ARCHIVES
Material Optimization in Formula One Seat Fit Based on Structural and Biomechanical Analysis
Published Online: March-April 2024
Pages: 37-44
Cite this article
↗ https://www.doi.org/10.59256/ijire.20240502006Abstract
F1 drivers have reported that they suffer a long-term lower back pain due to ‘Porpoising’ effect, it is a series of bounce that is generated due to Aerodynamic downforce. This downforce is part of drag reduction principle that pulls the air underneath the vehicle, thereby creating an incredible speed of 200 to 230 mph. Also at 200 mph speed, the driver experiences a high amount G-forces up to six times during the race. This can be simply avoided by increasing the ground clearance of vehicle but at the same time it also reduces the max speed of the vehicle. Hence, without compromising the speed, Design optimization is done for driver’s seat-fit through Computational methods of Biomechanical modelling and simulation for determining the optimum Seat Angle for Postural Ergonomics. As an additional Reinforcement and Shock absorption in seat material to protect driver’s spine from the resulting dynamics, Material optimization is done by selecting Graphene as the suitable material over the existing Carbon fiber material. Finite element method was carried out for structural analysis of seat-fit model. The stress, strain and deformation values were found to be lesser in Graphene model when compared to Carbon fiber. The simulation results will provide a solution for eliminating the higher risk of spine injury or pathological condition of a sportsman and thereby improves the sporting performance
Related Articles
2024
Embedding Artificial Intelligence for Personal Voice Assistant Using NLP
2024
Analysis of Pedestrian Steel Bridge subjected the Seismic Load and Wind Load using Damper at different Span
2024
Review Paper on Comparison of Asymmetric and Symmetric RCC Building with Soil Structure Interaction by Dynamic Loading
2024
BLYNK RFID and Retinal Lock Access System
2024
ML-Driven Facial Synthesis from Spoken Words Using Conditional GANs
2024