Artificial road input data generation tool for vehicle durability assessment using artificial intelligence

Ogunoiki, Adebola Oluwaseyi (2015). Artificial road input data generation tool for vehicle durability assessment using artificial intelligence. University of Birmingham. Ph.D.

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Abstract

Vehicle durability assessment in the automotive industry requires a good knowledge of the road load input the vehicle will experience while in service. This research explores the approach of artificial intelligence for predicting the road load input for road load simulation in the CAE environment prior to the development of a vehicle prototype.
The multi-body dynamics (MBD) simulation of a quarter vehicle test rig, built with the specification of a commercial SUV, and the full vehicle of the same SUV were modelled and validated in SIMPACK using a simple tyre model developed using the tri-axial tyre test rig at the University of Birmingham. The models were used to carry out a road load data characterisation based on the variation in vehicle parameters.
An artificial road input tool (ARIT) based on an optimised NARX artificial neural network architecture was developed to predict the road input for variants of vehicle for a particular vehicle behaviour over a road event.
The results of the ARIT were used to run MBD simulations and compared with those from drive file iteration. The results of this research show a successful method of artificial intelligence for the generation of road load data from CAE simulations.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

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