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A reduced kinetic reaction mechanism for the autoignition of dimethyl ether is presented in this paper. Dimethyl ether has proven to be one of the most attractive alternatives to traditional fossil fuels for compression ignition engines. It can either be produced from biomass or from fossil oil. For dimethyl ether, Fischer et al. (Int. J.Chem. Kinet. 32 ( 12 ) (2000) 713-740) proposed a detailed reaction mechanism consisting of 79 species and 351 elementary reactions. In the present work, this detailed mechanism is systematically reduced to 31 species and 49 reactions. The reduced mechanism is discussed in detail with special emphasis on the high temperature thermal decomposition of dimethyl ether and on the fuel specific depleting reactions, which produce the methoxymethyl radical. In addition, a reaction pathway analysis for low temperature combustion is applied, where hydroperoxy-methylformate is found to be the dominating parameter for the low temperature regime.

Especially for internal combustion engine simulations, various combustion models rely on the laminar burning velocity. With respect to computational time needed for CFD, the calculation of laminar burning velocities using a detailed chemical mechanism can be replaced by incorporation of approximation formulas, based on rate-ratio asymptotics. This study revisits an existing analytical approximation formula [1]. It investigates applicable temperature, pressure, and equivalence ratio ranges with special focus on engine combustion conditions. The fuel chosen here is methane and mixtures are composed of methane and air. The model performance to calculate the laminar burning velocity are compared with calculated laminar burning velocities using existing state of the art detailed chemical mechanisms, the GRI Mech 3.0 [2], the ITV RWTH [3], and the Aramco mechanism [4].

Premixed charged compression ignition (PCCI) is an advanced combustion strategy, which has the potential to achieve ultra-low nitrogen oxide and soot emissions at high thermal efficiencies. PCCI combustion is characterized by a complex nonlinear chemical-physical process, which indicates that a physical description involves significant development times and also high computation cost. This paper presents a method to use cylinder pressure data and engine operations parameters for prediction of PCCI engine emissions by unsupervised learning and nonlinear identification techniques. The proposed method first uses principal component analysis (PCA) to reduce the dimension of the cylinder-pressure data. Based on the PCA analysis, a multi-input multi-out model was developed for nitrogen oxide and soot emission prediction by multi-layer perceptron (MLP) neural network.

Ethanol currently remains the leading biofuel in the transportation sector, with special focus on spark ignition engines, as a pure as well as a blend component. In order to provide reliable numerical simulations of gasoline combustion processes under the influence of ethanol for modern engine research, it is mandatory to develop well validated detailed kinetic combustion models. One key parameter for the numerical simulation is the laminar burning velocity. Under the aspect of minimizing the general simulation effort for burning velocities, well-validated models have to be reduced. As a base kinetic mechanism for the reduction and optimisation process with respect to burning velocity calculations, a detailed model presented by Zhao et al. (Int. J. Chem. Kin. 40 (1) (2007) 1-18) is chosen. The model has been extensively validated against shock tube, rapid compression machine and burning velocity data. The detailed model consists of 55 species and 290 reactions.

One of the main challenges in internal combustion engine design is the simultaneous reduction of all engine pollutants like carbon monoxide (CO), total unburned hydrocarbons (THC), nitrogen oxides (NOx), and soot. Low-temperature combustion (LTC) concepts for compression ignition (CI) engines, e.g., premixed charged compression ignition (PCCI), make use of pre-injections to create a partially homogenous mixture and achieve an emission reduction. However, they present challenges in the combustion control, with the usage of in-cylinder pressure sensors as feedback signal is insufficient to control heat release and pollutant emissions simultaneously. Thus, an additional sensor, such as an ion-current sensor, could provide further information on the combustion process and effectively enable clean and efficient PCCI operation.

Renewable fuels, such as bio- and e-fuels, are of great interest for the defossilization of the transport sector. Among these fuels, methanol represents a promising candidate for emission reduction and efficiency increase due to its very high knock resistance and its production pathway as e-fuel. In general, reliable simulation tools are mandatory for evaluating a specific fuel potential and optimizing combustion systems. In this work, a previously presented methodology (Esposito et al., Energies, 2020) has been refined and applied to a different engine and different fuels. Experimental data measured with a single cylinder engine (SCE) are used to validate RANS 3D-CFD simulations of gaseous engine-out emissions. The RANS 3D-CFD model has been used for operation with a toluene reference fuel (TRF) gasoline surrogate and methanol. Varying operating conditions with exhaust gas recirculation (EGR) and air dilution are considered for the two fuels.