python, or C++). Some familiarity (or interest) in design optimization is desired.
Schuman, T., De Weck, O., Sobieski, J. (2005) Integrated System-Level Optimization for Concurrent Engineering with Parametric Subsystem Modeling AIAA 2005-2199.
De Weck, O. (2004), Multidisciplinary System Design Optimization (MSDO): Decomposition and Coupling, Module 6 Notes, MIT OpenCourseWare 16.888 / ESD.77, Massachusetts Institute of Technology.
Cramer, E. J., J. E. Dennis, Jr., P. D. Frank, R. M. Lewis, G. R. Shubin (1994), Problem formulation for multidisciplinary optimization, SIAM Journal of Optimization 4 (4): 754-776.