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Beside the automotive industry, where 2-cylinder inline engines are catching attention again, twin-cylinder configurations are quite usual in the small engine world. From stationary engines and range-extender use to small motorcycles up to big cruisers and K-Cars this engine architecture is used in many types of applications. Because of very good overall packaging, performance characteristics and not least the possibility of parts-commonality with 4-cylinder engines nearly every motorcycle manufacturer provides an inline twin in its model range. Especially for motorcycle applications where generally the engine is a rigid member of the frame and vibrations can be transferred directly to the rider an appropriate balancing system is required.

The acoustics of automotive intake and exhaust systems is typically modeled using linear acoustics or gas-dynamics simulation. These approaches are preferred during basic sound design in the early development stages due to their computational efficiency compared to complex 3D CFD and FEM solutions. The linear acoustic method reduces the component being modelled to an equivalent acoustic two-port transfer matrix which describes the acoustic characteristic of the muffler. Recently this method was used to create more detailed and more accurate models based on a network of 3D cells. As the typical automotive muffler includes perforated elements and sound absorptive material, this paper demonstrates the extension of the 3D linear acoustic network description of a muffler to include the aforementioned elements. The proposed method was then validated against experimental results from muffler systems with perforated elements and sound absorptive material.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

Engine simulation can be performed using model approaches of different depths in capturing physical effects. The present paper presents a comprehensive comparison study on seven different engine models. The models range from transient 1D cycle resolved approaches to steady-state non-dimensional maps. The models are discussed in the light of key features, amount and kind of required input data, model calibration effort and predictability and application areas. The computational performance of the different models and their capabilities to capture different transient effects is investigated together with a vehicle model under real-life driving conditions. In the trade-off field of model predictability and computational performance an innovative approach on crank-angle resolved cylinder modeling turned out to be most beneficial.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values. This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).

Efficient integration of mechanics and microelectronics components is nowadays a must within the automotive industry in order to minimize integration risks and support optimization of the entire system. We propose in this work a cross domain co-simulation platform for the efficient analysis of mechatronic systems. The interfacing of two state-of-the-art simulation platforms provides a direct link between the two domains at an early development stage, thus enabling the validation and optimization of the system already during modeling phase. The proposed cross-domain co-simulation is used within our TEODACS project for the analysis of the FlexRay technology. We illustrate using a drive-by-wire use case how the different architecture choices may influence the system.

Beside hard facts as performance, emissions and fuel consumption especially the brand specific attributes such as styling and sound are very emotional, unique selling prepositions. To develop these emotional characters, within the given boundary conditions of the future pass-by regulation, it is necessary to define them at the very beginning of the project and to follow a consequent development process. The following paper shows examples of motorcycle NVH development work on noise cleaning and sound engineering using a hybrid development process combining front loading, simulation and testing. One of the discussed solutions is the investigation of a piston pin offset in combination with a crankshaft offset for the reduction of friction. The optimization of piston slap noise as a result of the piston secondary motion was performed by simulation. As another example a simulation based development was performed for the exhaust system layout.

Microprocessors and microcontrollers are now widely used in automobiles. Microprocessor systems contain sources of interrupt and interrupt service routines, which are software components executed in response to the assertion of an interrupt in hardware. A major problem in designing the software of microprocessor systems is the analytical treatment of interrupt latency. Because multiple interrupt service routines are executed on the same CPU, they compete for the CPU and interfere with each other's latency requirements. Here, interrupt latency is defined as the delay between the assertion of the interrupt in hardware and the start of execution of the associated interrupt service routine. It is estimated that 80% of intermittent bugs in small microprocessor software loads are due to improper treatment of interrupts. Until this work, there is no analytic method for analyzing a particular system to determine if it may violate interrupt latency requirements.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

The paper focuses on various important decisions of verification and testing plans of the product during its design and production stages. In most of the product and process development projects, decisions on verification and testing are ad-hoc or based on traditions. Such decisions never guarantee the performance of the product as planned, during its whole life cycle. We propose an analytical approach to provide the concrete base for such crucial decisions of verification planning. Accordingly, a mathematical model is presented. Also, a case study of an automotive Electro-mechanical product is included to illustrate the application of the model.

Periprosthetic fractures refer to the fractures that occur in the vicinity of the implants of joint replacement arthroplasty. Most of the fractures during an automotive frontal collision involve the long bones of the lower limbs (femur and tibia). Since the prevalence of persons living with lower limb joint prostheses is increasing, periprosthetic fractures that occur during vehicular accidents are likely to become a considerable burden on health care systems. It is estimated that approximately 4.0 million adults in the U.S. currently live with Total Knee Replacement (TKR) implants. Therefore, it is essential to study the injury patterns that occur in the long bone of a lower limb containing a total knee prosthesis. The aim of the present study is to develop an advanced finite element model that simulates the possible fracture patterns that are likely during vehicular accidents involving occupants who have knee joint prostheses in situ.

The European Emissions Stage 5b standard for diesel passenger cars regulates particulate matter to 0.0045 g/km and non-volatile part/km greater than 23 nm size to 6.0x10₁₁ as determined by the PMP procedure that uses a heated evaporation tube to remove semi-volatile material. Measurement artifacts associated with the evaporation tube technique prevents reliable extension of the method to a lower size range. Catalytic stripper (CS) technology removes possible sources of these artifacts by effectively removing all hydrocarbons and sulfuric acid in the gas phase in order to avoid any chemical reactions or re-nucleation that may cause measurement complications. The performance of a miniature CS was evaluated and experimental results showed solid particle penetration was 50% at 10.5 nm. The sulfate storage capacity integrated into the CS enabled it to chemically remove sulfuric acid vapor rather than rely on dilution to prevent nucleation.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

A new friction testing system has been designed and built to simulate the actual engine conditions in friction and wear test of piston-ring and cylinder liner assembly. Experimental data has been developed as Friction Coefficient / Crank Angle Degree diagrams including the effects of running speed (500 and 700 rpm) and ring normal load. Surface roughness profilocorder traces were obtained for tested samples. Mixed lubrication regime observed in the most part of the test range. New cylinder bore materials and lubricants can be screened easily and more reliable simulated engine friction data can be collected using this technique.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.

The goal of optimization in vehicle design is often blurred by the myriads of requirements belonging to attributes that may not be quite related. If solutions are sought by optimizing attribute performance-related objectives separately starting with a common baseline design configuration as in a traditional design environment, it becomes an arduous task to integrate the potentially conflicting solutions into one satisfactory design. It may be thus more desirable to carry out a combined multi-disciplinary design optimization (MDO) with vehicle weight as an objective function and cross-functional attribute performance targets as constraints. For the particular case of vehicle body structure design, the initial design is likely to be arrived at taking into account styling, packaging and market-driven requirements.