• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Sep 30, 2019 News!Vol 9, No 6 has been published with online version. 12 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2017 Vol.7(2): 92-97 ISSN: 2010-3697
DOI: 10.7763/IJMO.2017.V7.565

Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm

Azfarizal Mukhtar, Ng Khai Ching, and Mohd Zamri Yusoff
Abstract—Ventilation shaft designs are the most effective devices used for ventilating underground shelter. A Kriging Metamodel assisted Multi-Objective Genetic Algorithm (MOGA) was utilised for the evaluation of an optimal design for the opening ventilation shaft, which improved the ventilation rate of a naturally-ventilated underground shelter. Computational Fluid Dynamics (CFD) was employed as a simulation tool, and the result was validated with experimental data from the previous literature. For the optimisation, three parameters were considered for the effectiveness of the ventilation rate. The generated results found an excellent performance of the strength correlation between parameters and the recommended optimised design. This revealed that an equal opening area has a better ventilation rate for naturally-ventilated underground shelters. Overall, these results can provide support selecting ventilation shaft opening areas in relation to the design of ventilation systems.

Index Terms—CFD, optimisation, kriging, multi-objective genetic algorithm, ventilation rate.

Azfarizal Mukhtar is with the Center for Fluid Dynamics, College of Engineering, UniversitiTenagaNasional (UNITEN), Putrajaya Campus, Jalan IKRAM-UNITEN, 43000 Kajang, Selangor, Malaysia (e-mail: azfarizal.mukhtar@gmail.com, PE20591@utn.edu.my)


Cite: Azfarizal Mukhtar, Ng Khai Ching, and Mohd Zamri Yusoff, "Optimal Design of Opening Ventilation Shaft by Kriging Metamodel Assisted Multi-objective Genetic Algorithm," International Journal of Modeling and Optimization vol. 7, no. 2, pp. 92-97, 2017.

Copyright © 2008-2019. International Journal of Modeling and Optimization. All rights reserved.
E-mail: ijmo@iacsitp.com