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Cylinder deactivation in multicylinder spark-ignition (SI) engines leads to increased fuel efficiency at part load by allowing fired cylinders to operate closer to their peak thermal efficiency compared to all-cylinder operation. Unlike traditional cylinder deactivation strategies that are limited to deactivating only certain cylinders, Dynamic Skip Fire (DSF) is an advanced cylinder deactivation control strategy that makes deactivation decisions for every cylinder on an individual firing opportunity basis to best meet driver torque demand while saving fuel and mitigating noise, vibration, and harshness (NVH). During DSF operation, inducted charge air mass can vary for each firing event due to the firing sequence history. To maximize efficiency, cylinder fueling should be adjusted for each firing event in DSF based on the inducted charge air mass for that event. This article discusses the development of a control algorithm to compensate fueling for charge air mass variations of individual firing events in DSF on a 1.8-liter, four-cylinder SI engine with intake camshaft phasing using a production-type engine controller. The control algorithm architecture and calibration methodology are discussed. The final algorithm was evaluated by performing engine dynamometer testing with and without the compensation algorithm for selected DSF firing patterns at a range of engine load at 2000 rpm. Optimal fueling compensation in DSF resulted in more complete combustion, evidenced by an increase in brake-specific carbon dioxide emission of up to 1.9% and a decrease in brake-specific carbon monoxide emission of up to 48%. Reductions in brake-specific fuel consumption (BSFC) were also observed with fueling compensation, averaging up to 1.1% reduction in BSFC from 1 to 4 bar brake mean effective pressure (BMEP) depending on the firing sequence. Engine operation was very stable with and without the compensation algorithm in the operating ranges considered.

This book provides an introduction to basic thermodynamic engine cycle simulations, and provides a substantial set of results. Key features includes comprehensive and detailed documentation of the mathematical foundations and solutions required for thermodynamic engine cycle simulations. The book includes a thorough presentation of results based on the second law of thermodynamics as well as results for advanced, high efficiency engines. Case studies that illustrate the use of engine cycle simulations are also provided.          

The development of Urban-Scale Energy Modelling (USEM) at the district or city level is currently the goal of many research groups due to the increased interest in evaluating the impact of energy efficiency measures in city environments. Because USEM comprises a great variety of analysis areas, the simulation programs that are able to model urban-scale energy systems actually consist of an assemblage of different particular sub-models. In order to simulate each of the sub-models in USEM, one can choose to use either existing specific simulation engines or tailor-made models. Engines or tools for simulation of urban-scale energy systems have already been overviewed in previous existing literature, however the distinction and classification of tools according to their functionalities within each analysis area in USEM has not been clearly presented. Therefore, the present work aims at reviewing the existing tools while classifying them according to their capabilities. The ultimate goal of this classification is to expose the available resources for implementing new co-simulation approaches in USEM, which may reduce the modelling effort and increase reliability as a result of using established and validated simulation engines.

Methanol as marine fuel represents one of the most cost-effective and practical solutions towards low-carbon shipping. Methanol fueled internal combustion engines have a high level of technological readiness and are already available on the market; however, technical data in terms of fuel consumption and emissions are not yet easily accessible. For this reason, the present study deals with the simulation of a virtual spark-ignition methanol engine, carried out in a Matlab-Simulink© R2023a environment to assess the CO2 emissions in several working conditions of a possible ship power system. The thermodynamic model of the methanol fueled engine is derived from a marine gas engine simulator, already validated by the authors in a previous work. This article presents the relevant modifications necessary to adapt the engine to the methanol fuel mode with regard to the different fuel characteristics. The simulation analysis compares the results of the virtual methanol engine with available data from a similar, existing gas engine, highlighting the differences in efficiency and carbon dioxide emissions.

This paper presents a study on the effect of the ratio of biodiesel and injection timing on the performance of diesel engines and their emissions. The research engine is a cylinder engine AVL-5402, simulated by software AVL-Boost. The simulated fuel includes fossil diesel and biodiesel blended with a replacement rate from 0% to 50%, with a simulation mode of 2200 (rev/min), at a rate of a 25%, 50% and 75% load. In this speed range, the engine has the lowest fuel consumption. The parameters to be evaluated are power, fuel consumption and emissions, based on the proportions of blended biodiesel. The results show that there is a relationship between the proportion of blended biodiesel, injection timing and the parameters of the engine. Specifically, the ratio of the biodiesel blend increases, injection timing tends to move closer to the top dead center (TDC), the tendency reduce engine power, fuel consumption increases, the emissions of CO and soot reduces, while NOx increases.

Nowadays reducing green-house gas emissions and pushing the fossil fuel savings in the field of light-duty vehicles is compulsory to slow down climate change. To this aim, the use of new combustion modes and dilution strategies to increase the stability of operations rich in diluent is an effective technique to reduce combustion temperatures and heat losses in throttled operations. Since the combustion behavior in those solutions highly differs from that of typical market systems, fundamental analyses in optical engines are mandatory in order to gain a deep understanding of those and to tune new models for improving the mutual support between experiments and simulations. However, it is known that optical accessible engines suffer from significant blow-by collateral flow due to the installation of the optical measure line. Thus, a reliable custom blow-by model capable of being integrated with both mono-dimensional and three-dimensional simulations was developed and validated against experimental data. The model can work for two different configurations: (a) stand-alone, aiming at providing macroscopic data on the ignitable mixture mass loss/recover through the piston rings; (b) combined, in which it is integrated in CFD engine simulations for the local analysis of likely collateral heat release induced by blow-by. Furthermore, once the model was validated, the effect of the engine speed and charge dilution on the blow-by phenomenon in the optical engine were simulated and discussed in the stand-alone mode.

Concept-phase planning of diesel-engined hybrid vehicles requires rapid engine synthesis, including brake-specific fuel consumption (BSFC) estimation, with minimal input data. Fuel savings from hybridization arise partly through engine downsizing and engine-off operation, so trade studies depend on knowing the dependence of BSFC on engine sizing and speed and load conditions. This paper presents a method for synthesizing hypothetical modern diesel engines of any given size for the purpose of trade studies. The synthesized engines match the performance and efficiency capabilities of commercially available units. Relationships are developed between rated power, rated speed, peak torque, displacement and cylinder count for four vehicle application classes. Together with a BSFC estimation method, these relationships form a complete engine synthesis chain from rated power to a full torque curve and BSFC map. Known values may be substituted, such as minimum BSFC, wherever published data are available. The method supports continuous scaling.

Hydrogen and its unique properties pose major challenges to the development of innovative combustion engines, while it represents a viable alternative when it is based on renewable energy sources. The present paper deals with the holistic approach of hydrogen combustion modeling from a 0D/1D reactor evaluation with Cantera up to complete engine simulations in the 3D-CFD tool QuickSim. The obtained results are referenced to the current literature and calibrated with experimental data. In particular, the engine simulations are validated against measurements of a single-cylinder research engine, which was specifically adapted for lean hydrogen operation and equipped with port fuel injection and a passive pre-chamber system. Special attention is hereby given to the influence of different engine loads and varying lambda operation. The focus of this work is the complementary numerical investigation of the hydrogen flame speed and its self-ignition resistance under the consideration of various reaction mechanisms. A detailed transfer from laminar propagation under laboratory conditions to turbulent flame development within the single-cylinder engine is hereby carried out. It is found that the relatively simple reaction kinetics of hydrogen can lead to acceptable results for all mechanisms, but there are particular effects with regard to the engine behavior. The laminar flame speed and induction time vary greatly with the inner cylinder conditions and significantly affect the entire engine’s operation. The 3D-CFD environment offers the opportunity to analyze the interactions between mixture formation and combustion progress, which are indispensable to evaluate advanced operating strategies and optimize the performance and efficiency, as well as the reliability, of the engine.

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component’s health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components.

The article describes a compression-ignition engine working with a dual-fuel system installed in diesel locomotive TEP70 BS. The model of the locomotive engine has been created applying AVL BOOST and Diesel RK software and engine performance simulations. Combustion characteristics have been identified employing the mixtures of different fuels. The paper compares ecological (CO2, NOx, PM) and energy (in-cylinder pressure, temperature and the rate of heat release (ROHR)) indicators of a diesel and fuel mixtures-driven locomotive. The performed simulation has shown that different fuel proportions increased methane content and decreased diesel content in the fuel mixture, as well as causing higher in-cylinder pressure and ROHR; however, in-cylinder temperature dropped. CO2, NOx and PM emissions decrease in all cases thus raising methane and reducing diesel content in the fuel mixture.