This paper is concerned with the influence of cavitation in the injection nozzle on combustion in diesel engines. After an overview of the fundamental definitions to characterize nozzles — where above all the injection pressure, the back pressure, the injection mass flow, and the spray momentum through the nozzle as well as the geometry play a role — the difference between a cavitating and a non-cavitating nozzle will be clarified both theoretically and based on engine measurements. To observe the influence of cavitation on combustion in isolation, a cavitating and a non-cavitating nozzle were designed in such a way that they possessed the same mass flow and the same nozzle discharge velocity. In addition to the manufacturer's measurement, the nozzles were measured using a combined flowrate—spray momentum device at levels of injection pressure and back pressure close to those in an engine. A single-cylinder research engine with a modern common rail injection system served as the test engine for the experiments. The experiments revealed striking differences in emission levels. Especially notable are the differences in the soot values. To explore in more detail these differences between the cavitating and non-cavitating nozzle, optical investigations were conducted in an injection chamber. CCD high-speed imaging was used to visualize mixture formation of the two different nozzles.
Robust engine operation with long maintenance intervals and low emissions is the key to meeting future engine requirements. At the same time, engines should be environmentally friendly, resource-friendly, and cost-effective to produce and operate.
To meet these market requirements, a central component in engine development is the ignition system and in particular the spark plug.
To increase the maintenance intervals of internal combustion engines, it is necessary to increase spark plug lifetime by reducing spark plug wear. The electrode materials used to date are often expensive and rare, and their mining is not without controversy.
Successful use of alternative spark plug electrode materials which are available in large quantities, inexpensive, and more resistant to wear than existing materials with similar ignition behavior would advance engine development so it can meet further economic and environmental requirements.
To this end, ceramic spark plug electrodes were investigated to determine their spark and ignition behavior as well as their wear. These materials seem to be an interesting alternative to existing spark plug electrode materials.
This paper presents a spark plug with ceramic spark plug electrodes that achieves conditions similar to those of standard spark plugs in terms of secondary voltage trace and ignition behavior. Furthermore, it introduces a sophisticated method for scientific, cost-effective, and application-oriented development.
Finally, it provides a promising outlook for the ignition behavior and combustion performance of engines with this spark plug.
Digitalization offers a large number of promising tools for large internal combustion engines such as condition monitoring or condition-based maintenance. This includes the status evaluation of key engine components such as cylinder liners, whose inner surfaces are subject to constant wear due to their movement relative to the pistons. Existing state-of-theart methods for quantifying wear require disassembly and cutting of the examined liner followed by a high-resolution microscopic surface depth measurement that quantitatively evaluates wear based on bearing load curves (also known as Abbott-Firestone curves). Such reference methods are destructive, time-consuming and costly. The goal of the research presented here is to develop simpler and nondestructive yet reliable and meaningful methods for evaluating wear condition. A deep-learning framework is proposed that allows computation of the surface-representing bearing load curves from reflection RGB images of the liner surface that can be collected with a simple handheld device, without the need to remove and destroy the investigated liner. For this purpose, a convolutional neural network is trained to estimate the bearing load curve of the corresponding depth profile, which in turn can be used for further wear evaluation.
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