This research is part of an ongoing collaboration between the Wind Engineering Research Laboratory at UIUC and Skidmore, Owings & Merrill LLP. The work done as part of this study is intended to be developed further into a scientific publication that may be utilized by
practitioners and researchers alike. It is understood that while chamfering has been explored in the work of previous scientific researchers, venting is a relatively novel concept with important real-world applications. Based on the experimental results achieved during this research, the following ideas have been identified as possible topics to further explore:
• Test the chamfered and vented building models at other wind angles of attack to investigate the effect of these aerodynamic treatments in such conditions.
• Continue incremental chamfering from the halfway point to the base of the building to identify a point of diminishing returns that may be reached due to the loss of cross-sectional variation (from octagon to square) with height.
• Study the impact of porosity on vented floors – addition of porous screens with varying openings to understand effect on reduced air flow through vents. This topic is especially relevant to the application of vented floors in real-world projects.
• Lastly, recognize ‘optimal’ vent dimensions within realistic limits.
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APPENDIX A: ADDITIONAL TABLES & FIGURES
(a) Spectral response in open terrain flow simulations
(b) Spectral response in suburban terrain flow simulations
Figure A.1. Crosswind response spectra for 7:1 models with single-vent treatments
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(a) Spectral response in open terrain flow simulations
(b) Spectral response in suburban terrain flow simulations
Figure A.2. Crosswind response spectra for 10:1 models with single-vent treatments
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(a) Response in open terrain flow simulations
(b) Response in suburban terrain flow simulations
Figure A.3. Normalized peak moments for 7:1 models with single-vent treatments
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(a) Response in open terrain flow simulations
(b) Response in suburban terrain flow simulations
Figure A.4. Normalized peak moments for 10:1 models with single-vent treatments
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Table A.1. Normalized static and dynamic moment coefficients for all models
Slenderness
Ratio Model Type Modification Length
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Table A.1 (cont.). Static and Dynamic Moment Coefficients for all models
Slenderness
* Note that models with greatest dynamic moment reduction for each aerodynamic treatment type tested are highlighted in bold
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APPENDIX B: CALIBRATION OF THE SOM WT
For the purpose of benchmarking against an industry standard, a calibration exercise was undertaken at the SOM wind tunnel. Results obtained were compared to those published in an international high-frequency base balance benchmark study (Holmes & Tse, 2014). This portion of the appendix is meant to serve as a standalone calibration to match other wind tunnels around the world. The experimental setup (spires, model scale, etc.) of the calibration exercise was separate from that described in Chapter 3 and these results have no influence on those generated for the rest of this research. The goals of the calibration exercise were to achieve similar results to an international standard while also understanding flow dynamics inside the SOM wind tunnel facility through extensive testing of a relatively simple building form. Lessons learned from the calibration exercise helped improve simulation and experiment quality for the rest of this research.
A.2 Experimental Setup & Comparison Criteria
To match prescribed ‘urban’ terrain flow conditions specified in the benchmark study, special spires were used upstream of the test section. A geometric scale of 1:500 was used to create a model per ‘basic building B’ specifications provided in the calibration study. Model details and global axes used in the SOM wind tunnel are shown in Figure B.1. The rectangular building model was rotated clockwise in 10° increments through 360° with response calculated in terms of the model’s local axes. A description of this local axis coordinate system and incident wind direction is given in Figure B.2.
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(a) Model dimensions & local axes orientation
(b) Photograph of the building model inside the SOM wind tunnel
Figure B.1. Model used for ‘Basic Building B’
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Figure B.2. Definition of local coordinate system with wind angle of attack
The velocity profile provided in the benchmark study was digitized and compared to that obtained in an urban flow, with a power law exponent of 0.23, simulated at SOM. This
comparison is provided in Figure B.3. Longitudinal turbulence intensity at model height was measured at 18%, which was higher than the prescribed value of 14.3%. This increased
turbulence of the simulated urban environment generated additional buffeting effects that were not seen in aerodynamic treatment tests in open and suburban environments for the rest of this research. Mode shapes were assumed to be linear for the basic building and were not corrected.
Finally, it must be noted that the global axes used in this calibration exercise do not match those used in the benchmark study. Wherever used, plots digitized from the benchmark study have been adjusted to facilitate comparison between the industry benchmark and SOM results.
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Figure B.3. Mean velocity profile for simulated urban terrain
A.3 Results
Mean moment coefficients for both sway directions are compared to the benchmark study in Figure B.4. Since only static and peak response data is provided, there was no way to
specifically compare the dynamic response.
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Figure B.4. Static moment coefficients for both sway directions
Both sway directions show good correlation between test data from the SOM wind tunnel and results from the benchmark study. Wind incident at corner points of the building appear to cause the most discrepancy in results – tests at angles around 45°, 135°, 225°, and 315° provide the least correlated results to those seen in the benchmark study. A possible rationale for this
behavior is the larger turbulence simulated in the SOM wind tunnel compared to that specified in the benchmark study. It must be noted that although the measured response does deviate from benchmark curves at these angles, symmetry is still maintained in the obtained results, indicating that the urban flow simulation was laterally uniform.
Peak response calculation involves the use of observed model response along with prescribed full-scale dimensions and dynamic properties. Although the full-scale properties used in this calibration exercise are identical to those specified in the published benchmark study, spectral measurements could not be validated from the calibration study. Differences in frequency-domain data processing such as use of windows and smoothing could alter the resonant dynamic response used in peak response calculation. Also, simulated urban flow
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conditions had a longitudinal turbulence intensity of 18% at the building height (180 m in full-scale) as compared to a lower value of 14.3% prescribed in the international benchmark paper.
Figure B.5 provides a comparison of peak full-scale moments obtained in the SOM wind tunnel to those published in the international calibration study.
(a) Peak (min) moments in the X-direction
(b) Peak (max) moments in the Y-direction
Figure B.5. Full-scale peak moment comparison in both sway directions
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While a similar trend to the benchmark results can be seen in both sway directions, peak (max) moments in the Y-direction have better correlation to the benchmark overall. Note that the two benchmark curves indicate an upper- and lower-bound of data published in the benchmark study.
Peak moments are obtained through the combination of static and dynamic (background and resonant) components per Eq. (12) described previously. A comparison to dynamic data would be required to determine the exact reason for the lack of correlation seen in peak (min) moments in the X-direction. A more traditional method of looking at wind tunnel results through full-scale static and peak moments is shown in Figure B.6 for both sway directions. Again, symmetry in the peak response is seen, indicating that both the static and dynamic responses obtained from the SOM wind tunnel were symmetric. The calibration exercise served to not only corroborate results obtained in the SOM wind tunnel to an industry benchmark but also to help build intuition of flow around simple bluff bodies in the wind tunnel. Lessons learned from this exercise laid the foundation for the architectural form study conducted in this research.
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(a) Moments in the X-direction
(b) Moments in the Y-direction
Figure B.6. Full-scale (max-mean-min) moments in both sway directions
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APPENDIX C: TIME AVERAGING OF WIND SPEEDS
To ensure proper scaling of wind tunnel simulation results to full-scale response
estimates, it was important to maintain similarity between simulation and full-scale parameters.
The following dimensionless parameter was defined:
� 𝐿𝐿
𝑈𝑈𝑇𝑇�𝑚𝑚 = � 𝐿𝐿
𝑈𝑈𝑇𝑇�𝑝𝑝 (𝐶𝐶1)
where the subscripts 𝑁𝑁 and 𝑝𝑝 denoted the model and prototype (full-scale) parameters respectively. While the length scale, 𝐿𝐿𝑝𝑝
𝐿𝐿𝑚𝑚 was fixed at 700, the time, 𝑇𝑇𝑝𝑝
𝑇𝑇𝑚𝑚 , and velocity, 𝑉𝑉𝑝𝑝
𝑉𝑉𝑚𝑚, scales varied to allow for calculation of full-scale peak moments over a range of wind speeds. 𝑇𝑇𝑝𝑝 was chosen to be 1 hour (3600 sec) to employ full-scale mean hourly time averaging of wind speeds.
𝑈𝑈𝑚𝑚 was fixed at 7m/s (15.6 mph) which was recorded at model roof height for flow over both suburban and open terrains simulated in the wind tunnel. Table C.1 lists the possible scaling combinations (values of 𝑇𝑇𝑚𝑚 and 𝑈𝑈𝑝𝑝) required to maintain the desired 1:700 geometric scale.
Table C.1. Simulation time and attainable full-scale velocity combinations that maintain geometric scaling 𝑻𝑻𝒑𝒑
Cobra Probe (TFI, 2015) measurements were made to determine mean wind speed readings at several heights in the wind tunnel. Such mean wind speed profiles are shown in Figure 3.4 previously. Wind speeds were typically recorded for 5 minutes (300 seconds), but to
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ensure geometric scaling while attaining reasonable full-scale speeds, averaging times of 20 to 90 seconds were used in the calculation of peak moments discussed in Chapter 4. A typical 5-minute wind speed record in the SOM wind tunnel, along with shorter time averages of wind speed are shown in Figure C.1. The coefficient of variance for both mean velocity and
longitudinal turbulence intensity values for the various averaging times was within 5% of values calculated from a full 5-minute record. Thus, proper scaling of parameters was ensured.
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(a) Sample 5-min wind speed record
(b) Magnified view of various time averages for the length of the record
Figure C.1. Time averaging applied to a wind speed record measured at model height (open terrain simulation)
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APPENDIX D: DATA FILTERING
The high frequency force balance technique requires stiff and lightweight models (i.e.
models with high natural frequencies) to accurately predict full-scale response based on model geometry. This is done to prevent the model’s local response from affecting the vortex shedding-induced excitation generated by the geometry of the model. All model tests in the wind tunnel were conducted at a high sampling rate of 500 Hz to determine any model resonance effects.
This response was then filtered through an 8th order low-pass Butterworth filter during post-processing in MATLAB. A sample crosswind moment response time history for the baseline square model is shown in Figure D.1 to demonstrate the effects of such filtering of the data in the time domain.
Figure D.1. Crosswind moment response time history for 8.5:1 baseline model
The low-pass frequency cutoff was set to 25 Hz for all models tested. This value was set by examining PSDs for all 43 models to satisfactorily delineate between the geometric body’s
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aerodynamic response to wind loading and the model’s resonant response. Data was not decimated (down-sampled) to retain the resolution of high-frequency sampling. Figure D.2 shows a sample spectrum for the square baseline model capturing the effect of such a filter in the frequency domain.
Full-scale peak moment calculations involved the use of terms obtained from such spectra. Such calculations were only made for a range of speeds that were within the maximum reduced frequency limit of 0.2. Hence, for the 7:1, 8.5:1, and 10:1 buildings, the minimum full-scale wind speed was 34 m/s (76 mph), 27 m/s (60 mph), and 22 m/s (48 mph) respectively.
Figure D.2 Crosswind moment response spectral density for 8.5:1 baseline model
Sample MATLAB code used to filter time history data in such a way is provided below:
fs = 500; %original sampling frequency (Hz) fc = 25; % low-pass cutoff frequency (Hz) order = 8; %filter order
[b,c]=butter(order,fc/(fs/2),'low'); %create low-pass filter x_fil = filtfilt(b,c,x); %filter data (x = original)