The phenomenon of knock is an abnormal combustion occurring in spark-ignition (SI) engines and forms a barrier that prevents an increase in thermal efficiency while simultaneously reducing CO${}_2$ emissions. Since knocking combustion is highly stochastic, a cyclic analysis of in-cylinder pressure is necessary. In this study we propose an approach for efficient and robust detection and identification of knocking combustion in three different internal combustion engines. The proposed methodology includes a signal processing technique, called continuous wavelet transformation (CWT), which provides a simultaneous analysis of the in-cylinder pressure traces in the time and frequency domains with coefficients. These coefficients serve as input for a convolutional neural network (CNN) which extracts distinctive features and performs an image recognition task in order to distinguish between non-knock and knock. The results revealed the following: (i) The CWT delivered a stable and effective feature space with the coefficients that represents the unique time-frequency pattern of each individual in-cylinder pressure cycle; (ii) the proposed approach was superior to the state-of-the-art threshold value exceeded (TVE) method with a maximum amplitude pressure oscillation (MAPO) criterion improving the overall accuracy by 6.15 percentage points (up to 92.62%); and (iii) The CWT + CNN method does not require calibrating threshold values for different engines or operating conditions as long as enough and diverse data is used to train the neural network.

Condition monitoring of components in internal combustion engines is an essential tool for increasing engine durability and avoiding critical engine operation. If lubrication at the crankshaft main bearings is insufficient, metal-to-metal contacts become likely and thus wear can occur. Bearing temperature measurements with thermocouples serve as a reliable, fast responding, individual bearing-oriented method that is comparatively simple to apply. In combination with a corresponding reference model, such measurements could serve to monitor the bearing condition. Based on experimental data from an MAN D2676 LF51 heavy-duty diesel engine, the derivation of a data-driven model for the crankshaft main bearing temperatures under steady-state engine operation is discussed. A total of 313 temperature measurements per bearing are available for this task. Readily accessible engine operating data that represent the corresponding engine operating points serve as model inputs. Different machine learning methods are thoroughly tested in terms of their prediction error with the help of a repeated nested cross-validation. The methods include different linear regression approaches (i.e., with and without lasso regularization), gradient boosting regression and support vector regression. As the results show, support vector regression is best suited for the problem. In the final evaluation on unseen test data, this method yields a prediction error of less than 0.4 °C (root mean squared error). Considering the temperature range from approximately 76 °C to 112 °C, the results demonstrate that it is possible to reliably predict the bearing temperatures with the chosen approach. Therefore, the combination of a data-driven bearing temperature model and thermocouple-based temperature measurements forms a powerful tool for monitoring the condition of sliding bearings in internal combustion engines.

This paper focuses on improving the 3D-Computational Fluid Dynamics (CFD) modeling of diesel ignited gas engines, with an emphasis on injection and combustion modeling. The challenges of modeling are stated and possible solutions are provided. A specific approach for modeling injection is proposed that improves the modeling of the ballistic region of the needle lift. Experimental results from an inert spray chamber are used for model validation. Two-stage ignition methods are described along with improvements in ignition delay modeling of the diesel ignited gas engine. The improved models are used in the Extended Coherent Flame Model with the 3 Zones approach (ECFM-3Z). The predictive capability of the models is investigated using data from single cylinder engine (SCE) tests conducted at the Large Engines Competence Center (LEC). The results are discussed and further steps for development are identified.

An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified.

Increasing the reliability of combustion engines while further reducing emissions and life cycle costs are the main drivers for optimizing lubricating oil consumption (LOC). However, in order to reduce the lube oil consumption of an engine, it is crucial to measure it accurately. Therefore, a LOC measurement device based on the use of the stable isotope deuterium has been developed. Previous publications have focused on the use of passenger car engines. This publication describes the first application of this newly developed method on a large gas engine. This is of particular interest as large-bore engines might show different oil consumption behavior, much higher LOC in gram per hour and the bigger oil reservoir need larger amounts of tracer. Additionally, a different type of fuel has an effect on oil consumption measurement as well, as presented in this paper. The results showed this method can be applied to large gas engines as well after conducting minor changes to the measurement setup. However, other than liquid fuels, the origin and isotopic composition of the natural gas has to be monitored. Ideally, gas from large storage is used for carrying out these measurements.