Optimization of Spring-Damper Orientation in Double-Wishbone Type Suspension Geometry Using Genetic Algorithm in Python BITS Pilani Hyderabad Campus

Format:

Book

Conference/Event

Author/Creator:

Madhan, Pratik, author.

Contributor:

Gandhi, Om

Conference Name:

International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility (2021-10-08 : Chennai, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Orientation of the spring-damper system in a suspension geometry is a critical but hidden factor in vehicle performance characteristics. Spring and damper mounting characteristics are the significant factors to ensure proper contact of the tire with the ground, maintaining ride height, minimizing forces on spring, smooth ride, and driver comfort. Determining the spring orientation is conventionally a long and iterative process that involves computational simulations and processing of analytical expressions, which should align with the practical vehicle constraints. Due to numerous possible orientations, the designer would randomly pick the orientation and do the simulation, which reduces the reliability of the solution and the better solutions remain unexplored. This paper proposes a new methodology to optimize spring damper orientation in a suspension geometry using a genetic algorithm in Python Programming Language. This model aims to use genetic algorithm to optimize the spring-damper system's orientation in double-wishbone type suspension geometry by maintaining optimal ride frequency and static ride height, minimizing shear forces on spring based on the constraints and variations in its mounting position.Further, the fitness of each is calculated based on performance characteristics developed through specific orientation. The algorithm provides the 5 best solutions out of hundreds of possible orientations, allowing designers to further narrow down from optimized results based on CAD simulations. Analytical equations used to calculate performance parameters in genetic algorithm predicted results much closer to that obtained on ADAMS software

Notes:

Vendor supplied data

Publisher Number:

2021-28-0256

Access Restriction:

Restricted for use by site license