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A method including Multi-Body Dynamics (MBD) and fatigue assessment process with modal approach was developed to predict Light Commercial Van (LCV) Rear French Doors open/close durability performance during early design stage to improve test detect ability. The nonlinear properties of joints, such as those on bolted housings or spot welds sheets and hem flange areas, can substantially influence the local and global results of a dynamic simulation. The Modal approach considers joint contact, by way of Joint Interface Modes (JIMs) by using Contact Subroutine (MAMBA) to co-simulate with MBD software to improve result quality. One of the main challenges is measuring the dynamic stiffness for the weather strip. A novel test method was used to measure the weather strip dynamic stiffness by conducting an “in-situ” test. For CAE simulation results, positive feedback was received from design and test engineers.

For most car manufacturers, aerodynamic noise is becoming the dominant high frequency noise source (≻ 500 Hz) at highway speeds. Design optimization and early detection of issues related to aeroacoustics remain mainly an experimental art implying high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the development of a reliable numerical prediction capability. This paper presents a computational approach that can be used to predict the vehicle interior noise from the greenhouse wind noise sources, during the early stages of the vehicle developmental process so that design changes can be made to improve the wind noise performance of the vehicle.

For most car manufacturers, aerodynamic noise is becoming the dominant high frequency noise source (≻500 Hz) at highway speeds. Design optimization and early detection of issues related to aeroacoustics remain an experimental art implying high cost prototypes, expensive wind tunnel sessions, and potentially late design changes. To reduce the associated costs as well as development times, there is strong motivation for the development of a reliable numerical prediction capability. This paper presents a computational approach that can be used to predict the vehicle interior noise from the greenhouse wind noise sources, during the early stages of the vehicle developmental process so that design changes can be made to improve the wind noise performance of the vehicle.

The high cost and competitive nature of automotive product development necessitates the search for less expensive and faster methods of predicting vehicle performance. Continual improvements in High Performance Computing (HPC) and new computational schemes allow for the digital evaluation of vehicle comfort parameters including wind noise. Recently, the commercially available Computational Fluid Dynamics (CFD) code PowerFlow, was evaluated for its accuracy in predicting wind noise generated by an external automotive tow mirror. This was accomplished by running simulations of several mirror configurations, choosing the quietest mirror based on the predicted performance, prototyping it, and finally, confirming the prediction with noise measurements taken in an aeroacoustic wind tunnel. Two testing methods, beam-forming and direct noise measurements, were employed to correlate the physical data with itself before correlating with simulation.