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The frequency spectral structure of turbulence spatial components was investigated in the cylinder of an automotive diesel engine with a high-squish reentrant in-piston bowl of the conical type and a helical inlet port. A sophisticated HWA technique using single- and dual-sensor probes was applied for instantaneous air velocity measurements along the injector axis at practical engine speeds, up to 3000 rpm, under motored conditions. The investigation was carried out for both cycle-resolved and conventional turbulence components, as were determined by different wire orientations, throughout the induction, the compression and the early stage of the expansion stroke. The anisotropy of turbulence spectral structure and its temporal evolution during the engine cycle were examined by evaluating the autospectral density functions and the time scales of each turbulence component in consecutive correlation crank-angle intervals.

Cycle-resolved numerical and experimental analyses were carried out to study the dynamics of an automotive diesel injection system with a distributor-type pump during transient control phases. The investigation had the following main objectives: to assess a flexible and efficient simulation program of high-pressure fuel-injection systems in unsteady operating conditions; to examine the system response to rapid load or speed variations, regardless of the control device which may cause them. The program, based on a fast implicit numerical algorithm with second-order accuracy, was an extension of the computational code NAIS which had been previously developed and validated for stationary operating conditions. The experiments were made on a test bench usually used by industry to evaluate diesel injection equipment.

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.