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EN
Waterlogging is related to rainfall intensity as well as drainage network design. In previous studies, rainfall intensity was dominantly considered, while the design return period with the lowest total social investment of drainage networks was generally neglected. In this study, Guangkai Street in Tianjin in northern China was selected as a case study to determine the optimal design return period of drainage networks. According to the drainage networks for different design return periods, the depth of waterlogging was simulated based on the FloodArea model under the conditions of the rainfall exceeding the design return period. Furthermore, traffic losses due to waterlogging were determined by using the traffic loss model. When the sum of traffic losses and drainage network investment is smallest (i.e., the lowest total social investment), the corresponding return period is considered as the optimal design return period of drainage networks. By comparing the simulated depths of waterlogging and observations of 17 waterlogging monitoring points, we found that the FloodArea model has efficient simulation in most areas. Accordingly, the FloodArea model was used to simulate the depths of waterlogging with different return periods in Guangkai Street. The results show that the total social investment, including traffic losses and initial investment of drainage networks, is the lowest with the return period of the drainage networks in the selected area being designed as 5 years. This suggests that the design return period of the drainage networks in Guangkai Street should be upgraded to 5 years. The approach in this study is based on high-precision simulation (1 m GIS data) and actual waterlogging depth to ensure the accuracy of simulation. The optimal design return period is calculated in combination with traffic losses and initial investment of drainage networks, providing reference for the design of drainage networks in specific areas.
EN
Exploring building energy demands under the conditions of climate change can provide a basis for promoting building energy efficiency. The heating and cooling loads of commercial and residential buildings with different energy-saving standards from 1961-2009 in a large city in northern China were simulated and their responses to climate change and variability were analyzed. The results showed that the heating load for commercial buildings significantly decreased from 1961 to 2009 (P<0.01), whereas the cooling load weakly but not significantly increased over these 49 years (P>0.05). This may indicate that continuous rising temperatures in the future may apparently decrease heating load, but not largely increase energy load for cooling. The heating loads in all types of residential buildings showed a large and significant decrease from 1961 to 2009 (P < 0.01). However, decreasing rate gradually decreased from the first- to the third-stage energy-saving buildings, indicating decreasing sensitivity to climate change with enhancement of energy-saving standards. The variations of heating loads are dominantly controlled by the mean air temperature, which can explain up to 90% of the heating load. The climate change influence on the cooling load of a commercial building is dependent on month. Cooling load is dominantly related to air temperature in June and September, whereas it relates to the combination of humidity and temperature in July and August. These results may indicate that improvement of energy efficiency for building cooling should be considered by the combined effects of humidity and temperature rather than a single temperature.
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