淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-3107201916485900
中文論文名稱 基於建築資訊模型(BIM)之建築自然通風決策系統開發
英文論文名稱 The Development of BIM-based Natural Ventilation Decision Information System
校院名稱 淡江大學
系所名稱(中) 土木工程學系碩士班
系所名稱(英) Department of Civil Engineering
學年度 107
學期 2
出版年 108
研究生中文姓名 賀鈞
研究生英文姓名 Jun-He
學號 607384020
學位類別 碩士
語文別 中文
口試日期 2019-07-06
論文頁數 103頁
口試委員 指導教授-蔡明修
委員-黎益肇
委員-陳建忠
委員-蔡明修
中文關鍵字 自然通風  計算流體力學  建築資訊模型(BIM)  物聯網(IOT)  營運維護  自然通風決策系統 
英文關鍵字 Natural ventilation  Computational Fluid Dynamics  Building Information Modeling(BIM)  Internet of Things  Operational maintenance  Building Natural Ventilation Decision System 
學科別分類 學科別應用科學土木工程及建築
中文摘要 在全球暖化的趨勢下,氣溫的逐步升高,建物使用者依賴空調之機械通風程度與日俱增,然而空調等機械通風技術的使用又會進一步加劇氣溫的升高,以此惡性循環。根據一項訪談的大型調查,發現引入自然通風模式建築中的乘客總體滿意度明顯優於空調建築。而自然通風是一種通過引入外部風場進入建物以達到室內外空氣交換目的之通風方式。一般而言,為了引導外部環境風場,除必須掌握環境風場之實際狀況外,亦須有符合該建物室內空間配置之風場模型及良善之空間系統整合介面,方能在適合的時機主動提供整體通風口之開關決策與空間資訊給建物管理人員,達到引入自然通風之目標。而為建立符合建物室內空間配置之風場模型,計算流體力學(Computational Fluid Dynamics, CFD)雖成為被廣泛應用之分析技術,但進行CFD模擬所需的專業知識和資源成本極高,亦無法完成建築物實時通風量之分析,因此目前仍僅在設計階段透過CFD之輔助完成設計,鮮少將分析成果導入建物使用維護階段。
根據上述自然通風之過程與問題,本研究針對建築物室內外通風特性,精進風場模擬技術,並開發物聯網(IOT)室外實時風場實場量測及室內空間通風環境量測裝置,藉以根據室外風場、室內環境參數及建築物使用者之期望,建立建物室內自然通風最佳化演算模型;進而利用建築資訊模型(building information model)作為建物通風即時資訊之空間資訊載體(container),提供室內通風控制之最佳化決策,達到節省能源、增進風能與舒適環境之效益。為此,本研究建立「建築自然通風決策系統」,針對目標建築物開發一個可即時收集建築室內外風場與環境資訊之「實場資訊量測系統」;並使用CFD模擬分析求得目標建物之自然通風數值模型,以此為基礎建立「建物自然通風數值模型資料庫」以提供決策之數據基礎;最後結合風場實測裝置所收集之即時資訊,以建築物BIM資訊模型為整合載體(container),開發一個可協助建物使用者取得室內最佳通風決策之輔助資訊系統。以此提供自然通風決策系統導入後續營運維護階段使用之參考。
英文摘要 Under the trend of global warming, the temperature gradually rises, and the degree of mechanical ventilation of building users relying on air conditioners is increasing day by day. However, the use of mechanical ventilation technology such as air conditioners will further increase the temperature rise, thus vicious circle. According to a large survey conducted by an interview, it was found that the overall satisfaction of passengers in the natural ventilation mode building was significantly better than that of air-conditioned buildings. Natural ventilation is a kind of ventilation method that uses the external wind field to enter the building to achieve indoor and outdoor air exchange purposes. In general, in order to guide the external environment wind field, in addition to the actual situation of the environmental wind field, it is necessary to have a wind field model that fits the indoor space configuration of the building and a space system integration interface for goodness, so that it can take the initiative at the right time. Provides overall venting switch decisions and spatial information to building management personnel to achieve the goal of introducing natural ventilation. Computational Fluid Dynamics (CFD) has become a widely used analytical technique in order to establish a wind field model that conforms to the indoor space configuration of the building. However, the professional knowledge and resources required for CFD simulation are extremely hard and the analysis of real-time ventilation of buildings cannot be completed. So the design is still completed by CFD only in the design stage, and the analysis results are rarely introduced into the construction maintenance phase.
According to the above-mentioned natural ventilation process and problems, this study is aimed at the indoor and outdoor ventilation characteristics of the building, refine wind field simulation technology, and develop the Internet of Things (IOT) outdoor real-time wind field measurement and indoor space ventilation environment measurement device. According to the outdoor wind field, indoor environmental parameters and the expectations of the building users, the natural ventilation optimization calculation model of the building interior is established; then the building information model is used as the spatial information carrier of the building ventilation real-time information, provides optimal decision-making for indoor ventilation control to save energy, enhance wind and comfort. To this end, this study established the "Building Natural Ventilation Decision System" to develop a "real-field information measurement system" for real-time collection of indoor and outdoor wind farms and environmental information for target buildings and use CFD simulation analysis is used to obtain the natural ventilation numerical model of the target building. Based on this, the “National Natural Ventilation Numerical Model Database” is established to provide the data foundation for decision-making. Finally, the real-time information collected by the wind field measuring device is used to integrate the building information model as a container develop an auxiliary information system that assists building users in obtaining optimal indoor ventilation decisions. This provides a reference for the introduction of the natural ventilation decision system into the subsequent operational maintenance phase.
論文目次 目录
目录 I
圖次 IV
表次 VI
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 5
1.3 研究方法與流程 7
1.4 研究範圍與限制 14
第二章 文獻回顧 15
2.1 建築室內自然通風之相關研究 15
2.2 BIM在自然通風之應用相關議題 18
2.3 物聯網(IOT)技術之相關研究 22
2.4 小結 23
第三章 建物室內通風最佳化決策模式 25
3.1 室內最低體感溫度估算方法 27
3.2 室內即時體感溫度估算方法 29
3.3 室內最佳開窗配置推估方法 31
第四章 實場資訊量測系統 33
4.1 實場資訊量測系統設計 33
4.2 實場資訊量測系統實作 39
第五章 建物自然通風數值模型資料庫 53
5.1 CFD模擬分析 53
5.1.1 通風模型建立 54
5.1.2 通風模型簡化 55
5.1.3 室外風場模擬 60
5.1.4 室內風場模擬 65
5.2 建物自然通風數值模型資料庫建置 79
第六章 建築自然通風決策系統 84
6.1 系統分析 84
6.2 系統設計 86
6.2.1 建築自然通風資料庫 86
6.2.2 通風管理應用程式設計 87
6.3 系統實做 90
第七章 結論與建議 96
7.1 研究結論 96
7.2 後續研究建議 97
第八章 參考文獻 98

圖次
圖1.1視覺化多維度資訊整合架構(VMDIIF)圖 9
圖1.2混合式資料庫架構圖 9
圖1.3視覺化多維度資訊應用程式三層式架構圖 11
圖1.4:研究流程圖 13
圖2.1 BIM在建築物永續性分析的七大主要功能 19
圖3.1 最佳化自然通風配置評估機制 26
圖3.2 室內最低體感溫度(Tmin)及最大換氣率(Emax)推估方法示意圖 27
圖3.3 室內即時體感溫度(Tr)及當前換氣率(Er)推估方法示意圖 29
圖3.4 室內最佳室內通風速率(Ūm)及其對應開窗配置推估方法示意圖 31
圖4.1實場資訊量測系統架構圖 34
圖4.2實時外部風場資訊量測裝置架構 36
圖4.3實時室內通風環境量測裝置架構 37
圖4.4 WindSonic 超聲波風速傳感器架設位置 41
圖4.5 電源供應裝置 41
圖4.6信號轉換裝置 42
圖4.7 DHT-22溫濕度傳感器與粉塵傳感器模組 44
圖4.8 LinkIt ONE開發板與藍牙wifi天線 47
圖4.9 LinkIt ONE開發板室外執行程式 47
圖4.10 室外實時資訊管理程式 48
圖4.11 自製窗戶與雌黃開關、DHT-22溫濕度傳感器 49
圖4.12 LinkIt ONE開發板室內執行程式 50
圖4.13 室內實時資訊管理程式 50
圖4.14 建物實場環境資料庫之實時外部風場資料表 51
圖4.15 建物實場環境資料庫之實時室內環境資料表 52
圖5.1 建物自然通風數值模型資料庫架構圖 53
圖5.2 假定目標建築BIM模型 54
圖5.3 假定目標建築4樓配置圖 55
圖5.4 假定目標建築之簡化模型 56
圖5.5 假定目標建築之簡化模型平面示意圖 56
圖5.6 四區塊風向角之示意圖 57
圖5.7 假定目標建築室外3D網格配置 60
圖5.8 室外風場模擬邊界條件設定示意圖 62
圖5.9 假定目標建築4F室內2D網格配置 65
圖5-10 全開(W4)狀況下之室內平均風速等值圖 68
圖5-11 關北窗(W3a)狀況下之室內平均風速等值圖 68
圖5-12 關東窗(W3b)狀況下之室內平均風速等值圖 69
圖5-13 關南窗(W3c)狀況下之室內平均風速等值圖 69
圖5-14 關東窗(W3d)狀況下之室內平均風速等值圖 70
圖5-15 開東西窗(W2a1)狀況下之室內平均風速等值圖 70
圖5-16 開南北窗(W2a2)狀況下之室內平均風速等值圖 71
圖5-17 開東北窗(W2b1)狀況下之室內平均風速等值圖 71
圖5-18 開東南窗(W2b2)狀況下之室內平均風速等值圖 72
圖5-19 開西南窗(W2b3)狀況下之室內平均風速等值圖 72
圖5-20 開西北窗(W2b4)狀況下之室內平均風速等值圖 73
圖5-19 Revit模型資料匯入BIM 3D模型資料庫流程圖 81
圖5.20 建物自然通風數值模型資料庫實體關聯圖 81
圖5.21 model_element資料表 83
圖5.22 ventilation_combination資料表 83
圖6.1 建物自然通風決策系統使用案例圖 85
圖6.2 建物自然通風決策系統架構圖 86
圖6.3 建物自然通風資料庫架構圖 87
圖6.6建物自然通風決策系統主界面 92
圖6.7建物自然通風決策系統第四樓層通風界面(UI) 92
圖6.8建物自然通風決策系統第四樓層通風界面(model) 94 

表次
表2.1 12個主要基於BIM之綠建築相關分析軟體及其功能與使用對象 20
表4.1實場資訊量測系統之開發環境 39
表4.2 WindSonic 超聲波風速傳感器規格表 40
表4.3 DHT-22溫度模組規格表 43
表4.4 Grove Dust Sensor PM2.5 粉塵傳感器傳感器規格表 44
表4.5 LinkIt ONE開發板規格表 45
表5.1 4W之風向角對應關係 58
表5.2 3Wa之風向角對應關係 58
表5.3 2Wa之風向角對應關係 59
表5.4 2Wb之風向角對應關係 59
表5.5 室外模擬參數設定 62
表5.6 模擬所得之建物4樓各開窗處表面風壓(P) 63
表5.7 建物4樓各開窗處表面風壓係數 64
表5-7 室內模擬參數設定 66
表5.8 全開(4W)之室內通風速率及室內換氣率表 73
表5.9 關北窗(3Wa)室內通風速率及室內換氣率圖表 74
表5.10 關東窗(3Wb)室內通風速率及室內換氣率圖表 74
表5.11 關南窗(3Wc)室內通風速率及室內換氣率圖表 75
表5.12 關東窗(3Wb)室內通風速率及室內換氣率圖表 75
表5.13 開東西窗(2Wa1)室內通風速率及室內換氣率圖表 76
表5.14 開南北窗(2Wa2)室內通風速率及室內換氣率圖表 76
表5.15開東北窗(2Wb1)室內通風速率及室內換氣率圖表 77
表5.16 開東南窗(2Wb2)室內通風速率及室內換氣率圖表 77
表5.17 開西南窗(2Wb3)室內通風速率及室內換氣率圖表 78
表5.18 開西北窗(2Wb4)室內通風速率及室內換氣率圖表 78
表6.1 建物自然通風決策系統之開發環境 90
表6.2系統PHP程式語言功用說明 95
參考文獻 1.江哲銘,1997,建築物理,三民書局印行,台北,89-94。
2.朱佳仁、秋英浩、陳彥志、王宇文(2009),建築開口對風壓通風影響之研究,中華民國建築學會建築學報,第69期,第17-33頁。
3.黃偉、馮琪惠、王喻正、蕭之彥、陳禹昕、謝文生,2017,萬物聯網新時代:IoT智慧聯網平台及應用,電工通訊,C.I.E.E Magazine DOI:10.6328/CIEE.2017.4.06,第51-57頁。
4.邱瓊萱(2004),通風管管頂型式對室內通風效益影響之研究,國立成功大學建築研究所碩士論文。
5.周伯丞(2000),建築軀殼開口部自然通風效果之研究,國立成功大學建築研究所博士論文。
6.劉雲浩編, 2010, 物聯網導論. 北京: 科學出版社. 2010-12: 4. ISBN 9787030292537。
7.陳彥志(2008),室外風場對風壓通風影響之研究,國立中央大學土木工程研究所碩士論文。
8.陳念祖(2000),「高架地板置換式自然通風對室內通風效率之影響」,國立成功大學建築研究所碩士論文。
9.陳念祖(2007),建築開口部裝設導風板對自然通風之效果,國立成功大學建築研究所博士論文。
10.蘇裕民 (2006),小型建築中庭空間浮力通風之解析,國立台灣科技大學建築研究所碩士論文。
11.羅紹誠(2018),基於建築資訊模型(BIM)之視覺化多維度品質查核系統之開發與應用,淡江大學土木研究所,碩士論文。
12.Alahmad M., Nader W., and Neal J., et al, 2010, Real time power monitoring & integration with BIM. IECON - th Annual Conference on IEEE Industrial Electronics Society
13.Allen J.G., MacNaughton P., Satish U., et al, 2016, Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments. Environmental Health Perspectives 124:805–812.
14.Allocca C., Chen Q., and Glicksman L.R., 2003, Design analysis of single-side natural ventilation, Energy & Building, 35, 785-795.
15.Atzori L., Iera A., Morabito G., 2010 , The Internet of Things: A survey (PDF). Computer Networks.
16.Brager G., and Baker L., 2009, Occupant satisfaction in mixed-mode buildings. Building Research & Information 37:369–380.
17.Broll G., Paolucci M., and Wagner M., 2009 Perci: Pervasive Service Interaction with the Internet of Things. IEEE INTERNET COMPUTING, pp 74-81
18.Chang T.J., Huang M.Y., Wu Y.T., and Liao C.M., 2003, Quantitative Prediction of Traffic Pollutant Transmission into Buildings, Journal of Environmental Science and Health Part A:Toxic/Hazardous Substance & Environmental Engineering, A38, 1025-1040.
19.Chen Q., and Jiang Z., 1992, Significant questions in predicting room air motion, ASHRAE Transaction, 98(1), 929-939.
20.Chen Q., 1996, Prediction of room air motion by Reynolds-stress models, Buildings & Environment, 31, 233-244.
21.Chen Y., Weiming S., and Xianbin W., 2017, The Internet of Things in Manufacturing: Key Issues and Potential Applications. IEEE Systems, Man, and Cybernetics Magazine. Jan. 2018. doi:10.1109/MSMC.
22.Chu C.R., Chiu Y.H., Chen Y.J., Wang Y.W., and Chou C.P., 2009, Turbulence effects on the discharge coefficient and mean flow rate of wind-driven cross-ventilation, Building & Environment, 44, 2064-2072.
23.Chu, C. R. Chiu, Y. H., and Wang, Y. W., 2010, An experimental study of wind-driven cross ventilation in partitioned buildings, Energy & Buildings, 42, 667-673.
24.Dascalaki, E., Santamouris M., Bruant M., Balaras C.A., Bossaer A., Ducarme D., and Wouters P., 1999, Modeling largr openings with COMIS, Energy & Buildings, 30, 105-115.
25.Evola G., and Popov V., 2006, Computational analysis of wind driven natural ventilation in buildings, Energy & Buildings, 38, 491-501.
26.Gartner Identifies Top 10 Strategic IoT Technologies and Trends. Gartner, 2018/11/7.
27.Gates W.H., Myhrvold N., Rinearson P., 2005 THE INTERNET OF THINGS. International Telecommunication Union. ITU INTERNET REPORTS.
28.Gao N. P., Niu J. L., Perino M., and Heiselberg P., 2008, The airborne transmission of infection between flats in high-rise residential buildings: Tracer gas simulation, Building & Environment, 43, 1805-1817.
29.Haghight F., Li Y., and Mergi A.C., 2001, Development and validation of a zonal model-POMA, Building & Environment, 36, 1039-1047.
30.Heiselberg P., Svild K., and Nielsen P.V., 2001, “Characteristics of airflow from open windows,” Building & Environment,36,859-869.
31.Holmström J., Singh V., and Främling K., 2015, BIM as Infrastructure in a Finnish HVAC Actor Network: Enabling Adoption, Reuse, and Recombination over a Building Life Cycle and between Projects. J Manage in Eng 31:A4014006–12.
32.Hu C. H., Ohba M., and Yoshie R., 2008, CFD modeling of unsteady cross ventilation flows using LES, Journal of Wind Engineering & Industrial Aerodynamics, 96, 1692-1706.
33.Issa J., Ramaji, John I., Messner, Robert M., and Leicht, 2016, Leveraging building information models in IFC to perform energy analysis in OpenStudio, Salt Lake City, pp. 251-258.
34.Jalaei F., 2014, Integrating Building Information Modeling (BIM) and Energy Analysis Tools with Green Building Certification System to Conceptually Design Sustainable Buildings. ITcon 19:494–519.
35.Jiang Y., Alexander D., Jenkins H., Arthur R., and Chen Q., 2003, Natural ventilation in building:measurement in a wind tunnel and numerical simulation with large-eddy simulation, Journal of Wind Engineering & Industrial Aerodynamics, 91, 331-353.
36.Kamel E, Memari AM., 2019, Review of BIM's application in energy simulation_ Tools, issues, and solutions. Automation in Construction 97:164–180.
37.Karava P.M., Stathopoulos T., and Athienitis A. K., 2007, Wind-induced natural ventilation analysis, Solar Energy, 81, 20-30.
38.Kurabuchi T., Ohba M., Endo T., Akamine Y., and Nakayama F., 2004, Local dynamic similarity of cross-ventilation, Part 1:Theoretical framework, International Journal of Ventilation, 2, 371-382.
39.Lu Y., Wu Z., Chang R., and Li Y., 2017, Building Information Modeling (BIM) for green buildings_ A critical review and future directions. Automation in Construction 83:134–148.
40.Mochida A., Yoshino H., Takeda T., Kakegawa T., and Miyauchi S., 2005, Method for controlling airflow in and around a building under cross- ventilation to improve indoor thermal comfort, Journal of Wind Engineering & Industrial Aerodynamics, 93, 437-449.
41.Newsham G.R., Mancini S., and Birt B.J., 2009, Do LEED-certified buildings save energy? Yes, but... Energy & Buildings 41:897–905.
42.Ohba M., Irie K., and Kurabuchi T., 2001, Study on airflow characteristics inside and outside a cross-ventilation model, and ventilation flow rates using wind tunnel experiments, Journal of Wind Engineering & Industrial Aerodynamics, 89, 1513-1524.
43.Schlueter A., and Thesseling F., 2009, Building information model based energy/exergy performance assessment in early design stages. Automation in Construction 18:153–163.
44.Seifert J., Li Y., Axley J., and Rosler M., 2006, Calculation of wind-driven cross ventilation in buildings with large openings, Journal of Wind Engineering & Industrial Aerodynamics, 94, 925-94715Tan G., and Glicksman L.R., 2005, Application of integrating multi-zone model with CFD, Energy and Buildings, 37, 1049-1057.
45.Somboonwit N., Boontore A., and Rugwongwan Y., 2017, Obstacles to the automation of building performance simulation: adaptive building integrated photovoltaic (BIPV) design. In: th AMER International Conference on Quality of Life. Bangkok, Thailand, pp 25–27.
46.Tuohy P.G., and Murphy G.B., 2015, Closing the gap in building performance: learning from BIM benchmark industries. Archit Sci Rev 58:47–56.
47.44Tu K. J., and Vernatha D., 2016, Application of building information modeling in energy management of individual departments occupying university facilities. Int. J. Civl Environ. Struct Constr Archit Eng 10:225–231.
48.Tung, Y. C., Shih, Y. C., Hu, S. C., and Chang, Y. L., 2010, Experimental performance investigation of ventilation schemes in a private bathroom, Building & Environment, 45, 243-251.
49.Pasini D., 2018, Connecting BIM and IoT for addressing user awareness toward energy savings. Journal of Structural Integrity and Maintenance 3:243–253.
50.Porter S., Tan, T., Wang X., and Pareek V., 2018, LODOS - Going from BIM to CFD via CAD and model abstraction. Automation in Construction 94:85–92.
51.Porter S., Tan T., Tan T., and West G., 2014, Breaking into BIM: Performing static and dynamic security analysis with the aid of BIM. Automation in Construction 40:84–95.
52.USGBC, 2013, Guide to LEED Certification.
53.Watson A, 2011, Digital buildings – Challenges and opportunities. Adv Eng Inf 25:573–581.
54.Woo J.H., Diggelman C., and Abushakra B., 2011, BIM-based energy mornitoring with XML parsing engine. In: Proceeding of the th ISARC. Seoul, Korea, pp 544–545.
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2019-08-13公開。
  • 同意授權瀏覽/列印電子全文服務,於2019-08-13起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2486 或 來信