Modal Analysis and Performance Optimization of Car Suspension Systems

In this project, I focused on analyzing and optimizing the dynamic performance of a car suspension system using modal analysis. Vehicle suspensions are critical for ensuring ride comfort and road handling, and understanding how they behave under dynamic loading is key to designing better systems. To begin, I modeled the suspension as a two-degree-of-freedom mass-spring-damper system, representing the sprung and unsprung masses of the car. I applied modal analysis to decompose the system into its natural frequencies and mode shapes, which allowed for better insight into how different vibration modes affect the system's performance.

To simulate how the suspension responds to real-world disturbances, I used Euler’s method to perform time integration on the decoupled equations. The dynamic response helped identify the limitations of the initial design, particularly in terms of settling time. To improve system performance, I implemented a gradient descent algorithm to optimize key parameters such as spring constants and damping coefficients. The result was a suspension configuration with significantly reduced settling time, making the system more stable and responsive. This project demonstrated how mathematical modeling and numerical optimization can be combined to solve complex engineering problems in vehicle dynamics.