Potential application of radiant floor cooling systems for residential buildings in different climate zones

A radiant floor cooling system(RFCS)is a high-comfort and low energy consumption system suitable for residential buildings.Radiant floor systems usually work with fresh air,and their operating performance is affected by climatic conditions.Indoor and outdoor environmental disturbances and the system’s control strategy affect the indoor thermal comfort and energy efficiency of the system.Firstly,a multi-story residential building model was established in this study.Transient system simulation program was used to study the operation dynamics of three control strategies of the RFCS based on the calibrated model.Then,the performance of the control strategies in five climate zones in China were compared using multi-criteria decision-making in combination.The results show that control strategy has a negligible effect on condensation risk,but the thermal comfort and economic performance differ for different control strategies.The adaptability of different control strategies varies in different climate zones based on the consideration of multiple factors.The performance of the direct-ground cooling source system is better in Hot summer and warm winter zone.The variable air volume control strategy scores higher in Serve cold and Temperate zones,and the hours exceeding thermal comfort account for less than 3%of the total simulation period.Therefore,it is suggested to choose the RFCS control strategy for residential buildings according to the climate zone characteristics,to increase the energy savings.Our results provide a reliable reference for implementing RFCSs in residential buildings.

Predicting moisture condensation risk on the radiant cooling floor of an office using integration of a genetic algorithm-back-propagation neural network with sensitivity analysis

Pre-dehumidification time(τ_(pre))and pre-dehumidification energy consumption(E_(pre))play important roles in preventing the condensation of moisture on the floors of rooms that use a radiant floor cooling(RFC)system.However,there are few theoretical or experimental studies that focus on these two important quantities.In this study,an artificial neural network(ANN)was used to predict condensation risk for the integration of RFC systems with mixed ventilation(MV),stratum ventilation(SV),and displacement ventilation(DV)systems.A genetic algorithm-back-propagation(GA-BP)neural network model was established to predict τ_(pre) and E_(pre).Both training data and validation data were obtained from tests in a computational fluid dynamics(CFD)simulation.The results show that the established GA-BP model can predict τ_(pre) and E_(pre) well.The coefficient of determination(R^(2))of τ_(pre) and of E_(pre) were,respectively,0.973 and 0.956.For an RFC system integrated with an MV,SV,or DV system,the lowest values of τ_(pre) and E_(pre) were with the DV system,23.1 s and 0.237 kWh,respectively,for a 67.5 m^(3) room.Therefore,the best pre-dehumidification effect was with integration of the DV and RFC systems.This study showed that an ANN-based method can be used for predictive control for condensation prevention in RFC systems.It also provides a novel and effective method by which to assess the pre-dehumidification control of radiant floor surfaces.

Numerical study on indoor environment and thermal comfort in train station waiting hall with two different air-conditioning modes

With the increasing size of the waiting hall,the large-area use of transparent envelope materials makes the interior surface temperature of the envelope too high,which puts forward higher requirements for the control of environment and thermal comfort indoors.In this paper,the characteristics of indoor temperature distribution,relative humidity distribution and thermal comfort under the all air system(AAs)and the radiant floor cooling and wall cooling combined with air supply system(RC/ASs)were investigated in the large spaces.The computational fluid dynamics(CFD)method was used,and the performance of the CFD model was validated by comparing the measured results with CFD simulation results in the AAs.The numerical results clearly showed that the temperature and relative humidity indoors could satisfy the design requirement both in the AAs and RC/ASs.The indoor air distribution in most areas under the RC/ASs was more uniform based on the indoor heat and humidity requirements under the cooling load of measured day.In the AAs,the total cooling capacity of air conditioning unit was the highest when indoor thermal comfort was the best,that meant that to achieve the best working condition,the air-conditioning system need high energy consumption.Meanwhile,the RC/ASs addressed the problem that the temperature around the seated passengers in the waiting area was relatively high in the AAs.This paper will provide reference for the air conditioning system design in the similar large spaces in the future.