Refine Your Search

Topic

Author

Affiliation

Search Results

Journal Article

Analysis of the Piston Group Friction in a Single-Cylinder Gasoline Engine When Operated with Synthetic Fuel DMC/MeFo

2022-03-29
2022-01-0485
Synthetic fuels for internal combustion engines offer CO2-neutral mobility if produced in a closed carbon cycle using renewable energies. C1-based synthetic fuels can offer high knock resistance as well as soot free combustion due to their molecular structure containing oxygen and no direct C-C bonds. Such fuels as, for example, dimethyl carbonate (DMC) and methyl formate (MeFo) have great potential to replace gasoline in spark-ignition (SI) engines. In this study, a mixture of 65% DMC and 35% MeFo (C65F35) was used in a single-cylinder research engine to determine friction losses in the piston group using the floating-liner method. The results were benchmarked against gasoline (G100). Compared to gasoline, the density of C65F35 is almost 40% higher, but its mass-based lower heating value (LHV) is 2.8 times lower. Hence, more fuel must be injected to reach the same engine load as in a conventional gasoline engine, leading to an increased cooling effect.
Journal Article

Assessing Low Frequency Flow Noise Based on an Experimentally Validated Modal Substructuring Strategy Featuring Non-Conforming Grids

2022-06-15
2022-01-0939
The continuous encouragement of lightweight design in modern vehicles demands a reliable and efficient method to predict and ameliorate the interior acoustic comfort for passengers. Due to considerable psychological effects on stress and concentration, the low frequency contribution plays a vital rule regarding interior noise perception. Apart other contributors, low frequency noise can be induced by transient aerodynamic excitation and the related structural vibrations. Assessing this disturbance requires the reliable simulation of the complex multi-physical mechanisms involved, such as transient aerodynamics, structural dynamics and acoustics. The domain of structural dynamics is particularly sensitive regarding the modelling of attachments restraining the vibrational behaviour of incorporated membrane-like structures. In a later development stage, when prototypes are available, it is therefore desirable to replace or update purely numerical models with experimental data.
Technical Paper

The New 12-Cylinder Hydrogen Engine in the 7 Series: The H2 ICE Age Has Begun

2006-04-03
2006-01-0431
Due to its high specific power density, immediate and lively throttle response, good efficiency and life cycles comparable to current powertrain concepts the hydrogen internal combustion engine (H2-ICE) will play a major role in future automotive propulsion systems. The new bi-fuel 12-cylinder hydrogen internal combustion engine for the 7 series is an important step in this direction. In this article engine design and the development of the engine functions of the new H2-12-cylinder will be shown in detail. In particular the engine operation strategy to achieve high efficiencies and very low tail pipe emissions will be presented. Finally potentials of the mono-fuel derivative will be discussed and an outlook for future engine concepts will be given.
Technical Paper

Gaussian Process Surrogate Models for Vibroacoustic Simulations

2024-06-12
2024-01-2930
In vehicle NVH development, vibroacoustic simulations with Finite Element (FE) models are a common technique. The computational costs for these calculations are steadily rising due to more detailed modelling and higher frequency ranges. At the same time, the need for multiple evaluations of the same model with different input parameters, e.g., for uncertainty quantification, optimization, or robustness investigations, is also increasing. Therefore, it is crucial to reduce the computational costs in these cases. A common technique is to use surrogate models that replace the computationally intensive FE model to perform repeated evaluations. Several different methods in this area are well established, but with the continuous advancements in the field of machine learning, interesting new methods like the Gaussian Process (GP) regression arises as a promising approach.
X