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Vibration and sound radiation characteristics of bead-stiffened panels are investigated. Rectangular panels with different bead configurations are considered. The attention is focused on various design parameters, such as orientation, depth, and periodicity, and their effects on equivalent bending stiffness, modal density, radiation efficiency and sound transmission. A combined FEA-SEA approach is used to determine the response characteristics of panels across a broad frequency range. The details of the beads are represented in fine-meshed FEA models. Based on predicted surface velocities, Rayleigh integral is evaluated numerically to calculate the sound pressure, sound power and then the radiation efficiency of beaded panels. Analytical results are confirmed by comparing them with experimental measurements. In the experiments, the modal densities of the panels are inferred from averaged mechanical conductance.

Regulations implemented by safety commissions throughout the world have resulted in extensive physical testing to protect the occupants during frontal impact events. Significant prototype and test costs aimed at optimizing structure and restraint systems are associated with meeting these regulations. To help reduce development costs, Computer Aided Engineering (CAE) is often applied. LS-DYNA [1] coupled with MADYMO [2] is widely used in crash and occupant safety simulation. An analysis technique which utilized a single model to design and optimize interiors (instrument panel, seats, visor, steering wheel, steering column) and restraints (airbag, seatbelts, retractor, pre-tensioner) was developed. The single model concept captures the global structural kinematics through minimal vehicle representation. Global vehicle modes such as pitch and roll can be represented by applying prescribed motion boundary conditions extracted from full vehicle models.

A predictive automatic gear shift system is currently under development. The system optimizes the gear shift process, taking the conditions of the road ahead into account, such that the fuel consumption is minimized. An essential part of the system is a module that predicts the vehicle speed dynamics: This calculates a speed trajectory, i.e. the most probable vehicle speed the driver will desire for the upcoming section of the route. In the paper the theoretical background for predicting the vehicle speed, and simulation results of the predictive shift algorithm are presented.

This paper presents a new approach to improve occupant response in a front impact event. Instead of designing a vehicle structure for maximum structural efficiency and safety and then engineer a restraint system for the vehicle, this paper proposes to use a systems approach. In this approach, the vehicle structural response during impact (i.e., pulse) and the restraint system are considered together in the optimization process. In this paper, the 35 mph front impact into a rigid barrier with belted occupants, which is the NHTSA NCAP test, will be used to demonstrate the proposed new approach.

The effects of vehicle “stiffness” and mass on the occupant response during a crash may be determined by evaluation of accident data. However, “stiffness” and mass may be correlated, making it difficult to separate their effects. In addition, a single-valued “stiffness”, although well defined for linear case, is not well defined for non-linear systems, such as in vehicle crash, making the separation task even more difficult. One approach to addressing the lack of a clear definition of stiffness is to use multiple definitions. Each stiffness definition can then be correlated with mass to look for trends. In this study, such an approach was taken, and the different stiffness definitions were given and their values were obtained from rigid barrier crash test data. No clear relationship between mass and stiffness appears to exist. All the stiffness measures reviewed show, at best, only a weak correlation with mass. A stiffness analysis among different vehicle types was also carried out.

As the standards for exhaust emissions have become more stringent, the quality control tools used to evaluate the performance of low level samplers and analyzers has become more important. The Vehicle Exhaust Emissions Simulator (VEES) was developed to evaluate the performance of vehicle or engine exhaust emissions sampling and analytical systems. The simulator emulates emissions from low-emitting gasoline vehicles by producing a simulated exhaust stream containing emission constituents (HC, CO, CO2, and NOx) injected via Mass Flow Controllers (MFCs). This paper discusses various applications of the VEES as a quality control tool for ULEV and SULEV testing. A comparison is made between the injected amount of exhaust species by the VEES and the amounts recovered by the different sampling systems. Different root cause scenarios are discussed as to the source of discrepancies between the results on the CVS and BMD for different driving cycles.

The goal of an OEM electrical/electronics (E/E) platform organization is to release reliable E/E systems that achieve high levels of customer satisfaction with minimum investment and system cost. Achieving this goal is made more challenging by rapid advances in E/E technology and features which impact the vehicle development business environment. This paper discusses the evolution of an OEM platform organization striving to achieve E/E system integrity in an ever-changing world and eventually achieved the world class electrical quality as measured by J. D. Power. The organizational evolution progresses through a series of philosophies and methodologies, adapting new initiatives and enablers seeking continuous improvement. The result is an OEM organization with: knowledge based on lessons learned, an understanding of E/E system architecture, and enabled by models and tools to provide high levels of customer satisfaction.

In this paper, cradle design functional objectives are briefly reviewed and a durability development process is proposed focusing on the cradle loads, stress, strain, and fatigue life analysis. Based upon the proposed design process, sample isolated and non-isolated cradle finite element (FE) models for a uni-body sport utility vehicle (SUV) under different design phases are solved and correlated with laboratory bench and proving ground tests. The correlation results show that the applied cradle models can be used to accurately predict the critical stress spots and fatigue life under various loading conditions.

The validation of vehicle aerodynamic simulation results to wind tunnel test results and simulation accuracy improvement attract considerable attention of many automotive manufacturers. In order to improve the simulation accuracy, a simulation model of the ground effects simulation system of the aerodynamic wind tunnel of the Shanghai Automotive Wind Tunnel Center was built. The model includes the scoop, the distributed suction, the tangential blowing, the moving belt and the wheel belts. The simulated boundary layer profile and the pressure distribution agree well with test results. The baseline model and multiple design changes of the new Buick Excelle GT are simulated. The simulation results agree very well with test results.

A challenge for the aerodynamic optimization of trucks is the limited availability of wind tunnels for testing full scale trucks. FAW wants to introduce a development process which is mainly based on CFD simulation in combination with some limited amount of wind tunnel testing. While maturity of CFD simulation for truck aerodynamics has been demonstrated in recent years, a complete validation is still required before committing to a particular process. A 70% scale model is built for testing in the Shanghai Automotive Wind Tunnel Center (SAWTC). Drag and surface pressures are measured for providing a good basis for comparison to the simulation results. The simulations are performed for the truck in the open road driving condition as well as in an initial digital model of the aerodynamic wind tunnel of SAWTC. A full size truck is also simulated in the open road driving condition to understand the scaling effect.

The implementation of an advanced process for the aerodynamic development of cab-over type heavy trucks at China FAW Group Corporation (FAW) requires a rigorous validation of the tools employed in this process. The final objective of the aerodynamic optimization of a heavy truck is the reduction of the fuel consumption. The aerodynamic drag of a heavy truck contributes up to 50% of the overall resistance and thus fuel consumption. An accurate prediction of the aerodynamic drag under real world driving conditions is therefore very important. Tools used for the aerodynamic development of heavy trucks include Computational Fluid Dynamics (CFD), wind tunnels and track and road testing methods. CFD and wind tunnels are of particular importance in the early phase development.

It is becoming increasingly difficult to obtain good repeatability from running lab vehicle correlation testing, since vehicle variability is so significant at the Low ULEV and SULEV emissions levels. These new emission standards are becoming so stringent that it makes it very difficult to distinguish whether a problem is a result of vehicle variability, test cell sampling or the analytical system. A vehicle exhaust emission simulator (VEES) developed by Horiba, can simulate emissions from low emitting gasoline vehicles by producing tailpipe flow rates containing emissions constituents ( HC, CH4, CO, NOx, CO2 ) injected at the tailpipe flow stream via mass flow controllers.

Diesel fuel is a blend of various middle distillate components separated at the refinery. The composition and characteristics of the diesel fuel blend changes daily if not hourly because of normal process variation, changing refinery processing conditions, changing crude oil diet or changing diesel fuel and kerosene market conditions. Regardless of the situation going on at the refinery or the market, the resultant diesel fuel must consistently meet established cloud point specifications. To consistently meet the cloud point specifications, refiners are forced to blend their diesel fuels in such a way that the resultant blend is always on the low side of the cloud point specification even when the refining process adversely changes the fuel characteristics. This practice has the effect of producing several degrees of cloud point “giveaway” when the refinery is not experiencing adverse swings in product quality.

The emerging Variable Valve Timing (VVT) technology complicates the estimation of air flow rate because both intake and exhaust valve timings significantly affect engine's gas exchange and air flow rate. In this paper, we propose to use Artificial Neural Networks (ANN) to model the air flow rate through a 2.4 liter VVT engine with independent intake and exhaust camshaft phasers. The procedure for selecting the network architecture and size is combined with the appropriate training methodology to maximize accuracy and prevent overfitting. After completing the ANN training based on a large set of dynamometer test data, the multi-layer feedforward network demonstrates the ability to represent air flow rate accurately over a wide range of operating conditions. The ANN model is implemented in a vehicle with the same 2.4 L engine using a Rapid Prototype Controller.

The Engine Oil Industry Base Oil Interchange (BOI) and Viscosity Grade Read Across (VGRA) guidelines developed by the American Petroleum Institute (API) provide a means to significantly reduce the time to market for current technology oils. The guidelines also allow conversion of a fraction of the millions of dollars spent each year on engine testing in pursuit of API engine oil licensing into research testing and the development of fundamental knowledge. In the past, guidelines have been developed based upon a general assessment of minimal engine test data. Recently, however, regression models have been used to assess Base Oil and Viscosity Grade effects. The use of statistical regression models and Virtual Tests in determining effects to establish BOI and VGRA has several advantages. These advantages, demonstrated through an example and a case study, include volume of data and breadth of data.

The management of surface water flows driven from the wind screen by the action of wipers and aerodynamic shear is a growing challenge for automotive manufacturers. Pressure to remove traditional vehicle features, such as A-Pillar steps for aesthetic, aeroacoustic and aerodynamic reasons increases the likelihood that surface water may be convected over the A-Pillar and onto the front side glass where it can compromise drivers’ vision. The ability to predict where and under which conditions the A-Pillar will be breached is important for making correct design decisions. The use of numerical simulation in this context is desirable, as experimental testing relies on the use of aerodynamics test properties which will not be fully representative, or late-stage prototypes, making it difficult and costly to correct issues. This paper provides an update on the ability of simulation to predict A-Pillar overflow, comparing physical and numerical results for a test vehicle.

One of the key functions of lubricating oil additives in diesel engines is to control oil thickening caused by soot accumulation. Over the last several years, it has become apparent that the composition of the base oil used within the lubricant plays an extremely important role in the oil thickening phenomenon. In particular, oil thickening observed in the Mack T-8 test is significantly affected by the aromatic content of the base oil. We have found that the Mack T-8 thickening phenomenon is associated with high electrical activity, i.e., engine drain oils which exhibit high levels of viscosity increase show significantly higher conductivities. These findings suggest that electrical interactions are involved in soot-induced oil thickening.

A validation study was performed comparing the simulation results of the Lattice-Boltzmann Equation (LBE) based flow solver, PowerFLOW®, to cooling cell measurements conducted at Volvo Trucks North America (VTNA). The experimental conditions were reproduced in the simulations including dynamometer cell geometry, fully detailed under-hood, and external tractor geometry. Interactions between the air flow and heat exchangers were modeled through a coupled simulation with the 1D-tool, PowerCOOL™, to solve for engine coolant and charge air temperatures. Predicted temperatures at the entry and exit plane of the radiator and charge-air-cooler were compared to thermocouple measurements. In addition, a detailed flow analysis was performed to highlight regions of fan shroud loss and cooling airflow recirculation. This information was then used to improve cooling performance in a knowledge-based incremental design process.

A traction test machine, developed for evaluation of traction-CVT fluids for the automotive consortium, USCAR, provides precision traction measurements to stresses up to 4 GPa. The high stress machine, WAMhs, provides an elliptical contact between AISI 52100 steel roller and disc specimens. Machine stiffness and positioning technology offer precision control of linear slip, sideslip and spin. A USCAR traction test methodology includes entrainment velocities from 2 to 10 m/sec and temperatures from -20°C to 140°C. The purpose of the USCAR machine and test methodology is to encourage traction fluid development and to establish a common testing approach for fluid qualification. The machine utilizes custom software, which provides flexibility to conduct comprehensive traction fluid evaluations.

Accurate motion prediction can be used to evaluate vibrations at seat track and steering wheel. This paper presents the prediction and correlation of truck frame motion from wheel force transducer (WFT) measurements. It is assumed that the method can be used to predict vibrations at seat track and steering wheel for unibody vehicles. Two durability events were used for calculation. WFT measurements were used as inputs applied on frame from suspension. Frame loads were then used as inputs to calculate frame motions using a FEA approach. The predicted frame motions are represented by four exhaust hangers and they are compared with measured motions of the same locations. The correlations include displacement, velocity, and acceleration. It is shown that good correlations are obtained in velocity and displacement. Acceleration shows bigger differences than velocity and displacement.