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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.

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.

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.

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.

This paper introduces a reliable method to calculate body mount loads from wheel-force-transducer (WFT) measurements on framed vehicles. The method would significantly reduce time and cost in vehicle development process. The prediction method includes two parts: Hybrid Load Analysis (HLA) that has been used by DaimlerChrysler Corporation and Body Mount Load Analysis (BMLA) that is introduced by this paper for the first time. The method is validated on a body-on-frame SUV and a pickup truck through one proving ground events. The example shown in this paper is for a SUV and one of the most severe events. In HLA, the loads at suspension-to-frame attachments are calculated from spindle loads measured by WFT. In BMLA, body mount loads were calculated using outputs of HLA with detailed finite-element-modeled frame and body. The loads are compared with measured body mount loads. The comparisons are conducted in range, standard deviation (S.D.), and fatigue pseudo-damage.

Digital engineering has been utilized in product development to improve the quality. The actual object was measured and digitized into the three-dimensional (3-D) data, and the requirement of evaluating and analyzing the CAD data has been increased in these activities. So, we developed the technology that measures the actual object and obtains the 3-D model data for general automotive parts. The features of this new system are high-speed and high-accuracy by using high energy X-ray CT technology and 3-D model data technology. 3-D model data can be obtained for about 5 hours in case of the engine block and the error is 0.1mm or less. We also show the examples of the new automotive parts development using this technology.

In an evaluation of crashworthiness for the cars made of aluminum alloys, the evaluation considering fracture phenomenon comes to be needed because conventional aluminum alloys have low fracture strain (10-20%). In case of the development of a B-Pillar made by die cast, if crack occurrence, furthermore, separation of a part can be estimated by using CAE in crashworthiness evaluations, we can reduce the number of prototype makings and the cost of development using expensive dies. Therefore, we performed crashworthiness evaluations by CAE using some sort of a damage & fracture material model. It is known as “Orthotropic damage & fracture model”.

Springback study on a Dodge Ram fender outer panel is detailed in this paper. A simple measurement fixture is designed for the panel, wherein non-contact laser scan technology is applied The measurement data are compared with the original CAD design surface and deviation contour maps are obtained. Consistency of measurement is studied at different sections among three samples. Details of FEA simulations are outlined. The comparison between measurement and simulation prediction is summarized. A method to describe the consistency of measurement and the accuracy of simulation prediction is proposed. The targets for measurement consistency and simulation accuracy are verified. A sensitivity analysis is also performed to investigate various simulation input parameters.

In this study, a full vehicle with durability tire model established with ADAMS is applied to simulate the dynamic behavior of the vehicle under severe rough road proving ground events, where the shock force-velocity characteristics are modeled as nonlinear curves and multi-stage representations, respectively. The shock forces and velocities at each corner are resolved and through full factorial DOE, the shock forces and velocities response surface models are established to analyze the sensitivities of shock force and velocity to the shock damping characteristics.

Today automotive thermal systems development is a joint effort between an OEM and its suppliers. This paper presents a pilot program showing how OEMs and suppliers can jointly develop a reliable and robust thermal system using CAE tools over the internet. Federated Intelligent Product Environment (FIPER) has been used to establish B2B communication between OEMs and suppliers. Suppliers remotely run thermal systems computer models at the OEM site using the FIPER B2B feature.

A process to achieve vehicle system level NVH objectives using CAE simulation tools is discussed. Issues of modeling methodology, already covered adequately in the literature, are less emphasized so that the paper can focus on the application of a process that encompasses objective setting, design synthesis, and performance achievement using simulation predictions. A reference simulation model establishes correlation levels and modeling methods that are applied to future predictions. The new model, called a “Digital Mule”, is an early new product “design intent” simulation used to arrive at subsystem goals to meet the vehicle level NVH objectives. Subsystem goals are established at discrete noise paths where structure borne noise enters the body subsystem. The process also includes setting limits on the excitation sources, such as suspension and powertrain.

As SULEV emission regulations approach, the bag mini-diluter (BMD) technology is gaining acceptance as a replacement for the existing constant volume sampler (CVS) for SULEV exhaust emission measurement and certification. The heart of the BMD system is the direct vehicle exhaust (DVE) flow measurement system. Due to the transient nature of vehicle exhaust during a standard FTP emission test cycle, the DVE must be capable of rapid and accurate response in order to track these varying exhaust flow rates. The DVE must also be robust enough to accurately measure flow rate despite variations in exhaust gas composition, pulsation effects, and rapid changes in both exhaust temperature and pressure. One of the primary DVE systems used on BMDs is the E-Flow, an ultrasonic flow meter manufactured by Flow Technologies, Inc.

In automotive industry, all passenger vehicles are treated with damping materials to reduce structure borne noise. The effectiveness of damping treatments depends upon design parameters such as choice of damping materials, locations and size of the treatment. This paper proposes a CAE (Computer Aided Engineering) methodology based on finite element analysis to optimize damping treatments. The developed method uses modal strain-energy information of bare structural panels to identify flexible regions, which in turn facilitates optimization of damping treatments with respect to location and size. The efficacy of the method is demonstrated by optimizing damping treatment for a full-size pick-up truck. Moreover, simulated road noise performances of the truck with and without damping treatments are compared, which show the benefits of applying damping treatment.

Traditionally, vehicle emission testing has used non-intelligent analyzers to meet government-regulated standards. Typically, these instruments would provide a 0 to 5-volt signal to a central test cell computer which would then handle all calibrations including analyzer linearization, zero and span corrections, stability checks, time delays, and sample readings. Modern gas analyzers now contain intelligence within each individual analyzer; this has caused the calibration methods to change dramatically. New methods were developed in the bench control system to take advantage of the intelligence of the analyzers by creating a distributed control architecture. The zeroing, spanning, and linearization methods are quite different from the previous protocols. The results, however, will provide more accurate reading to be used in calculating vehicle emissions.

In the early stages of a vehicle program, it is a common practice to set target ranges for the global body, suspension and powertrain modes. This modal management process allows engineers to avoid potential noise and vibration problems stemming from strong overlap of major global modes. Before the first prototype hardware is built, finite element models of the body, suspension and powertrain are usually exercised to compare predicted versus targeted ranges of the major system modes in the form of a modal management chart. However, uncertainty associated with the design parameters, manufacturing process and other sources can lead to a major departure from the design intent when the first hardware prototype is built. In this study, a first order reliability method is used to predict variance of the eigen values due to parameter uncertainties. This allows the CAE engineers to add a “three sigma” bound on the eigen values reported in the modal management chart.

This paper describes a lightweight magnesium intensive automobile body structure concept developed at DaimlerChrysler to support a high fuel-efficiency vehicle project. This body structure resulted in more than 40% weight reduction over a conventional steel structure while achieving significantly improved structural performance as evaluated through CAE simulations. A business case analysis was conducted and showed promising results. One concept vehicle was built for the purpose of demonstrating concept feasibility. The paper also identifies areas for further development to enable such a vehicle to become a production reality at a later time.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

In 1998, Rouhana et al. described development of a new device, called the IR-TRACC (InfraRed - Telescoping Rod for Assessment of Chest Compression). In its original concept, the IR-TRACC uses two infrared LEDs inside of a telescoping rod to measure deflection. One LED serves as a light transmitter and the other as a light receiver. The output from the receiver LED is converted to a linear function of chest compression using an analog circuit. Tests have been performed with IR-TRACC units at various labs around the world since 1998. A first-generation IR-TRACC system was retrofit into a Q3 dummy by TNO. Similarly, a mid sized male Hybrid III dummy thorax and a small female Hybrid III dummy thorax have been designed by First Technology Safety Systems (FTSS) such that each contains 4 second-generation IR-TRACC units. The second-generation IR-TRACC is the result of continued development by FTSS, especially in the areas of the analysis circuit, manufacturing and calibration methods.

To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

Generally, pass-by noise levels measured outdoors vary according to the influence of weather conditions, background noise and the driver’s skill. Manufactures, therefore, are trying to reproduce proving ground driving conditions on a chassis dynamometer. The tire noise that occurs on actual road surfaces, however, is difficult to reproduce in indoor tests. In 2016, new pass-by noise regulations (UN R51-03) will take effect in Europe, Japan and other countries. Furthermore, stricter regulations (2dB) will take effect in 2020. In addition to the acceleration runs required under current regulations, UN R51-03 will require constant speed runs. Therefore, an efficient measurement methods are necessary for vehicle development. To solve the above mentioned issues, an indoor evaluation system capable of reproducing the tire noise that occurs on road surfaces has been developed.