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Squeak and rattle (SAR) noise audible inside a passenger car causes the product quality perceived by the customer to deteriorate. The consequences are high warranty costs and a loss in brand reputation for the vehicle manufacturer in the long run. Therefore, SAR noise must be prevented. This research shows the application and experimental validation of a novel method to predict SAR noise on an actual vehicle interior component. The novel method is based on non-linear theories in the frequency domain. It uses the harmonic balance method in combination with the alternating frequency/time domain method to solve the governing dynamic equations. The simulation approach is part of a process for SAR noise prediction in vehicle interior development presented herein. In the first step, a state-of-the-art linear frequency-domain simulation estimates an empirical risk index for SAR noise emission. Critical spots prone to SAR noise generation are located and ranked.

Finite element simulation (FE) makes it possible to analyze the structural dynamic behavior of vehicle seat structures in early design phases to meet Noise-Vibration-Harshness (NVH) requirements. For this purpose, linear simulations are usually used, which neglect many nonlinear mechanical properties of the real structure. These models are trimmed to fit global vibration behavior based on the complex description of contact or jointed definitions. Targeted design is therefore only possible to a limited extent. The aim of this work is to characterize the entire seat structure and its sub-components in order to identify the main contributors using experimental and simulative data. The Lagrange Multiplier Frequency Based Substructuring (LM-FBS) method is used for this purpose. Therefore, the individual subsystems of seat frame, seat backrest and headrest are characterized under different conditions.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

Transfer path analyses of vehicle bodies are widely considered as an important tool in the noise, vibration and harshness design process, as they enable the identification of the dominating transfer paths in vibration problems. It is highly beneficial to model uncertain parameters in early development stages in order to account for possible variations on the final component design. Therefore, parameter studies are conducted in order to account for the sensitivities of the transfer paths with respect to the varying input parameters of the chassis components. To date, these studies are mainly conducted by performing sampling-based finite element simulations. In the scope of a sensitivity analysis or parameter studies, however, a large amount of large-scale finite element simulations is required, which leads to extremely high computational costs and time expenses. This contribution presents a method to drastically reduce the computational burden of typical sampling-based simulations.

Uncertainties play a major role in vibroacoustics - especially in car body design in the preliminary development because of the overall spread in the production that should be covered with one simulation model. Therefore, we use uncertain input parameters to determine the stochastically distributed admittance of the car body before each part of the car is fully designed. To gain a stochastic result - the stochastically distributed admittance curve - we calculate a deterministic finite element simulation several times with sets of stochastically distributed input parameter values. To reduce simulation time and cost of the car model with many million degrees of freedom we focus on the uncertain parameters that show a significant influence on the admittance curve. It is therefore necessary to be able to accurately estimate for each parameter if its influence on the admittance of the car body plays a major role for the noise vibration harshness simulation.

Raising demands towards lightweight design paired with a loss of originally predominant engine noise pose significant challenges for NVH engineers in the automotive industry. From an aeroacoustic point of view, low frequency buffeting ranks among the most frequently encountered issues. The phenomenon typically arises due to structural transmission of aerodynamic wall pressure fluctuations and/or, as indicated in this work, through rear vent excitation. A possible workflow to simulate structure-excited buffeting contains a strongly coupled vibro-acoustic model for structure and interior cavity excited by a spatial pressure distribution obtained from a CFD simulation. In the case of rear vent buffeting no validated workflow has been published yet. While approaches have been made to simulate the problem for a real-car geometry such attempts suffer from tremendous computation costs, meshing effort and lack of flexibility.

Substructuring approaches are helpful methods to solve and understand vibro-acoustic problems involving systems as complex as a vehicle. In that case, the whole system is split into smaller, simpler to solve, subsystems. Substructuring approaches allow mixing different modeling “solvers” (closed form solutions, numerical simulations or experiments). This permits to reach higher frequencies or to solve bigger systems. Finally, one of the most interesting features of substructuring approaches is the possibility to combine numerical and experimental descriptions of subsystems. The latter point is particularly interesting when dealing with subdomains that remain difficult to model with numerical tools (assembly, trim, sandwich panels, porous materials, etc.). The Patch Transfer Functions (PTF) method is one of these substructuring approaches. It condenses information (impedance matrix) of subsystems on their coupling surfaces.

The Noise Vibration and Harshness (NVH) characteristics and requirements of vehicles are changing as the automotive manufacturers turn their focus from developing and producing cars propelled by internal combustion engines (ICE) to electrified vehicles. This new strategic orientation enables them to offer products that are more efficient and environmentally friendly. Although electric powertrains have many advantages compared to their established predecessors they also bring new challenges that increase the difficulty of matching the high quality requirements of premium car producers especially regarding NVH. Electric motors are one of the most important sources of vibrations in electric vehicles.

Classical decentralized architectures based on large networks of microprocessor-based Electronic Control Units (ECU), namely those used in self-driving cars and other highly-automated applications used in the automotive industry, are becoming more and more complex. These new, high computational power demand applications are constrained by limits on energy consumption, weight, and size of the embedded components. The adoption of new embedded centralized electrical/electronic (E/E) architectures based on dynamically reconfigurable hardware represents a new possibility to tackle these challenges. However, they also raise concerns and questions about their safety. Hence, an appropriate evaluation must be performed to guarantee that safety requirements resulting from an Automotive Safety Integrity Level (ASIL) according to the standard ISO 26262 are met. In this paper, a methodology for the evaluation of dynamically reconfigurable systems based on centralized architectures is presented.

As a key part of numerical analysis, the modeling process has a tremendous influence on the quality of the results. While there is general awareness concerning uncertainties that arise during modeling, their quantity and sensitivity rarely are known. Hence, modeling quickly can become inaccurate and inefficient. The scope of the present paper is to innovate predictive modeling processes concerning the dynamics of real complex structures by means of linear modal analysis with the finite element method (FEM). The aim is to offer a transparent design catalog relating specific uncertainties to each model component in order to achieve error prevention for engineers dealing with comparable systems. A complex system is simplified and investigated for different levels of detail. Only after the model uncertainties for one level of detail are obtained, the next level of complexity is approached.

Within the pre-development phase of a vehicle validation process, the role of computational simulation is becoming increasingly prominent in efforts to ensure thermal safety. This gain in popularity has resulted from the cost and time advantages that simulation has compared to experimental testing. Additionally many of these early concepts cannot be validated through experimental means due to the lack of hardware, and must be evaluated via numerical methods. The Race Track Simulation (RTS) can be considered as the final frontier for vehicle thermal management techniques, and to date no coherent method has been published which provides an efficient means of numerically modeling the temperature behavior of components without the dependency on statistical experimental data.

The thermal prediction of a vehicle under-body environment is of high importance in the design, optimization and management of vehicle power systems. Within the pre-development phase of a vehicle's production process, it is important to understand and determine regions of high thermally induced stress within critical under-body components. Therefore allowing engineers to modify the design or alter component material characteristics before the manufacture of hardware. As the exhaust system is one of the primary heat sources in a vehicle's under-body environment, it is vital to predict the thermal fluctuation of surface temperatures along corresponding exhaust components in order to achieve the correct thermal representation of the overall under-body heat transfer. This paper explores a new method for achieving higher accuracy exhaust surface temperature predictions.

A test center for aging analysis and characterization of Lithium-Ion batteries for automotive applications is optimized by means of a dedicated cell tester. The new power tester offers high current magnitude with fast rise time in order to generate arbitrary charge and discharge waveforms, which are identical to real power net signals in vehicles. Upcoming hybrid and electrical cars show fast current transients due to the implemented power electronics like inverter or DC/DC converter. The various test procedures consider single and coupled effects from current profile, state of charge and temperature. They are simultaneously applied on several cells in order to derive statistical significance. Comprehensive safely functions on both the hardware and the software level ensure proper operation of the complex system.

Nowadays there is an increasing need to streamline CAE processes. One such process consists of translating a Multibody Dynamics System (MBS) model into an equivalent Finite Element Analysis (FEA) model. Typically, users start with the creation of a MBS model which is set at a desired operating point by means of running simulations in the MBS domain (e.g. dynamics, statics.) The MBS model is then further translated into an equivalent FEA model which is used to perform simulations in the FEA domain (e.g. passive safety/crash, noise vibration harshness/NVH.) Currently, the translation of the MBS model into a FEA model is done either manually or by means of using a user-written script. This paper shows that a user-written script that translates a MBS model into a FEA model can not provide a high fidelity translation. In general, it is found that eigenvalues computed by the FEA code would not match eigenvalues computed by the MBS code.

Today digital 3D human models are widely used to support the development of future products and in planning and designing production systems. However, these virtual models are generally not sufficiently intuitive and configuring accurate and real body postures is very time consuming. Furthermore, additionally using a human model to virtually examine manual assembly operations of a vehicle is currently synonymous with increased user inputs. In most cases, the user is required to have in-depth expertise in the deployed simulation system. In view of the problems described, in terms of human-computer interaction, it is essential to research and identify the requirements for simulation with digital human models. To this end, experienced staff members gathered the requirements which were then evaluated and weighted by the potential user community. Weaknesses of the simulation software will also be detected, permitting optimisation recommendations to be identified.

This paper presents an integrated simulation process which has been performed in order to assess the riding comfort performance of a vehicle seat system virtually. Present methods of seat comfort design rely on the extensive testing of numerous hardware prototypes. In order to overcome the limitations of this expensive and time-consuming process, and to fasten innovation, simulation-based design has to be used to predict the seat comfort performance very early in the seat design process, leading to a drastic reduction in the number of physical prototypes. The accurate prediction of the seat transfer function by numerical simulation requires a complete simulation chain, which takes into account the successive stages determining the final seat behaviour when submitted to vibrations. First the manufacturing stresses inside the cushion, resulting from the trimming process, are computed.

AM-SC1™ is a high temperature Mg alloy that was originally developed as a sand casting alloy for automotive powertrain applications. The alloy has been selected as the engine block material for both the AVL Genios LE and the USCAR lightweight magnesium engine projects. The present work assesses the potential of this alloy for permanent-mould die cast applications. Thermo-physical and mechanical properties of AM-SC1 were determined for material derived from a permanent-mould die casting process. The mechanical properties determined included: tensile, creep, bolt load retention/relaxation and both low and high cycle fatigue. To better assess the creep performance, a comparative analysis of the normalized creep properties was carried out using the Mukherjee-Dorn parameter, which confirmed the high viscoplastic performance of AM-SC1 compared with common creep resistant high pressure die cast (HPDC) Mg-alloys.

In large-scale industrial simulations the numerical prediction of fracture in sheet metal forming operations as well as in crash events is still a challenging task of high social and economic relevance. Among several approaches presented in literature, the authors and their colleagues developed a model which accounts each for three different mechanisms leading finally to fracture in thin sheet metals: the local instability (necking), ductile normal fracture and ductile shear fracture. The focus of this paper is to develop and validate a new approach to improve the predictive capabilities for fracture triggered by localized necking for a wide variety of steel grades. It is well known that after the onset of a local instability additional strain is still necessary to induce fracture. In a numerical simulation using shell elements this post instability strain becomes of increasing importance when the ratio of the characteristic shell element edge length to its thickness decreases.

A computational analysis of underbody windnoise sources on a production automobile at 180 km/h free stream air speed and 0° yaw is presented. Two different underbody geometry configurations were considered for this study. The numerical results have been obtained using the commercial software PowerFLOW. The simulation kernel of this software is based on the numerical scheme known as the Lattice-Boltzmann Method (LBM), combined with a two-equation RNG turbulence model. This scheme accurately captures time-dependent aerodynamic behavior of turbulent flows over complex detailed geometries, including the pressure fluctuations causing wind noise. Comparison of pressure fluctuations levels mapped on a fluid plane below the underbody shows very good correlation between experiment and simulation. Detailed flow analysis was done for both configurations to obtain insight into the transient nature of the flow field in the underbody region.

Comfort and well-being have always been connected with a flawless interior acoustic, free of any background noise or BSR, (buzz, squeak and rattle). BSR noises dominate the interior acoustic and represent one of the main sources for discomfort often causing considerable warranty costs. Traditionally BSR issues have been identified and rectified through extensive hardware testing, which by its nature intensifies toward the end of the car development process. In the following paper the integration of a virtual BSR validation technique in a modern development process by the use of appropriate CAE methods is presented. The goal is to shift, in compliance with the front loading concept, the development activities into the early phase. The approach is illustrated through the example of an instrument panel, from the early concept draft for single components to an assessment of the complete assembly.