Unlike the case of air systems where the cooling load is purely convective, the cooling load for radiant systems consists of both convective and radiant components. The main objectives of this energy simulation study were to investigate whether the same design cooling load calculation methods can be used for radiant and air systems by studying the magnitude of the cooling load differences between radiant and air systems over a range of configurations and to suggest potential improvements in current design guidelines. Simulation results show that 1) zone level 24-hour total cooling energy of radiant systems can be 5-15% higher than air systems due to differences in conduction load through the building envelope; 2) peak cooling load at the radiant system hydronic level can be 7-31% higher than air system for zones without solar load. The differences can increase up to 93% at the hydronic level for floor system in zones with solar load; 3) the cooling load differences between the two systems originate from: a) radiant cooling surface(s) directly remove part of the radiant heat gain and reduce heat accumulation in the building mass; b) only part of the convective heat gain becomes instantaneous cooling load. This indicates that simplified methods such as Radiant Time Series Method is not appropriate for cooling load calculation in radiant system design. Radiant systems should be modeled using a dynamic simulation tool that is capable of capturing radiant heat transfer for cooling load calculation.

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Impacts of solar shortwave radiation are not taken into account in the standardized design methods in the current radiant system design guidelines. Therefore, the current methods are not applicable for cases where incident solar is significant. The goals of this study are to: 1) use dynamic simulation tools to investigate the impacts of solar radiation on floor cooling capacity, and 2) develop a new simplified method to calculate radiant floor cooling capacity when direct solar radiation is present. We used EnergyPlus to assess the impacts of solar for different design conditions. The simulation results showed that the actual cooling capacities are in average 1.44 times higher than the values calculated with the ISO 11855 method, and 1.2 times higher than the ASHRAE method. A simplified regression model is developed to improve the predictability of ISO methods. The new model calculates the increased capacity as a function of the zone transmitted solar and the characteristic temperature difference between the hydronic loop and room operative temperature.

(Jingjuan (Dove) Feng) Highlights We experimentally compared cooling loads between radiant and air systems  Radiant system has on average 18%-21% higher cooling rates compared to air system  A new definition must be used for radiant system cooling load  HB approach should be used for radiant system cooling load calculation  RTS or weighting factor methods may lead to incorrect results for radiant systems AbstractRadiant cooling systems work fundamentally differently from air systems by taking advantage of both radiant and convective heat transfer to remove space heat. This paper presents an experiment investigating how the dynamic heat transfer in rooms conditioned by a radiant system is different from an air system, and how such differences affect the sensible cooling load and cooling load calculation methods for radiant systems. Four tests with two heat gain profiles were carried out in a standard climatic chamber. For each profile, two separate tests were carried out to maintain a constant operative temperature: one with radiant chilled ceiling panels; and a second with an overhead mixing air distribution system. Concrete blocks were used to create a thermal mass effect. The experiments show that, during the periods the heat gain was on, the radiant system has on average 18%-21% higher instantaneous cooling rates compared to the air system, and 75-82% of total heat gains were removed, while for the air system only 61-63% were removed. Based on the study, we conclude that a new definition must be used for radiant system cooling load, which should be characterized as the combined radiant and convective heat removal at the cooled surface. Calibrated dynamic energy simulation based on a fundamental heat balance approach showed good accuracy. Simplified cooling load calculation methods, such as RTS or weighting factor method, may lead to incorrect results for radiant systems.

The measurement of the concentration of suspended sediment in a water body is a very important content in the observation of hydrological elements, and it is also one of the important parameters for calculating the sediment resuspension flux. In order to accurately predict the distribution of lake sediment, this paper uses satellite remote sensing data to invert the suspended sediment concentration. The key to the quantitative inversion is the atmospheric correction and the suspended sediment concentration inversion algorithm. In this paper, satellite remote sensing technology and Internet of Things technology are combined to establish a new type of lake suspended sediment concentration distribution model. First of all, this paper combines the results of satellite remote sensing inversion and the results of on-site water sample inspections of the Internet of Things to obtain the original hydrological data of suspended sediment in the lake. Secondly, this paper combines ADAM with deep learning technology to simulate the lake flow field and predict the dynamic process of suspended sediment pollution under different conditions. Finally, through experimental simulation and field sampling experiments, the validity of the lake suspended sediment concentration model established in this paper is verified. This model can provide assistance for relevant agencies to grasp the temporal and spatial distribution of suspended sediment concentration in regional lakes in a comprehensive and timely manner, and can obtain the overall characteristics of the study area and the impact of humanistic engineering construction.INDEX TERMS Lake sediment concentration, Internet of Things, temporal and spatial distribution, satellite remote sensing, ADAM