TY  - JOUR
T1  - Performance Study of Kriging Based Surrogate Models
AU - Muruganandham, A. AU - Mukesh, R. AU - Lingadurai, K. AU - Selvakumar, U. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 17
SP  - 3134
EP  - 3152
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3134.3152
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3134.3152
KW  - Surrogate model
KW  -OK
KW  -PSOA
KW  -MPSOA
KW  -aerodynamic efficiency
AB  - The use of optimisers in the Kriging based surrogate models has become popular in full-scale aerospace systems development. Computational modelling through high-fidelity simulations provides a possible approach towards efficient implementation of the design specifications but the associated computational cost restricts its applicability to full-scaled systems. In this present research a Computational Fluid Dynamics (CFD) optimisation strategy based on surrogate modelling is proposed for obtaining high-fidelity predictions of aerodynamic forces (Cl, Cd) and aerodynamic Efficiency (E). An Aerodynamic Shape Optimisation (ASO) problem is formulated and solved using Particle Swarm Optimisation Algorithm (PSOA) and Modified Particle Swarm Optimisation Algorithm (MPSOA) with the inclusion of constructed surrogate models in the place of actual CFD algorithms. Ordinary Kriging (OK) approach is used to construct the surrogate models. PARametric SECtion (PARSEC) approach is implemented to mathematically describe the geometry of the airfoil. The results of two optimisers and an airfoil shape optimisation problem shows that this approach, known as MPSOA can significantly enhance the accuracy of Kriging models when compared to the normal PSOA.
ER  -