Search Results

Nowadays, the influence of aerodynamics on vehicles capabilities is mostly studied in terms of energy efficiency; maximum speed; maximum linear accelerations; cooling capacity of brake systems; resistance and deformation of elements exposed to aerodynamic forces; stability during lateral wind and during braking for straight ahead maneuvers; noise caused by airflow; proper evacuation of exhaust gases and aesthetics of cars. Generally, a model for CFD analysis is used and six constant coefficients are determined. However, there is insufficient information about the interaction of vehicle aerodynamics with vehicle suspension and the effects that this interaction generates on the dynamic behavior of the vehicle. In this work this interaction is studied, and there is an analysis of how vehicle aerodynamic characteristics impact on suspension behavior and how suspension characteristics could diminish or amplify aerodynamic.

Automotive industry has shown, in the recent years, a dramatically increase of competition at emergent markets. The incoming of new Brands, for example in the Brazilian market, is causing the OEMs to decrease costs while increase quality, which represents a big challenge nowadays. In this challenging scenario, virtual simulation has become mandatory. While cutting costs since no physical prototypes are required, virtual models also reduces development time. Time to time, as computers processing capacity grows, virtual models are becoming more and more accurate, being able to capture even high non-linear phenomena, which ten years ago would not be feasible. It is also known the natural tendency of vehicle dynamics engineers to develop shock absorber tuning only by means of subjective evaluation. Many reasons can be raised to this tendency, but one of them is the lack of representation of the entire shock absorber behavior in the virtual models.

The lateral drift of a vehicle while driving straight ahead has been a major concern of the OEMs on the South America market. Due to its natural way to evaluate and also to different types of roads on the market (roads with different types of bank angle), a vehicle with slight tendency to drift will certainly be a reason of customer complaint. Since such a vehicle dynamics property is very sensible to small variation of some parameters, such as road bank angle, alignment setting, etc, sometimes the subjective evaluation tends to become worthless due to the parameters control. In this scenario, simulation becomes important. As it is a quite difficult subject, since there is big influence of small parameters variation, the usage of statistical approach allow to obtain better understanding of the phenomenon. This work presents a statistical approach for simulation based on DOE analysis and Monte Carlo method.

Through the new Automotive Regulation recently deployed by ANFAVEA, vehicles selled in Brazil will have strong incentives to reduce fuel consumption. OEMs are being pushed to develop alternatives to achieve lower levels of fuel consumption either by new engines or reduce overall weight for the vehicles. Reducing the weight is being the logical and first choice by automakers, since it represents a cost reduction as well. The problem lies on the crash resistance (Latin NCAP) that forces the vehicle to have reinforcements, especially in the near future for the lateral crash standard, still to be deployed in the market. Thus, in the opposite direction, there is a natural tendency to increase sprung mass. In this way, the solution is to reduce non-sprung masses. The non-sprung parts likely to be reduced are normally components that impact the customer feeling the least, for example: dampers, stabilizer bars, tires, wheels, etc.

Functional vehicle dynamics simulation differs from the regular multi-body simulation especially by means of modeling parameters. The models are usually parametric, involving suspension properties that are commonly used by OEM's. Input data for a multi-body simulation (MBS) is, normally, raw information about suspension hard points coordinates, flexible elements stiffness (such as bushing and springs), etc. As for a functional simulation, the input data can be one outcome of a multi-body one, such as K&C data or even measured data of a bench test. However, the results of both ways of simulation must be the similar. Functional simulation software does not solve the multi-body equations for each integration step as any regular MBS software and, as such, can run the model in a faster way. In this way, software-in-the-loop and hardware-in-the-loop starts to become a clear advantage of these art of simulation.