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Precise prediction of combustion parameters such as peak firing pressure (PFP) or crank angle of 50% burned mass fraction (MFB50) is essential for optimal engine control. These quantities are commonly determined from in-cylinder pressure sensor signals and are crucial to reach high efficiencies and low emissions. Highly accurate in-cylinder pressure sensors are only applied to test rig engines due to their high cost, limited durability and special installation conditions. Therefore, alternative approaches which employ virtual sensing based on signals from non-intrusive sensors retrieved from common knock sensors are of great interest. This paper presents a comprehensive comparison of selected approaches from literature, as well as adjusted or further developed methods to determine engine combustion parameters based on knock sensor signals. All methods are evaluated on three different engines and two different sensor positions.

High-pressure direct injection (HPDI) of natural gas into the combustion chamber enables a non-premixed combustion regime known from diesel engines. Since knocking combustion cannot occur with this combustion process, an increase in the compression ratio and thus efficiency is possible. Due to the high injection pressures required, this concept is ideally suited to applications where liquefied natural gas (LNG) is available. In marine applications, the bunkering of and operation with LNG is state-of-the-art. Existing HPDI gas combustion concepts typically use a small amount of diesel fuel for ignition, which is injected late in the compression stroke. The diesel fuel ignites due to the high temperature of the cylinder charge. The subsequently injected gas ignites at the diesel flame. The HPDI gas combustion concept presented in this paper is of a monovalent type, meaning that no fuel other than natural gas is used.

The transient operation of gas engines is of paramount importance to sustainable power generation as it increases the share of renewable energy. Fast-reacting and highly flexible power plants are an integral component of scenarios for the smart power generation of the future. Modern gaseous fueled large bore engines already adapt to fluctuating load demands quickly and also provide high efficiency throughout all load conditions. However, future energy systems that integrate predominantly fluctuating renewables will require even further improved transient capabilities of these engines. The goal is to be competitive with diesel engines in applications with the highest transient requirements and to meet the high transient requirements while simultaneously generating significantly less emissions than other fossil generation facilities to support the future sustainable power supply.

Digital technologies are capable of making a significant contribution to improving large internal combustion engine technology. In particular, methods from the field of artificial intelligence are opening up new avenues. So-called “intelligent” engine components rely on advanced instrumentation and data analytics to create value-added data, which in turn can serve as the basis for applications such as condition monitoring, predictive maintenance and controls. For related components and systems, these data may also allow for novel condition monitoring approaches. This paper describes the use of value-added data from an intelligent diesel fuel injection valve that give detailed information about the injection process for real-time prediction of key combustion parameters such as indicated mean effective pressure, maximum cylinder pressure and combustion phasing.

Increasing demands regarding the efficiency and emissions of internal combustion engines will require higher peak firing pressures and increased indicated mean effective pressures in the future. Adaptation of these parameters will result in higher thermal and mechanical loads that act on core engine components. To meet the future requirements, it is essential to make changes to the design of the tribological system, which is composed of the piston, piston rings, liner and lube oil, while maintaining the robustness and reliability of the engine and its components. Modification of the tribological system requires in-depth knowledge of wear and friction. This paper presents the setup of a model of the tribological system (piston, piston rings, liner and lube oil) of a large gas engine in the commercial software AVL EXCITE™ Piston&Rings as well as its calibration and validation with data obtained from a test bed.

The advent of digitalization opens up new avenues for advances in large internal combustion engine technology. Key engine components are becoming "intelligent" through advanced instrumentation and data analytics. By generating value-added data, they provide deeper insight into processes related to the components. An intelligent common rail diesel fuel injection valve for large engine applications in combination with machine learning allows reliable prediction of key combustion parameters such as maximum cylinder pressure, combustion phasing and indicated mean effective pressure. However, fault-related changes to the injection valve also have to be considered. Based on experiments on a medium-speed four-stroke single-cylinder research engine with a displacement of approximately 15.7 liter, this study investigates the extent to which the intelligent injection valve can improve the reliability of combustion parameter predictions in the presence of injection valve faults.

To increase reliability and the maintenance interval of an internal combustion engine while operating it with the lowest possible emissions, spark plug wear must be reduced. In this context, information about the spark plug center and the ground electrode temperature is key. Several measurement devices have been developed that measure the temperature of spark plug electrodes. The great challenge is to measure the temperature of the center electrode; on the one hand, the measurement device must be insulated and capable of withstanding the high voltage of the ignition system, and on the other hand, the device should not influence the ignition system. All previously studied devices presented in this paper have in common that major reconstruction of the ignition system and/or spark plugs whose design is very different from the standard engine spark plug were necessary.

Using natural gas in large bore engines reduces carbon dioxide emissions by up to 25% at a lower fuel cost than diesel engines. In demanding applications with highly transient operating profiles, however, premix gas engines have disadvantages compared to diesel engines because of the potential for knocking and misfire to occur. Operating a gas engine using the diesel cycle requires high gas injection pressures. Furthermore, a source of ignition is needed due to the high autoignition temperature of methane. State-of-the-art solutions inject a small quantity of diesel fuel before introducing the natural gas. One monofuel alternative ignites the gas jets with flame torches that originate in a prechamber. This paper presents the simulation based development of a prechamber ignited high pressure direct injection (HPDI) gas combustion concept and subsequent experimental validation.

It is critical for gas and dual fuel engines to have improved transient characteristics in order that they can successfully compete with diesel engines. Testing of transient behavior as well as of different control strategies for the multi-cylinder engine (MCE) should already be done on the single cylinder engine (SCE) test bed during the development process. This paper presents tools and algorithms that simulate transient MCE behavior on a SCE test bed. A methodology that includes both simulation and measurements is developed for a large two-stage turbocharged gas engine. Simple and fast models and algorithms are created that are able to provide the boundary conditions (e.g., boost pressure and exhaust back pressure) of a multi-cylinder engine in transient operation in real-time for use on the SCE test bed. The main models of the methodology are discussed in detail.