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The initial presence and dynamic formation of internal voids in human body models have been subjects of discussion within the human body modeling community. The relevant physics of the human body are described and the importance of capturing this physics for modeling of internal organ interactions is demonstrated. Basic modeling concepts are discussed along with a proposal of simulation setups designed to verify model behavior in terms of volume and pressure between internal organs.

As for passenger cars, the overall noise and vibration comfort in commercial trucks and busses becomes an increasingly important sales argument. In order to effectively reduce the noise and vibration levels it is required to identify possible NVH issues at an early stage in the vehicle development process. For this reason a so-called “Virtual Transfer Path Analysis” (VTPA) method has been implemented which combines the results obtained from the conventional multi-body simulation and finite element method approaches. The resulting VTPA tool enables Daimler Trucks to systematically investigate and predict the complex interaction between powertrain excitation and the resulting vehicle response well before hardware prototypes become available. An overview of the theory is presented as well as the practical application and outcome of the technique applied in a past product development.

Measured vehicle loads have traditionally been used as the basis for development of component, subsystem and vehicle level durability tests. The use of measured loads posed challenges due to the availability of representative hardware, scheduling, and other factors. In addition, stress was placed on existing procedures and methods by aggressive product development timing, variety in tuning and equipment packages, and higher levels of design optimization. To meet these challenges, General Motors developed new processes and technical competencies which enabled the direct substitution of analytically synthesized loads for measured data. This process of Virtual Road Load Data Acquisition (vRLDA) enabled (a) conformance to shortened product development cycles, (b) greater consistency between design targets and validation requirements, and (c) more comprehensive data.

There is an increasing demand in automated manufacturability analysis of metal castings at the initial stages of their design. This paper presents a system developed for virtual manufacturability analysis of casting components. The system can be used by a casting designer to evaluate manufacturability of a part designed for various manufacture processes including casting, heat treatment, and machining. The system uses computational geometrics and geometric reasoning to extract manufacturing features and geometry characteristics from a part CAD model. It uses an expert system and a design database consisting of metal casting, heat treatment and machining process knowledge and rules to present manufacturability analysis results and advice to the designer. Application of the system is demonstrated for the manufacturability assessment of automotive cast aluminum components.

In the last few years, the effect of diabatic test conditions on compressor performance maps has been widely investigated, leading some Authors to propose different correction models. The accuracy of turbocharger performance map constitute the basis for the tuning and validation of a numerical method, usually adopted for the prediction of engine-turbocharger matching. Actually, it is common practice in automotive applications to use simulation codes, which can either require measured compression ratio and efficiency maps as input values or calculate them “on the fly” throughout specific sub-models integrated in the numerical procedures. Therefore, the ability to correct the measured performance maps taking into account internal heat transfer would allow the implementation of commercial simulation codes used for engine-turbocharger matching calculations.

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

The comfort assessment of seats in the automotive industry has historically been accomplished by subjective ratings. This approach is expensive and time consuming since it involves multiple prototype seats and numerous people in supporting processes. In order to create a more efficient and robust method, objective metrics must be developed and utilized to establish measurable boundaries for seat performance. Objective measurements already widely accepted, such as IFD (Indentation Force Deflection) or CFD (Compression Force Deflection) [1], have significant shortcomings in defining seat comfort. The most obvious deficiency of these component level tests is that they only deal with a seats' foam rather than the system response. Consequently, these tests fail to take into account significant factors that affect seat comfort such as trim, suspension, attachments and other components.

The increasing functionality and complexity of future electric/electronic architectures requires efficient methods and tools to support design decisions, which are taken in early development phases 6. For the past four years, a holistic approach for architecture development has been established at Mercedes-Benz Cars R&D department. At its core is a seamless design flow, including the conception, the analysis and the documentation for electric/electronic architectures. One of the actual challenges in the design of electric/electronic architectures concerns communication behavior and network topologies. The increasing data exchange between the ECUs creates high requirements for the networks. With the introduction of FlexRay 21 and Ethernet the automotive network architecture become a lot more heterogeneous. Especially gateways must fulfill many new requirements to handle the strict periodic schedule of FlexRay and the partly event-triggered communication on CAN-busses 23.

In modern vehicles the architecture of electronics is growing more and more complex because both the number of electronic functions – e.g. implemented as software modules – as well as the level of networking between electronic control units (ECUs) is steadily increasing. This complexity leads to greater propagation of failure symptoms, and diagnosing the causes of failure becomes a new challenge. Diagnostics aims at detecting failures such as defect sensors or faulty communication messages. It is subdivided into diagnosis algorithms on an ECU and algorithms running offboard, e.g. on a diagnostic tester. These algorithms have to complement each other in the best possible way. While in the past the diagnosis algorithm was developed late in the development process, nowadays there are efforts to start the development of such algorithms earlier – at least in parallel to developing a new feature itself. This would allow developers to verify the diagnosis algorithms in early design stages.

Digital or virtual prototyping by means of a multibody simulation model (MBS) is a standard part of the automotive design process. A high-fidelity model is built and often correlated against test data to increase its accuracy. Once built the MBS model can then be used for high fidelity analysis in ride comfort, handling as well as durability. Next to the MBS model, current industry practice is to develop a reduced degree of freedom model for the design and validation of control or intelligent systems. The models used in the control system design are required to execute in hardware-in-the-loop (HIL) simulations where it is necessary to run real-time. The reason for the creation of the reduced degree of freedom models so far has been that the high-fidelity or off-line model does not execute fast enough to be used in an HIL simulation.

A simulation approach to predict the amount of snow which is penetrating into the air filter of the vehicle’s engine is important for the automotive industry. The objective of our work was to predict the snow ingress based on an Eulerian/Lagrangian approach within a commercial CFD-software and to compare the simulation results to measurements in order to confirm our simulation approach. An additional objective was to use the simulation approach to improve the air intake system of an automobile. The measurements were performed on two test sites. On the one hand we made measurements on a natural test area in Sweden to reproduce real driving scenarios and thereby confirm our simulation approach. On the other hand the simulation results of the improved air intake system were compared to measurements, which were carried out in a climatic wind tunnel in Stuttgart.

This paper presents a model updating method based on experimental receptances. The presented method minimises the so called ‘indirect receptance difference’. First, the reduced analytical dynamic stiffness matrix is expressed as an approximate, linearised function of the updating parameters. In a numerically stable, iterative procedure, this reduced analytical dynamic stiffness matrix is changed in such a way that the analytical receptances match the experimental receptances at the updating frequencies. The updating frequencies are a set of selected frequency points in the frequency range of interest. Some considerations about an optimal selection of the updating frequencies are given. Finally, a mixed static-dynamic reduction scheme is discussed. Dynamic reduction of the analytical dynamic stiffness matrix at each updating frequency is physically exact, but it involves a great computational effort.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

Delivering the appropriate material data for CAE analysis of plastic components is not as straight forward as it would seem to be. While a few of the properties typically used by resin manufacturers and material engineers to describe a plastic are useful to the analysis community (density, CLTE), most are not (flexural modulus, notched izod). In addition some properties such as yield stress are defined differently by the analysis community than by the materials community. Lastly, secondary operations such as painting or chrome plating significantly change the behavior of components with plastic substrates. The materials engineering community and the CAE analysis community must work together closely to develop the material data necessary to increase the capability of the analysis. This paper will examine case studies where these issues have required modifications to the material property data to increase the fidelity of the CAE analysis.

While CAE methods allow improving nominal product design using virtual prototypes, uncertainty and variability in properties and manufacturing processes lead to scatter in actual performances. Uncertainty must hence be incorporated in the CAE process to guarantee the robustness and reliability of the design. This paper presents an overview of uncertainty-based design in automotive and aerospace engineering. Fuzzy methods take uncertainty into account, whereas reliability analysis and a reliability-based design optimization framework can deal with variability. Key enabling technologies to alleviate the computational burden, such as workflow automation, substructuring and design of experiments, are discussed, and industrial applications are presented.

In this paper, a low-cost means to improve fuel economy in conventional vehicles by employing ultracapacitor based Active Energy Recovery Buffer (AERB) scheme will be presented. The kinetic energy of the vehicle during the coast down events is utilized to charge the ultracapacitor either directly or through a dc-dc converter, allowing the voltage to increase up to the maximum permissible level. When the vehicle starts after a Stop event, the energy stored in the capacitor is discharged to power the accessory loads until the capacitor voltage falls below a minimum threshold. The use of stored capacitor energy to power the accessory loads relieves the generator torque load on the engine resulting in reduced fuel consumption. Two different topologies are considered for implementing the AERB system. The first topology, which is a simple add-on to the conventional vehicle electrical system, comprises of the ultracapacitor bank and the dc-dc converter connected across the dc bus.

Spot welds have two separation modes: interfacial and button pullout. Most of existing publications [8,9,10,11,12] focused on button pullout. This is because for the same sheet metal and gage combination, button pullout leads to higher separation load than interfacial separation. With the push for lighter vehicles, high strength and ultra high strength steels are used. To further reduce mass, welding flanges are getting narrower. The welding tips are getting smaller. The weld nugget diameters are smaller as a result. The separation mode for certain load cases is no longer nugget pullout, but interfacial instead. This lowers the weld's maximum load capacity. In order for CAE simulated prediction to correlate to physical behaviors of vehicle structures, it is important to define and reconfirm separation criteria. New tests and analyses are necessary.

Trivial Principal Component method (TPC) was developed recently to model a system based on measured data. It is a statistical method that utilizes Eigen-pairs of covariance matrix obtained from the measured data. It determines linear coefficients of a model by using the trivial eigenvector corresponding to the least eigenvalue. In general, linear modeling accuracy depends on the strength of nonlinearity and interaction terms as well as measurement error. In this paper, the TPC method is extended to analyze residual (error) vector to identify significant higher order and interaction terms that contribute to the modeling error. Subsequently, these additional terms are included for constructing a robust system model. Also, an iterative TPC analysis is proposed for the first time to correct the model gradually till the least eigenvalue becomes minimum.

Automobile drivers/passengers perceive automatic transmission (AT) shift quality through the torque transferred by the transmission. Clearly, torque regulation is important for transmission control. Unfortunately, a physical torque sensor has been too costly for production applications. With no torque measurement for feedback, controls in AT is mainly implemented in an open-loop fashion. Therefore, complicated adaptation algorithms are necessary while undesired shifts may still occur. To further simplify the controls and enhance its consistency and robustness, a direct torque feedback has long been desired in transmission control synthesis and development. A “virtual” torque sensor (VTS) algorithm has recently been developed to show a good potential in estimating relative torque along transmission output shaft using transmission output speed sensor and wheel speed sensors.

Due to the multitude of external design constraints, such as increasing fuel economy standards, and the increasing number of global vehicle programs, developers of automotive transmission controls have had to cope with increasing levels of system complexity while at the same time being forced by the marketplace to improve system quality, reduce development costs, and improve time to market. General Motors Powertrain (GMPT) chose to meet these challenges through General Motors Company's Road-to-Lab-to-Math (RLM) strategy, particularly the Math-based method of a virtual vehicle simulation environment called System Simulation. The use of System Simulation to develop transmission control algorithms has enabled GMPT to improve product quality and reduce development times and costs associated with the dependence on physical prototypes. Additionally, System Simulation has facilitated the reuse of GMPT controls development assets, improving overall controls development efficiency.