The On-site Test Of Heat Transfer Coefficient Of Building Envelope And Analysis Of Heat Transfer Process

The energy crisis and environmental degradation have become a threat to people’s daily lifeas two most important factors.Along with China entered into the period of rapid development ofindustrialization and urbanization, the contradiction between population, resources, environmentand this rapid development become more and more obvious. According to the "China StatisticalYearbook" of2012, China’s building energy consumption improved to620million tons in2010from280million tons in2000. At the meantime, construction’s manufacture and use also causedserious consequences to the environment such as Energy consumption, waste emission, ecologicalenvironment deterioration. The earth we survived has overwhelmed, energy-saving and emissionreduction is imminent.HVAC energy consumption accounts for more than20%of total energy consumption.Building indoor heating, air conditioning cooling and heating load is mainly composed of loadcaused by heating transfer of envelope structure, heating load of inner heat source and fresh airload. One of the most important part is heat transfer load caused by building envelope. Therefore,the heat transfer coefficient of building envelope affects the indoor and outdoor heat transferprocess. Engineering quality testing is also a very important part for the evaluation of buildingenergy efficiency.The purpose of this research is to solve the problem of how to reduce the testing time and toimprove the success rate of the on-site test of building envelope’s thermal performance in theover-season and the southern regionThis topic firstly do some on-site detections in Shanghai area in early summer and Guangzhou area in early winter as the typical data in different areas and different seasons,successfully getting a great number of data. When adopting new testing equipment for wall on-sitedetection, using ANSYS14.0software to simulate building wall heat transfer process, comparedwith field test data analysis. Concluding that for typical wall, testing data of the24hours beforetest started is meaningless for data processing, removing it can be obtained faster and moreaccurate heat transfer coefficient. Then, compared and analyzed with the method to detecting thebuilding wall heat transfer coefficient field, it is obvious that the new testing equipment this topicchosen has advantages. Making a theoretical analysis on the building wall heat transfer process,the data provides a theoretical basis for the following analysis. It’s faster and more accurate to getbuilding wall heat transfer coefficient values after carrying detailed discussion and comparativeanalysis on the existing data processing method and improving the dynamic analysis method,which greatly improved the thermal performance of building surrounding structure parameters offield testing equipment and the chances of the success of on-site testing.The main topic of this study is to use the new testing equipment to the construction site of thewall detection experiments, analysis verification, and thermal analysis of a large number of fieldtest data ANSYS was conducted. Improved dynamic analysis method is more complicated to dealwith the case than the arithmetic average method or the least squares, but for the medium-sized,heavy structural element performance testing, and changes in the surrounding environmentdetection element fluctuation, it can reduce the detection time, get the accurate value quicker.Coupled with the development of computers, the advantages of dynamic analysis has becomeincreasingly evident.Typical data for data processing in this article is two field test data: early summer inShanghai and early winter in Guangzhou. Through the comparative analysis of various dataprocessing methods, and the error analysis, dynamic analysis method, it can be faster to get anaccurate detection value of the detection time by nearly one day than other data processing method.For the climate variability in southern areas, the use of dynamic analysis of field test data for dataprocessing and analysis improves the chances of a successful test.