Dynamic prediction of the pre-dehumidification of a radiant floor cooling and displacement ventilation system based on computational fluid dynamics and a back-propagation neural network: A case study of an office room
Restricted accessResearch articleFirst published online December, 2022
Dynamic prediction of the pre-dehumidification of a radiant floor cooling and displacement ventilation system based on computational fluid dynamics and a back-propagation neural network: A case study of an office room
This study was carried out to solve the problem of condensation in radiant floor cooling systems. Computational fluid dynamics simulation and back-propagation neural network prediction were employed to conduct thorough research to predict the effects of the displacement ventilation dehumidification phase in an office building located in Jinan, China. The effects of the air supply temperature (Tas), air supply flow rate (Vas), air supply humidity ratio (Has), floor temperature (Tfloor), initial indoor temperature (Tini) and relative humidity (Hini) on the duration and energy consumption of pre-dehumidification were investigated. The big data show the air dew point temperature (Tad) produced the most significant effect on the pre-dehumidification duration and energy consumption, while Tas would cause the least significant effect. With the decrease of Tad, the pre-dehumidification duration and energy consumption were, respectively, decreased by 59.1% and 44.2%. Furthermore, with the variation of Vas, the energy consumption exhibited a fluctuating trend. This study provides a novel and effective method to assess the pre-dehumidification control of radiant floor surfaces by considering different initial indoor conditions and air supply parameters.
LiXYuCWF. China’s building energy efficiency targets: challenges or opportunities?Indoor Built Environ2012; 21(5): 609–613. DOI: 10.1177/1420326x12460463
2.
RheeKNKimKW. A 50 year review of basic and applied research in radiant heating and cooling systems for the built environment. Build Environ2015; 91: 166–190. DOI: 10.1016/j.buildenv.2015.03.040
3.
KimJTYuCWF. Sustainable development and requirements for energy efficiency in buildings - the Korean perspectives. Indoor Built Environ2018; 27(6): 734–751. DOI: 10.1177/1420326x18764618
4.
ZhaoMKangWBLuoXLYuCWFMengXZGuZL. Performance comparison of capillary mat radiant and floor radiant heating systems assisted by air source heat pump in residential buildings. Indoor Built Environ2017; 26(9): 1292–1304. DOI: 10.1177/1420326x16674517
5.
SuJLiJLuoXYuCWGuZ. Experimental evaluation of a capillary heating bed driven by an air source heat pump and solar energy. Indoor Built Environ2020; 29(10): 1399–1411. DOI: 10.1177/1420326x19878576
6.
XieDTianLLiaoMLYuCWWangHQ. The thermal environment of non-steady state radiation heat transfer with a capillary ceiling radiation cooling system. Indoor Built Environ2019; 28(4): 443–453. DOI: 10.1177/1420326x18804012
7.
XieDWangCYuCWWangH. Performance of capillary ceiling cooling panel on ceiling surface temperature and indoor thermal environment. Indoor Built Environ2020; 29(6): 881–894. DOI: 10.1177/1420326x20917740
8.
KimMKLiuJCaoS-J. Energy analysis of a hybrid radiant cooling system under hot and humid climates: a case study at Shanghai in China. Build Environ2018; 137: 208–214. DOI: 10.1016/j.buildenv.2018.04.006.
9.
LiuJLiZKimMKZhuSZhangLSrebricJ. A comparison of the thermal comfort performances of a radiation floor cooling system when combined with a range of ventilation systems. Indoor Built Environ2020; 29(4): 527–542. DOI: 10.1177/1420326X19869412
10.
LiHGongGXuCSuHCaoZZhouMYuCWF. Thermal and humid environment and moisture condensation characteristics of cold surfaces. Indoor Built Environ2014; 23(3): 474–484. DOI: 10.1177/1420326X19869412
11.
TangHLiuX-HLiHZhouYJiangY. Study on the reduction of condensation risks on the radiant cooling ceiling with superhydrophobic treatment. Build Environ2016; 100: 135–144. DOI: 10.1016/j.buildenv.2016.02.008
12.
XingDLiNCuiHZhouLLiuQ. Theoretical study of infrared transparent cover preventing condensation on indoor radiant cooling surfaces. Energy2020; 201: 117694. DOI: 10.1016/j.enery.2020.117694
13.
GuoSTianYFanDWuWZhaoJJinG. A novel operating strategy to avoid dew condensation for displacement ventilation and chilled ceiling system. Appl Therm Eng2020; 176: 115344. DOI: 10.1016/j.applthermaleng.2020.115344
14.
JinWMaJJiaLWangZ. Dynamic variation of surface temperatures on the radiant ceiling cooling panel based on the different supply water temperature adjustments. Sustain Cities Soc2020; 52: 101805. DOI: 10.1016/j.scs.2019.101805
15.
SongDKimTSongSHwangSLeighSB. Performance evaluation of a radiant floor cooling system integrated with dehumidified ventilation. Appl Therm Eng2008; 28: 1299–1311.
16.
LimHKangYKJeongJW. Application of a phase change material to a thermoelectric ceiling radiant cooling panel as a heat storage layer. J Build Eng2020; 32: 101787. DOI: 10.1016/j.jobe.2020.101787
17.
ZhongZNiuJMaSYaoSYangMWangZ. An experimental study of condensation on an aluminum radiant ceiling panel surface with superhydrophobic treatment. Energy Build2021; 252(7734): 111393. DOI: 10.1016/j.enbuild.2021.111393
18.
FauchouxMBansalMTalukdarPSimonsonCJTorviD. Testing and modelling of a novel ceiling panel for maintaining space relative humidity by moisture transfer. Int J Heat Mass Transf2010; 53(19/20): 3961–3968. DOI: 10.1016/j.ijheatmasstransfer.2010.05.015
19.
XieDTianLYuCWLiaoMWangH. Indoor thermal environment due to non-steady-state radiation heat transfer of a capillary ceiling radiation cooling system. Indoor Built Environ2019; 29(4): 443–453. DOI: 10.1177/1420326X18804012
20.
KaracavusBAydinK. Numerical investigation of general and local thermal comfort of an office equipped with radiant panels. Indoor Built Environ2019; 28(6): 208–214. DOI: 10.1177/1420326X18799834
21.
FengJSchiavonSBaumanF. New method for the design of radiant floor cooling systems with solar radiation. Energy Build2016; 125: 9–18. DOI: 10.1016/j.enbuild.2016.04.048
22.
TangHLiuX-HJiangY. Theoretical and experimental study of condensation rates on radiant cooling surfaces in humid air. Build Environ2016; 97(15): 1–10. DOI: 10.1016/j.buildenv.2015.12.003
23.
LimJ-HJoJ-HKimY-YYeoMSKimKW. Applieation of the control methods for radiant floor cooling system in residential buildings. Build Environ2006; 41(1): 60–73. DOI: 10.1016/j.buildenv.2005.01.019
24.
JeongCYeoMKimKW. Feasibility of a radiant floor cooling system for residential buildings with massive concrete slab in a hot and humid climate. Int J Concr Struct M2018; 12(1): 3–14. DOI: 10.1186/s40069-018-0314-z
25.
WilkinsCKosonenR. Cool ceiling system: a European air-conditioning alternative. ASHRAE J1992; 34: 41–45.
26.
ZongJLiuJAiZKimMK. A review of human thermal plume and its influence on the inhalation exposure to particulate matter. Indoor Built Environ2022: 1420326X2210803. DOI: 10.1177/1420326X221080358
27.
EngelmanaPKalzDSalvalaiG. A review of CFD analysis methods for personalized ventilation (PV) in indoor built environments. Sustainability2019; 11(15): 4166. DOI: 10.3390/su11154166
28.
AwbiHB. Energy efficient room air distribution. Renew Energ1998; 15: 293–299. DOI: 10.1016/S0960-1481(98)00176-1
29.
XingHHattonAAwbiHB. A study of the air quality in the breathing zone in a room with displacement ventilation. Build Environ2001; 36: 809–820. DOI: 10.1016/S0360-1323(01)00006-3
30.
RenJLiuJZhouSKimMKSongS. Experimental study on control strategies of radiant floor cooling system with direct-ground cooling source and displacement ventilation system: a case study in an office building. Energy2022; 239(15): 122410. DOI: 10.1016/j.energy.2021.122410
31.
LiuJKimMKSrebricJ. Numerical analysis of cooling potential and indoor thermal comfort with a novel hybrid radiant cooling system in hot and humid climates. Indoor Built Environ2022; 31(4): 929–943. DOI: 10.1177/1420326x211040853
32.
LiuJDalgoDZhuSLiHZhangLSrebricJ. Performance analysis of a ductless personalized ventilation combined with radiant floor cooling system and displacement ventilation. Build Simul2019; 12(5): 905–919. DOI: 10.1007/s12273-019-0521-9
33.
NasruddinSSatrioPMahliaTMIGiannettiNSaitoK. Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm. Sustain Energy Technol Assess2019; 35: 48–57. DOI: 10.1016/j.seta.2019.06.002
34.
SholahudinSHanH. Heating load predictions using the static neural networks method. Int J Technol2015; 6(6): 946–953. DOI: 10.14716/ijtech.v6i6.1902
35.
MbaLMeukamPKemajouA. Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region. Energy Build2016; 121: 32–42. DOI: 10.1016/j.enbuild.2016.03.046
36.
ZhaoR. Concise air conditioning design manual. Beijing: China Architecture & Building press, 1998.
37.
CengelTMY. A practical approach. Columbus, GA, USA: Mc-Graw Hill Education, 2003.
38.
ChenQ. Comparison of different k-ε models for indoor air flow computations. Numer Heat Tr B-fund1998; 28(3): 353–369. DOI: 10.1080/10407799508928838
39.
DavidsonL. An introduction to turbulence models. Goteborg, Sweden: Chalmers University of Technology, 2018.
40.
GB 50365-2019. Ministry of Housing and Urban-Rural Development of the People‘s Republic of China (MOHURD). Standard for operation and management of central air conditioning system. Beijing: China Architecture and Building press, 2019.
ZhangCPomianowskiMHeiselbergPKYuT. A review of integrated radiant heating/cooling with ventilation systems - Thermal comfort and indoor air quality. Energy Build2020; 223(15): 110094. DOI: 10.1016/j.enbuild.2020.110094
44.
XuHTNiuJL. Numerical procedure for predicting annual energy consumption of the under-floor air distribution system. Energy Build2006; 38: 641–647. DOI: 10.1016/j.enbuild.2005.10.003
45.
JGJ/T 449-2018. Ministry of Housing and Urban-Rural Development of the People‘s Republic of China (MOHURD). Standard for green performance calculation of civil buildings. Beijing: China Architecture and Building press, 2018.
46.
ZhangTHariQZhaoQ. The use of the interval pareto sorting method in simulation-based design of an indoor environment. Sci Technol Built En2018; 24: 410–417. DOI: 10.1080/23744731.2017.1394142
47.
ZhouLHaghighatF. Optimization of ventilation system design and operation in office environment, Part I: methodology. Build Environ2009; 44: 651–656. DOI: 10.1016/j.buildenv.2008.05.009
48.
XuYYangXYangCSrebricJ. Contaminant dispersion with personal displacement ventilation Part I: base case study. Buid Environ2009; 44(10): 2121–2128. DOI: 10.1016/j.buildenv.2009.03.006
49.
MaXLiXShaoXJiangX. An algorithm to predict the transient moisture distribution for wall condensation under a steady flow field. Buid Environ2013; 67: 56–58. DOI: 10.1016/j.buildenv.2013.04.028
50.
NasruddinSSatrioPMashliaTMIMahliaTMIGiannettiNSaitoK. Optimization of HVAC system energy consumption in a building using artificial neural network and multi-objective genetic algorithm. Sustain Energ Technol Assess2019; 35: 48–57. DOI: 10.1016/j.seta.2019.06.002
51.
SaidSAM. Degree-day base temperature for residential building energy prediction in Saudia Arabia. ASHRAE Trans Res1992; 98(1): 346–353.
52.
ImranTShafiqurRKhaledB. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew Energ2002; 35(4): 545–554. DOI: 10.1016/S0960-1481(01)00082-9
53.
RenLLiuYRuiZ. Application of Elman neural network and MATLAB to load forecasting. In: International Conference on Information Technology and Computer Science,Beijing, 04 August 2009, pp. 55–59. DOI: 10.1109/ITCS.2009.20
54.
MohanrajMJayarajSMuraleedharanC. Applications of artificial neural networks for refrigeration, air-conditioning and heat pump systems—a review. Renew Sustain Energ Rev2012; 16(2): 1340–1358. DOI: 10.1016/j.rser.2011.10.015
55.
QiA. Research on prediction model of improved BP neural network optimized by genetic algorithm. In: 4th International Conference on Machinery, Materials and Computer 2017. Shanxi. Xian, 27–29 November 2017. DOI: 10.2991/macmc-17.2018.145
56.
ZhaoKLiuX-HJiangY. Dynamic performance of water-based radiant floors during start-up and high-intensity solar radiation. Sol Energ2014; 101: 232–244. DOI: 10.1016/j.solener.2013.12.033
57.
VerbekeSAudenaertA. Thermal inertia in buildings: a review of impacts across climate and building use. Renew Sustain Energ Rev2018; 82: 2300–2318. DOI: 10.1016/j.rser.2017.08.083
58.
ChenYT. Application of adaptive predictive control to a floor heating system with a large thermal lag. Energy Build2002; 34(01): 45–51. DOI: 10.1016/S0378-7788(01)00076-7
59.
LiuDZhouHHuAZhangQLiuNWenJ. Study on the intermittent operation mode characteristic of a convection-radiation combined cooling system in office buildings. Energy Build2022; 255: 111669. DOI: 10.1016/j.enbuild.2021.111669
60.
ChenQLiN. Fast simulation and high-fidelity reduced-order model of the multi-zone radiant floor system for efficient application to model predictive control. Energy Build2021; 248: 111210. DOI: 10.1016/j.enbuild.2021.111210
61.
DuJFangFPainCCNavonIMZhuJHamDA. POD reduced-order unstructured mesh modeling applied To 2D and 3D fluid flow. Comput Math Appl2013; 65(3): 362–379. DOI: 10.1016/j.camwa.2012.06.009
62.
ZhuXLiuJZhuXWangXDuYMiaoJ. Experimental study on operating characteristic of a combined radiant floor and fan coil cooling system in a high humidity environment. Buildings2022; 12(4): 499, https://www.mdpi.com/2075-5309/12/4/499
