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Using renewable fuels is a reliable approach for decarbonization of combustion engines. iso-Butanol and n-butanol are known as longer chain alcohols and have the potential of being used as gasoline substitute or a renewable fraction of gasoline. The combustion behavior of renewable fuels in modern combustion engines and advanced combustion concepts is not well understood yet. Low-temperature combustion (LTC) is a concept that is a basis for some of the low emissions-high efficiency combustion technologies. Fuel ɸ-sensitivity is known as a key factor to be considered for tailoring fuels for these engines. The Lund ɸ-sensitivity method is an empirical test method for evaluation of the ɸ-sensitivity of liquid fuels and evaluate fuel behavior in thermal. iso-Butanol and n-butanol are two alcohols which like other alcohol exhibit nonlinear behavior when blended with (surrogate) gasoline in terms of RON and MON.

Predicting and preventing combustion anomalies leads to safe and efficient operation of the hydrogen internal combustion engine. This research presents the application of three machine learning (ML) models – K-Nearest Neighbors (KNN), Random Forest (RF) and Logistic Regression (LR) – for the prediction of combustion anomalies in a hydrogen internal combustion engine. A small experimental dataset was used to train the models and posterior experiments were used to evaluate their performance and predicting capabilities (both in operating points -speed and load- within the training dataset and operating points in other areas of the engine map). KNN and RF exhibit superior accuracy in classifying combustion anomalies in the training and testing data, particularly in minimizing false negatives, which could have detrimental effects on the engine.

Dual-mode dual-fuel combustion is a promising combustion concept to achieve the required emissions and CO2 reductions imposed by the next standards. Nonetheless, the fuel formulation requirements are stricter than for the single-fuel combustion concepts as the combustion concept relies on the reactivity of two different fuels. This work investigates the effect of the low reactivity fuel sensitivity (S=RON-MON) and the octane number at different operating conditions representative of the different combustion regimes found during the dual-mode dual-fuel operation. For this purpose, experimental tests were performed using a PRF 95 with three different sensitivities (S0, S5 and S10) at operating conditions of 25% load/950 rpm, 50%/1800 rpm and 100%/2200 rpm. Moreover, air sweeps varying ±10% around a reference air mass were performed at 25%/1800 rpm and 50%/1800 rpm. Conventional diesel fuel was used as high reactivity fuel in all the cases.

The Homogeneous Charge Compression Ignition (HCCI) is the third alternative for combustion in the reciprocating engine. Here, a homogeneous charge is used as in a spark ignited engine, but the charge is compressed to auto-ignition as in a diesel. The main difference compared with the Spark Ignition (SI) engine is the lack of flame propagation and hence the independence from turbulence. Compared with the diesel engine, HCCI has a homogeneous charge and hence no problems associated with soot and NOX formation. Earlier research on HCCI showed high efficiency and very low amounts of NOX, but HC and CO were higher than in SI mode. It was not possible to achieve high IMEP values with HCCI, the limit being 5 bar. Supercharging is one way to dramatically increase IMEP. The influence of supercharging on HCCI was therefore experimentally investigated. Three different fuels were used during the experiments: iso-octane, ethanol and natural gas.

The ability to predict cyclic variations is certainly useful in studying engine operating regimes, especially under unstable operating conditions where one single cycle may differ from another substantially and a single simulation may give rather misleading results. PDF based models such as Stochastic Reactor Models (SRM) are able to model cyclic variations, but these may be overpredicted if discretization is too coarse. The range of cyclic variations and the dependence of the ability to correctly assess their mean values on the number of cycles simulated were investigated. In most cases, the average values were assessed correctly on the basis of as few as 10 cycles, but assessing the complete range of cyclic variations could require a greater number of cycles. In studying average values, variations due too coarse discretization being employed are smaller than variations originating from changes in physical parameters, such as heat transfer and mixing parameters.

With ever-demanding emission legislations in Compression Ignition (CI) engines, new premixed combustion strategies have been developed in recent years seeking both, emissions and performance improvements. Since it has been shown that in-cylinder air flow affects the combustion process, and hence the overall engine performance, the study of swirling structures and its interaction with fuel injection are of great interest. In this regard, possible Turbulent Kinetic Energy (TKE) distribution changes after fuel injection may be a key parameter for achieving performance improvements by reducing in-cylinder heat transfer. Consequently, this paper aims to gain an insight into spray-swirl interaction through the analysis of in-cylinder velocity fields measured by Particle Image Velocimetry (PIV) when PCCI conditions are proposed. Experiments are carried out in a single cylinder optical Diesel engine with bowl-in-piston geometry.

Spray modeling is among the main aspects of mixture formation and combustion in internal combustion engines. It plays a major role in pollutant formation and energy efficiency although adequate modeling is still under development. Strong grid dependence is observed in the droplet-based stochastic spray model commonly used. As an alternative, an interactive model called 'SprayLet' is being developed for spray simulations based on one-dimensional integrated equations for the gas and liquid phases, resulting from cross-sectionally averaging of multi-dimensional transport equations to improve statistical convergence. The formulated one-dimensional cross-section averaged system is solved independently of the CFD program to provide source terms for mass, momentum and heat transfer between the gas and liquid phases. The transport processes take place in a given spray cone where the nozzle exit is automatically resolved.

In this work a soot source term tabulation strategy for soot predictions under Diesel engine conditions within the zero-dimensional Direct Injection Stochastic Reactor Model (DI-SRM) framework is presented. The DI-SRM accounts for detailed chemistry, in-homogeneities in the combustion chamber and turbulence-chemistry interactions. The existing implementation [1] was extended with a framework facilitating the use of tabulated soot source terms. The implementation allows now for using soot source terms provided by an online chemistry calculation, and for the use of a pre-calculated flamelet soot source term library. Diesel engine calculations were performed using the same detailed kinetic soot model in both configurations. The chemical mechanism for n-heptane used in this work is taken from Zeuch et al. [2] and consists of 121 species and 973 reactions including PAH and thermal NO chemistry. The engine case presented in [1] is used also for this work.

The subject of this work is 3D numerical simulations of combustion and soot emissions for a passenger car diesel engine. The CFD code STAR-CD version 3.26 [1] is used to resolve the flowfield. Soot is modeled using a detailed kinetic soot model described by Mauss [2]. The model includes a detailed description of the formation of polyaromatic hydrocarbons. The coupling between the turbulent flowfield and the soot model is achieved through a flamelet library approach, with transport of the moments of the soot particle size distribution function as outlined by Wenzel et al. [3]. In this work we extended this approach by considering acetylene feedback between the soot model and the combustion model. The model was further improved by using new gas-phase kinetics and new fitting procedures for the flamelet soot library.

In the past few years’ various studies have shown how the application of a highly premixed dual fuel combustion for CI engines leads a strong reduction for both pollutant emissions and fuel consumption. In particular a drastic soot and NOx reduction were achieved. In spite of the most common strategy for dual fueling has been represented by using two different injection systems, various authors are considering the advantages of using a single injection system to directly inject blends in the chamber. In this scenario, a characterization of the behavior of such dual-fuel blend spray became necessary, both in terms of inert and reactive ambient conditions. In this work, a light extinction imaging (LEI) has been performed in order to obtain two-dimensional soot distribution information within a spray flame of different diesel/gasoline commercial fuel blends. All the measurements were conducted in an optically accessible two-stroke engine equipped with a single-hole injector.

This paper focuses on the performance and efficiency of an HCCI (Homogenous Charge Compression Ignition) engine system running on natural gas or landfill gas for stationary applications. Zero dimensional modeling and simulation of the engine, turbo, inlet and exhaust manifolds and inlet air conditioner (intercooler/heater) are used to study the effect of compression ratio and exhaust turbine size on maximum mean effective pressure and efficiency. The extended Zeldovich mechanism is used to estimate NO-formation in order to determine operation limits. Detailed chemical kinetics is used to predict ignition timing. Simulation of the in-cylinder process gives a minimum λ-value of 2.4 for natural gas, regardless of compression ratio. This is restricted by the NO formation for richer mixtures. Lower compression ratios allow higher inlet pressure and hence higher load, but it also reduces indicated efficiency.

In more or less all aspects of life and in all sectors, there is a generalized global demand to reduce greenhouse gas (GHG) emissions, leading to the tightening and expansion of existing emissions regulations. Currently, non-road engines manufacturers are facing updates such as, among others, US Tier 5 (2028), European Stage V (2019/2020), and China Non-Road Stage IV (in phases between 2023 and 2026). For on-road applications, updates of Euro VII (2025), China VI (2021), and California Low NOx Program (2024) are planned. These new laws demand significant reductions in nitrogen oxides (NOx) and particulate matter (PM) emissions from heavy-duty vehicles. When equipped with an appropriate exhaust aftertreatment system, natural gas engines are a promising technology to meet the new emission standards.

Worldwide, there is the demand to reduce harmful emissions from non-road vehicles to fulfill European Stage V+ and VI (2022, 2024) emission legislation. The rules require significant reductions in nitrogen oxides (NOx), methane (CH4) and formaldehyde (CH2O) emissions from non-road vehicles. Compressed natural gas (CNG) engines with appropriate exhaust aftertreatment systems such as three-way catalytic converter (TWC) can meet these regulations. An issue remains for reducing emissions during the engine cold start where the CNG engine and TWC yet do not reach their optimum operating conditions. The resulting complexity of engine and catalyst calibration can be efficiently supported by numerical models. Hence, it is required to develop accurate simulation models which can predict cold start emissions. This work presents a real-time engine model for transient engine-out emission prediction using tabulated chemistry for CNG.

We numerically simulate a Homogeneous Charge Compression Ignition (HCCI) engine fuelled with a blend of ethanol and diethyl ether by means of a stochastic reactor model (SRM). A 1D CFD code is employed to calculate gas flow through the engine, whilst the SRM accounts for combustion and convective heat transfer. The results of our simulations are compared to experimental measurements obtained using a Caterpillar CAT3401 single-cylinder Diesel engine modified for HCCI operation. We consider emissions of CO, CO2 and unburnt hydrocarbons as functions of the crank angle at 50% heat release. In addition, we establish the dependence of ignition timing, combustion duration, and emissions on the mixture ratio of the two fuel components. Good qualitative agreement is found between our computations and the available experimental data.

A self-calibrating model for Diesel engine simulations is presented. The overall model consists of a zero-dimensional direct injection stochastic reactor model (DI-SRM) for engine in-cylinder processes simulations and a package of optimization algorithms (OPAL) suitable for solving various optimization, automatization and search problems. In the DI-SRM, based on an extensive model parameters study, the mixing time history that affects the level of in-cylinder turbulence was selected as a main calibration parameter. As targets during calibration against the experimental data, in-cylinder pressure history and engine-out emissions, including nitrogen oxides and unburned hydrocarbons were chosen. The calibration task was solved using DI-SRM and OPAL working as an integrated tool. Within OPAL, genetic algorithms (GA) were used to determine model constants necessary for calibrating. Engine-out emissions in DI-SRM were calculated based on the reduced mechanism of n-heptane.

In contrast to the currently primarily used liquid fuels (diesel and gasoline), methane (CH4) as a fuel offers a high potential for a significant reduction of greenhouse gas emissions (GHG). This advantage can only be used if tailpipe CH4 emissions are reduced to a minimum, since the GHG impact of CH4 in the atmosphere is higher than that of carbon dioxide (CO2). Three-way catalysts (TWC - stoichiometric combustion) and methane oxidation catalysts (MOC - lean combustion) can be used for post-engine CH4 oxidation. Both technologies allow for a nearly complete CH4 conversion to CO2 and water at sufficiently high exhaust temperatures (above the light-off temperature of the catalysts). However, CH4 combustion is facing a huge challenge with the planned introduction of Euro VII emissions standard, where stricter CH4 emission limits and a decrease of the cold start starting temperatures are discussed.

During cold start of natural gas engines, increased methane and formaldehyde emissions can be released due to flame quenching on cold cylinder walls, misfiring and the catalyst not being fully active at low temperatures. Euro 6 legislation does not regulate methane and formaldehyde emissions. New limits for these two pollutants have been proposed by CLOVE consortium for Euro 7 scenarios. These proposals indicate tougher requirements for aftertreatment systems of natural gas engines. In the present study, a zero-dimensional model for real-time engine-out emission prediction for transient engine cold start is presented. The model incorporates the stochastic reactor model for spark ignition engines and tabulated chemistry. The tabulated chemistry approach allows to account for the physical and chemical properties of natural gas fuels in detail by using a-priori generated laminar flame speed and combustion chemistry look-up tables.

Methanol is today considered a viable green fuel for combustion engines because of its low soot emissions and the possibility of it being produced in a CO2-neutral manner. Methanol as a fuel for combustion engines have attracted interest throughout history and much research was conducted during the oil crisis in the seventies. In the beginning of the eighties the oil prices began to decrease and interest in methanol declined. This paper presents the emission potential of methanol. T-Φ maps were constructed using a 0-D reactor with constant pressure, temperature and equivalence ratio to show the emission characteristics of methanol. These maps were compared with equivalent maps for diesel fuel. The maps were then complemented with engine simulations using a stochastic reactor model (SRM), which predicts end-gas emissions. The SRM was validated using experimental results from a truck engine running in Partially Premixed Combustion (PPC) mode at medium loads.

By dividing the combustion process into several phases with phase optimized skeletal mechanisms (POSM), gains in calculation speed were realized with virtually no loss in accuracy. A skeletal mechanism is a reduced mechanism where only the significant species, determined through a set of parameters (one for each species), remain with respect to a detailed mechanism. The parameter is based on a combination of sensitivity and flow analysis. Within the POSM method machine learning algorithms are used to automatically determine and recognize the major phases. Reduction is achieved by keeping only the significant species with respect to each phase. Each phase has a different mechanism, derived from the original and each is smaller than the original.

Pure biodiesel is non-toxic, biodegradable and greenhouse gas neutral alternative fuel with potential successful future but reduced quantitative information is available about the impact of biodiesel on engine durability and long period usage effects. In this study, a commercial light duty Diesel engine installed on an engine test bench has been operated fuelled with pure biodiesel (B100 referred to the EN-14214 standard) during a period test of 300 hours in order to analyze engine performance and components behavior. A engine characterization has been completed using conventional diesel fuel (EN 590). Then, following a specific defined operation cycle and fuelled with pure biodiesel, a study over different engine components such as: engine oil, fuel filters, Diesel Particulate Filter (DPF), so on, has been done in order to obtain possible negative effects and modifications required over maintenance policies applied to them.