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# Propeller and its slipstream. The slipstream affects the aerodynamic behaviour and stability of the aircraft as the wing and the tailplane lie inside its influence [22]

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Due to the significance that propeller propulsion holds in the current aviation market, owing to the cost and other advantages that it provides, it was considered important to continue research in field of propeller propulsion. And, as the propeller slipstream significantly affects the aircraft's aerodynamic behavior, it was considered necessary to...

## Contexts in source publication

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... stated above, the airflow behind the propeller has a changed momentum compared to the ambient incoming airflow. This airflow behind the propeller, which comprises swirl velocity and an increased axial velocity, is called the propeller slipstream. Fig. 3 shows how the slipstream flows. We can observe from Fig. 3 that the wing and tailplane lie within the slipstream, and thus the slipstream affects the aerodynamic behaviour and stability of the ...

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... stated above, the airflow behind the propeller has a changed momentum compared to the ambient incoming airflow. This airflow behind the propeller, which comprises swirl velocity and an increased axial velocity, is called the propeller slipstream. Fig. 3 shows how the slipstream flows. We can observe from Fig. 3 that the wing and tailplane lie within the slipstream, and thus the slipstream affects the aerodynamic behaviour and stability of the ...

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... can see that a convergence is achieved for a wake length of 11 times the propeller diameter, i.e. for a wake length of 2.871 m. Convergence study by changing the wake length We can see that a convergence is achieved at 0.01 seconds Figure 23 represents the convergence study based on time step for VRM approach using the self-designed propeller. For this study, the time step was varied while keeping the number of panels per blade and the wake length constant at 60 panels and 11*Propeller Diameter respectively. ...

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... number of panels, the wake length and the time step were varied in Whirl to get the following results. Figure 30 represents the convergence study based on the number of panels per blade for Helical Wake Modelling approach using the Graupner CAM 9x4 propeller. For this study, the number of panels per blade varied while keeping the wake length and time step constant at 1*Propeller Diameter and 0.9 seconds respectively. ...

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... study for change in time step for Helical Wake Modelling approach (with Graupner CAM 9x4 propeller). We can see that a convergence is achieved at 0.001 seconds Figure 32 represents the convergence study based on time step blade for Helical Wake Modelling approach using the Graupner CAM 9x4 propeller. For this study, the time step was varied while keeping the number of panels per blade and wake length constant at 272 panels and 4*Propeller Diameter respectively. ...

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... from [34] Values obtained from Whirl Figure 34 represents the comparison of results obtained from Whirl, using the Helical Wake Modelling approach, with the experimental results obtained from [34] for the Graupner CAM 9x4 propeller. We can see that the results obtained from Whirl have an error, but unlike in the VRM approach, the error is almost consistent with different advance ratios. ...

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... on the number of panels was achieved at around the same number of panels for both the self-designed propeller and the Graupner propeller for the VRM approach. From Fig. 22 and Fig. 28, we can observe that the convergence based on the wake length was achieved at the same value of the wake length for both the propellers in the VRM approach. From Fig. 23 and Fig. 29, we can observe that the convergence based on time step was achieved at the same value of time step for both the propellers in VRM approach. Thus, we can see that though the geometry of the Graupner propeller is more complex (sweep and twisting of blades) as compared to the self-designed propeller, the convergence studies, ...

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... Fig. 24. And Fig. 30, we can observe that the convergence based on the number of panels was achieved at around the same number of panels for both the propellers in the Helical Wake Modelling approach. From Fig. 25 and Fig. 31, we can observe that the convergence based on the wake length was achieved at the same value of the wake length for both the propellers in the Helical Wake Modelling approach. ...

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... number of panels was achieved at around the same number of panels for both the propellers in the Helical Wake Modelling approach. From Fig. 25 and Fig. 31, we can observe that the convergence based on the wake length was achieved at the same value of the wake length for both the propellers in the Helical Wake Modelling approach. From Fig. 26 and Fig. 32, we can observe that the convergence based on the time step was achieved at the same value of time step for both the propellers in the Helical Wake Modelling approach. Thus, we can see that like the VRM approach, the Helical Wake Modelling approach is independent of the geometry of the propeller regarding the convergence studies. As ...

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... the VRM and the Helical Wake Modelling approaches are independent of the geometry of the propeller with respect to convergence studies, we can observe that these two approaches yield different convergence results when compared with each other. We can observe from Fig. 21, Fig. 24 and Fig. 27 and Fig. 30 that the number of panels at which the convergence is achieved is much higher for the Helical Wake Modelling approach as compared to the VRM approach. This happens because of the simplicity that the VRM approach provides. As each panel has its two trailing vortices formed circles in the VRM and two helixes in the Helical Wake Modelling ...

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... these wakes have the same radius. From Fig. 23, Fig. 26 and Fig. 29, Fig. 32, we can observe that the time step value at which convergence is achieved is much smaller for the Helical Wake Modelling approach as compared to the VRM. In VRM, the results are influenced by the trailing vortices forming circles and also the vortex rings, and in the Helical Wake Modelling approach, it is only the helical wake that influences the results. ...

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... to the Helical Wake Modelling approach. The reason this happens is because the distance till which the Vortex rings have their influence on the propeller is larger as compared to the helical wake. Thus, VRM gives a much longer wake as compared to the Helical Wake Modelling approach. Both these wakes have the same radius. From Fig. 23, Fig. 26 and Fig. 29, Fig. 32, we can observe that the time step value at which convergence is achieved is much smaller for the Helical Wake Modelling approach as compared to the VRM. In VRM, the results are influenced by the trailing vortices forming circles and also the vortex rings, and in the Helical Wake Modelling approach, it is only the helical wake that ...

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... Fig. 33 and Fig. 34 we can observe that the results obtained from Whirl have a margin of error, regardless of which of the two approaches is used, when compared with the results from [34]. However, from Fig. 33 we can observe that the error in the results obtained from Whirl using the VRM approach is not consistent with the change in flow ...

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... Fig. 33 and Fig. 34 we can observe that the results obtained from Whirl have a margin of error, regardless of which of the two approaches is used, when compared with the results from [34]. However, from Fig. 33 we can observe that the error in the results obtained from Whirl using the VRM approach is not consistent with the change in flow conditions, i.e. ...

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... Fig. 33 and Fig. 34 we can observe that the results obtained from Whirl have a margin of error, regardless of which of the two approaches is used, when compared with the results from [34]. However, from Fig. 33 we can observe that the error in the results obtained from Whirl using the VRM approach is not consistent with the change in flow conditions, i.e. advance ratios. And, from Fig. 34 we can observe that the error in the results obtained from Whirl using the Helical Wake Modelling approach is consistent with the change in advance ...

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... that the results obtained from Whirl have a margin of error, regardless of which of the two approaches is used, when compared with the results from [34]. However, from Fig. 33 we can observe that the error in the results obtained from Whirl using the VRM approach is not consistent with the change in flow conditions, i.e. advance ratios. And, from Fig. 34 we can observe that the error in the results obtained from Whirl using the Helical Wake Modelling approach is consistent with the change in advance ...

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... obtain the results from Whirl using the VRM that are presented in Fig. 33, the number of segments forming the trailing vortices' circles at every panel were changed for every advance ratio. It was observed that if the number of segments were kept constant for different advance ratios, the results obtained for different advance ratios don't vary much and become redundant. Thus, we can see that with the change ...

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... must be changed for different flow conditions. If the number of segments forming the helical wake are kept constant with different flow conditions, the results obtained, like in the VRM, don't vary much and thus become redundant. Thus, a change in the number of segments forming the helical wake is a must when the flow conditions are changed. From Fig. 33 and Fig. 34 we can observe that the error in results is more consistent in the Helical Wake Modelling approach as compared to the VRM approach. This happens because in the Helical Wake Modelling approach the change in the results with the change in the number of segments forming the helical wake is more consistent and follows a ...

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... for different flow conditions. If the number of segments forming the helical wake are kept constant with different flow conditions, the results obtained, like in the VRM, don't vary much and thus become redundant. Thus, a change in the number of segments forming the helical wake is a must when the flow conditions are changed. From Fig. 33 and Fig. 34 we can observe that the error in results is more consistent in the Helical Wake Modelling approach as compared to the VRM approach. This happens because in the Helical Wake Modelling approach the change in the results with the change in the number of segments forming the helical wake is more consistent and follows a trendline. However, ...

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... of the wake and camber of the propeller blades are not included in Whirl cause the error in the results obtained from both these approaches. In Whirl, the compressibility effects are addressed by dividing the coefficient of thrust of the whole propeller by the compressibility correction factor, which is the simplest way. It can be observed from Fig. 35 that the blade tip velocities of the Graupner CAM 9x4 propeller don't reach anywhere near 0.3 Mach for the flow conditions that are used to obtain the results, in the Validation study, in Fig. 33 and Fig. 34. Thus, the compressibility effects are insignificant and the results presented in Sections 4.2.1 and 4.2.2 are devoid of ...

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... dividing the coefficient of thrust of the whole propeller by the compressibility correction factor, which is the simplest way. It can be observed from Fig. 35 that the blade tip velocities of the Graupner CAM 9x4 propeller don't reach anywhere near 0.3 Mach for the flow conditions that are used to obtain the results, in the Validation study, in Fig. 33 and Fig. 34. Thus, the compressibility effects are insignificant and the results presented in Sections 4.2.1 and 4.2.2 are devoid of compressibility ...

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... coefficient of thrust of the whole propeller by the compressibility correction factor, which is the simplest way. It can be observed from Fig. 35 that the blade tip velocities of the Graupner CAM 9x4 propeller don't reach anywhere near 0.3 Mach for the flow conditions that are used to obtain the results, in the Validation study, in Fig. 33 and Fig. 34. Thus, the compressibility effects are insignificant and the results presented in Sections 4.2.1 and 4.2.2 are devoid of compressibility ...

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... effects become significant, at velocities greater than 0. As the compressibility effects become significant in the compressible region, the density of air increases; and this increase in density leads to higher forces being calculated by the Kutta-Joukowski theorem (16), and subsequently higher thrust coefficients values as observed in Fig. 37. It must be noted that Whirl works only in the subsonic region, and if the blade tip velocities reach the transonic region, the coefficient of thrust values calculated would be wrong as Whirl doesn't take into account the effects of shock ...

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... Fig. 38 and Fig. 39, we can observe that the vorticity increases from the blade root to the tip for both the VRM and the Helical Wake Modelling approaches. This happens because the total flow velocity (19) increases from the blade root to the tip. The reason as to why relatively high values of vorticity are found at the blade tips is because ...

**Context 25**

... Fig. 38 and Fig. 39, we can observe that the vorticity increases from the blade root to the tip for both the VRM and the Helical Wake Modelling approaches. This happens because the total flow velocity (19) increases from the blade root to the tip. The reason as to why relatively high values of vorticity are found at the blade tips is because of the fact ...

**Context 26**

... Wake Modelling approaches. This happens because the total flow velocity (19) increases from the blade root to the tip. The reason as to why relatively high values of vorticity are found at the blade tips is because of the fact that the gradient in the loading is the highest at the tips, as mentioned in Section 2.1.4. We can also observe from Fig. 38 and Fig. 39 that the increase in vorticity is more gradual for the Helical Wake Modelling approach as compared to the VRM approach. This can be attributed to the unstable nature of the VRM ...

**Context 27**

... approaches. This happens because the total flow velocity (19) increases from the blade root to the tip. The reason as to why relatively high values of vorticity are found at the blade tips is because of the fact that the gradient in the loading is the highest at the tips, as mentioned in Section 2.1.4. We can also observe from Fig. 38 and Fig. 39 that the increase in vorticity is more gradual for the Helical Wake Modelling approach as compared to the VRM approach. This can be attributed to the unstable nature of the VRM ...

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... direction, as mentioned in Section 2.1.1. It can also be observed form Fig. 40 and Fig. 42 that the axial velocity becomes constant after the modelled wake length of 10*propeller diameter is reached. Thus, we can say that Whirl calculates axial velocity accurately up till the modelled wake length, which is 10*propeller diameter in Fig. 40 and Fig. 43, and beyond the modelled wake length, the axial velocity calculated by Whirl would be null and void, for both the ...

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... 41 and Fig. 43, we can observe that the tangential velocity remains almost constant in the wake stream-wise direction for both the Helical Wake Modelling and the VRM approaches. This happens because unlike the axial velocity, the tangential velocity doesn't rely on the vortex system length that increases in the wake stream-wise direction. From Fig. ...

## Citations

... In order to estimate rate derivatives of FWMAV with propeller effects, Tornado was selected as a primary software. Tornado has been tested and found to give satisfactory results for propwash affected vehicles [86]. Vortex Ring Modeling (VRM) and the Helical Wake Modeling (HWM) are the two approaches which this software can employ to couple propwash effects on vehicle aerodynamics, refer [87], [88] and [89]. ...

In this research effect of propeller induced flow on aerodynamic characteristics of low aspect ratio flying wing micro aerial vehicle has been investigated experimentally in subsonic wind tunnel. Left turning tendencies of right-handed propellers have been discussed in literature, but not much work has been done to quantify them. In this research, we have quantified these tendencies as a change in aerodynamic coefficient with a change in advance ratio at a longitudinal trim angle of attack using subsonic wind tunnel. For experimental testing, three fixed pitch propeller diameters (5 inch, 6 inch and 7 inch), three propeller rotational speeds (7800, 10800 and 12300 RPMs) and three wind tunnel speeds (10, 15 and 20 m/s) have been considered to form up 27 advance ratios. Additionally, wind tunnel tests of 9 wind mill cases were conducted and considered as baseline. Experimental uncertainty assessment for measurement of forces and moments was carried out before conduct of wind tunnel tests. Large variation in lift, drag, yawing moment and rolling moment was captured at low advance ratios, which indicated their significance at high propeller rotational speeds and large propeller diameters. Side force and pitching moment did not reflect any significant change. L/D at trim point was found a nonlinear function of propeller diameter to wingspan ratio D/b, and propeller rotational speed. Rate and control derivatives were obtained using unsteady vortex lattice method with propeller induced flow effect modeled by Helical Vortex Modeling approach. In this research, we have proposed improved 6-DOF equations of motion, with a contribution of advance ratio J. It is concluded, that propeller induced flow effects have a significant contribution in flight dynamic modeling for vehicles with large propeller diameter to wingspan ratio, D/b of 22% or more.