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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.

Trim materials are often used for vibroacoustic energy absorption purposes within vehicles. To estimate the sound impact at a driver’s ear, the substructuring approach can be applied. Thus, transfer functions are calculated starting from the acoustic source to the car body, from the car body to the trim and, finally, from the trim to the inner cavity where the driver is located. One of the most challenging parts is the calculation of the transfer functions from the car body inner surface to the bottom trim surface. Commonly, freely laying mass-spring systems (trims) are simulated with a fixed boundary and interface phenomena such as friction, stick-slip or discontinuities are not taken into consideration. Such an approach allows for faster simulations but results in simulations strongly overestimating the energy transfer, particularly in the frequency range where the mass-spring system’s resonances take place.

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.

Accurately predicting real-world vehicle energy consumption is essential for optimizing vehicle designs, enhancing energy efficiency, and developing effective energy management strategies. This paper presents a data-driven approach that utilizes machine learning techniques and a comprehensive dataset of vehicle parameters and environmental factors to create precise energy consumption prediction models. The methodology involves recording real-world vehicle data using data loggers to extract information from the CAN bus systems for ICE and hybrid electric, as well as hydrogen and battery fuel cell vehicles. Data cleaning and cycle-based analysis are employed to process the dataset for accurate energy consumption prediction. This includes cycle detection and analysis using methods from statistics and signal processing, and then pattern recognition based on these metrics.

Internal combustion (IC) engines still power most of the vehicles on road and will likely to remain so in the near future, especially for heavy duty applications in which electrification is typically more challenging. Therefore, continued improvements on IC engines in terms of efficiency and longevity are necessary for a more sustainable transportation sector. Two important design objectives for heavy duty engines with wet liners are to reduce friction loss and to lower the risks of cavitation damages, both of which can be greatly influenced by the piston-liner clearance and the design of the piston skirt. However, engine design optimization is difficult due to the nonlinear interactions between the key design variables and the design objectives, as well as the multi-physics and multi-scale nature of the mechanisms that are relevant to the design objectives.

Lubricating oil consumption (LOC) is a direct source of hydrocarbon and particulate emissions from internal combustion engines. LOC also inhibits the lifetime of exhaust aftertreatment system components, preventing their ability to effectively filter out other harmful emissions. Due to its influence on piston ring- bore conformability, bore distortion is arguably the most critical parameter for engine designers to consider in prevention of LOC. Bore distortion also has a significant influence on the contact forces between the piston ring and cylinder wall, which determine the wear rate of the ring and cylinder wall and can cause durability issues. Two drivers of bore distortion: thermal expansion and head bolt stresses, are routinely considered in conformability and contact analyses. Separately, bore distortion/vibration due to piston impact and combustion/cylinder pressures has been previously analyzed in wet liner engines for coolant cavitation and noise considerations.

In an engine system, the piston pin is subjected to high loading and severe lubrication conditions, and pin seizures still occur during new engine development. A better understanding of the lubricating oil behavior and the dynamics of the piston pin could lead to cost- effective solutions to mitigate these problems. However, research in this area is still limited due to the complexity of the lubrication and the pin dynamics. In this work, a numerical model that considers structure deformation and oil cavitation was developed to investigate the lubrication and dynamics of the piston pin. The model combines multi-body dynamics and elasto-hydrodynamic lubrication. A routine was established for generating and processing compliance matrices and further optimized to reduce computation time and improve the convergence of the equations. A simple built-in wear model was used to modify the pin bore and small end profiles based on the asperity contact pressures.

Protecting the piston ring and liner interface is critical to the proper operation of internal combustion engines. Specifically, the dry region, which is the portion of the liner above the Top Dead Center (TDC) of the Oil Control Ring (OCR), needs proper lubrication to reduce wear and to maintain sustainability. However, the mechanisms by which oil is distributed to such region have not been investigated. This paper presents the first attempt to understand dry region lubrication by means of the oil-gas interaction below the top ring gap through a combination of experimental and modeling approaches. An optical engine with 2D Laser Induced Fluorescence (2D-LIF) technique was applied to visualize the oil flow below the top ring gap. It was observed that the two vortices downstream the top ring gap can cause oil bridging towards the liner, providing lubrication to the ring-liner interface.

The thermal operational safety (TOS) of a vehicle ensures that no component exceeds its critical temperature during vehicle operation. To enhance the current TOS validation process, a data-driven approach is proposed to predict maximum component temperatures of a new vehicle project by leveraging the historical thermal wind tunnel data from previous vehicle projects. The approach intends to support engineers with temperature predictions in the early phase and reduce the number of wind tunnel tests in the late phase of the TOS validation process. In the early phase, all measurements of the new vehicle project are predicted. In the late phase, a percentage of measurements with the test vehicle used for the model training and the remaining tests are predicted with the trained ML model. In a first step, data from all wind tunnel tests is extracted into a joint dataset together with metadata about the vehicle and the executed load case.

The increasing demand for environmentally friendly and fuel-efficient transportation and power generation requires further optimization and minimization of friction power losses. With up to 50% of the overall friction, the piston cylinder unit (PCU) shows most potential within the internal combustion engine (ICE) to increase mechanical efficiency. Calculating friction of internal combustion engines, especially the friction contribution from piston rings and skirt, requires detailed knowledge of the dynamics and lubrication regime of the components being in contact. Part I of this research presents a successful match of simulated and measured piston inter-ring pressures at numerous operation points [1] and constitutes the starting point for the comparison of simulated and measured piston group friction forces as presented in this research.

The NVH-development cycle of vehicle components often requires a source characterization separated from the vehicle itself, which leads to the implementation of test bench setups. In the context of frequency based substructuring and transfer path analysis, a component can be characterized using Blocked Forces. The following paper provides a comparison of methods between an acceleration-based in-situ and a new hybrid in-situ Blocked Force determination, using measurements of an artificially excited electric power steering (EPS). Under real-life conditions on a test rig, the acceleration-based in-situ approach often shows limitations in the lower frequency range, due to relatively bad signal-to-noise ratio at the indicator sensors, while delivering accurate results in the higher spectrum. Due to considerable loads on components in operation, the stiffness of the test-rig cannot be decreased arbitrarily.

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.

Knock in gasoline engines at higher loads is a significant constraint on torque and efficiency. The anti-knock property of a fuel is closely related to its research octane number (RON). Ethanol has superior RON compared to gasoline and thus has been commonly used to blend with gasoline in commercial gasolines. However, as the RON of a fuel is constant, it has not been used as needed in a vehicle. To wisely use the RON, an On-Board Separation (OBS) unit that separates commercial gasoline with ethanol content into high-octane fuel with high ethanol fraction and a lower octane remainder has been developed. Then an onboard Octane-on-demand (OOD) concept uses both fuels in varying proportion to provide to the engine a fuel blend with just enough RON to meet the ever changing octane requirement that depends on driving pattern.

Eigenvalues of a simple rotating flexible disk-shaft system are obtained using different methods. The shaft is supported radially by non-rigid bearings, while the disk is situated at one end of the shaft. Eigenvalues from a finite element and a multi-body dynamic tool are compared against an established analytical formulation. The Campbell diagram based on natural frequencies obtained from the tools differ from the analytical values because of oversimplification in the analytical model. Later, detailed whirl analysis is performed using AVL Excite multi-body tool that includes understanding forward and reverse whirls in absolute and relative coordinate systems and their relationships. Responses to periodic force and base excitations at a constant rotational speed of the shaft are obtained and a modified Campbell diagram based on this is developed. Whirl of the center of the disk is plotted as an orbital or phase plot and its rotational direction noted.

Gear profile deviation is the difference in gear tooth profile from the ideal involute geometry. There are many causes that result in the deviation. Deflection under load, manufacturing, and thermal effects are some of the well-known causes that have been reported to cause deviation of the gear tooth profile. The profile deviation caused by gear tooth profile deformation due to interference-fit assembly has not been discussed previously. Engine timing gear trains, transmission gearboxes, and wind turbine gearboxes are known to use interference-fit to attach the gear to the rotating shaft. This paper discusses the interference-fit joint design and the mechanism of tooth profile deformation due to the interference-fit assembly in gear trains. A new analytical method to calculate the profile slope deviation change due to interference-assembly of parallel axis spur gears is presented.

Three-piece oil control rings (TPOCR) are widely used in the majority of modern gasoline engines and they are critical for lubricant regulation and friction reduction. Despite their omnipresence, the TPOCRs’ motion and sealing mechanisms are not well studied. With stricter emission standards, gasoline engines are required to maintain lower oil consumption limits, since particulate emissions are strongly correlated with lubricant oil emissions. This piqued our interest in building a numerical model coupling TPOCR dynamics and oil transport to explain the physical mechanisms. In this work, a 2D dynamics model of all three pieces of the ring is built as the main frame. Oil transport in different zones are coupled into the dynamics model. Specifically, two mass-conserved fluid sub-models predict the oil movement between rail liner interface and rail groove clearance to capture the potential oil leakage through TPOCR. The model is applied on a 2D laser induced fluorescence (2D-LIF) engine.

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.