||On the simulation of 3-D flapping-wing insect's flight performance under abnormal atmospheric conditions
||Department of Aerospace Engineering
在本研究中我們依據名為Morpho peleides的蝴蝶建立其幾何外形，使用GAMBIT和ANSYS前處理軟體產生網格，並結合軟體FLUENT中UDF(User Define Function)及動態網格機制來模擬蝴蝶在不同攻角的拍翼飛行，大雨計算則是運用FLUENT內建DPM模組(Discrete Phase Model)來模擬蝴蝶在惡劣天氣下飛行空氣動力性能變化。
本研究中吾人嘗試使用四種不同網格數的網格來進行驗證，結果不管是在升力係數還是阻力係數全都得出相近且幾乎一樣的數值。根據幾個蝴蝶固定在不同攻角下晴天飛行的結果，我們得知當蝴蝶在飛行時隨著攻角的上升，整個拍動週期的平均升力也會跟著增加，且升力係數曲線的趨勢會與真實擺動身體飛行的蝴蝶更加近似。最後我們比較在晴天及大雨下的飛行狀況，蝴蝶在大雨的情況下飛行，升力係數會較晴空時顯著的下降，並且升力減少的百分比會隨著攻角的上升而減少。在降雨量為25 g/m3時，升力減少的百分比會從618.6%減少至60.389%。當降雨量為39 g/m3時，則會從1057%降至101.537%。因此，吾人認為拍翼飛行受到大雨的影響程度可以透過提高拍翼的攻角而得到緩解。
|| In recent years, flapping wing technology is becoming more popular, and Micro Air Vehicle (MAV) has received great attention from researchers. For most people their desired MAV performance is limited in the flapping mode of small birds and insects under clear weather situation. But in past years Taiwan has experienced many disasters caused by detrimental and severe weather, such as extremely heavy rain and very thick fog. In such cases if MAV could put into use to help the rescue mission, it could significantly improve the efficiency of rescue. However, the insect-like MAV is very small and light, and it's very sensitive to sudden change of atmospheric surroundings. Therefore, maintain the flapping wing MAV flight quality in extreme weather will be an important issue.
In this study, we constructed a geometric model of the butterfly based on a true Morpho peleides and created a grid system by GAMBIT and ANSYS preprocessing software, then use the CFD software FLUENT combine with the User Define Function (UDF) to analyze the relationship between the flow field and other aerodynamic phenomenon. For our simulation, we programing a grid convergence process first to verify the simulation of our 3-D butterfly flapping motion, and the flapping-wing aerodynamic parameters such as lift coefficient and drag coefficient are almost same with four grid systems in clam atmospheric condition.
According to several cases, we can find some result. When the butterfly increases the pitch angle of flapping motion in forward flight, the lift coefficient will increase too. Then the trend of the lift coefficient curve will more approximate with the lift coefficient of the true butterfly in forward flight. In our abnormal atmospheric cases, we used the Discrete Particles Model (DPM) in FLUENT to simulate the 3-D butterfly flapping motion in heavy rain condition. In our result, the average values of lift coefficient under the heavy rain condition are lower than the case in normal atmospheric condition. When the liquid water content (LWC) is 25 g/m3, the reduction rate of lift coefficient would be reduced to 60.389% from 618.6% with the rising angle of attack. In the situation of the liquid water content is 39 g/m3, the reduction rate of lift coefficient will decrease from 1057% to 101.537% with the gradual increase in the angle of attack. Therefore, the effect of the heavy rain can be relieved by changing the angle of attack in forward flight, and the higher angle of attack can relieve more impact of heavy rain.
List of Tables VII
List of Figures VIII
Chapter 1 1
Chapter 2 4
Research Background 4
2-1 Literature Review of Flapping 4
2-2 Heavy Rain 9
2-3 Fog 10
Chapter 3 13
Numerical Modeling 13
3-1 Geometry Model Construction and Grid Generation 13
3-2 Preprocessing 17
3-3 Governing Equations 20
3-4 Dynamic Mesh 20
3-5 Flow Solver 22
3-6 Discrete Phase Model 26
3-7 Case of Heavy Rain 30
Chapter 4 33
Results and Discussion 33
4-1 The Verification of 2-D Simulation 33
4-2 The Verification of 3-D Simulation 35
4-3 Numerical Simulation under the Different Angles of Attack 36
4-4 Numerical Simulation of the Heavy Rain and Fog 38
Chapter 5 68
Appendix A 74
Appendix B 76
List of Tables
Table 3.1 The parameters of the geometric half butterfly mode. 14
Table 3.2 The number of the unstructured grids 14
Table 3.3 The parameter of the flapping motion 18
Table 4.1 Classification of the cases 41
Table 4.2 Mean lift in different numerical simulation 41
Table 4.3 Classification of the different grids 41
Table 4.4 Average lift and drag coefficient in different case 41
Table 4.5 The lift coefficient in three different time 42
Table 4.6 The drag coefficient in three different time 42
Table 4.7 The parameters of the heavy rain. 42
Table 4.8 Numerical results for flapping wing of lift coefficients degradation percentage for heavy rate case (LWC= 25 g/m3) 43
Table 4.9 Numerical results for flapping wing of lift coefficients degradation percentage for heavy rate case (LWC= 39 g/m3) 43
Table 4.10 Numerical results for flapping wing of lift coefficients for heavy rate case in different time (LWC= 25 g/m3) 44
Table 4.11 Numerical results for flapping wing of lift coefficients for heavy rate case in different time (LWC= 25 g/m3) 44
Table 4.12 The lift coefficient of case 2 in heavy rain condition 45
Table 4.13 The lift coefficient of case 3 in heavy rain condition 45
Table 4.14 The lift coefficient of case 4 in heavy rain condition 45
Table 4.15 The lift coefficient of case 5 in heavy rain condition 46
Table 4.16 The parameters of fog 46
Table A.1 Average lift and drag coefficient in different case 74
List of Figures
Fig. 1.1 Reynolds number range for flight vehicles. 3
Fig. 2.1 Clap and fling Mechanism 5
Fig 2.2 Flapper flow visualization with smoke released from the leading edge wing at different time. 6
Fig. 2.3 Compare the 2-D linear translation with 3-D flapping translation 7
Fig. 2.4 Saturation vapor pressure 12
Fig. 3.1 (a) A real butterfly model and (b) a geometric butterfly model. 15
Fig. 3.2 The Calculated domain. 16
Fig. 3.3 Grids around the flapping-wing 16
Fig 3.4 The 3-D flapping motion. 18
Fig. 3.5 The solutions loop for FLUENT solve process 19
Fig. 3.6 1D control volume 25
Fig. 3.7 Heat, mass, and momentum transfer between discrete and continuous phase 29
Fig. 4.1 Grids and calculated domain 47
Fig. 4.2 The 2-D flapping motion 47
Fig. 4.3 Lift vs. period comparing with references 48
Fig. 4.4 Drag vs. period comparing with references 48
Fig. 4.5 Pressure contour at different instants 49
Fig. 4.6 The lift and drag coefficient in different grid types. 50
Fig. 4.7 The lift and drag coefficient in different grid types. 50
Fig. 4.8 The forward flight butterflies lift and drag coefficient. 51
Fig. 4.9 The lift and drag coefficient profile in one period 51
Fig. 4.10 The lift coefficient profile in six periods 52
Fig. 4.11 The drag coefficient profile in six periods 52
Fig. 4.12 The pressure contour of case 2 53
Fig. 4.13 The pressure contour of case 3 54
Fig. 4.14 The pressure contour of case 4 55
Fig. 4.15 The pressure contour of case 5 56
Fig. 4.16 The lift and drag coefficient profile in one period (LWC=25 g/m3) 57
Fig. 4.17 The lift and drag coefficient profile in one period (LWC=39 g/m3) 57
Fig. 4.18 The lift coefficient profile in one period (LWC = 25&39 g/m3) 58
Fig. 4.19 The pressure contour of case 2 in the heavy rain conditions (LWC=25 g/m3) 59
Fig. 4.20 The pressure contour of case 3 in the heavy rain conditions (LWC=25 g/m3) 60
Fig. 4.21 The pressure contour of case 4 in the heavy rain conditions (LWC=25 g/m3) 61
Fig. 4.22 The pressure contour of case 5 in the heavy rain conditions (LWC=25 g/m3) 62
Fig. 4.23 The pressure contour of case 2 in the heavy rain conditions (LWC=39 g/m3) 63
Fig. 4.24 The pressure contour of case 3 in the heavy rain conditions (LWC=39 g/m3) 64
Fig. 4.25 The pressure contour of case 4 in the heavy rain conditions (LWC=39 g/m3) 65
Fig. 4.26 The pressure contour of case 5 in the heavy rain conditions (LWC=39 g/m3) 66
Fig. 4.27 The lift coefficient profile in ten periods 66
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