Efficient simulation and auto-calibration of soot particle processes in Diesel engines

Abstract

Parameters describing soot particle processes are generally derived from a limited number of experimental studies. These parameters then have to be carefully calibrated for different operating conditions in internal combustion engine applications. This paper presents an innovative calibration procedure for soot

Modified Hooke-Jeeves optimization of operating parameters for required slider speeds and displacements in a feeder slider-crank mechanism

“…It is observed that Hooke-Jeeves method is essentially a direct numerical optimization method, which has been reliably and extensively implemented in many industrial problems such as these [11][12][13][14][15][16][17]19]. Due to the fact that numerical data are available from the numerical solutions of a highly non-linear differential equation, minimization of the errors defined previously requires the use of a commensurate method.…”

“…Thus, a review of basic literature on Hooke-Jeeves direct search method appears to be in order here. Within this context, Wu et al [11] applied Hooke-Jeeves optimization method to the calibration of seven soot model parameters used in the model of soot formation and oxidation in diesel engines. Mazouz et al [12] showed application of Hooke-Jeeves method for the optimization of PI regulator gains in offshore wind energy production with permanent magnet synchronous generator.…”

“…In all these studies on Hooke-Jeeves optimization method [11][12][13][14][15][16][17], it is commonly shared that (i) an increment amount is randomly defined for exploratory search by the user; (ii) a multiplier of one is taken for the pattern move from the base point; (iii) the process is stopped as soon as no move is established by repeatedly dividing the increment amount, which may not make sure that optimization objective is mathematically achieved. However, all these quantities mentioned above are numerically determined here in this work on a mathematical basis such that finally the difference between the actual and desired objects, referred to as the objective function, is practically zero, implying to the achievement of optimum.…”