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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.

A realistic simulation of the wall heat transfer is an imperative condition for the accurate analysis and simulation of the working process of IC engines. Due to its simplicity in application, zero-dimensional wall heat transfer models dominate engine cycle simulation in practice. However, experience shows that existing zero-dimensional models for wall heat transfer do not yield satisfactory results in certain applications. This is mainly due to a lack of consideration of the actual flow field in the cylinder. In this paper a quasi-dimensional heat transfer model, which is based on a detailed description of the turbulent flow field in the combustion chamber, is described. The model presents a consistent approach for the high pressure as well as the low pressure part of the cycle. The results of the heat transfer model are compared with results from the correlation by Woschni/Huber and with experimental results from various DI Diesel engines.

As emission limits become increasingly stringent and the price of gaseous fuels decreases, more emphasis is being placed on promoting gas engines. In the field of large engines for power generation, dual fuel combustion concepts that run on diesel/natural gas are particularly attractive. Knock in diesel/natural gas dual fuel engines is a well known yet not fully understood complex phenomenon that requires consideration in any attempt to increase load and efficiency. Thus combustion concept development requires a reliable yet robust methodology for detecting knock in order to ensure knock-free engine operation. Operating parameters such as rail pressure, start of injection and amount of diesel injected are the factors that influence oscillations in the in-cylinder pressure trace after the start of combustion. Oscillations in the pre-mixed combustion phase, or ringing, are caused by the rapid conversion of large parts of the injected diesel.

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.

Hydrogen is seen as a promising energy carrier for a future mobility scenario. Applied as fuel in IC engines with internal mixture formation, hydrogen opens up new vistas for the layout of the combustion system. The 3D CFD simulation of internal mixture formation as well as combustion helps to understand the complex in-cylinder processes and provides a powerful tool to optimize the engine's working cycle. The performance of standard simulation models for mixture formation as well as the performance of a user-defined combustion model applied in a commercial CFD-code is discussed within this article. The 3D CFD simulations are validated with measurements obtained from a thermodynamic and from an optical research engine respectively.

This paper shows extracts from the development process of a lean-burn gaseous fuelled engine for combined heat and power generation. The aim was to optimize the mixture formation, the charge motion and the combustion of an existing multi-cylinder engine. Therefore, experimental investigations on a single cylinder research engine and numerical simulations based on 3-dimensional CFD methods were carried out. The use of CFD methods for the optimization of the engine required intensive development efforts in the field of combustion simulation. In particular, the combustion model developed by Magnussen and Hjertager [1] was modified. Through comparison with the PDF model and results of the engine process calculation, the suitability of this modified combustion model was shown. In addition, a knock model was also developed and implemented in the CFD code in order to determine the knock tendency of different engine concepts.

A realistic modeling of the wall heat transfer is essential for an accurate analysis and simulation of the working cycle of internal combustion engines. Empirical heat transfer formulations still dominate the application in engine process simulations because of their simplicity. However, experiments have shown that existing correlations do not provide satisfactory results for all the possible operation modes of hydrogen internal combustion engines. This paper describes the application of a flow field-based heat transfer model according to Schubert et al. [1]. The models strength is a more realistic description of the required characteristic velocity; considering the influence of the injection on the global turbulence and on the in-cylinder flow field results in a better prediction of the wall heat transfer during the compression stroke and for operations with multiple injections. Further an empirical hypothesis on the turbulence generation during combustion is presented.

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.

When developing and later using simulation models for combustion prediction in internal combustion engines, it is first of all necessary to determine the model constants. This paper describes the development of a method for the automated determination of model parameters which can be applied to any internal combustion simulation model. The work is not aimed at developing a new optimizing algorithm but at adjusting and adapting an existing optimizer to the special needs and convergence problems, which occur when applied to combustion models. Consequently, the paper describes the set-up of the objective function and several methods for improving the convergence. Finally, an outline for a strategy which uses the optimizing tool for model development is presented.

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.

Virtual sensing refers to the processing of desired physical data based on measured values. Virtual sensors can be applied not only to obtain physical quantities which cannot be measured or can only be measured at an unreasonable expense but also to reduce the number of physical sensors and thus lower costs. In the field of spark ignited internal combustion engines, the virtual sensing approach may be used to predict the cylinder pressure signal (or characteristic pressure values) based on the acceleration signal of a knock sensor. This paper presents a method for obtaining the cylinder pressure signal in the high-pressure phase of an internal combustion engine based on the measured acceleration signal of a knock sensor. The approach employs a partial differential equation to represent the physical transfer function between the measured signal and the desired pressure. A procedure to fit the modeling constants is described using the example of a large gas engine.

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.

Optimization of engine warm-up behavior has traditionally made use of experimental investigations. However, thermal engine models are a more cost-effective alternative and allow evaluation of the fuel saving potential of thermal management measures in different driving cycles. To simulate the thermal behavior of engines in general and engine warm-up in particular, knowledge of heat distribution throughout all engine components is essential. To this end, gas-side heat transfer inside the combustion chamber and in the exhaust port must be modeled as accurately as possible. Up to now, map-based models have been used to simulate heat transfer and fuel consumption; these two values are calculated as a function of engine speed and load. To extend the scope of these models, it is increasingly desirable to calculate gas-side heat transfer and fuel consumption as a function of engine operating parameters in order to evaluate different ECU databases.

Lean burn combustion concepts with high mean effective pressures are being pursued for large gas engines in order to meet future stringent emission limits while maintaining high engine efficiencies. Since severe boundary conditions for the ignition process are encountered with these combustion concepts, the processes of spark ignition and flame initiation are important topics of applied research, which aims to avoid misfiring and to keep cycle-to-cycle combustion variability within reasonable limits. This paper focuses on the fundamental investigation of early flame kernel development using different ignition system settings. The investigations are carried out on a rapid compression-expansion machine in which the spark ignition process can be observed under engine-like pressure and excess air ratio conditions while low flow velocities are maintained.

Hydrogen is frequently cited as a future energy carrier. Hydrogen allows a further optimization of internal combustion engines, especially with direct injection. In order to assess various concepts, detailed thermodynamic analyses were carried out. Effects, which can be neglected with conventional fuels (e.g. losses due to injection during compression stroke) are considered. These basics as well as several results from test bed investigations are described within this article. Wall heat losses were found to have a major influence on overall efficiency and are thus investigated in detail, based on local surface temperature measurement. Finally, concepts that allow an increase in engine efficiency and lowest NOx emissions are demonstrated.

This paper summarizes the results of several investigations on in-cylinder heat transfer during high-pressure and gas exchange phases as well as heat transfer in the inlet and outlet ports for a number of different engine types (DI Diesel, SI and gaseous fueled engine). The paper contains a comparision of simulation results and experimental data derived from heat flux measurements. Numerical results were obtained from zero-, one- and three-dimensional simulation methods. Time and spatially resolved heat fluxes were measured applying the surface temperature method and special heat flux sensors. The paper also includes an assessment of different sensor types with respect to accuracy and applicability.

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.

The 3D CFD method has become an essential and reliable tool for the development of modern large gaseous-fuelled engines. This holds especially true for the optimization of mixture formation and charge motion in prechamber engines to ensure suitable conditions near the spark plug at ignition time. In order to initialize a quick combustion process, an ignitable mixture with high turbulence but moderate velocity must prevail round the spark plug. However, suitable models for combustion and heat transfer are inevitable for a realistic simulation of the whole engine cycle. Within 3D CFD codes the combustion process is usually calculated using the PDF (probability density function) - model; heat transfer is modeled based on the logarithmic wall function. Experimental investigations were carried out on a single cylinder research engine in order to validate the combustion model used and different heat transfer models.

The incorporation of a detailed engine process calculation that takes into account thermal behavior of the engine and exhaust system is essential for a realistic simulation of transient vehicle operation. This is the only possible way to have a precise preliminary calculation of fuel consumption and emissions. Therefore, a comprehensive thermal network of the engine based on the lumped capacity method has been developed. The model allows the computation of component temperatures in steady state operation as well as in transient engine studies, e.g. investigations of engine warm-up. The model is integrated in a co-simulation environment consisting of a detailed vehicle and engine cycle simulation code. The paper describes the procedure of the co-simulation and presents several examples of warm-up simulations.

To achieve high power output and good efficiency and to comply with increasingly stricter emission standards, modern combustion engines require a more complex engine design, which results in a higher number of control parameters. As the measurement effort and the number of sensors for engine development at the test bed continue to increase, it is becoming nearly impossible for the test bed engineer to manually check measurement data quality. As a result, automated methods for analysis and plausibility checks of measurement data are necessary in order to find faults as soon as they occur and to obtain test results of the highest possible quality. This paper presents a methodology for automated fault diagnosis on engine test beds. The methodology allows reliable detection of measurement faults as well as the identification of the root cause of faults.