Research Article  Open Access
Sreenadh Chevula, Sankeerth Chillamcharal, Satya Prasad Maddula, "A Computational Design Analysis of UAV’s Rotor Blade in LowTemperature Conditions for the Defence Applications", International Journal of Aerospace Engineering, vol. 2021, Article ID 8843453, 16 pages, 2021. https://doi.org/10.1155/2021/8843453
A Computational Design Analysis of UAV’s Rotor Blade in LowTemperature Conditions for the Defence Applications
Abstract
This paper discusses about the critical situations faced by the Defence operations with drones in the area of Siachen Glacier in the Himalayas. The reasons for the structural failures in drone’s rotor blades and the lowperformance efficiency of the drones at lowtemperature conditions are highlighted. A possible solution to the abovementioned problems has been addressed by introducing a new boundary design in the rotor blades and composite materials. The results which are shown in this paper are obtained by the computational analysis facility located at the Department of Aerospace Engineering, School of Technology, GITAM (Deemed to be University), Hyderabad. By mimicking the Siachen Glacier atmosphere conditions, the proposed rotor blade design has been analysed in CFD.
1. Introduction
Unmanned air vehicles (UAV) and drones play a major role in various Defence operations, namely in remote sensing, surveillance, and data collection in the critical areas. In such operations, the lifetime of the drones plays a major role. It is a fact that various parameters of the drone’s operational conditions show huge influence on the performance and structural integrity of the drones. In terms of structural components, the main component of the drones and most of the UAV are rotor blades, and in the design of these blades, the condition of the operational temperature is one of the major parameter. As a study of temperature influence and to overcome the structural failures in the rotor blades, in this paper, the case study of Siachen Glacier is under the India Army control [1], located at 35.421226° N, 77.109540° E. The glacier is nothing but moving location of densely formed ice which has been classified by their thermal characteristics, morphology, and behaviour [2]. The selected location map of the Siachen Glacier can be found in Figure 1 [3].
The temperature conditions (see Figure 2) [4, 5] for the proposed work in this paper have been identified from the Siachen Glacier with latitude 35,500000  35, 30.000000 N  N35 30 00; longitude 77.000000  77 0.000000E  E0 77 00 00; and field elevation 22,000 ft/6706 m MSL. In the selected area, the winter snow fall will approximately 35 ft from the surface and the temperature can drop up to 50°C (58°F).
Considering the applications of UAV and drones in glacier conditions, world widely, many studies had been conducted, such as analysing the snow depth [6–9]. A lowcost UAV has been used to test the SFM performance in topographic and lighting conditions of different types of snows which has been discussed in Cimoli et al. [6]. A comparison of photogrammetric maps collected by the UAV has been performed by Avanzi et al. [10]. A flow velocity over glacier land has been conducted by Jones C et al. [11]. Another studies relevant to identifying and analysing changes of surface features such as ice cliffs have been conducted by Steiner, J.F. et. al., Buri, P et al., and Brun, et al. [12–14],
Beyond various studies conducted by worldwide researchers in terms of durability and structural integrity of the UAV’s and drones in glacier conditions, this paper focuses on the computational design analysis of rotor blades (further mentioned as propellers in this paper). An existing propeller blade design (Design1) and proposed new designs (Design2) and (Design3) are created in Catia V5 (geometry information can be found in the following sections), and computational analysis and results are obtained by using ANSYS 16.0. A comparison between the results obtained from the computational analysis of Design1 (existing rotor blade model), Design2, and Design3 (Proposed model) is shown in “Obtained Results.” Finally, conclusions has been plotted in “Conclusion.”
2. Designing the Solution
Several methods are used to calculate the performance parameters of a propeller, namely, momentum theories [15], blade element theories [16], hybrid blade element momentum theories [17, 18], and lifting line theories [19]. Without computational analysis, using some specified mathematical models will give the values of few propeller performance parameters. But in terms of high accuracy and optimization of the procedures, some new methods have been introduced which can be obtained only by using computational analysis. It is a wellknown fact that a propeller acts like a rotating wing, and its crosssection is an airfoil. In terms of optimizing, a propeller performance optimizes the airfoil shapes used along the blade span, and an optimization of an airfoil shape can be challenging, if the shape is described using individual points. A common approach to describing the number of parameters used to describe an airfoil is to parameterize the shape. A study can be found with respective to the common approach can be found in Kulfan et al. [20]. This approach has been adopted in this paper and aimed at studying the propeller optimization with a coupled electric motor, and also, a hybrid blade element momentum theory has been proposed to estimate the propeller performance analysis.
The lifting surface on a propeller is called a blade, and a propeller can have any number of blades. Most propellers have two to four blades. Any given point along a blade the crosssection has all the same characteristics as an airfoil such as leading and trailing edges, mean camber line, chord line, and thickness, where the blades connect is called the hub which is directly attached either to an engine or to a transmission. The root is the area between the hub and the blade, and the tip is end of the blade opposite the hub, as shown in Figure 3 [21, 22] and Figure 4 [21, 22]. The propeller performance parameters, namely, the blade angle, , is the “resultant angle between the free stream and rotational velocity components,” the effective pitch, , is “the distance a propeller advances in one rotation,” the geometric pitch, , is “the theoretical distance an element of a propeller blade would travel in one rotation”( is not constant along the length of blade), and the advance ratio, , is “the ratio between the distance the propeller moves forward through one rotation and the blade diameter.” where is in rotations per second, is the velocity, and is the diameter of the blade); the aspect ratio (AR) is the tip radius divided by the maximum blade width. A spinner is a conical or parabolic shaped fairing that is mounted over the canter of the centre of the propeller where it is connected to the hub, the thrust or driving face is the blade face is the lower surface of the propeller airfoil, and the blade back is the upper surface of the propeller. Most of the parameters can be visually found in Figure 5.
Based on the operational areas of the drones or UAV, propellers are classified in four ways, namely, tractor, pusher propellers (based on thrust production), and fixed and variable pitched propellers (based on the pitch geometry). A variable pitch propeller’s blades can be adjusted either on the ground or during fight to allow the propeller to operate at maximum performance throughout its operation range.
As mentioned above, to obtain the propeller performance parameters values with more accuracy, several computational methods are used in the design of propellers, which will utilizes some nondimensional numbers, namely, the thrust coefficient , power coefficient, , and the efficiency . where is the rotation speed, is the diameter of the propeller, is the density of air, and is the advance ratio.
3. Blade Element Rotor Theory
In the propeller performance parameters analysis, the blade element theory is one of the simplest method. A relatively simple method of predicting the more detailed performance of a helicopter rotor using blade element theory can be found in ref [23]. From Figures 6 and 7, the difference in angle between thrust and lift directions is defined as where is the geometric pitch angle, is the angle of attack with respective to the flow, and is the difference between the angles.
is the axial flow at propeller disk, is the section local flow velocity vector, summation of vectors and , and is the angular flow velocity vector.
The elemental thrust and torque of this blade element can thus be written as
Case 1. For a single blade.
Substituting section data ( and for the given ) then
where is the air density, and is the blade chord so that the lift producing area of the blade element is .
Case 2. If the number of propeller blades is then the area generating the lift will be the element area multiplied by the number of blades covering the circuit, and the lift coefficient can be assumed to be linear.
The overall propeller thrust and torque can be found by the summation of the radial blade element values, i.e.,
The relation between nondimensional thrust () and torque coefficients () and the efficiency of the propeller () and advance ratio can be found in Equations (1), (2), (3), and (4). The blade element theory is an iterative solution procedure and starts with an initial guess of induced flow component . With the guess of , the flow angle on the blade will be estimated, and based on the use of blade section properties, the element thrust and torque will be estimated. The approximated values obtained from the guessed values and the force balance can be used to give improved estimates of the induced velocity . Until the value of got converged to within a specified tolerance, the iteration process will be continued. (It should also be noted that convergence for a nonlinear system of equations is not guaranteed.) In this paper, linear airfoil section properties have been used to design propellers. So to obtain required results, a convergence enhancing technique (CrankNicholson underrelaxation) has been proposed to use.
4. Computational Analysis of the Propellers
Various experimental and computational analysis are available to obtain the results of the propeller analysis. Firstly, in an experimental method, the propeller blades will be tested in both static and advancing flow conditions in a wind tunnel setup. Secondly, in a computational analysis adopts threedimensional computational fluid dynamics (CFD) simulation, utilizing the Reynoldsaverage Navier–Stokes (RANS) equation. CFD tools are the useful tools for propeller design and analysis. In this paper, a flow simulation (under lowtemperature conditions) on propeller blades is proposed to conduct by using ANSYS 16.0. The geometry conditions of propellers Design1 (Figure 8), Design2 (Figure 9), and Design3 (Figure 10) can be found in Table 1. In general, the standard geometry of the propellers has a 0.254 m diameter. So in this paper, for computational design, approximate values of 0.254 m in diameter propellers have been designed in Catia V5.

In the above given three designs, the pitch of 0.1778 m provides a pitchtodiameter ratio of 0.7, which is common for an offshelf propeller type. The value of the Reynolds number for the testing flow condition is proposed to set for 50804 which can be obtained by a rotational speed of the propeller 3008 RPM and 75% of blade station from the chord.
5. Computational Setup
The computational predictions have been obtained from the ANSYS 16.0 CFD solver. The approach of the multiple reference frame model (MRF) has been incorporated to obtain the predicted flow around the propellers which can be shown in the following figures (see Figures 11–13). The computational domain has been split into a global, rotating region, and local domains. The rotational domain can be found where the propeller blade and hub enclosed without openings (see Figures 11–13).
Considering the full development (without any unexpected interruptions) of the upstream and downstream flow simulations and results around the propellers, a good configuration of the flow domain plays a major role in the computational setup. In this line, for the proposed computational setup of this paper, the stationary regions inlet and outlet boundaries are located at 4D from the origin of the propellers, and the enclosure of the rotating domain is set to be 1.1D and 0.4D.
Grid generation is another important factor to obtain good and accurate results in computational analysis. The rate of convergence, computational time, and performance depends on the quality of the computational grid generation. In this paper, the mesh grid has been generated using Ansys FLUENT 16.0 (see Figures 14–17). The cell size of the mesh kept smaller size or range around the propeller blade in rotational domain. From the rotational domain, a gradual increment in cell size towards the stationary region has been implemented in the grid generation with enough grid refinements across the interface.
In the computational analysis, especially in the grid generation, most of the researchers will select the unstructured tetrahedral grids because of its compatibility to solve complex geometries by discretizing. This paper also selected unstructured tetrahedral grids in the grid generation. Table 2 shows the details of the grid generation of this work.

6. Obtained Results
Defence operational conditions for UAV such as operational altitude, geographical areas, and mission operational profiles can be found in Figure 18.
Operational Area Siachen Glacier is known as the world’s highest battlefield, and soldiers have been deployed at an elevation of up to 6000 m (19685 feet) from sea level. In this paper, based on the operational temperature with respective change in the altitudes (with gradual increment of 250) (see Figure 18), the vertical drone operational area has been divided in to five zones. Complete information about the five zones, temperature, and required RPM of the Drone propellers can be found in Table 3.
 
The altitude and temperatures are taken from above 6000 m from the sea level. 
6.1. Pressure Contour for Propeller Design1 (Zone1 to Zone5), Respectively
For the mimicking condition of Zone1 with 0°C and velocity of 8 m/s obtained by 5000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone2 with 25°C and velocity of 8 m/s obtained by5500 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone3 with 50°C and velocity of 8 m/s obtained by6000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone4 with 75°C and velocity of 8 m/s obtained by6500 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone5 with 100°C and velocity of 8 m/s obtained by7000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
Note: the obtained pressure contour for the propeller Design2 (Zone1 to Zone5) is almost similar to Design1. So those results are not shown in this paper.
6.2. Pressure Contour for Propeller Design3 (Zone1 to Zone5), Respectively
For the mimicking condition of Zone1 with 0°C and velocity of 8 m/s obtained by5000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone2 with 25°C and velocity of 8 m/s obtained by5500 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone3 with 50°C and velocity of 8 m/s obtained by6000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone4 with 75°C and velocity of 8 m/s obtained by6500 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
For the mimicking condition of Zone5 with 100°C and velocity of 8 m/s obtained by7000 RPM, the results of the pressure contour influence vary from the hub to the tailing edge of the rotor ( to ).
With the gradual increment in the RPM obtained the required velocity of 8 m/s with respect to the increments in the zone’s altitude (Zone1 to Zone5). The influence of the pressure distribution from the leading edge to the tailing edge in Design1 and Design3 has a drastic change, and finally, the proposed Design3 performed smooth pressure distribution profiles (Figures 19–22) in all operational conditions which are simulated for Siachen Glacier geographical location.
6.3. Results with Respect to the Influencing Parameters
The obtained results with respect to the drone’s performance influencing the parameters of Design1, Design2, and Design3 are summarized (see Equations (1)–(4) and Equation (9)) in the tables and the graphical representation.
From Figure 23, we can find that Design3 and Design2 have a similar performance at temperature 25°C. But in other temperature conditions, low performance has been identified due to its geometrical design conditions. Finally, the proposed Design3 results the better performance in the comparison of existing Design1 and Design2 with respect to the all operational conditions which are simulated for Siachen Glacier geographical location.
7. Conclusion
The aim of the paper is to perform a computational analysis by Ansys 16.0 of newly designed rotor blades at very low temperate conditions (which can fit the atmospheric conditions of Siachen Glacier, in India) which has been achieved. From the obtained pressure contours of Design1 (Figures 19–23; see Section 6.1) and Design3 (Figures 24–28, see Section 6.2), it can be clearly found that Design3 is the best solution for very lowtemperature conditions with highquality pressure distribution around the propeller surface in high altitudes at lowtemperature conditions. From the obtained values of the performance parameters (, , , , , and from Tables 4–6, and Figures 29–31), it can also be found that the values of the thrust coefficient and the efficiency of Design3 result in reliable and more durable values compared to Design1 and Design2. Table 7 and Figure 32 show the comparison of the obtained values of the Design1, Design2, and Design3 efficiencies, and as a result of comparison, we can again find that Design3 is the best design propeller for very lowtemperature operational conditions such as in Siachen Glacier, in India. As a continuation of this work, a static structural analysis of Design3 has been proposed which is going to be presented in the upcoming paper.




Data Availability
(a) Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies. (b) All data, models, and code generated or used during the study appear in the submitted article.
Conflicts of Interest
The authors whose names are listed in this work certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patentlicensing arrangements) or nonfinancial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.
References
 A. Shukla, 846 Indian soldiers have died in Siachen since 1984, BusinessStandard, 2012, Archived from the original on 9 April 2018. Retrieved 13 April 2018 – via Business Standard.
 M. E. Brown, H. Ouyang, S. Habib et al., “HIMALA: climate impacts on glaciers, snow, and hydrology in the Himalayan region,” Mountain Research and Development, vol. 30, no. 4, pp. 401–404, 2010. View at: Publisher Site  Google Scholar
 https://www.jagranjosh.com/generalknowledge/whatisthesiachenglacierdispute15282919521.
 https://www.dailyexcelsior.com/wpcontent/uploads/2019/09/page323.jpg.
 https://keralakaumudi.com/webnews/en/2019/11/NMAN0107123/image/siachen.1574093958.jpg.
 E. Cimoli, M. Marcer, B. Vandecrux, C. E. Bøggild, G. Williams, and S. B. Simonsen, “Application of lowcost UASs and digital photogrammetry for highresolution snow depth mapping in the Arctic,” Remote Sensing, vol. 9, no. 11, p. 1144, 2017. View at: Publisher Site  Google Scholar
 C. De Michele, F. Avanzi, D. Passoni et al., “Microscale variability of snow depth using U.A.S. technology,” The Cryosphere Discussions, vol. 9, no. 1, pp. 1047–1075, 2015. View at: Publisher Site  Google Scholar
 Y. Bühler, M. S. Adams, A. Stoffel, and R. Boesch, “Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying nearinfrared UAS imagery,” International Journal of Remote Sensing, vol. 38, no. 810, pp. 3135–3158, 2017. View at: Publisher Site  Google Scholar
 M. KorczakAbshire, A. Zmarz, M. Rodzewicz, M. Kycko, I. Karsznia, and K. J. Chwedorzewska, “Study of Fauna population changes on Penguin Island and turret point oasis (King George Island, Antarctica) using an unmanned aerial vehicle,” Polar Biology, vol. 42, no. 1, pp. 217–224, 2019. View at: Publisher Site  Google Scholar
 F. Avanzi, A. Bianchi, A. Cina et al., “Centimetric accuracy in snow depth using unmanned aerial system photogrammetry and a multistation,” Remote Sensing, vol. 10, no. 5, p. 765, 2018. View at: Publisher Site  Google Scholar
 C. Jones, J. Ryan, T. Holt, and A. Hubbard, “Structural glaciology of Isunguata Sermia, West Greenland,” Journal of Maps, vol. 14, no. 2, pp. 517–527, 2018. View at: Publisher Site  Google Scholar
 J. F. Steiner, F. Pellicciotti, P. Buri, E. S. Miles, W. W. Immerzeel, and T. D. Reid, “Modelling icecliff backwasting on a debriscovered glacier in the Nepalese Himalaya,” Journal of Glaciology, vol. 61, no. 229, pp. 889–907, 2015. View at: Publisher Site  Google Scholar
 P. Buri, F. Pellicciotti, J. F. Steiner, E. S. Miles, and W. W. Immerzeel, “A gridbased model of backwasting of supraglacial ice cliffs on debriscovered glaciers,” Annals of Glaciology, vol. 57, no. 71, pp. 199–211, 2016. View at: Publisher Site  Google Scholar
 F. Brun, P. Wagnon, E. Berthier et al., “Ice cliff contribution to the tonguewide ablation of Changri Nup Glacier, Nepal, central Himalaya,” The Cryosphere, vol. 12, no. 11, pp. 3439–3457, 2018. View at: Publisher Site  Google Scholar
 H. Glauert, The Elements of Aerofoil and Airscrew Theory, Cambridge University Press, Cambridge, UK, 1947.
 D. O. Dommasch, Element of Propeller and Helicopter Aerodynamics, Sir Isaac Pitman & Sons, LTD, 1953.
 C. N. Adkins and R. H. Liebeck, “Design of optimum propellers,” Journal of Propulsion and Power, vol. 10, no. 5, pp. 676–682, 1994. View at: Publisher Site  Google Scholar
 H. Glauert, Aerodynamic Theory, Springer, Berlin, Heidelberg, 1935. View at: Publisher Site
 C. Burger, Propeller Performance Analysis and Multidisciplinary Optimization using a Genetic Algorithm, [Ph.D. thesis], Auburn University, 2007.
 B. M. Kulfan, “Universal parametric geometry representation method,” Journal of Aircraft, vol. 45, no. 1, pp. 142–158, 2008. View at: Publisher Site  Google Scholar
 W. C. Nelson, Airplane Propeller Principles, John Wiley and Sons, 1944.
 F. E. Weick, Aircraft Propeller Design, McGrawHill, 1930.
 http://www.aerodynamics4students.com/propulsion/bladeelementrotortheory.php.
 https://www.aircraftsystemstech.com/2017/04/aircraftpropellers.html.
Copyright
Copyright © 2021 Sreenadh Chevula et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.