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[en] Hydrogen-fueled internal combustion engines are a possible solution to make transportation more ecological. Only emissions of oxides of nitrogen (NOx) occur at high loads, being a constraint for power and efficiency optimization. A thermodynamic model of the engine cycle enables a cheap and fast optimization of engine settings. It needs to accurately predict the heat transfer in the engine because the NOx emissions are influenced by the maximum gas temperature. However, the existing engine heat transfer models in the literature are developed for fossil fuels and they have been cited to be inaccurate for hydrogen. We have measured the heat transfer inside a spark ignited engine with a thermopile to investigate the heat transfer process of hydrogen and to find the differences with a fossil fuel. This paper describes the effects of the compression ratio, ignition timing and mixture richness on the heat transfer process, comparing hydrogen with methane. A convection coefficient is used to separate the effect of the temperature difference between the gas and the wall from the influence of the gas movement and combustion. The paper shows that the convection coefficient gives more insight in the heat transfer process in a combustion engine despite the assumptions involved in its definition. The comparison between hydrogen and methane demonstrates, in contrast to what is believed, that the heat loss of hydrogen can be lower. - Research highlights: → Heat flux measurements in internal combustion engine with thermopile. → Comparing hydrogen with methane using convection coefficient. → Convection coefficient gives more information than heat flux trace alone. → Heat loss of hydrogen can be lower than that of methane.
[en] Past investigations have shown that the current type-approval test cycles are not representative for real-world vehicle usage. Consequently, the emissions and fuel consumption of the vehicles are underestimated. Therefore, a new cycle is being developed in the UNECE framework (World-harmonised Light-duty Test Procedure, WLTP), aiming at a more dynamic and worldwide harmonised test cycle. To provide recommendations for the new cycle, we have analysed the noxious emission results of a test programme of seven vehicles on the test cycles NEDC (New European Driving Cycle) and CADC (Common Artemis Driving Cycles). This paper presents the results of that analysis to show the zones of the cycle that are causing the highest emissions, using two different approaches. Both approaches show that the zones with the highest emissions of modern vehicles differ from vehicle to vehicle. Consequently, a representative test cycle has to contain as many combinations of vehicle speed and acceleration that occur in real-world traffic as possible to prevent that a vehicle does not perform well for certain combinations because they are not included in the test cycle. Furthermore, the paper demonstrates that it is important to include a cold start to ensure rapid warm up of the catalysts. - Highlights: ► Vehicle emissions on the NEDC and CADC type-approval cycles are analysed. ► The zones within the cycles that produce the highest emissions are investigated. ► It is shown that these zones can differ significantly from one vehicle to another. ► The WLTP cycle should contain as many of the real-world driving zones as possible.
[en] Highlights: ► We obtained models for estimation of cetane number of biodiesel. ► Twenty-four neural networks using two topologies were evaluated. ► The best neural network for predict the cetane number was selected. ► The best accuracy was obtained for the selected neural network. - Abstract: Models for estimation of cetane number of biodiesel from their fatty acid methyl ester composition using multiple linear regression and artificial neural networks were obtained in this work. For the obtaining of models to predict the cetane number, an experimental data from literature reports that covers 48 and 15 biodiesels in the modeling-training step and validation step respectively were taken. Twenty-four neural networks using two topologies and different algorithms for the second training step were evaluated. The model obtained using multiple regression was compared with two other models from literature and it was able to predict cetane number with 89% of accuracy, observing one outlier. A model to predict cetane number using artificial neural network was obtained with better accuracy than 92% except one outlier. The best neural network to predict the cetane number was a backpropagation network (11:5:1) using the Levenberg–Marquardt algorithm for the second step of the networks training and showing R = 0.9544 for the validation data.