The use of the interval Pareto sorting method in simulation-based design of an indoor environment

Zhang, Tianhu; Hari, Qiga; Zhao, Qingxin

doi:10.1080/23744731.2017.1394142

Cited by 11 publications

(1 citation statement)

References 19 publications

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“…Only the cooling energy portion used to condition the outdoor fresh air to the air supply state was included. According to the calculation of cooling energy consumption in previous studies, 46,47 the calculation of this portion of energy consumption was according to equation ( 13) 48 E vent ¼ V as ρðh out À h as Þτ pre (13) where E vent is the portion of cooling energy used to condition the outdoor fresh air to the air supply state (kWh), ρ is the air density, the value of which was assumed to be 1.29 kg/m 3 , and h out and h as are, respectively, the specific enthalpies of the outdoor and supply air (kJ/kg). As a supplement to the previous statement, the following assumptions were imposed on the energy analysis for the ventilation system.…”

Section: ) Cooling Energy Consumptionmentioning

confidence: 99%

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

Liu

Zhou

et al. 2022

Indoor and Built Environment

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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 ( T as), air supply flow rate ( V as), air supply humidity ratio ( H as), floor temperature ( T floor), initial indoor temperature ( T ini) and relative humidity ( H ini) on the duration and energy consumption of pre-dehumidification were investigated. The big data show the air dew point temperature ( T ad) produced the most significant effect on the pre-dehumidification duration and energy consumption, while T as would cause the least significant effect. With the decrease of T ad, the pre-dehumidification duration and energy consumption were, respectively, decreased by 59.1% and 44.2%. Furthermore, with the variation of V as, 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.

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