Although many energy-saving technologies and sustainable building designs have successfully reduced building energy consumption and improved user satisfaction, little consideration has been given to the impact of building spatial configuration on the indoor environment. This study focused on a fitness centre and employed an optimization methodology based on computational fluid dynamics (CFD) simulation. The optimization aims of this study were to enhance interior ventilation efficiency and regulate temperature, resulting in the optimal building spatial characteristics. This work presents the homologous clustering optimization algorithm (HCO) as a solution to the limitations of existing optimization approaches. It also compares HCO with the multi-objective evolutionary mating algorithm (MOMA). HCO with a coefficient of eight performed better than MOMA. The Pareto frontier generated by HCO was more succinct, and the computational time was reduced by 52%. Moreover, an analysis of the CFD simulation results revealed that, in the context of this study, the influence of the interface width coefficient was greater than that of the interface height, and the impact of the cavity width was greater than that of the cavity height. The relationship between changes in air age and variations in geometric parameters was similar to that of indoor temperature.
RichardS. Air pollution. Counting the cost of London's killer smog. Science2002; 298(5601): 2106.
2.
ZhuWWangMZhangB. The effects of urbanization on PM2.5 concentrations in China's Yangtze River Economic Belt: new evidence from spatial econometric analysis. J Clean Prod2019; 239: 118065.
3.
SheQCaoSZhangSZhangJZhuHBaoJMengXLiuMLiuY. The impacts of comprehensive urbanization on PM2.5 concentrations in the Yangtze River Delta, China. Ecol Indicat2021; 132: 108337.
4.
LiQZhangLZhangLWuX. Optimizing energy efficiency and thermal comfort in building green retrofit. Energy2021; 237: 121509.
5.
SpiruPSimonaLPAlexandruS. Increasing the energy efficiency of a building by thermal insulation to reduce the thermal load of the micro-combined cooling, heating and power system. Energy Rep2021; 7(S5): 286–298.
6.
MostafaviFTahsildoostMZomorodianZ. Energy efficiency and carbon emission in high-rise buildings: a review (2005-2020). Build Environ2021; 206: 108329.
7.
AbadieMOMendesN. Numerical assessment of turbulence effect on the evaluation of wind-driven rain specific catch ratio. Int Commun Heat Mass Tran2008; 35(10): 1253–1261.
8.
AmatullahNAMüslümAAzhailiB. A review on the factors influencing energy efficiency of mosque buildings. J Clean Prod2021; 292: 126010.
9.
YuntaoLZhangJTingRZhongweiWShuanglinS. Application of ventilation simulation to spontaneous combustion control in underground coal mine: a case study from Bulianta colliery. Int J Min Sci Technol2018; 28(02): 231–242.
10.
AttiaSGratiaEHerdeDAHensenJLM. Simulation-based decision support tool for early stages of zero-energy building design. Energy Build2012; 49: 2–15.
11.
TianZZhangXJinXZhouXSiBShiX. Towards adoption of building energy simulation and optimization for passive building design: a survey and a review. Energy Build2018; 158: 1306–1316.
12.
LinSGerberDJ. Evolutionary energy performance feedback for design: Multidisciplinary design optimization and performance boundaries for design decision support. Energy Build2014; 84: 426–441.
13.
ØstergårdTJensenLRMaagaardES. Building simulations supporting decision making in early design – a review. Renew Sustain Energy Rev2016; 61: 187–201.
14.
ShiXTianZChenWSiBJinX. A review on building energy efficient design optimization rom the perspective of architects. Renew Sustain Energy Rev2016; 65: 872–884.
15.
NguyenAReiterSRigoP. A review on simulation-based optimization methods applied to building performance analysis. Appl Energy2014; 113: 1043–1058.
16.
ShiZFonsecaJASchlueterA. A review of simulation-based urban form generation and optimization for energy-driven urban design. Build Environ2017; 121: 119–129.
17.
KheiriF. A review on optimization methods applied in energy-efficient building geometry and envelope design. Renew Sustain Energy Rev2018; 92: 897–920.
18.
LuSGuMFanZ. Evaluation index of indoor environment comfort of health sports building based on perceptual phenomenology. Western Journal of Human Settlements2019; 34(2): 8.
19.
HeCLiuD. Research on thermal comfort environment standard of indoor national fitness facilities. Cryogenic building technology2019; 41(11): 4.
20.
WeiZ. Formation and natural ventilation of national fitness centre in hot and humid area. Central China Architecture2013; 31(8): 5.
21.
AhadiASaghafiMRTahbazM. The optimization of light-wells with integrating daylight and stack natural ventilation systems in deep-plan residential buildings: a case study of Tehran. J Build Eng2018; 18: 220–244.
22.
HarkoussFFardounFBiwolePH. Passive design optimization of low energy buildings in different climates. Energy2018; 165(Part A): 591–613.
23.
YuWLiBJiaHZhangMWangD. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build2015; 88: 135–143.
24.
FlagerFLWelleBBansalPSoremekunGHaymakerJ. Multidisciplinary process integration and design optimization of a classroom building. Itcon2012; 14(38): 595–612.
25.
MartinsATAdolpheLBastosEL. From solar constraints to urban design opportunities: optimization of built form typologies in a Brazilian tropical city. Energy Build2014; 76: 43–56.
26.
YangDRenSTurrinMSariyildizSSunY. Multi-disciplinary and multi-objective optimization problem re-formulation in computational design exploration: a case of conceptual sports building design. Autom ConStruct2018; 92: 242–269.
27.
ImanRFangC. A review on constraint Handling techniques for population-based algorithms: from single-objective to multi-objective optimization. Arch Comput Methods Eng2023; 30(3): 2181–2209.
28.
PengCLitingSZhangWTomizukaM. Optimization-based constrained iterative learning control with application to building temperature control systems. In Proceedings of the IEEE International Conference on Advanced intelligent Mechatronics. Banff, AB, Canada, 2016; 709–715.
29.
FelknerJChatziEKotnikT. Interactive truss design using Particle Swarm Optimization and NURBS curves. J Build Eng2015; 4: 60–74.
30.
SavsaniVJTejaniGPatelVKSavsaniP. Modified meta-heuristics using random mutation for truss topology optimization with static and dynamic constraints. Journal of Computational Design and Engineering2016; 4(2): 106–130.
31.
VítorPJoséSFernandaLEscórcioP. Using BIM to improve building energy efficiency – a scientific metric and systematic review. Energy Build2021; 250: 111292.
32.
ZhuangDZhangXLuYWangCJinXZhouXShiX. A performance data integrated BIM framework for building life-cycle energy efficiency and environmental optimization design. Autom ConStruct2021; 127: 103712.
33.
HollandJH. Adaptation in natural and artificial systems: an Introductory analysis with applications to Biology. In: Control, and artificial intelligence. Cambridge, Massachusetts: The MIT Press, 1992.
34.
FlagerFLWelleBBansalPSoremekunGHaymakerJ. Multidisciplinary process integration and design optimization of a classroom building. Itcon2012; 14(38): 595–612.
35.
CoffeyB. A development and testing framework for simulation-based supervisory control with application to optimal zone temperature ramping demand response using a modified genetic algorithm. PhD Thesis. Montreal, Canada: Concordia University, 2008.
36.
HuangWLamH. Using genetic algorithms to optimize controller parameters for HVAC systems. Energy Build1997; 26(3): 277–282.
37.
WeiminWRaduZHuguesR. Applying multi-objective genetic algorithms in green building design optimization - ScienceDirect. Build Environ2005; 40(11): 1512–1525.
38.
YangJMKaoCY. A robust evolutionary algorithm for training neural networks. Neural Comput Appl2001; 10(3): 214–230.
39.
LachanceJ. Hardy–weinberg equilibrium and random mating. Encyclopedia of Evolutionary Biology2016: 208–211.
40.
YuWLiBLeiYLiuM. Analysis of a residential building energy consumption demand model. Energies2011; 4(3): 475–487.
41.
DorseyREJohnsonJDMayerWJ. A genetic algorithm for the training of feed forward neural networks. Advances in Artificial Intelligence in Economics, Finance, and Management1994; 1: 93–111.
42.
RodríguezJEnriqueÁ. A new analytical approach for simplified thermal modelling of buildings: self-Adjusting RC-network model. Energy Build2016; 130: 85–97.
43.
ZhuangJChenYChenX. A new simplified modeling method for model predictive control in a medium-sized commercial building: a case study. Build Environ2018; 127: 1–12.
44.
RouchierS. Solving inverse problems in building physics: an overview of guidelines for a careful and optimal use of data. Energy Build2018; 166: 178–195.
45.
WangSXuX. Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm. Energy Convers Manag2006; 47(13/14): 1927–1941.
46.
XuXWangS. Optimal simplified thermal models of building envelope based on frequency domain regression using genetic algorithm. Energy Build2007; 39(5): 525–536.
47.
PetriHMikaRKaukoL. A Dynamic model for indoor temperature prediction in buildings. Energies2018; 11(6): 1477.
48.
O’DwyerETommasiDLKouramasKCychowskiMLightbodyG. Modelling and disturbance estimation for model predictive control in building heating systems. Energy Build2016; 130: 532–545.
49.
SpentzouECookMJEmmittS. Modelling natural ventilation for summer thermal comfort in Mediterranean dwellings. Int J Vent2019; 18(1): 28–45.
50.
AlvaGLinYFangG. An overview of thermal energy storage systems. Energy2018; 144: 341–378.
51.
SunYLinGZongZ. Characteristics of wind speed and wind energy resources in Shenyang area from 1951 to 2010. Energy Saving2013; 32(01): 7–9.
52.
BaiX. Research on spatial form optimization of National Fitness Centre based on natural ventilation performance. PHD Thesis. Harbin, China: Harbin industrial university, 2020.
53.
ZhengC. Research on Stadium design strategy based on natural ventilation simulation. PhD Thesis. Harbin, China: Harbin Institute of Technology, 2017.
