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A simulation method is presented for the analysis of combustion in spark ignition (SI) engines operated at elevated exhaust gas recirculation (EGR) level and employing multiple spark plug technology. The modeling is based on a zero-dimensional (0D) stochastic reactor model for SI engines (SI-SRM). The model is built on a probability density function (PDF) approach for turbulent reactive flows that enables for detailed chemistry consideration. Calculations were carried out for one, two, and three spark plugs. Capability of the SI-SRM to simulate engines with multiple spark plug (multiple ignitions) systems has been verified by comparison to the results from a three-dimensional (3D) computational fluid dynamics (CFD) model. Numerical simulations were carried for part load operating points with 12.5%, 20%, and 25% of EGR. At high load, the engine was operated at knock limit with 0%, and 20% of EGR and different inlet valve closure timing.

This paper will describe the development of the 3-load cell tumble meter. This is a new method for measuring the tumble component of in-cylinder mixture motion. In-cylinder mixture motion is an important parameter for understanding and improving combustion stability of piston engines.

In scooter/motorbike engines coherent and stable tumble motion generation is still considered an effective mean in order to both reduce engine emissions and promote higher levels of combustion efficiency. The scientific research also assessed that squish motion is an effective mean for speeding up the combustion in a combustion process already fast. In a previous technical paper the authors demonstrated that for an engine having a high C/D ratio the squish motion is not only not necessary but also detrimental for the stability of the tumble motion itself, because there is a strong interaction between these two motions with the consequent formation of secondary vortices, which in turn penalizes the tumble breakdown and the turbulent kinetic energy production.

In this paper three-dimensional Large Eddy Simulations (i.e., LES) by using a PLIC-VOF method have been adopted to investigate the atomization process of round liquid jets issuing from automotive multi-hole injector-like nozzles. LES method is used to compute directly the effect of the large flow structure, being the smallest one modelled. A mesh having a cell size of 4 μm was used in order to derive a statistics of the detached liquid structures, i.e. droplets and ligaments. The latter have been identified by using an algorithm coded by authors. Cavitation modeling has not been included in the present computations. Two different mean injection nozzle flow velocities of 50 m/s and 270 m/s, corresponding to two mean nozzle flow Reynolds numbers of 1600 and 8700, respectively, have been considered in the calculations as representative of laminar and turbulent nozzle flow conditions.

A flash evaporation model is being developed to capture the effects of bubble nucleation and growth inside multi-hole injector nozzles to investigate the flash evaporation in fuel injector sprays in Gasoline Direct Injection (GDI). The 1D flash evaporation model is a key tool for providing the 3D Eulerian-Eulerian or Lagrangian spray simulation model with the right droplet size in order to properly predict the effect of degree of superheating on mixture formation. Super heating conditions are likely to be found under partial load conditions in GDI applications or they might be deliberately induced to enhance fuel atomization and vaporization. A quasi-1D nozzle flow model has been developed to help quantifying the effects of main physical and geometrical parameters in promoting fuel flash evaporation. This model is based on an weakly compressible homogenous two-phase mixture assumption. A non-equilibrium model is used to predict the vapour formation rate along the nozzle.

Traditional User/Maintenance Manuals provide useful information when dealing with simple machines. However, when dealing with complex systems of systems and highly miniaturized technologies, like UAVs, or with machines with millions of parts, a commercial aircraft is a case in point, new technologies taking advantage of Augmented Reality can rapidly and effectively support the maintenance operations. This paper presents a User/Maintenance Manual based on Augmented Reality to help the operator in the detection of parts and in the sequence to be followed to assemble/disassemble systems and subsystems. The proposed system includes a handheld device and/or an head mounted display or special goggles, to be used by on-site operators, with software management providing data fusion and overlaying traditional 2D user/maintenance manual information with an augmented reality software and appropriate interface.

A hydrogen economy is an increasingly popular solution to lower global carbon dioxide emissions. Previous research has been focused on the economic conditions necessary for hydrogen to be cost competitive, which tends to neglect the effectiveness of greenhouse gas mitigation for the very solutions proposed. The holistic carbon footprint assessment of hydrogen production, distribution, and utilization methods, otherwise known as “well-to-wheels” carbon intensity, is critical to ensure the new hydrogen strategies proposed are effective in reducing global carbon emissions. When looking at these total carbon intensities, however, there is no single clear consensus regarding the pathway forward. When comparing the two fundamental technologies of steam methane reforming and electrolysis, there are different scenarios where either technology has a “greener” outcome.

A process for simultaneously optimizing the mechanical performance and minimizing the weight of an automotive body-in-white will be developed herein. The process begins with appropriate load path definition though calculation of an optimized topology. Load paths are then converted to sheet metal, and initial critical cross sections are sized and shaped based on packaging, engineering judgment, and stress and stiffness approximations. As a general direction of design, section requirements are based on an overall vehicle “design for stiffness first” philosophy. Design for impact and durability requirements, which generally call for strength rather than stiffness, are then addressed by judicious application of the most recently developed automotive grade advanced high strength steels. Sheet metal gages, including tailored blanks design, are selected via experience and topometry optimization studies.

The laminar burning speed is an important intrinsic property of an air-fuel mixture determining key combustion characteristics such as turbulent flame propagation. It is a function of the mixture composition (mixture fraction and residual gas mass fraction) and of the thermodynamic conditions. Experimental measurements of Laminar Flame Speeds (LFS) are common in literature, but initial pressure and temperature are limited to low values due to the test conditions: typical pressure values for LFS detection are lower than 25 bar, and temperature rarely exceeds 550 K. Actual trends in spark ignition engines are to increase specific power output by downsizing and supercharging, thus the flame front involves even more higher pressure and temperature since the beginning of combustion.

Time-averaged temperatures at critical locations on the piston of a direct-fuel injected, two-stroke, 388 cm3, research engine were measured using an infrared telemetry device. The piston temperatures were compared to data [7] of a carbureted version of the two-stroke engine, that was operated at comparable conditions. All temperatures were obtained at wide open throttle, and varying engine speeds (2000-4500 rpm, at 500 rpm intervals). The temperatures were measured in a configuration that allowed for axial heat flux to be determined through the piston. The heat flux was compared to carbureted data [8] obtained using measured piston temperatures as boundary conditions for a computer model, and solving for the heat flux. The direct-fuel-injected piston temperatures and heat fluxes were significantly higher than the carbureted piston. On the exhaust side of the piston, the direct-fuel injected piston temperatures ranged from 33-73 °C higher than the conventional carbureted piston.

The performance optimization of modern Spark Ignition engines is limited by knock occurrence: heavily downsized engines often are forced to work in the Knock-Limited Spark Advance (KLSA) range. Knock control systems monitor the combustion process, allowing to achieve a proper compromise between performance and reliability. Combustion monitoring is usually carried out by means of accelerometers or ion sensing systems, but recently the use of cylinder pressure sensors is also becoming frequent in motorsport applications. On the other hand, cylinder pressure signals are often available in the calibration stage, where SA feedback-control based on the pressure signal can be used to avoid damages to the engine during automatic calibration. A predictive real-time combustion model could help optimizing engine performance, without exceeding the allowed knock severity.

A correlation of aerodynamic wind tunnels was initiated between Chrysler, Ford and General Motors under the umbrella of the United States Council for Automotive Research (USCAR). The wind tunnels used in this correlation were the open jet tunnel at Chrysler's Aero Acoustic Wind Tunnel (AAWT), the open jet tunnel at the Jacobs Drivability Test Facility (DTF) that Ford uses, and the closed jet tunnel at General Motors Aerodynamics Laboratory (GMAL). Initially, existing non-competitive aerodynamic data was compared to determine the feasibility of facility correlation. Once feasibility was established, a series of standardized tests with six vehicles were conducted at the three wind tunnels. The size and body styles of the six vehicles were selected to cover the spectrum of production vehicles produced by the three companies. All vehicles were tested at EPA loading conditions. Despite the significant differences between the three facilities, the correlation results were very good.

The European Commission is going to publish the new Euro7 standard shortly, with the target of reducing the impact on pollutant emissions due to transportation systems. Besides forcing internal combustion engines to operate cleaner in a wider range of operating conditions, the incoming regulation will point out the role of On-Board Monitoring (OBM) as a key enabler to ensure limited emissions over the whole vehicle lifetime, necessarily taking into account the natural aging of involved systems and possible electronic/mechanical faults and malfunctions. In this scenario, this work aims to study the potential of data-driven approaches in detecting emission-relevant engine faults, supporting standard On-Board Diagnostics (OBD) in pinpointing faulty components, which is part of the main challenges introduced by Euro7 OBM requirements.

Due to the integration of many interacting subsystems like hybrid vehicle management, energy management, distance management, etc. into the VCU platform the design steps for function development and calibration become more and more complex. This makes an aid necessary to relieve the development. Therefore, the aim of the proposed simulation-based development and calibration design is to improve the time-and-cost consuming development stages of modern VCU platforms. A simulation-based development framework is shown on a complex function development and calibration case study using an advanced powertrain concept with a plug-in hybrid electric vehicle (PHEV) concept with two electrical axles. Presenter Thomas Boehme, IAV GmbH

Due to the integration of many interacting subsystems like hybrid vehicle management, energy management, distance management, etc. into the VCU platform the design steps for function development and calibration become more and more complex. This makes an aid necessary to relieve the development. Therefore, the aim of the proposed simulation-based development and calibration design is to improve the time-and-cost consuming development stages of modern VCU platforms. A simulation-based development framework is shown on a complex function development and calibration case study using an advanced powertrain concept with a plug-in hybrid electric vehicle (PHEV) concept with two electrical axles.

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

A physically based simulation program developed by IAV makes a notable reduction of test bed measurements for the calibration of the cylinder charge calculation possible. Based upon geometric engine parameters and camshaft profiles, the cylinder charge is calculated from thermodynamic relationships taking into account the contribution of residual gas. After successful engine-specific calibration of the simulation model on the basis of a reduced set of test bed measurements, it is possible to calculate the cylinder air mass over the entire range of valve timing settings and operating points (engine load and speed). The simulation-generated “virtual” measurements can then be used for calibration of the control unit software over the entire operating range.

As vehicle development timelines continue to shorten, it is necessary for the full vehicle NVH engineer to be able to predict performance without actual prototypes. There has been significant advancement in the accuracy of finite element modeling techniques of trimmed bodies; however accuracy is still low in the road noise mid frequency range from 150-400Hz. Also, calculation times for these frequencies are long, with very large results files in some cases. To alleviate these limitations, a Hybrid approach has been used, where a finite element suspension and drive train model is coupled with a test based Frequency Response Function (FRF) model of the trimmed body. The predicted road noise level was compared to actual vehicle tests and exhibited excellent correlation.

Added masses computation is a crucial aspect to be considered when the density of a body moving in a fluid is comparable to the density of the fluid displaced: added mass can be defined as the inertia added to a system because an accelerating or decelerating body displaces some volume of neighboring fluid as it moves through it. The motion of vehicles like airships and ships can be addressed only by keeping into account the effect of added masses, while in case of aircrafts and helicopters this contribution is usually neglected. Lighter Than Air flight simulation, unmanned airships flight control system, airships flight dynamics are typical applications in which added masses are fundamental to achieve an effective and realistic modeling. A panel based method using the mesh of an airship external shape is developed to account for the added massed.

Robustness testing of Advanced Driver Assistance Systems (ADAS) features is a crucial step in ensuring the safety and reliability of these systems. ADAS features include technologies like adaptive cruise control, lateral and longitudinal controls, automatic emergency braking, and more. These systems rely on various sensors, cameras, radar, lidar, and software algorithms to function effectively. Robustness testing aims to identify potential vulnerabilities and weaknesses in these systems under different conditions, ensuring they can handle unexpected scenarios and maintain their performance. Mileage accumulation is one of the validation methods for achieving robustness. It involves subjecting the systems to a wide variety of real-world driving conditions and driving scenarios to ensure the reliability, safety, and effectiveness of the ADAS features.