SIMULATION OF THE FLOW FIELD CHARACTERISTICS OF TRANSIENT FLOW

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1 The Seventh Asia-Pacific Conference on Wind Engineering, November 8-12, 2009, Taipei, Taiwan SIMULATION OF THE FLOW FIELD CHARACTERISTICS OF TRANSIENT FLOW S. Cao 1, Y. Zhao 2, H. Ozono, Y. Tamura, A.Kareem 1 Professor, Department of Bridge Engineering, State Key Lab for Disaster Reduction in Civil Engineering, Tongji University, Shanghai , China, shuyang@tongji.edu.cn 2 PhD Candidate, Department of Civil Engineering, Harbin Institute of Technology, Harbin 10090, China, zhaoyanghit@1.com Professor, Department of Applied Physics, Miyazaki University, Miyazaki , Japan, ozono@phys.miyazaki-u.ac.jp Professor, Wind Engineering Research Center, Tokyo Polytechnic University, Kanagawa 2-021, Japan, yukio@arch.t-kougei.ac.jp Professor, Department of Civil Engineering and Geological Sciences, University of Notre Dame, IN USA, kareem@nd.edu ABSTRACT Modeling the downburst boundary layer, even only its statistical properties, is very important to investigate the wind load on structures in transient flow. In this study, we try to model the turbulence statistics of a downburst boundary layer in an actively-controlled wind tunnel that has 99 fans controlled individually through a computer. The flow, at the beginning, is controlled to be of conventional boundary layer type. A sudden abrupt velocity change is then introduced into the flow which has a positive shear on the lower part and minus shear in the upper part of the boundary layer, which is a typical vertical distribution of wind speed in a downburst. The evolutionary process of the gust front is not considered at this stage. The results show that this wind tunnel is advantageous to simulate the turbulence statistics of transient flow although the acceleration is not great enough to satisfy the similarity law. KEYWORDS: DOWNBURST, TURBULENCE STATISTICS, ACTIVELLY CONTROL, WIND TUNNEL Introduction Wind load on a structure is usually investigated in turbulent boundary layer flow simulated in a boundary layer wind tunnel. However, the strong wind that causes damages on structures is usually transient wind, for instance, downburst wind with a gust front, whose mechanics differs significantly from the conventional atmospheric boundary layer, and results in different profiles of turbulence statistics. As pointed out by Kareem, extreme loads on structures are potentially sensitive to the influence of transient flow, i.e., the load coefficients may be enhanced based on the gust form and the resulting localized, rapid changes in the surrounding flow. The investigation of wind-reduced force on structures in transient flow is thus very important at the point of wind hazard mitigation. Although great improvements of modeling and simulation of the transient flow have been achieved by the analytical approach due to the works of Wang and Kareem (200) and Chen and Letchford (200), physical modeling in a laboratory with flow mechanism similar to natural phenomenon is still a very big challenge, and reliable investigation of the wind load on structures in transient flow till now is very few (Mason et al. 200; Sengupta and Sarkar 2008). Compared with the

2 conventional atmospheric boundary layer flow, the most distinguish features of the speed of transient wind are the sudden changes of wind speed and direction, and different profiles of turbulence statistics. In this study, we try to model the turbulence statistics of a downburst boundary layer in an actively-controlled wind tunnel which has 99 fans controlled individually through a computer (Cao et al. 2002). The flow, at the beginning, is controlled to be of conventional boundary layer type. A sudden abrupt velocity change is then introduced into the flow which has a positive shear on the lower part and minus shear in the upper part of the boundary layer, which is a typical vertical distribution of wind speed in a downburst. The down process of a downburst is not considered. Although the velocity profile is actually time-dependent after acceleration due to the evolution of the gust front and will recover to general boundary layer type finally, we considered constant velocity profile after acceleration for modeling. The reason is that there are no successful measurements of the spatial and temporal structures of a downburst which can be used as target for simulation. Another reason is that the stronger shear is possibly more unfavorable to the structures. In addition, considering a simple however essential feature of the downburst flow will be much more helpful to understand the wind load characteristics on a structure in transient flow. In this paper, we will describe the flow-generating technique and study on the characteristics of the simulated flow. Method of modeling the flow The actively controlled wind tunnel of Miyazaki University, Japan is an open-circuit one with 99 fans of 270mm in diameter at the front. The fans are arranged in a 9 wide by 11 high matrixes. The fans are driven by AC servo-motors of high quality through a computer. The servo-motors can be controlled at different frequencies of up to approximately 2Hz, thus achieving fluctuating air flow in the test section. Since 99 fans can be programmed independently to deliver variable flows, phase shifts can be introduced among the fans, allowing transverse and vertical turbulence to be generated. This is the primary process by which representative turbulent characteristics would be generated. The test section is 100mm long (maximum), 200mm wide, and 1800mm high. The length of the test section can be adjusted, and it is set to be 100mm downstream from the honeycomb in this experiment. The generated velocity field is measured at 800mm downstream from the honeycomb, by using multiple-channel hot wire anemometer. Modelling the turbulence statistics of a downburst boundary layer includes: 1) Simulation of the conventional atmospheric boundary layer. The recommendations given by Architecture Institute of Japan (AIJ) were adopted in this study to determine the target power spectrum, and target profiles of mean velocity and turbulence intensity. 2) Simulation of sudden velocity change. ) Simulation of non-synoptic profile of wind characteristics. ) Simulation of combination of above 2) and ) Details of the simulation methods are described below. 1. Simulation of the conventional atmospheric boundary layer In the ABL, the statistical parameters change with height, so the fans should be controlled and modified individually when the ABL is simulated. As mentioned above, random phase is introduced into the fans in this study. Only the longitudinal turbulence is actively controlled, while the turbulence in vertical and lateral direction is generated naturally due the instantaneous velocity shear. Control of the longitudinal turbulence is achieved by controlling the input data of the fans, whose procedure is described briefly below. Suppose that a uniform turbulence with mean velocity U=m/s, turbulent intensity I u =10% and integral scale L u =1.m

3 is expected in the wind tunnel. A time series velocity data can be simulated by utilizing a superposition of trigonometric functions with random phase angles (FFT). Fig.1 shows an example of velocity series. In this process, the Karman type power spectrum suggested by AIJ is adopted. This data is inputted to the motors of the fans through AD converters. Because of the mechanical inertia of the motor and because of the inertia of the air inside the tunnel, the velocity history appearing in the wind tunnel cannot be the same as the voltage data inputted to the motors. For instance, Fig.2(a) shows the velocity history generated by the input data shown in Fig.1. The correlation between them is only 72.%. If the power spectrum and phase of the generated velocity history agree with the target ones well at each frequency, the generated velocity history will be very similar to the target one, and high correlation will be achieved between them. It is clear from Fig.2(b) that the power spectrum of the generated flow deviates from the target spectrum gradually when the frequency becomes larger. The spectrum compositions at high frequency numbers are not reproduced as expected. In order to let the power spectrum to be well reproduced, the power spectrum of the input data of the fans are modified as shown in Eq.1, U=.0m/s, I=10.0%, L=1.m Fig.1. Target velocity history. U=.12m/s, I=7.2%, L=2.m S u [m 2 /s] Target flow Generated flow f [Hz] (a) velocity history (b) power spectrum Fig.2. Generated velocity history and power spectrum without modification. U=.9m/s, I=10.1%, L=1.2m f [Hz] (a) velocity history (b) power spectrum Fig.. Generated velocity history and power spectrum with modification. S u [m 2 /s] Target flow Generated flow

4 ( k + 1) S ( f ) ( ) S ( f ) t k i = S ( f ) (1) ( k) i Sm ( f ) where f is the frequency, S (k) m (f) is the power spectrum of the generated flow resulted from the input data with S (k) i (f) and S t (f) is the target power spectrum. Modification of the phase data is also necessary if good simulation of the velocity history is expected. By doing inverse FFT with the modified spectrum and phase, new input data of the fans can be obtained. Modification of the input data should be repeated several times until satisfied results can be achieved. For instance, velocity history shown in Fig.(a) is achieved by conducting the modification of power spectrum three times and phase twice. The generated flow has a high correlation at 9.8% with the target. This means good reproduction of the raw velocity history. Meanwhile, the power spectrum is compared with the target in Fig.(b). The turbulent parameters of the generated flow are U=.9m/s, I u =10.1%, L u =1.2m, respectively. The statistical characteristics are well reproduced. From the unique turbulence-generating method shown above, it can be found that this wind tunnel is efficient for the simulation of the velocity fluctuation, and the values of U, I and L can be changed just by generating different velocity fluctuations. This feature makes it useful for basic studies in which the effect of varying some flow characteristics independently of the others can be studied in detail. It can also be realized that the characteristics of the motors are crucial for this type of wind tunnel. Motors with quicker responses are more advantageous to the reproduction of the velocity fluctuations. 2. Simulation of the step-like velocity change When modeling the step-like speedup in a wind tunnel, a normalized time is necessary U τ to be considered, which is defined as τ 0 = 2, where U 2 is the wind speed after B acceleration, τ is the acceleration time, B is the referee length scale of the structure whose wind force will be investigated in transient flow, as shown in Fig.. For a natural transient flow, say, a tornado, if we assume U 2 =0m/s, τ =2s, B =20m, the normalized acceleration time will be τ 0 =. Shorter acceleration time inside the wind tunnel is advantageous to model the step-like flow. As for the actively controlled with tunnel, its acceleration time is dependent on the length of the test section and the initial wind speed from which the acceleration starts. It is easy to understand that the shorter test section is advantageous to achieve greater acceleration. Fig. shows the performance of the wind tunnel in producing the transient flow with step-like velocity change, which increases from m/s to 1m/s during about 0.18s. When a model B=0.2m is tested in this step-like flow, the normalized acceleration time will be This acceleration time is almost the maximum available value that can be obtained in this wind tunnel. However, it is obviously too great compared to the natural transient wind. Fig. Normalized acceleration time

5 u [m/s] % t [sec] Fig. Performance of the wind tunnel in producing transient flow. Simulation of turbulence statistics of downburst boundary layer Oseguera suggested the wind speed profile during a downburst. Because of the movement of gust front, the downburst has a maximum wind speed at about 100m with a positive or negative shear below or above this height. Fig. shows the velocity profiles before and after the speedup of the simulated flow. Non-synoptic profile of wind speed can be easily simulated. Fig.7 shows the velocity history at the height of gust front. The wind speed accelerates from.m/s to 10.0m/s in about 0.7s. Wind load on high, medium and low buildings are being investigated at this flow. H/H m U/U m Fig. Mean velocity profile ( : Before acceleration; : After acceleration) Fig.7 Velocity time series Conclusion An actively controlled wind tunnel is utilized to model the turbulence statistics of a downburst boundary layer. The flow, at the beginning, is controlled to be of conventional boundary layer type. A sudden abrupt velocity change is then introduced into the flow which has a positive shear on the lower part and minus shear in the upper part of the boundary layer, which is a typical vertical distribution of wind speed in a downburst. The evolutionary process of the gust front is not considered at this stage. The results show that this wind tunnel is advantageous to simulate the turbulence statistics of transient flow although the acceleration is not great enough to satisfy the similarity law.

6 Acknowledgements This research is partly supported by the Grant-in-Aid for Scientific Research (C) of JSPS (1908).This research is partly supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan, through the Global Center of Excellence Program, References Wang L., Kareem A. (200) Modelling and simulation of transient winds: downbursts/hurricanes, Proc. of the 10 th American Conf. on wind engineering, Baton Rouge, LA, CD-ROM Chen L., Letchford C.W. (200) A deterministic-stochastic hybrid model of downbursts and its impact on a cantilevered structure, Engineering Structures 2(), Cao S., Nishi A., Kikugawa H., Matsuda Y. (2002) Reproduction of wind velocity in a multiple fan wind tunnel, Journal of wind engineering and industrial aerodynamics, 90, M.S. Mason, C.W. Letchford, D.L. James. (200) Pulsed wall jet simulation of a stationary thunderstorm downburst, Journal of Wind Engineering and Industrial Aerodynamics, 9(7), Sengupta A., Sarkar P. (2008) Experimental measurement and numerical simulation of an impinging jet with application to thunderstorm microburst winds, Journal of Wind Engineering and Industrial Aerodynamics, 9(), -. Oseguera R.M, Bowles R.L. A simple analytic -dimentioanl downburst model based on boundary layer stagnation flow. NASA Technical Memorandum 1002, July 1988.

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