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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.

NVH targets for future vehicles are often defined by utilizing a competitive benchmarking vehicle in conjunction with an existing production and/or reference vehicle. Mode management of full vehicle modes is one of the most effective and significant NVH strategies to achieve such targets. NVH dynamic characteristics of a full vehicle can be assessed and quantified through experimental modal testing for determination of global body mode resonance frequency, damping property, and mode shape. Major body modes identified from full vehicle modal testing are primarily dominated by the vehicle's body-in-white structure. Therefore, an estimate of BIW modes from full vehicle modes becomes essential, when only full vehicle modes from experimental modal testing exist. Establishing BIW targets for future vehicles confines the fundamental NVH behavior of the full vehicle.

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.

An accurate air flow rate model is critical for high-quality air-fuel ratio control in Spark-Ignition engines using a Three-Way-Catalyst. Emerging Variable Valve Timing technology complicates cylinder air charge estimation by increasing the number of independent variables. In our previous study (SAE 2004-01-3054), an Artificial Neural Network (ANN) has been used successfully to represent the air flow rate as a function of four independent variables: intake camshaft position, exhaust camshaft position, engine speed and intake manifold pressure. However, in more general terms the air flow rate also depends on ambient temperature and pressure, the latter being largely a function of altitude. With arbitrary cam phasing combinations, the ambient pressure effects in particular can be very complex. In this study, we propose using a separate neural network to compensate the effects of altitude on the air flow rate.

Carefully controlled intake valve deposit (IVD) cleanup testing is found to be an effective method for differentiating the effect of the deposit control additives on combustion chamber deposits (CCD). The IVD buildup procedure produces a consistent initial level of CCD that the cleanup additive, the additive of interest, continues to build on until the end of the cleanup test. This “end of cleanup” CCD is found to be as repeatable and differentiable a measurement as tests run under the more common “keep clean” type operation. While IVD cleanup testing induces a mid-test disturbance in the form of the end of buildup measurement, it aligns well with two key CCD protocols in terms of the higher additive treat rates used and the extended total test length. In an analysis of results from IVD cleanup tests run using four different engine/vehicle procedures on seven different additives, several findings stood out.

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.

A new approach for improving estimates of the kinematic response of ATDs (anthropomorphic test devices) to vehicle crash events has been developed. This approach employs the Kalman Filter; a state model based estimation approach that has been widely applied to system dynamics problems ranging from navigation to missile guidance. The Kalman Filter approach combines measurements of crash event phenomena (acceleration and displacement), kinematic models of ATD behavior and statistics of sensor noise to create precise estimates of ATD motion during a crash. This paper presents an implementation of a state model and Kalman Filter for a sensor data collected from the chest of an ATD during an out-of-position airbag deployment test. Favorable comparisons are made between the Kalman Filter model approach and traditional methods involving numerical integration and differentiation.

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 operating conditions of a typical motorcycle are considerably different than those of a typical passenger car and thus require an oil capable of handling the unique demands. One primary difference, wet clutch lubrication, is already addressed by the current JASO four-stroke motorcycle engine oil specification (JASO T 903:2011). Another challenge for the oil is gear box lubrication, which may be addressed in part with the addition of a gear protection test in a future revision to the JASO specification. A third major difference between a motorcycle oil and passenger car oil is the more severe conditions an oil is subjected to within a motorcycle engine, due to higher temperatures, engine speeds and power densities. Scooters, utilizing a transmission not lubricated by the crankcase oil, also place higher demands on an engine oil, once again due to higher temperatures, engine speeds and power densities.

The use of a soy-based diesel fuel (biodiesel) has potential advantages over the use of a conventional petroleum diesel fuel including: Reduced dependence on foreign petroleum Lowering of greenhouse gas emissions Less air pollution and related public health risks in urban areas A life cycle study performed by the U.S. Department of Energy's National Renewable Energy Laboratory, the U.S. Department of Agriculture's Office of Energy and Ecobalance (completed in May 1998) helped to quantify the environmental benefits of the “cradle-to-grave” production and use of biodiesel. The study showed, for example, that substituting 100% biodiesel for petroleum diesel in urban buses reduced the life cycle emissions of carbon dioxide (CO2) by 78%. The study also pointed out some trade-offs of using biodiesel including increased life cycle hydrocarbon and NOx emissions.

The Association des Constructeurs Européen d'Automobiles (ACEA) light duty diesel engine specifications requires a kinematic viscosity measurement technique for Peugeot XUD-11 BTE drain oils. This viscosity measurement is used to define the medium temperature dispersivity of soot in the drain oil.(1) This paper discusses the use of rotational rheology methods to measure the Newtonian character of XUD-11 drain oils. The calculation of the rate index using the Hershel Bulkley model indicates the level of non-Newtonian behavior of the drain oil and directly reflects the level of soot dispersion or agglomeration. This study shows that the more non-Newtonian the drain oil the greater the difference between kinematic and rotational viscosity measurements Oscillation (dynamic) rheological techniques are used to characterize build up of soot structure.

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.