Search Results

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.

Physics-based models in a closed-loop feedback control of a premixed charge compression ignition (PCCI) engine can improve the combustion efficiency and potentially reduce harmful NOx and soot emissions. A stand-alone multi-zone combustion model has been proposed in the literature using a physics-based mixing approach. The scalar dissipation rate emerged as the determining parameter in the model for mixing among different zones in the mixture fraction space. However, the calculation of the scalar dissipation rate depends on three approaches: three-dimensional computational fluid dynamics (3-D CFD) combustion simulations based on representative interactive flamelet (RIF) model, tabulation, or an empirical algebraic model of the scalar dissipation rate fitted for the given operating conditions of the engine. While the 3-D CFD approach provides accurate results, it is computationally too expensive to use the multi-zone model in closed-loop control.

In the foreseeable future, the transportation sector will continue to rely on internal combustion engines. Therefore, reduction of engine-out emissions and increase in engine efficiency are important goals to meet future legislative regulations and restricted fuel resources. One viable option, which provides lower peak temperatures and increased mixture homogeneity and thus simultaneously reduces nitric oxide as well as soot, is a low-temperature combustion (LTC) concept. However, this might result in an increase of unburnt hydrocarbon, carbon monoxide, and combustion noise due to early combustion phasing and lower engine efficiency. Various studies show that these drawbacks can be compensated by advanced injection strategies, e.g. by employing multiple injections. The aim of this work is to identify the optimum injection strategy, which enables a wide range of engine operating points in LTC mode with reduced engine-out emissions.

For compliance with legislative regulations as well as restricted resources of fossil fuel, it is essential to further reduce engine-out emissions and increase engine efficiency. As a result of lower peak temperatures and increased homogeneity, premixed Low-Temperature Combustion (LTC) has the potential to simultaneously reduce nitrogen oxides (BSNOx) and soot. However, LTC can lead to higher emissions of unburnt total hydrocarbons (BSTHC) and carbon monoxide (BSCO). Furthermore, losses in efficiency are often observed, due to early combustion phasing (CA50) before top dead center (bTDC). Various studies have shown possibilities to counteract these drawbacks, such as split-injection strategies or different nozzle geometries. In this work, the combination of both is investigated. Three different nozzle geometries with included spray angles of 100°, 120°, and 148° and four injection strategies are applied to investigate the engine performance.