Database enrichment for multi-objective optimization Patricia Klotz?†, Nathalie Bartoli†, Laurence Cornez†, Renaud Lecourt† and Nicolas Savary‡ †ONERA - Toulouse France, ‡TURBOMECA - Bordes France Abstract The problem of optimization of complex phenom- ena is studied by using response surface approxi- mation. Design of experiments are elaborated for database generation of samples, but it can be im- proved to obtain more adapted response surfaces through a sequential enrichment based on bootstrap techniques. A multi-objective optimization is per- formed on a two phase flow configuration for the optimization of injection parameters in a Low Pre- mixed Prevaporized (LPP) injection system for a combustor chamber. keywords: multi-objective optimization, re- sponse surface, neural network, design of experi- ments, bootstrap 1 Introduction This paper outlines some optimization research done at ONERA within the context of an ON- ERA Internal project (PRF DOOM) and a Euro- pean project (INTELLECT D.M.). Because opti- mization requires a lot of computations, the use of response surface model (RSM) is a good solution to decrease CPU time. The set of points for fitting RSM to each objective function is provided by a de- sign of experiments. We use here Latin Hypercube Sampling (LHS) which combines deterministic and random sampling in order to reduce the variance of the RSM. The number of necessary points and their best location can be both improved.
- taking into
- sponse surface
- multi-objective optimization
- phase flow
- ac- cordingly
- results obtained
- rate