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Copyright

by

Liangchung James Lo

2012


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The Dissertation Committee for Liangchung James Lo Certifies that this is the approved version of the following dissertation:


Predicting wind driven CROSS ventilation in buildings with small openings


Committee:




Atila Novoselac, Supervisor

Jeffrey Siegel

Richard L. Corsi

Ofodike A. Ezekoye

David Banks

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Predicting wind driven Cross ventilation in buildings with small openings




by

Liangchung James Lo, B.S. Arch.E; M.S.E.


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Dissertation

Presented to the Faculty of the Graduate School of

The University of Texas at Austin

in Partial Fulfillment

of the Requirements

for the Degree of


Doctor of Philosophy

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The University of Texas at Austin

August 2012

Dedication


Ashlee and Alex,

I love you to pieces,

to you I dedicate this thesis.


Acknowledgements


I would like to acknowledge, first and foremost, my adviser, Dr. Atila Novoselac, for giving me an opportunity as a Ph.D. student and providing continuous support and advice since that time. It has been an enlightening and productive partnership that I believe will hopefully continue long into the future.

I would also like to acknowledge all of the previous and current members of my research group, particularly Dr. Donghyun Rim, Dr. Neil Crain, Dr. Brent Stephens, Elliott Gall, Brandon Boor, Jordan Clark, Yirui Liang and Shichao Liu, as well as the faculty members, Dr. Jeff Siegel, Dr. Rich Corsi and Dr. Ying Xufor their support. I would also like to especially thank Dori Eubank for her dedication and amazing ability in managing all the various crises that arise in our group.

I would like to acknowledge my funding sources, including the American Society of Heating, Refrigerating and Air-Conditioning Engineers (Grant-in-Aid award) and the National Science Foundation (IGERT #DGE-0549428).

Additionally, I would like to acknowledge out of department members of my committee, Dr. Ofodike Ezekoye and Dr. David Banks, for their guidance and contribution to my growth as a researcher.

Finally, I thank my family for their continued support; my friends for providing the encouragements when I needed it the most; and my wife, Ashlee Chao, and my son Alex Lo for their understandings and patience while I complete the last leg of my formal education.

Predicting wind driven Cross ventilation in buildings with small openings


Liangchung James Lo

The University of Texas at Austin, 2012


Supervisor: Atila Novoselac


Designing for wind driven cross ventilation for a building is challenging due to the dynamic characteristics of wind. While numerous studies have studied various aspects of cross ventilation, few have had an opportunity to examine the topic with a holistic approach utilizing multiple research techniques. Thus, this study combined three different investigation methods: wind tunnel analysis, full scale experiments and computational fluid dynamics (CFD) to examine the physics of wind driven cross ventilation.

Following the systematic approach of the three methods, this study first conducted full scale measurements of wind properties, façade pressures, air flow rates through small window openings, and tracer gas concentrations in a multi-zone test house. Secondly, a scaled model of the test house was studied in a boundary layer wind tunnel (BLWT) for its façade pressures and ventilation rate under various wind incident angles. Finally, a CFD model of the test house was simulated under various constraints to determine the factors which affect indoor air distribution during wind driven cross ventilation events.

The full scale experimental results showed a strong correlation between the cross ventilation rate and the wind velocity component normal to the inlet openings. This correlation suggested that the cross ventilation flow rate could be estimated from wind conditions alone. A closer examination of the wind characteristics also revealed that the cyclical pattern of changing wind direction could be impacted by obstructions which are kilometers upwind, suggesting that distant landscapes could have an impact on cross ventilation flows.

The combination of CFD and full scale measurements also showed that local heat sources can generate significant buoyancy driven flow and affect indoor mixing during wind-driven cross ventilation scenarios. Experimentally validated parametric CFD analyses demonstrated the effect of interior heat loads in driving internal airflow, and suggest that a small source (35W/m2) can increase the indoor mixing from less than 1 ACH to 8 ACH between indoor spaces.

Finally, the wind tunnel and CFD coupled analysis was found to predict the cross ventilation flow which was also validated against the full scaled measurements. The prediction, which may only be applicable to similar building types with small openings, showed significant agreement that such method has potential as an innovative design tool for natural ventilation in buildings.

Table of Contents

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1.Introduction 11

2.Literature Review 17

2.1.Investigation Methods 17

2.1.1.Theoretical Modeling 17

2.1.2.Full-scale Field Experiments 19

2.1.3.Boundary Layer Wind Tunnels 21

2.1.4.Computational Fluid Dynamics 23

2.1.5.Hybrid Investigation Approach 25

2.2.Factors to affect indoor air distribution 26

3.Specific Research Objectives 29

4.Investigation Methods 31

4.1.Summary of Methods for full scale experiments 31

4.1.1.Test Scenarios and Procedures for Full Scale Experiments 33

5.1.1.Instrumentation 36

6.1.Summary of Wind Tunnel Test Methods 41

6.2.Summary of CFD Simulation Methods 46

6.2.1.CFD Parametric Analysis Scenarios 47

8.1.1.Models and mesh 48

8.1.2.Steady Boundary Conditions and Heat Flux 49

8.1.3.Fluctuating Boundary Conditions 50

9.Summary of Research Findings 53

9.1.Quality of Data for the Full Scale Experiments 53

9.1.1.Uncertainty of flow rate measurement at openings 53

9.1.2.Uncertainty of tracer gas tests 56

9.2.Wind properties that affect cross ventilation flow 57

10.1.1.Correlation between the approaching wind and the resulting flow rate 59

10.1.2.Effect of wind incident angle on flow rate 61

10.2.Effect of local buoyancy flow on cross ventilation 63

10.2.1.Validation of CFD models by experiments with local buoyancy sources 64

10.2.2.Effect of indoor buoyancy sources with increased intensity 68

10.3.Combined wind tunnel and CFD flow prediction method 70

10.3.1.Cross ventilation rate prediction using wind tunnel tracer test 71

10.3.2.Validation of steady state CFD simulation 72

10.3.3.Validation of transient CFD simulation 75

10.3.4.Impact of time averaging in wind fluctuation 78

10.3.5.Improvement of air distribution from transient CFD simulation 80

11.Conclusions 82
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