PMID- 37554836
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230810
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 8
DP  - 2023 Aug
TI  - A machine learning-based comparative analysis of surrogate models for design 
      optimisation in computational fluid dynamics.
PG  - e18674
LID - 10.1016/j.heliyon.2023.e18674 [doi]
LID - e18674
AB  - Complex computer codes are frequently used in engineering to generate outputs 
      based on inputs, which can make it difficult for designers to understand the 
      relationship between inputs and outputs and to determine the best input values. 
      One solution to this issue is to use design of experiments (DOE) in combination 
      with surrogate models. However, there is a lack of guidance on how to select the 
      appropriate model for a given data set. This study compares two surrogate 
      modelling techniques, polynomial regression (PR) and kriging-based models, and 
      analyses critical issues in design optimisation, such as DOE selection, design 
      sensitivity, and model adequacy. The study concludes that PR is more efficient 
      for model generation, while kriging-based models are better for assessing max-min 
      search results due to their ability to predict a broader range of objective 
      values. The number and location of design points can affect the performance of 
      the model, and the error of kriging-based models is lower than that of PR. 
      Furthermore, design sensitivity information is important for improving surrogate 
      model efficiency, and PR is better suited to determining the design variable with 
      the greatest impact on response. The findings of this study will be valuable to 
      engineering simulation practitioners and researchers by providing insight into 
      the selection of appropriate surrogate models. All in all, the study demonstrates 
      surrogate modelling techniques can be used to solve complex engineering problems 
      effectively.
CI  - (c) 2023 The Authors. Published by Elsevier Ltd.
FAU - Mukhtar, Azfarizal
AU  - Mukhtar A
AD  - Institute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, 
      Jalan IKRAM-UNITEN, 43000 Kajang, Malaysia.
AD  - College of Engineering, Putrajaya Campus, Universiti Tenaga Nasional, Jalan 
      IKRAM-UNITEN, 43000 Kajang, Malaysia.
FAU - Yasir, Ahmad Shah Hizam Md
AU  - Yasir ASHM
AD  - Faculty of Resilince, Rabdan Academy, 65, Al Inshirah, Al Sa'adah, 22401, PO Box: 
      114646, Abu Dhabi, United Arab Emirates.
FAU - Nasir, Mohamad Fariz Mohamed
AU  - Nasir MFM
AD  - STARE Resources Sdn. Bhd., Wisma Rampai, 2-4-29, Fourth Floor, Jalan 34/26, Taman 
      Sri Rampai 53300 Wilayah Persekutuan Kuala Lumpur, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230726
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10405017
OTO - NOTNLM
OT  - -Surrogate model
OT  - Design of experiment (DOE)
OT  - Kriging-based model
OT  - Polynomial regression (PR)
OT  - Underground shelter
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/08/09 06:43
MHDA- 2023/08/09 06:44
PMCR- 2023/07/26
CRDT- 2023/08/09 04:06
PHST- 2023/04/01 00:00 [received]
PHST- 2023/07/22 00:00 [revised]
PHST- 2023/07/25 00:00 [accepted]
PHST- 2023/08/09 06:44 [medline]
PHST- 2023/08/09 06:43 [pubmed]
PHST- 2023/08/09 04:06 [entrez]
PHST- 2023/07/26 00:00 [pmc-release]
AID - S2405-8440(23)05882-6 [pii]
AID - e18674 [pii]
AID - 10.1016/j.heliyon.2023.e18674 [doi]
PST - epublish
SO  - Heliyon. 2023 Jul 26;9(8):e18674. doi: 10.1016/j.heliyon.2023.e18674. eCollection 
      2023 Aug.