As Systems Architect for the 2021 FSAE racecar, I owned high-level vehicle design, and I led a subteam of 10 engineers to optimize and select key powertrain and battery parameters. Under my direction, the simulation and motor selection process was completed in 2.5 months, compared to 4 months in a previous year.
One innovation that helped accelerate this process was the use of partial derivative sensitivities to drive initial downselection. With a small number of one-dimensional sweeps of ODE simulations, I determined the sensitivity of system performance to various parameters, without the need for costly sweeps through multidimensional parameter space as had been attempted in previous years. Early intuition and quantitative direction from these sensitivities served to reduce the time spent performing detailed simulations and considering component-specific qualitative factors.
I presented this work at the fall 2020 MIT Mechanical Engineering Research Exhibition (MERE) and won the prize for best first-time presenter. My poster presentation is shown below (information that may be of interest to competitor FSAE teams is redacted).
