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Analysis Of Applicable Passive Solar Heating Technology Types In Different Regions

Posted on:2021-03-01Degree:MasterType:Thesis
Country:ChinaCandidate:J YangFull Text:PDF
GTID:2392330611989521Subject:Heating, Gas Supply, Ventilation and Air Conditioning Engineering
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Passive solar heating technology is a technology that uses the heat-collecting members provided on the south side of the building body to effectively absorb and store solar energy,and converts solar energy into heat energy to provide heat for the room.It has good economic benefits by the advantages of simple structure and no need to use machinery power which is widely used in China.However,due to the vast territory of our country,there are significant differences between the climate and application effect of passive solar heating technology in different regions.Therefore,the climate suitability of passive solar heating in China is divided and the type of passive solar heating technology selected in different regions is very necessary.At present,the climate partition of passive solar heating is mainly based on radiation and temperature and most of them ignore the influence of heating demand on passive technology application,thus causing problems such as summer overheating and resource waste when passive solar heating is used in low-demand areas.The results do not fully consider the influence of outdoor temperature fluctuation on the selection of passive solar heating technology,and only the regional suitability of passive solar heating can be obtained,which cannot be accurately used in the selection of applicable passive technologies.Based on this,this study uses theoretical analysis,mathematical statistics and simulation methods to study the types of applicable technologies in passive solar heating areas.This study used coldest month southward radiation temperature difference ratio,heating degree days and winter outdoor temperature day difference degree days,respectively reprensent climate suitability,differences in heating demand and winter outdoor temperature fluctuations.K-means cluster analysis methods was used to analyze indicators of 270 meteorological stations in our country for typical meteorological year parameters and 22 regions with different suitability,heating demand and temperature fluctuations was divided.After areas with low heating demand and areas where passive solar heating were not applicable were excluded,the corresponding typical cities were selected for each region and TRNSYS software was used to establish models to simulate indoor temperature and heating load respectively.Indoor temperature fluctuation range,average temperature,maintenance time greater than 15? and heating load of the coldest typical day in different typical cities were selected as evaluation indexes,and the suitable passive solar heating technology types were selected for different regions based on the comprehensive attribute evaluation method of maximum deviation.In view of the problem that the applicable technologies selected in some areas cannot meet the requirements of solar heating buildings,this research analyzes the application effect of passive solar heating technologies in different heating seasons in different areas and puts forward the recommended passive solar heating strategies in different periods.The main research conclusions are as follows:1)China is divided into 22 passive solar heating areas with different suitability,heating demand and temperature fluctuation.There are 5 types of regions with different suitability,3 types of regions with different heating needs and 3 types of regions with different outdoor temperature fluctuations in winter.Among them,central and southern Tibet,northern and parts of southern Qinghai are highly suitable for passive solar heating areas with high demand and high temperature fluctuations in winter,which are applicable to use passive solar heating and need to avoid excessive fluctuations in room temperature.Central inner Mongolia,central Xinjiang,southwest Heilongjiang,Chongqing,Southwest Guangxi,southern Guangdong and southern Fujian are not suitable for passive solar heating areas.2)The research obtained the types of applicable passive solar heating technology in different regions.Among them,western Xinjiang,most part of Gansu,southwestern inner Mongolia,northern Shandong,southern Hebei and northern Shaanxi are applicable with additional attached sunspace technology and indoor temperature fluctuations are small.In most part of Qinghai,northern Xinjiang,most part of Tibet,most part of Sichuan,northern inner Mongolia,eastern Heilongjiang,and most part of Shanxi Province,the combined passive solar heating technology of “heat-storage wall plus direct benefit window(window-to-wall ratio of 0.3)” is appropriately applied which has good effect on heat gain and heat storage effects.In central inner Mongolia,northeastern Xinjiang,northeast Qinghai and western Heilongjiang,the combined passive solar heating technology of “heat storage wall plus direct benefit window(window-to-wall ratio of 0.5)” is adopted.The outdoor temperature in these areas is very low in winter and increasing the indoor temperature is the main goal.3)Based on the principle of “priority to use passive technology and use passive technology to determine active technology”,the study has drawn four application strategies of passive technology that apply passive solar heating technology but can not meet the heating requirements in different periods of the district heating period: only use passive heating technology;only use passive heating technology and require shading measures or night insulation;use the passive technology during the day and use active technology at night;use the combination of active and passive heating all-day.The types of passive heating technology and recommended technology application strategies in different regions suitable for climate in China obtained in the study will provide design and application references for engineers and technicians of passive solar heating in China.
Keywords/Search Tags:passive solar heating, climate partition, applicable technology, k-means clustering, multi-attribute decision making
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