Search Results

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.

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.

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.