Search Results

Accurate prediction of in-vehicle powertrain bounce mode is necessary to ensure optimum responses are achieved at driver’s touch points during 4post shake or rough road shake events. But, during the early stages of vehicle development, building a detailed vehicle finite element (FE) model is not possible and often powertrain bounce modes are computed assuming the powertrain to be a stand-alone unit. Studies conducted on FE models of a large SUV with body on frame architecture showed that the stand-alone approach overestimates the powertrain bounce mode. Consequently, there is a need for a simplified version of vehicle model which can be built early on to compute powertrain modes. Previously, representing all the major components as rigid entities, simplified unibody vehicle models have been built to compute powertrain modes. But such an approach would be inaccurate here, for a vehicle with body on frame architecture due to the flexible nature of the frame (even at low frequencies).

In the NVH domain, the cooling module is an important subsystem in ground vehicles. Recently, with the development of small high output turbocharged internal combustion (IC) engines, cooling module noise and vibration has become more challenging. Furthermore, with plug-in hybrid electric vehicle (PHEV), in some cases the cooling fan could be operational while the IC engine is not running. This poses a significant challenge for cabin noise enhancement. Small turbocharged IC engines typically require higher cooling capacity resulting in larger fan size designs with higher speed. Accurate prediction of the unbalance loads generated by cooling fan and loads transferred to the body are critical for the Noise Vibration and Harshness (NVH) performance of the vehicle. If the NVH risk of cooling module operation is not well quantified and addressed early in the program, attempts to find solutions in post launch stage could be very expensive and not as effective.

Idle shake is an important NVH attribute. Vehicles with good NVH characteristics are designed to perform excellent in IDLE and SHAKE conditions. Typically, tactile vibrations at idle are measured at the driver seat and steering wheel. Vibrations caused by engine excitation at idle are passed through several paths to the body structure. The dominant paths being the engine mounts and the half-shafts, either one of them or both can be a major factor influencing the perceived idle vibration in a vehicle. In the past, modeling the half-shafts accurately has been a challenge and often time has been ignored because of modeling complexity. This has led to idle CAE predictions not correlating with test data. The aim of this paper is to describe a finite element modeling method of half-shaft to predict idle vibrations levels.

It has been previously shown that a detailed representation of the half-shaft correlates with test data. Developed detailed half-shaft models have shown improvement in capturing the half-shaft path at vehicle idle condition. Since the detailed half-shaft model needs to capture many components and requires detailed solid geometry for each component represented, full CAD model from half-shaft supplier or part scanning is required. Furthermore, despite the availability of CAD geometry, the detailed half-shaft will require solid meshing of the CV joints, the shaft, linearized springs and manual creation of the complex coordinate systems for orientation of contact points. This paper proposes an automated method to reduce the half-shaft model to a semi-elastic rigid body elements model with linearized spring components. The simplified model reduces the modeling time by eliminating solid meshing of components and automating complex coordinate system development without losing accuracy.

Electronic Stop-Start (ESS) system automatically stops and restarts the engine to save energy, improve fuel economy and reduce emissions when the vehicle is stationary during traffic lights, traffic jams etc. The stop and start events cause unwanted vibrations at the seat track which induce discomfort to the driver and passengers in the vehicle. These events are very short duration events, usually taking less than a second. Time domain analysis can help in simulating this event but it is difficult to see modal interactions and root cause issues. Modal transient analysis also poses a limitation on defining frequency dependent stiffness and damping for multiple mounts. This leads to inaccuracy in capturing mount behavior at different frequencies. Most efficient way to simulate this event would be by frequency response analysis using modal superposition method.

This paper describes a reliable CAE methodology to model the linear vibratory behavior of windshields. The windshield is an important component in vehicle NVH performance. It plays an integral role in interior cabin noise. The windshield acts as a large panel typically oriented near vertical at the front of vehicle’s acoustic cavity, hence modeling it accurately is essential to have a reliable prediction of cabin interior noise. The challenge to model the windshield accurately rises from the structural composition of different types of windshields. For automotive applications, windshields come in several structural compositions today. In this paper, we will discuss two types of windshield glass used primarily by automotive manufacturers. First type is the typical laminated glass with polyvinyl butyral (PVB) layer and second type is the acoustic glass with PVB and vinyl layers. Acoustic glass improves acoustic characteristics of the glass in a frequency range of ~ 1200 Hz to ~4000 Hz.

Modern engines have high torque outputs and have low RPM due to increased demand for fuel efficiency. Vibrations caused by such engines have to be mitigated. Decoupling the roll mode from the remaining five rigid body modes results in a response which is predominantly about the torque roll axis (TRA) and helps reduce vibrations. Therefore, placing the mounts on the TRA early in the design phase is crucial. Best NVH performance can be obtained by optimizing the powertrain mount parameters viz; Position, Orientation and Stiffness. Many times, packaging restricts the mounts to be placed about the TRA resulting in degradation in NVH performance. Assuming that the line through the engine mount (Body side) centers is the desired TRA, we propose a novel method of shifting the TRA by adding mass modifying the powertrain inertia such that the new TRA is parallel to and on top to the desired TRA. This in turn will decouple the roll mode and reduce vibrations.

Weight reduction is very important in automotive design because of stringent demand on fuel economy. Structural optimization of dynamic systems using finite element (FE) analysis plays an important role in reducing weight while simultaneously delivering a product that meets all functional requirements for durability, crash and NVH. With advancing computer technology, the demand for solving large FE models has grown. Optimization is however costly due to repeated full-order analyses. Reanalysis methods can be used in structural vibrations to reduce the analysis cost from repeated eigenvalue analyses for both deterministic and probabilistic problems. Several reanalysis techniques have been introduced over the years including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA) and the Epsilon algorithm, among others.

To improve fuel economy, there is a trend in automotive industry to use light weight, high strength materials. Automotive body structures are composed of several panels which must be downsized to reduce weight. Because this affects NVH (Noise, Vibration and Harshness) performance, engineers are challenged to recover the lost panel stiffness from down-gaging in order to improve the structure borne noise transmitted through the lightweight panels in the frequency range of 100-300 Hz where most of the booming and low medium frequency noise occurs. The loss in performance can be recovered by optimized panel geometry using beading or damping treatment. Topography optimization is a special class of shape optimization for changing sheet metal shapes by introducing beads. A large number of design variables can be handled and the process is easy to setup in commercial codes. However, optimization methods are computationally intensive because of repeated full-order analyses.

Finite element analysis is a standard tool for deterministic or probabilistic design optimization of dynamic systems. The optimization process requires repeated eigenvalue analyses which can be computationally expensive. Several reanalysis techniques have been proposed to reduce the computational cost including Parametric Reduced Order Modeling (PROM), Combined Approximations (CA), and the Modified Combined Approximations (MCA) method. Although the cost of reanalysis is substantially reduced, it can still be high for models with a large number of degrees of freedom and a large number of design variables. Reanalysis methods use a basis composed of eigenvectors from both the baseline and the modified designs which are in general linearly dependent. To eliminate the linear dependency and improve accuracy, Gram Schmidt orthonormalization is employed which is costly itself.