Modelling nonlinear vehicle dynamics with neural networks

The nonlinear modelling ability of neural networks has been widely recognised as an effective tool to identify and control dynamic systems, with applications including nonlinear vehicle dynamics which this paper focuses on using multi-layer perceptron networks. Existing neural network literature does not detail some of the factors which effect neural network nonlinear modelling ability. This paper investigates into and concludes on required network size, structure and initial weights, considering results for networks of converged weights. The paper also presents an online training method and an error measure representing the network's parallel modelling ability over a range of operating conditions. Copyright © 2010 Inderscience Enterprises Ltd.

Analysis of high pressure premixed flames using Equivalent Reactor Networks for predicting NOx emissions

This paper describes a computational study of lean premixed high pressure methane-air flames, using Computational Fluid Dynamics (CFD) together with a reactor network approach. A detailed chemical reaction mechanism is employed to predict pollutant concentrations, placing emphasis on nitrogen oxide emissions. The reacting flow field is divided into separate zones in which homogeneity of the physical and chemical conditions prevails. The defined zones are interconnected forming an Equivalent Reactor Network (ERN). Three flames are examined for which experimental data is available. Flame A is characterised by an equivalence ratio of 0.43 while Flames B and C are richer with equivalence ratios of 0.5 and 0.56 respectively. Computations are performed for a range of operating conditions, quantifying the effect in the emitted NOx levels. Model predictions are compared against the available experimental data. Sensitivity analysis is performed to investigate the effect of the network size, in order to define the optimum number of reactors for accurate predictions of the species mass fractions. © 2012 Elsevier Ltd. All rights reserved.