The design of complex engineering problems requires computation of several optimal parameters that is generally very time consuming and computationally expensive process. When using computationally expensive simulation programs/algorithms in engineering design for optimization, sometimes it becomes impractical to rely exclusively on simulation codes only. This study involved the designing of a suitable metamodel by using Kriging response surfaces which will be used for global optimization purposes. The study also covers the implementation of Kriging mathematical model in the form of a computer algorithm which is written in MATLAB.
Published in | American Journal of Aerospace Engineering (Volume 1, Issue 2) |
DOI | 10.11648/j.ajae.20140102.11 |
Page(s) | 8-15 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2014. Published by Science Publishing Group |
Surrogate/Meta Model, System Function, Design of Experiments
[1] | A Taxonomy of Global Optimization Methods Based on Response, DONALD R. JONES, General Motors Corporation, Journal of Global Optimization 21: 345–383, 2001, Kluwer Academic Publishers. |
[2] | http://www.hesc.it/tnt/tutorial/surfmodl-ENG.pdf |
[3] | http://lazarus.elte.hu/hun/digkonyv/havas/mellekl/vm25/vma07.pdf |
[4] | DACE, A MATLAB Kriging Toolbox Version 2.0, August 1, 2002, Søren N. Lop-Haven, Hans Bruun Nielsen, Jacob Søndergaard |
APA Style
Johar Daudi. (2014). Application of Response Surface Methodology during Conceptual Design for Mission Analysis. American Journal of Aerospace Engineering, 1(2), 8-15. https://doi.org/10.11648/j.ajae.20140102.11
ACS Style
Johar Daudi. Application of Response Surface Methodology during Conceptual Design for Mission Analysis. Am. J. Aerosp. Eng. 2014, 1(2), 8-15. doi: 10.11648/j.ajae.20140102.11
AMA Style
Johar Daudi. Application of Response Surface Methodology during Conceptual Design for Mission Analysis. Am J Aerosp Eng. 2014;1(2):8-15. doi: 10.11648/j.ajae.20140102.11
@article{10.11648/j.ajae.20140102.11, author = {Johar Daudi}, title = {Application of Response Surface Methodology during Conceptual Design for Mission Analysis}, journal = {American Journal of Aerospace Engineering}, volume = {1}, number = {2}, pages = {8-15}, doi = {10.11648/j.ajae.20140102.11}, url = {https://doi.org/10.11648/j.ajae.20140102.11}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajae.20140102.11}, abstract = {The design of complex engineering problems requires computation of several optimal parameters that is generally very time consuming and computationally expensive process. When using computationally expensive simulation programs/algorithms in engineering design for optimization, sometimes it becomes impractical to rely exclusively on simulation codes only. This study involved the designing of a suitable metamodel by using Kriging response surfaces which will be used for global optimization purposes. The study also covers the implementation of Kriging mathematical model in the form of a computer algorithm which is written in MATLAB.}, year = {2014} }
TY - JOUR T1 - Application of Response Surface Methodology during Conceptual Design for Mission Analysis AU - Johar Daudi Y1 - 2014/07/20 PY - 2014 N1 - https://doi.org/10.11648/j.ajae.20140102.11 DO - 10.11648/j.ajae.20140102.11 T2 - American Journal of Aerospace Engineering JF - American Journal of Aerospace Engineering JO - American Journal of Aerospace Engineering SP - 8 EP - 15 PB - Science Publishing Group SN - 2376-4821 UR - https://doi.org/10.11648/j.ajae.20140102.11 AB - The design of complex engineering problems requires computation of several optimal parameters that is generally very time consuming and computationally expensive process. When using computationally expensive simulation programs/algorithms in engineering design for optimization, sometimes it becomes impractical to rely exclusively on simulation codes only. This study involved the designing of a suitable metamodel by using Kriging response surfaces which will be used for global optimization purposes. The study also covers the implementation of Kriging mathematical model in the form of a computer algorithm which is written in MATLAB. VL - 1 IS - 2 ER -