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Sector mesh modeling is the dominant computational approach for combustion system design optimization. The aim of this work is to quantify the errors descending from the sector mesh approach through three geometric modeling approaches to an optical diesel engine. A full engine geometry mesh is created, including valves and intake and exhaust ports and runners, and a full-cycle flow simulation is performed until fired TDC. Next, an axisymmetric sector cylinder mesh is initialized with homogeneous bulk in-cylinder initial conditions initialized from the full-cycle simulation. Finally, a 360-degree azimuthal mesh of the cylinder is initialized with flow and thermodynamics fields at IVC mapped from the full engine geometry using a conservative interpolation approach. A study of the in-cylinder flow features until TDC showed that the geometric features on the cylinder head (valve tilt and protrusion into the combustion chamber, valve recesses) have a large impact on flow complexity.

The Composite Lightweight Automotive Suspension System is a composite rear suspension knuckle/tieblade consisting of UD prepreg (epoxy resin), SMC (vinylester resin) carbon fibre and a steel insert to reduce the weight of the component by 35% and reduce Co2. The compression moulding manufacturing process and CAE optimisation are unique and ground-breaking for this product and are designed to allow high volume manufacture of approx. 30,000 vehicles per year. The manufacturing techniques employed allow for multi-material construction within a five minute cycle time to make the process viable for volume manufacture. The complexities of the design lie in the areas of manufacturing, CAE prediction and highly specialised design methods. It is a well-known fact that the performance of a composite part is primarily determined by the way it is manufactured.

The interaction of fuel sprays and in-cylinder flow in direct-injection engines is expected to alter kinetic energy and integral length scales at least during some portions of the engine cycle. High-speed particle image velocimetry was implemented in an optical four-valve, pent-roof spark-ignition direct-injection single-cylinder engine to quantify this effect. Non-firing motored engine tests were performed at 1300 RPM with and without fuel injection. Two fuel injection timings were investigated: injection in early intake stroke represents quasi-homogenous engine condition; and injection in mid compression stroke mimics the stratified combustion strategy. Two-dimensional crank angle resolved velocity fields were measured to examine the kinetic energy and integral length scale through critical portions of the engine cycle. Reynolds decomposition was applied on the obtained engine flow fields to extract the fluctuations as an indicator for the turbulent flow.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

Realistically experiencing the sound and vibration data through actually listening to and feeling the data in a full-vehicle NVH simulator remarkably aids the understanding of the NVH phenomena and speeds up the decision-making process. In the case of idle vibration, the sound and vibration of the idle condition are perceived simultaneously, and both need to be accurately reproduced simultaneously in a simulated environment in order to be properly evaluated and understood. In this work, a case is examined in which a perceived idle quality of a vehicle is addressed. In this case, two very similar vehicles, with the same powertrain but somewhat different body structures, are compared. One has a lower subjective idle quality rating than the other, despite the vehicles being so similar.

A CAE method has been developed to address engine tonal noise and whine due to the excitation from a gerotor oil pump. The method involves a multidisciplinary approach including CFD, frequency-response structural analysis and acoustic analysis. The results from the application of the method applied to a couple of pumps with different designs are discussed. Engine tonal noise improvement through reduction in the excitation source from the pump and also stiffening the excitation path from the pump to the engine are studied. The effect of component modal alignment with oil pump orders is addressed as well.

Drivelines used in modern pickup trucks commonly employ universal joints. This type of joint is responsible for second driveshaft order vibrations in the vehicle. Large displacements of the joint connecting the driveline and the rear axle have a detrimental effect on vehicle NVH. As leaf springs are critical energy absorbing elements that connect to the powertrain, they are used to restrain large axle windup angles. One of the most common types of leaf springs in use today is the multi-stage parabolic leaf spring. A simple SAE 3-link approximation is adequate for preliminary studies but it has been found to be inadequate to study axle windup. A vast body of literature exists on modeling leaf springs using nonlinear FEA and multibody simulations. However, these methods require significant amount of component level detail and measured data. As such, these techniques are not applicable for quick sensitivity studies at design conception stage.

Lightweighting of vehicle panels enclosing vehicle cabin causes NVH degradation since engine, road, and wind noise acoustic sources propagate to the vehicle interior through these panels. In order to reduce this NVH degradation, there is a need to develop new NVH sound package materials and designs for use in lightweight vehicle design. Statistical Energy Analysis (SEA) model can be an effective CAE design tool to develop NVH sound packages for use in lightweight vehicle design. Using SEA can help engineers recover the NVH deficiency created due to sheet metal lightweighting actions. Full vehicle SEA model was developed to evaluate the high frequency NVH performance of “Vehicle A” in the frequency range from 200 Hz to 10 kHz. This correlated SEA model was used for the vehicle sound package optimization studies. Full vehicle level NVH laboratory tests for engine and tire patch noise reduction were also conducted to demonstrate the performance of sound package designs on “Vehicle A”.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

The performance of an organic Rankine cycle (ORC) that recovers heat from the exhaust of a heavy-duty diesel engine was simulated. The work was an extension of a prior study that simulated the performance of an experimental ORC system developed and tested at Oak Ridge National laboratory (ORNL). The experimental data were used to set model parameters and validate the results of that simulation. For the current study the model was adapted to consider a 15 liter turbocharged engine versus the original 1.9 liter light-duty automotive turbodiesel studied by ORNL. Exhaust flow rate and temperature data for the heavy-duty engine were obtained from Southwest Research Institute (SwRI) for a range of steady-state engine speeds and loads without EGR. Because of the considerably higher exhaust gas flow rates of the heavy-duty engine, relative to the engine tested by ORNL, a different heat exchanger type was considered in order to keep exhaust pressure drop within practical bounds.

As the world's largest auto producer and consumer, China is both the most promising and complex market given the country's rapid economic growth, huge population, and many regional and segment preference differences. This research is aimed at developing data-driven demand models for customer preference analysis and prediction under a competitive market environment. Regional analysis is first used to understand the impact of geographical factors on customer preference. After a comprehensive data exploration, a customer-level mixed logit model is built to shed light on fast-growing vehicle segments in the Chinese auto market. By combining the data of vehicle purchase, consideration, and past choice, cross-shopping behaviors and brand influence are explicitly modeled in addition to the impact of customer demographics, usage behaviors, and attributes of vehicles.

Designing a vehicle body involves meeting numerous performance requirements related to different attributes such as NVH, Durability, Safety, and others. Multi-Disciplinary Optimization (MDO) is an efficient way to develop a design that optimizes vehicle performance while minimizing the weight. Since a body design evolves in course of the product development cycle, it is essential to repeat the MDO process several times as a design matures and more accurate data become available. This paper presents a real life application of the MDO process to reduce weight while optimizing performance over the design cycle of the 2015 Mustang. The paper discusses the timing and results of the applied Multi-Disciplinary Optimization process. The attributes considered during optimization include Safety, Durability and Body NVH. Several iterations of MDO have been performed at different milestones in the design cycle leading to a significant weight reduction of the already optimized design by over 16kg.

Motivated by a combination of increasing consumer demand for fuel efficient vehicles, more stringent greenhouse gas, and anticipated future Corporate Average Fuel Economy (CAFE) standards, automotive manufacturers are working to innovate in all areas of vehicle design to improve fuel efficiency. In addition to improving aerodynamics, enhancing internal combustion engines and transmission technologies, and developing alternative fuel vehicles, reducing vehicle weight by using lighter materials and/or higher strength materials has been identified as one of the strategies in future vehicle development. Weight reduction in vehicle components, subsystems and systems not only reduces the energy needed to overcome inertia forces but also triggers additional mass reduction elsewhere and enables mass reduction in full vehicle levels.

Flow bench and engine testing can be used to detect flow induced noise, but understanding the fundamental mechanisms of such noise generation is necessary for developing an effective design. This paper describes Computational Aero-Acoustic (CAA) analyses performed to obtain the broad-band and BPF noise sources A computational aero-acoustics simulation on the aerodynamic noise generation of an automotive radiator fan assembly is carried out. Three-dimensional Computational Fluid Dynamics (CFD) simulation of the unsteady flow field was performed including the entire impeller and shroud to obtain the source of an audible broad-band flow noise between 2 to 4 kHz. Static pressure probes placed around the outer-periphery and at the center of the impeller inlet side and, at the shroud cavities to capture the noise sources. The static pressure at all probe locations were FFT (Fast Fourier Transform) processed and sound pressure level (SPL) was calculated.

Transfer or response equations are important as they provide relationships between the responses of different surrogates under matched, or nearly identical loading conditions. In the present study, transfer equations for different body regions were developed via mathematical modeling. Specifically, validated finite element models of the age-dependent Ford human body models (FHBM) and the mid-sized male Hybrid III (HIII50) were used to generate a set of matched cases (i.e., 192 frontal sled impact cases involving different restraints, impact speeds, severities, and FHBM age). For each impact, two restraint systems were evaluated: a standard three-point belt with and without a single-stage inflator airbag. Regression analyses were subsequently performed on the resulting FHBM- and HIII50-based responses. This approach was used to develop transfer equations for seven body regions: the head, neck, chest, pelvis, femur, tibia, and foot.

Underhood thermal management is a challenging problem in automotive industry. In order to make sure that vehicle works efficiently, there should be enough airflow through the cooling system so that the consequent heat rejection would be adequate. In idle condition the required air flow is provided by the cooling fan so a better understanding and an accurate predictive CAE tool for fan is very beneficial. Computational Fluid Dynamics (CFD) has been extensively used in predicting aerodynamic performance of automotive components. In the current work, the airflow performance of a fan, shroud and radiator assembly was simulated using Moving Reference Method (MRF) method. Although it is less expensive than Sliding Mesh (SM) method, the CAE results compare well with the test data. The simulation was carried out over 10+ different shrouds and the effect of geometrical parameters on airflow was investigated.

A deeper understanding of the complex phenomenology associated with the multiphase flow-induced noise and vibration in a dynamic valve is of critical importance to the automotive industry. To this purpose, a two-dimensional axisymmetric numerical model has been developed to simulate the complex processes that are responsible for the noise and vibration in a poppet valve. More specifically, an Eulerian multiphase flow model, a dynamic mesh and a user-defined function are utilized to facilitate the modeling of this complicated two-phase fluid-structure interaction problem. For a two-phase flow through the valve, our simulations showed that the deformation and breakup of gas bubbles in the gap between the poppet and the valve seat generates a vibration that arises primarily from the force imbalance between the spring and the two-phase fluid flow induced forces on the poppet.

The stable operation of turbocharger compressor at low flow rates is important to provide low end engine torque for turbocharged automotive engines. Therefore, it is important to be able to predict the lowest flow rates at different turbocharger speeds at which the surge phenomenon occurs. For this purpose, three-dimensional Computational Fluid Dynamics (CFD) simulations were performed on the turbocharger compressor including the entire compressor wheel and volute. The wheel consisted of six main and six splitter blades. Historically, flow bench and engine testing has been used to detect surge phenomenon. However a complete 3D CFD analysis can be performed upfront in the design to calculate low end compressor surge performance. The analyses will help understand the fundamental mechanisms of stalled flow, the surge phenomenon, and impact of compressor inlet conditions on surge.