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Technical Paper

Computational Process for Wind Noise Evaluation of Rear-View Mirror Design in Cars

2014-04-01
2014-01-0619
A computational approach to evaluate rear-view mirror performance on wind noise in cars is presented in this paper. As a comfort metric at high speeds, wind noise needs to be addressed, for it dominates interior noise at mid-high frequencies. The impetus on rear-view mirror design arises from its crucial role in the flow field and the resulting pressure fluctuations on the greenhouse panels. The motivation to adopt a computational approach arises from the need to evaluate mirror designs early in vehicle design process and thus in conjunction with different vehicle shapes. The current study uses a Lattice Boltzmann method (LBM) based computational fluid dynamics(CFD) solver to predict the transient flow field and a statistical energy analysis(SEA) solver to predict interior noise contribution from the greenhouse panels. The accuracy of this computational procedure has been validated and published in the past.
Technical Paper

Design of Roof-Rack Crossbars for Production Automobiles to Reduce Howl Noise using a Lattice Boltzmann Scheme

2007-05-15
2007-01-2398
A computational design study, performed in conjunction with experiments, to reduce the howl noise caused by the roof rack crossbars of a production automobile is presented. This goals were to obtain insight into the flow phenomenon causing the noise, and to do a design iteration study that would lead to a small number of cross-section recommendations for crossbars that would be tested in the wind tunnel. The flow condition for this study is 0 yaw at 30 mph inlet speed, which experimentally gives the strongest roof rack howl for the vehicle considered for this study. The numerical results have been obtained using the commercial CFD/CAA software PowerFLOW. The simulation kernel of this software is based on the numerical scheme known as the Lattice Boltzmann Method (LBM), combined with a two-equation RNG turbulence model.
Technical Paper

Integration of SEA Tire Model with Vehicle Model

1999-05-17
1999-01-1700
Statistical energy analysis (SEA) has recently emerged as an effective tool for design assessment in the automotive industry. Automotive OEM companies develop vehicle models to aid design of body and chassis systems. The tire and wheel suppliers develop and supply component models to OEM companies in the engineering stage. In the model development process, some information on the vehicle side or component side is necessary for model development and correlation. A suitable termination representation of the vehicle characteristics on the tire/wheel model is required. This termination should account for the dissipation of energy on vehicle body and chassis side, otherwise the component model will overestimate the vibration responses and energy levels. On the vehicle model side, a representative simplified tire/wheel model may be sufficient for full vehicle road noise simulation.
Technical Paper

Multi-Disciplinary Aerodynamics Analysis for Vehicles: Application of External Flow Simulations to Aerodynamics, Aeroacoustics and Thermal Management of a Pickup Truck

2007-04-16
2007-01-0100
During the design process for a vehicle, the CAD surface geometry becomes available at an early stage so that numerical assessment of aerodynamic performance may accompany the design of the vehicle's shape. Accurate prediction requires open grille models with detailed underhood and underbody geometry with a high level of detail on the upper body surface, such as moldings, trim and parting lines. These details are also needed for aeroacoustics simulations to compute wall-pressure fluctuations, and for thermal management simulations to compute underhood cooling, surface temperatures and heat exchanger effectiveness. This paper presents the results of a significant effort to capitalize on the investment required to build a detailed virtual model of a pickup truck in order to simultaneously assess performance factors for aerodynamics, aeroacoustics and thermal management.
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