A Numerical Methodology for the Multi-Objective Optimization of an Automotive DI Diesel Engine 2013-24-0019
Nowadays, an automotive DI Diesel engine is demanded to provide an adequate power output together with limit-complying NOx and soot emissions so that the development of a specific combustion concept is the result of a trade-off between conflicting objectives. In other words, the development of a low-emission DI diesel combustion concept could be mathematically represented as a multi-objective optimization problem. In recent years, genetic algorithm and CFD simulations were successfully applied to this kind of problem. However, combining GA optimization with actual CFD-3D combustion simulations can be too onerous since a large number of simulations is usually required, resulting in a high computational cost and, thus, limiting the suitability of this method for industrial processes. In order to make the optimization process less time-consuming, CFD simulations can be more conveniently used to build a training set for the learning process of an artificial neural network which, once correctly trained, can be used to forecast the engine outputs as a function of the design parameters during a GA optimization performing a so-called virtual optimization. In this paper, a numerical methodology for the multi-objective virtual optimization of the combustion inside an automotive DI Diesel engine, based on artificial neural networks combined with genetic algorithms, is presented.
Citation: Costa, M., Bianchi, G., and Forte, C., "A Numerical Methodology for the Multi-Objective Optimization of an Automotive DI Diesel Engine," SAE Technical Paper 2013-24-0019, 2013, https://doi.org/10.4271/2013-24-0019. Download Citation
Author(s):
Marco Costa, Gian Bianchi, Claudio Forte
Affiliated:
University of Bologna
Pages: 12
Event:
11th International Conference on Engines & Vehicles
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Diesel / compression ignition engines
Optimization
Neural networks
Combustion and combustion processes
Mathematical models
Computational fluid dynamics
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