Wetland Monitoring Using Unmanned Aerial Vehicles with Electrical Distributed Propulsion Systems

Abstract

The inspection of wetlands in the Ecuadorian highlands has gained importance due to the environmental issues linked to the growth of human activities and the expansion of the agricultural and livestock frontiers. In this sense, unmanned aerial vehicles (UAVs) have been amply used in monitoring activities such as the supervision of threatened ecosystems, where cyclic measurements and high-resolution imagery are needed. However, the harsh operating conditions in the Andean highlands and sensitive ecosystem restrictions demand efficient propulsion configurations with low environmental impact. Electrical distributed propulsion (EDP) systems have surged as a forefront alternative since they offer benefits in both the propulsive and aerodynamic performance of fixed-wing UAVs. In this chapter, an EDP system is sized for a design point at the Andean operating conditions. Thereafter, two propulsion configurations were established based on off-the-shelf components, and their performance was characterized through analytical approaches. These results highlight the trends in power consumption and performance when the number of propulsors is increased. A significant contribution of this work is to exhibit important patterns in the performance of electric propulsion by using commercial components, and to set the operating limitations that can be further explored for analogous configurations in larger UAVs.

1. Introduction

The Andean region, which comprises paramos¹ [1] and wetlands, is considered a biodiversity hotspot that contains about one-sixth of earth’s plant life [2, 3]. This extension of land is of great importance, since it represents the main water reservoir for major cities in Colombia, Ecuador, and Peru [4, 5]. Both wetlands and paramos are endangered ecosystems, and, hence, efficient and suitable monitoring solutions are urgently needed. In this way, different monitoring techniques including satellite imagery and the use of high-resolution cameras mounted on manned airplanes have been utilized. Nonetheless, the aforesaid methods are not commonly affordable because they are costly and require long setup times.

The advent of unmanned aerial vehicles (UAVs) has encouraged periodical and low-cost management of threatened ecosystems through real-time data acquisition. The incursion of aerial platforms into forestry remote sensing [6] has had a positive impact thanks to the usage of high-resolution sensors [7, 8] to gather data regarding flora health, species inventory, or mapping in a periodic way. In this respect, multicopters have been seen as the first option for monitoring; however, their low autonomy limits the area covered per flight. Conversely, fixed-wing UAVs have been introduced to overfly larger areas. The imagery provided by these tools has been collected using different payloads, ranging from basic RGB cameras to sophisticated radars. Nevertheless, the time employed for a specific mission profile is higher when operating at the Andean highlands because of the harsh atmospheric conditions, which constrain the UAV autonomy and performance.

Commercial UAVs usually perform under sea level conditions with low wind gusts (lower than 16 m/s) and higher air density. This denotes that an improvement in some UAV subsystems is required to tailor them for high-altitude monitoring applications [9, 10]. Among the different characteristics that need to be upgraded to enhance the UAV performance, the following can be summarized: robust flight control system able to withstand the strong wind gusts (18 m/s), aerodynamic and high volumetric fuselage to store the avionics and payload, and high-efficient and eco-friendly propulsion system, which reduces energy consumption. The two latter options are linked, and, thus, their implementation into the conceptual design requires the assessment of their suitability to explore synergies for a more efficient UAV configuration.

The purpose of the present chapter is to investigate the performance of an electric-powered blended wing body (BWB)² UAV deployed on the aforesaid ecosystems. This baseline configuration has been selected based on previous research [11], where it has been found that BWB configurations offer high volumetric efficiency while providing good aerodynamic performance [12] as a result of the elliptical lift distribution improvement over the whole airframe [13, 14, 15]. Furthermore, the BWB model facilitates the integration of different propulsion architectures, which results in a broader spectrum of configurations for distributed propulsion [16].

Regarding the power source, the electric option has been seen attractive because of the reduction of polluting gas emissions, moderate cost, lighter weight, and high reliability. In the next section, a deeper explanation about the propulsion configuration for this conceptual design is described.

2. UAV design methodology

2.1 Distributed electric propulsion

Reconnaissance and surveillance of endangered environments through overflight missions require short setup time, versatility, and noise mitigation. In this sense, electric propulsion has emerged as a reliable and potential solution to accomplish the aforesaid requirements thanks to its high-efficiency, moderate cost, and eco-friendly essence. Nonetheless, the capacity of commercial batteries remains to be an issue because of their lower energy density compared with their counterpart, the fossil fuels [17, 18].

The distributed propulsion is a revolutionary technology that seeks to reduce the noise and weight of an aircraft by means of replacing large propulsors with a moderate amount of small ones along the airframe [16] as depicted in Figure 1. This offers the possibility of increasing the propulsive efficiency because a larger propulsive area is considered, which in turn, implies a lower jet velocity. Its application on small fixed-wing UAVs has not been formally studied³ [19], and, consequently, the present chapter aims to assess the performance of small UAV configurations with electric distributed propulsion. The study will focus mainly on power consumption and performance improvements to demonstrate the feasibility of employing this technology in small UAVs. Propulsive efficiency has not been considered as a figure of merit in the present study, because of the low operating speeds and the electrical propulsion system, where the use of this parameter does not capture well the improvement in the aircraft performance, as it does for turbofan engines.

Figure 1.

Difference between distributed and non-distributed propulsion in a BWB.

It is important to note that distributed propulsion may offer other numerous benefits such as the elimination of the aircraft control surfaces (thrust vectoring), flexible maintenance, decrease in noise, and reduction in aircraft weight through inlet-wing integration [20]. The study of these advantages is beyond the scope of the present work, since this chapter is aimed at setting the basic conceptual configurations and assessing their suitability for the case study. Nonetheless, these various features will be implemented in further research, where the selected conceptual configurations will be assessed using a more holistic perspective.

For the assessment of suitable UAV configurations for wetland monitoring, parametric sizing and aerodynamic assessment approaches were implemented into the conceptual design stage. Then, a brief insight about the influence of electric distributed propulsion into the performance of the UAV configuration using a semiempirical approach is exposed. In the next sections, initial sizing and modelling of the UAV systems are further explained.

2.2 Initial sizing

The design procedure starts by defining the mission requirements such as flight altitude, velocities, and payload sensors. In this sense, a precise study of wetlands and paramos demands the usage of special sensors applied in monitoring activities such as crop scouting, precision agriculture, surveillance, and air quality monitoring. Some sensors used in these monitoring tasks are listed in Table 1. In this work, the payload?s weight was assumed to be 1 kg for practical purposes

Main camera	Mass [g]	Resolution [MP]
Logitech C510	225	8
Canon PowerShot S60	230	5
Kodak Professional DCS Pro Back	770	16
Sony DSC-R1	929	10.3

Table 1.

Monitoring sensors applied for wetlands and forestry [21, 22, 23, 24, 25].

Next, the UAV layout is carried out, and main aircraft characteristics such as preliminary weight (W_TOp), wing planform area (S), and preliminary power required (P_Rp) are delineated through the constraint analysis technique [26, 27]. This method consists of a matching plot that allows defining the design space of the aircraft depending on their performance requirements such as stall speed, maximum speed, takeoff run, and ceiling altitude [28]. The outcomes of this design stage represent the general characteristics of a preliminary aircraft architecture and will be employed to size other parts.

Afterwards, the wing shape is outlined by defining its geometrical parameters and sectional airfoils [26, 29]. The wing geometry was set according to technical and semiempirical correlations [18], and then, the obtained results were contrasted with corresponding data of commercial UAVs with similar characteristics [6]. In this way, the aerodynamic assessment was accomplished through the employment of the open-source Athena Vortex Lattice (AVL) software which incorporates the vortex lattice method (VLM) [30]. On the other hand, due to the lack of suitable analytical methods to calculate the weight of small UAV configurations, the preliminary weight was estimated as a function of the internal volume and the structural material?s density of the aircraft [26].

At the end of this stage, the external shape of a conceptual model is obtained. Thereafter, it is necessary to define a proper propulsion system through the match of the thrust required and the thrust available. Finally, the weight of the resulting architecture is assessed through a refined model that takes into account the propulsion system and the power source weight. Figure 2 depicts the road map of the methodology employed to generate and characterize a conceptual BWB UAV model. It is worth to mention that all the symbols utilized along the chapter are reported in the nomenclature section at the end.

Figure 2.

General methodology of initial aircraft sizing.

2.3 Propulsion modeling

The main function of aircraft propulsion systems is to generate enough thrust to overcome the drag and maintain a steady flight. For this work, firstly, suitable propellers were selected based on operating conditions and performance requirements of the UAV model. Then, the rest of propulsion elements (motor, electronic speed control and battery) was outlined based on propeller’s characteristics. Finally, the established propulsion set is evaluated to verify that both the power and the thrust available satisfy the requirements for cruise condition. It is important to mention that, at this conceptual stage, the distortion and momentum drag reduction of the incoming flow to the propeller has not been considered and will be studied in future work.

The propellers are commonly characterized by the thrust (C_T) and power (C_P) coefficients through semiempirical models at early stages of design [31]. For this study, the aforesaid parameters were obtained from an experimental database of low Reynolds propellers [32] by using the advance ratio (J) as the key driver for the selection routine. This latter parameter relates the freestream velocity, the propeller diameter (ϕ_prop), and its rotational speed (ω). For this work, the freestream velocity was set according to the desired cruise speed. In this way, the proposed method involves an iterative scheme that consists of estimating the thrust and power generated by a preselected propeller through the variation of ϕ_prop and ω for the desired freestream velocity (V_c). The iterative loop stops when the thrust and power required are met by a certain configuration. Finally, these results were used to select appropriate electric motors and batteries which can adapt well to the design requirements. This semiempirical scheme was preferred since most of the available techniques [34, 35] are focused on large propeller assessment and, hence, they present limitations for their implementation into small aerial platforms.

Brushless electric motors are commonly employed for small UAVs considering their simple design, potential to downsize, little maintenance, and independent performance of the flight altitude. In addition, their purely inductive nature and their outrunner configuration (rotor with magnets that surrounds the fixed coils of the stator) enable them to generate high torque at a low rotational speed, eliminating the need of a gearbox and facilitating their integration and test at early stages of UAV design [18, 36]. In this context, appropriate motors were outlined based solely on basic parameters provided by manufacturers like rotational speed and torque. The selected motor must be able to generate the torque required by the propeller for its adequate functioning at a certain rotational speed [37]. Once a motor has been selected, various operating parameters like no-load current, voltage constant, and internal resistance, together with torque and rotational speed that were taken from the datasheets, were employed to estimate the required voltage (U_m) and current (I_m) of the motor [36].

The motor current (I_m) is then employed to select a proper electronic speed control (ESC) device and a lithium-polymer (LiPo) battery. It is important to highlight that batteries for small UAVs are almost exclusively lithium-based because they offer high capacity, low weight, and high discharge rates [38]. For the battery selection, two different scenarios were explored in this work. The first consisted of defining a nominal battery capacity based on commercial off-the-shelf devices to estimate the flight endurance. The second scenario aims to determine a suitable battery by giving a target endurance. This latter approach was employed to assess the maximum endurance that could be achieved by the UAV, without the constraints of off-the-shelf electronic components. Figure 3 illustrates the road map to establish the electric propulsion system during the conceptual design stage.

Figure 3.

Electric propulsion definition and performance assessment methodology.

Once the propulsion system and the aircraft external shape have been framed, it is necessary to estimate the UAV total weight in a more refined and accurate way. This value is then contrasted with the admissible weight stated in the design requirements. Since typical procedures are focused on civil aviation, their application cannot be extended to small aerial platforms. Instead, this work proposes a method that individually accounts for the airframe, propulsion system, battery, and payload weights and then adds each contribution to obtain the total weight as stated in Eq. (1).

The structural weight of the airframe was calculated with respect to the fuselage internal volume and the material’s density. The former was estimated through the convex hull method [39], and high-density foam was assumed as the major airframe material [26]. The weight of remaining components from Eq. (1) was readily obtained from manufacturer’s datasheets.

$$W_{\mathit{\text{TO}}}=W_{\mathit{\text{airframe}}}+W_{\mathit{\text{propulsion}}}+W_{\mathit{\text{payload}}}+W_{\mathit{\text{battery}}}\left[\mathrm{N}\right]$$ (1) E1

2.4 Performance evaluation

The performance analysis is an engineering discipline that relies on inputs from aerodynamic and propulsion assessments. In this sense, the performance evaluation aims to verify if a propulsion set (battery, motor and propeller) meets the mission requirements such as endurance and range. For this purpose, both the power required (P_R) and the power available (P_A) are determined. The former depends of the weight and the aerodynamic efficiency of an aircraft, while P_A depends on the propulsion system and its power source [35].

The power required (P_R) is calculated by means of Eq. (2) [34, 35], where ρ_alt is the air density at a desired altitude, S is the planform wing area, W_TO is the takeoff gross weight, and C_D and C_L are the drag and lift aerodynamic coefficients. Note that W_TO and S were previously defined in the initial sizing phase through the constraint analysis and the aerodynamic coefficients were estimated through the employment of the AVL software and parametric characterization [40]. This term represents the power required for steady cruise condition. However, the target power available (P_A) must be greater than P_R to consider a more demanding flight condition such as the takeoff phase. This excess of power is linked to the rate of climb (RC) as Eq. (3) shows [35]:

$$P_R=\sqrt{\frac{2}{{\rho }_{\mathit{alt}}\bullet S}}\left({W_{\mathit{\text{TO}}}}^{3/2}\right)\left(\frac{C_D}{C_L^{3/2}}\right)\left[W\right]$$ (2) E2

$$P_A=P_R+\mathit{RC}\bullet W_{\mathit{\text{TO}}}\left[W\right]$$ (3) E3

The power available (P_A), which depends of the propeller, motor, and battery characteristics, was estimated through analytical relationships regarding non-dimensional coefficients (C_T, C_P, and J) [35]. The computed value of P_A was verified to be greater or equal to P_R in order to guarantee that the aircraft reaches the absolute ceiling altitude⁴ at the desired rate of climb as explained before. Note that P_R will be less for the cruise condition because the airplane is not climbing anymore and, thus, the excess of power is zero. Finally, for the distributed propulsion case, the total power available is estimated by multiplying the number of propellers by their generated power, respectively.

It is important to highlight that the battery must provide a greater power than P_A to account for energy losses as shown in Eq. (4), where η_prop represents the propeller efficiency, η_e is the efficiency of the electric set (motor, electronic speed driver and battery), and P_bat is the power supplied by the battery:

$$P_A={\eta }_{\mathit{\text{prop}}}{\eta }_e{P}_{\mathit{bat}}\left[W\right]$$ (4) E4

The endurance and range are the key performance parameters because they reflect the time and distance that the aircraft is able to fly without recharging. Their estimation for electric-powered airplanes through analytical models has not received special attention because the devices for the efficient energy storage are still under development and research. Nevertheless, few authors have introduced distinct and elaborated methods to predict the aforesaid parameters from aerodynamic characteristics and battery working conditions [38, 41].

For this case, a simplified but good enough approach has been employed to estimate the endurance and range [36]. This method assumes that the voltage remains constant and the battery capacity is decreased linearly. In this sense, Eq. (5) was used to calculate the endurance at cruise condition, where C_min represents the battery minimum capacity that can be reached in a safety margin and I_b is the battery current. The former was supposed to be 20% of the total battery capacity because lithium-based batteries can be damaged if discharged more than 80% [42]. On the other hand, I_b is a function of the motor current, avionics current, and internal resistance. Its calculation is further explained in Ref. [36], and, hence, it will not be addressed in this work. The numeric value (0.06) in Eq. (5) represents a unit conversion factor because the capacity of batteries is commonly given in milliamperes-hour, I_b in amperes, and the computed time is given in minutes. It is important to highlight that only a single battery device was considered and its number of cells was determined based on the voltage required by the motor. The range was calculated, by using the cruise speed the endurance through the assumption of a rectilinear displacement:

$$E=0.06\left(\frac{C_b-C_{\mathit{min}}}{I_b}\right)\left[\mathit{min}\right]$$ (5) E5

3. Results and discussion

In this section, the methodology previously explained is implemented to set the propulsion configurations for wetland monitoring at the Andean highlands. The obtained results from the UAV conceptual design, aerodynamic assessment, electric propulsion evaluation, and performance analysis are presented.

3.1 Case study

The case study was carried out for wetlands located between 3500 and 4500 masl in the Antisana volcano region from Ecuador. Table 2 enlists some of the key operating conditions and design requirements that the aerial platform needs to fulfill in order to ensure a successful performance. Both cruise and stall speeds were set to ensure good-quality data gathering [43]. The payload mass was set not to exceed 1 kg after making a brief survey of common sensors (Table 1) that UAVs employ for forest monitoring [6]. These data will serve to determine the starting design point by means of the constraint analysis. It is important to mention that the maximum speed was set to be 1.25 times the cruise speed [26].

Parameter	Lower bound	Upper bound
Cruise speed [m/s]	18	22
Maximum speed [m/s]	22	26
Stall speed [m/s]	10	15
Absolute ceiling [m]	3500	4500
Aspect ratio*	4.5	5.5
Payload mass [kg]	1	1

Table 2.

Operating conditions and design requirements.

*

The aspect ratio (AR) is defined as the ratio between the wing span and its mean aerodynamic chord [33].

3.2 Conceptual model

The constraint analysis illustrated in Figure 4 shows the variation of the weight-to-power ratio (W/P) with respect to the wing loading (W/S) for various performance parameters such as the maximum speed (V_max), stall speed (V_s), and ceiling altitude (h_c) [26, 28]. The intersection of these curves enables to define an acceptable region of design and hence, establish a starting point for the aircraft initial sizing. It is worth to mention that for a propeller-driven aircraft, the acceptable region is located below the set of the aforesaid curves. A higher relation W/P is beneficial because this yields the smallest electric motor in terms of power required [26]; however, this is limited by the stall speed for each case. For this work, as the maximum velocity (V_max) function depends on the air density, a curve for each flight altitude analyzed (Table 2) was drawn. Thus, a total of four plots were sketched, involving two values of V_max at two different flight altitudes (h_c).

Figure 4.

Constraint analysis to determine multiple design points.

Two design points were defined within a permissible range of wing loading for small UAVs [27, 44] while maintaining a weight-to-power ratio as high as possible. The W/S range was established by considering the strong linkage between the structural and aerodynamic behavior of the airplane. These design points will allow to investigate the variation of wing planform area and propulsion power for different design requirements and operating conditions (Table 2) to contrast distinct scenarios. Note that the propulsion power computed through the constraint analysis (Table 3) is a preliminary estimate. A more accurate value is calculated once the geometrical parameters and the aerodynamic coefficients are defined.

Parameter	Design point 1 case A	Design point 2 case B
Wing loading [N/m^2]	87.97	165.37
Power loading [N/W]	0.145	0.141
Reference area [m^2]	0.52	0.19
Wingspan [m]	1.60	0.99
Aspect ratio	4.96	5.22
Preliminary WTO [N]	33	20
Preliminary power required [W]	224	244

Table 3.

Initial sizing parameters obtained from the constraint analysis.

Afterwards, the entire airframe (wing and fuselage) was outlined through classical methods of fixed-wing aircraft design [26]. Major geometrical parameters were obtained with respect to the preliminary weight (W_TOp) and wing planform area (S), previously computed in the constraint analysis. Moreover, non-dimensional parameters such as wing aspect ratio (AR) and taper ratio⁵ (λ) were initially established with regard to similar UAV architectures [12] and permissible ranges for this application [26]. The airfoil selected was the NACA 64A210 because it has proven to be suitable for small BWB models [45]. No twist angle was considered for this study. Main parameters from the initial sizing stage are summarized in Table 3.

The geometrical parameters from Table 3 were employed to generate a three-dimensional shape of the proposed airframes as depicted in Figure 5. This resulted in two different models whose primary difference is the size.

Figure 5.

Graphical representation of BWB airframe for two design cases (dimension in mm).

3.3 Aerodynamic assessment

The aerodynamic coefficients from both design concepts were estimated using the AVL open-source software. For this aim, these coefficients were obtained regarding the variation of the attack (α), that is the angle between the freestream velocity vector and the flight path as shown in Figure 6.

Figure 6.

Angle of attack between the freestream and a half wing.

The drag polar obtained from the aerodynamic assessment of both conceptual UAV models (Table 3 and Figure 5) is depicted in Figure 7. In this sense, the left plot illustrates the variation of both lift (C_L) and drag (C_D) coefficients with respect to the angle of attack. It is important to highlight that the AVL software employs the Vortex Lattice Theory to predict the aerodynamic coefficients and, thus, it does not predict the stall behavior at high angles of attack. In addition, this code does not calculate the zero-lift drag coefficient, and, therefore, the total drag coefficient was estimated through semiempirical methods [40, 46].

Figure 7.

Drag polars of UAV conceptual models.

On the other hand, the right plot of Figure 7 depicts the aerodynamic relation lift-to-drag (L/D) as a function of the angle of attack. This relation is a common way of reflecting the aerodynamic efficiency of an aircraft as a function of geometrical configuration and flight conditions. Moreover, this relation is highly important because it directly impacts on the endurance and range. The maximum L/D ratios obtained for cases A and B are 15.5 and 15.8, respectively. For this case, the minimum drag condition has been set for the cruise flight regime [35]. Finally, as seen in Figure 7, an L/D ratio of 8 at cruise condition is expected for both cases, which is in accordance with commercial UAV models [6].

On the other hand, the target power available, estimated by means of Eq. (3), was 302 W for both cases. This value is the same for cases A and B because their weight, aerodynamic coefficients, and operating conditions are almost equal, and hence an equivalent behavior is expected. Note that the estimated value through Eq. (3) is greater than the preliminary power required computed in the constraint analysis (Table 3) because during the initial sizing stage, several parameters were assumed based on historical and statistical data. Therefore, the power required calculated with Eq. (3) is more accurate because it considers the real performance and geometrical characteristics of the conceptual UAV.

3.4 Propulsion modelling

To investigate the integration of electric distributed propulsion into small UAV concepts, the available space on the trailing edge of the fuselage, as shown in Figures 1 and 6 has been considered. This allows estimating a suitable diameter for the propeller Φ_prop (Figure 8) and setting the quantity of motors that fits adequately the available space and generate the power needed.

Figure 8.

Main components of propeller geometry.

The thrust that each propeller must generate, in conjunction with their diameter, is presented in Table 4 for both cases (A and B) with three distinct configurations: single and distributed propulsion with double and triple propulsors. Note that when the propulsion system possesses more than one propulsor, the thrust required per propeller (T_req,i) is obtained by dividing the total thrust required (T_req) by the number of installed propellers. Besides, it is important to mention that the term T_req was obtained by dividing the power required for the desired value for cruise speed (15 m/s for both cases).

Cases			Np
			1	2	3
Case A	Treq,T = 20.1 [N] @ 15 [m/s]	Treq,i [N]	20.10	10.05	6.70
		Φprop [in]	18.00	8.50	5.50
Case B	Treq,T = 20.1 [N] @ 15 [m/s]	Treq,i [N]	20.10	10.05	6.70
		Φprop [in]	13.00	6.00	3.75

Table 4.

Thrust required for distributed propulsion.

3.4.1 Propeller selection

Tables 5 and 6 summarize the performance characteristics of selected propellers for cases A and B, respectively. The suitable models were established regarding the freestream velocity, their diameter, and the rotational speed. Special attention was paid to guarantee that the considered propeller arrangements fit the available space and, at the same time, generate the thrust required. The cruise speed was set for minimum drag condition [35].

Np	1		2		3
Propeller	17 × 10	18 × 10	8 × 6	8 × 10	5 × 3	5 × 5
RPM × 10^3	5	5	13	12	27	25
J	0.44	0.42	0.36	0.4	0.26	0.3
CT	0.069	0.061	0.1078	0.1195	0.1145	0.1213
CP	0.0415	0.0353	0.068	0.095	0.0642	0.0851
ηP	0.73	0.72	0.57	0.5	0.47	0.42
Pshaft [W]	432.5	499.6	294.5	320.6	238.1	249.1
PA,i [W]	315.7	359.7	167.9	160.3	111.9	104.6
PA [W]	315.7	359.7	335.8	320.6	335.7	313.8
Q [Nm]	0.831	0.954	0.216	0.257	0.083	0.095
Ti [N]	20.2	22.7	10.5	10.1	7.4	6.7
T [N]	20.2	22.7	21	20.2	22.2	20.1
Pbat [kW]	0.432	0.499	0.589	0.641	0.714	0.747

Table 5.

Propeller selection—case A.

Np	1		2
Propeller	12 × 6.5	11 × 7	6 × 4	6 × 3
RPM × 10^3	9	10	25	23
J	0.326	0.33	0.23	0.26
CT	0.0979	0.1036	0.0949	0.1166
CP	0.0544	0.0657	0.0473	0.0627
ηP	0.58	0.52	0.46	0.48
Pshaft [W]	589.1	634.5	344.5	355.7
PA,i [W]	335.8	329.9	158.5	170.7
PA [W]	335.8	329.9	316.9	341.5
Q [Nm]	0.627	0.605	0.131	0.148
Ti [N]	23.1	21.5	10.8	10.9
T [N]	23.1	21.5	21.6	21.8
Pbat [kW]	0.589	0.634	0.689	0.711

Table 6.

Propeller selection—case B.

For each propulsion set, several parameters from propellers, such as: non-dimensional coefficients (J, C_T, and C_P), efficiency, torque, shaft power, and thrust generated were extracted from the experimental database at desired operating conditions. Notice that the power available of individual propellers (P_A,i) was obtained by multiplying the power shaft for its corresponding propeller efficiency (η_p). Likewise, the total power available (P_A) is a function of the number or propellers (N_p) implemented, and it was obtained by multiplying N_p for its corresponding individual power (P_A,i). Finally, the power that battery must supply to the propulsion system was obtained by multiplying the power shaft for the number of propellers implemented. Note that the shaft power and the battery power will be the same for a single propeller configuration because only one motor is considered and the electrical efficiency losses were neglected for practical purposes.

Note that, in Tables 5 and 6, the propeller efficiency is dramatically affected as its diameter is reduced. As observed, the lesser the propeller’s size, the higher the rotational speed is needed to generate the required thrust. For instance, smaller models (e.g., three propellers 5 × 3) must operate at a high RPM (27,000) to produce the thrust required (Table 4). In contrast, a single larger propeller (e.g., a propeller 17 × 10) operates at lower RPM (5000) to generate the same thrust. These aspects also reflect the variation of propeller efficiency due to its direct linkage to the cruise speed, rotational speed (ω), and propeller diameter (ϕ_prop) [47, 48]. In this sense, the decrease of propeller efficiency (as its diameter is lower) is attributed to the increase of the induced velocity at the propeller tips, which in turn increments the tip losses due to drag [47].

In addition, the lower propeller efficiency, the higher power that needs to be delivered by the motor (P_shaft). As observed in Tables 5 and 6, the large drop in efficiency (around 20% for the increment of one propeller) produces an increment in total power consumed. Nonetheless, the total power available (P_A) for all the cases remains almost constant, and it agrees the target power available (302 W).

Table 6 shows similar results as Table 5; however, it is worth to highlight that propeller efficiencies are even lower due to the smaller propeller’s diameters for this case, which results in higher values of power shaft compared to case A. Additionally, note that the three-propeller configuration for this case was not assessed due to the unsuitability of allocating more than two propellers within the airframe trailing edge.

3.4.2 Motor selection

The selection of an electric motor for each set of propellers was accomplished through a catalogue-search of different manufacturers. The key parameters for motor selection and propeller-motor matching were the revolutions per minute (RPM), power shaft, and voltage constant. The latter represents a motor constant which correlates the RPMs and the operating voltage. Finally, when the set of the propeller and electric components is found, recommended values given by manufacturers for propeller pairing were used to check if the established arrangement meets the requirements.

Tables 7 and 8 present the motor devices for the propulsion configurations of cases A and B, respectively. As observed, for the distributed propulsion systems with three propellers of case A (Table 7) and with two propellers of case B (Table 8), it was not possible to find any off-the-shelf electrical motor that fulfills the propeller requirements, and, hence, they were not used in further analysis. For all favorable cases (first and second arrangements of case A and first set of case B), adequate motor models were decided, and their operating parameters were employed to size and select a suitable battery to perform a given mission.

Np	Propeller	Motor	RPM	Battery cells	KV	Im0 [A]	Um0 [V]	Rm [Ohm]
1	17 × 10	A40-14L V4 14-Pole	6000	6S	355	0.85	8.4	0.050
2	8 × 6	A40-12S V4 8-Pole	12,820	3S	1350	1.94	8.4	0.018
3	5 × 3	No motor matching	–	–	–	–	–	–

Table 7.

Motor selection—case A.

Np	Propeller	Motor	RPM	Battery cells	KV	Im0 [A]	Um0 [V]	Rm [Ohm]
1	12 × 6.5	A30 8 XL V4	9500	3S	1100	2.8	8.4	0.015
2	6 × 3	No motor matching	–	–	–	–	–	–

Table 8.

Motor selection—case B.

3.5 Performance assessment

As mentioned, the performance of an electric aerial vehicle mainly depends on the battery capacity and the total current draw supplied to the propulsion system. For this work, a single battery device was considered for each configuration, even in the case of distributed propulsion. In this way, the battery current (I_B) was computed as a function of several motor parameters such as voltage constant (K_v), no-load current (I_m0), no-load voltage (U_mo), and motor resistance (Tables 7 and 8). Another parameter employed for the battery selection was its nominal voltage (U_B), which is directly linked to the number of internal cells.

For case A, when a single propeller is considered, the battery voltage is almost twice the voltage of the distributed propulsion scenario. This is because a larger propeller demands higher torque to work properly, and hence a larger motor that works with a higher voltage is employed. Nonetheless, it is also observed that the current supplied by the battery to the motors (I_B) for the distributed propulsion configuration is twice the value of the single propeller set. This is because two motors that work in a parallel are employed in the first case. Moreover, it is seen that the motor of case B-1 requires a lower voltage than the case A-1, because of the difference in size of the propellers employed (Tables 5 and 6). The number of cells of the battery for each configuration was set according to the manufacturer suggestion. Table 9 compiles the results of general battery characteristics such as the number of cells and total current that battery supplies.

Case	Np	Cells	UB [V]	Ueo [V]	Im [A]	IB [A]
CA-1	1	6	22.2	19.1	31.9	28.4
CA-2	2	3	11.1	10.7	32.6	63.8
CB-1	1	3	11.1	10.8	75.4	74.2

Table 9.

Parameter for battery selection—cases A and B.

Regarding the takeoff gross weight estimation, it can be appreciated that the higher the nominal voltage, the higher number of cells from the battery and hence the aircraft weight increases. In this sense, two different scenarios were investigated to select a battery. The first one consisted of selecting a desired capacity from commercial datasheets, and, from this, the vehicle performance was assessed. For this approach, Table 10 and Figure 9 present the UAV weight estimation and its breakdown for the three configurations once the battery characteristics have been outlined. Meanwhile, Table 11 shows the estimated range and endurance under these battery characteristics.

Case	Np	Wmotor [kg]	Wpropeller [kg]	WESC [kg]	Wbattery [kg]	Wfuselage [kg]	Wpayload [kg]	WTO [kg]
CA-1	1	0.275	0.063	0.05	2.024	0.538	1	3.95
CA-2	2	0.190	0.014	0.05	1.055	0.538	1	3.10
CB-1	1	0.177	0.041	0.06	1.055	0.118	1	2.45

Table 10.

Weight assessment—cases A and B.

Figure 9.

UAV—weight breakdown. (a) Case A-1 propeller, (b) case A-2 propeller, and (c) case B-1 propeller.

Cases	Method 1			Method 2
	Input	Output		Input		Output
	CB [mAh]	E [min]	R [km]	E [min]	R [km]	CB [mAh]
CA-1	16,000	27.1	24.39	30	27	17731
CA-2	16,000	12.0	10.8	30	27	39,892
CB-1	16,000	10.3	9.27	30	27	46,378

Table 11.

Battery selection—cases A and B.

It is interesting to note that for case A, the takeoff gross weight (W_TO) of the aircraft is lower when the distributed propulsion system is implemented; however, the weight fractions of the components are altered. For instance, the propulsion system mass fraction increments from 9.8 to 16.4%, and, in similar way, the fuselage mass fraction increases from 25.3 to 32.3%. Although the lessening of weight that resulted from the implementation of the distributed propulsion system, this is overshadowed by the lessening of the endurance and range of the aircraft because the power consumed by the motors in the distributed propulsion scenario is higher, as showed in Table 11.

At last, comparing the single propeller configurations (Figure 9a and c) indicates that fuselage mass fraction is decreased for case B because of its smaller geometry. Note that the battery mass fraction is lower for case B because a 3-cell battery was employed for this configuration; meanwhile, for case A, a 6-cell configuration was utilized, which results in a weight increment.

The second performance approach determined the minimum battery capacity required for a target endurance and range. Table 11 (Method 2) shows the results for case A (one and two-propeller configuration) and case B (one-propeller configuration). As can be seen, for the same target endurance, the battery capacity for the distributed propulsion configuration is dramatically raised compared with the single propeller setting. In other words, the capacity lasts less due to the augment of current draw that the battery must provide as consequence of the loss in propeller efficiency. When comparing the one-propeller configuration of both cases (A and B), it is observable that the endurance reduces for case B because of the aerodynamic and propulsion drawbacks arising from a lesser wetted area and lower efficiency of the propeller.

The results from the second method of performance assessment showed that the required battery capacity to accomplish a specific mission increases for distributed propulsion arrangements. As observed in Table 11, the capacity to perform the same mission flight is different for all three cases because of the notable increment of the current draw (Table 9).

4. Summary

Wetland monitoring at the Andean highlands presents imperious needs in terms of more efficient and environmentally friendly UAV designs. The challenges imposed by the hard-operating requirements make commercial electrical small UAVs not suitable for monitoring tasks. However, their lower environmental impact and low costs encourage their improvements through the implementation of different technologies. In this context, this work has assessed the performance of two different configurations using electrical distributed propulsion system. Since for this particular application the payload required is small, the search for architectures with a large number of propulsors was not suitable, and hence the maximum number of propellers that could be allocated was set to three. The results showed that the propeller’s size reduction affected dramatically its performance, outweighing the benefits in weight and propulsive efficiency. The results from the performance analysis show that endurance, range, and weight decrease for distributed propulsion configurations. The reduction on total aircraft weight is beneficial; however, this was outweighed by the lower thermal performance of the propellers, which reflected on higher total power consumption for the distributed propulsion cases. It is important to highlight that distributed propulsion may be a good option when the propeller’s size does not decrease so dramatically as in the configurations studied. This aspect is a key variable in order to assess a recommendable range for distributed propulsor size, where the benefits obtained from weight, reliability, control, and aerodynamic aspects are not to be affected by the thermal propulsors’ performance.

Another aspect to highlight in this study is that conversely to other aviation sectors, the small UAV category allows to implement or design an important variety of electronic propulsion components, which can be easily tailored for the operating requirements. This aspect opens up the door to generate more flexible designs which incorporates forefront technology such as thrust vectoring, boundary layer ingestion (BLI), and propulsion airframe embedded (PAE) designs, among others in exchange of moderate costs. In this sense, it is important to implement a versatile and flexible optimization methodology, which enables the assessment of different geometrical configurations accounting for the aerodynamic and propulsion integration at system engineering perspective. These routines will enable to evaluate a larger range of aircraft configurations, and it will contribute to establish ideal models that fulfill the requirements from a synergetic standpoint.

Finally, since this study was implemented at conceptual level, the distortion features and momentum reduction produced by the ingestion of the boundary layer have not been considered. Nonetheless, further studies to determine the compromise between size and propeller performance incorporating BLI aspects need to be carried out.

Acknowledgments

The authors would like to thank to Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia (CEDIA) for the financial support given to the present research, development, and innovation work through its CEPRA program, especially for the CEPRA XII-2018-12 fund. Furthermore, the authors gratefully acknowledge the financial support provided by Escuela Politécnica Nacional for the development of the internal projects: PIJ 15-11 and PIS 16-20.

Abbreviations and nomenclature

ρ_alt

air density at desired altitude [kg/m³]

ω

motor/propeller rotational speed [RPM]

ϕ_prop

propeller diameter, [in]

η_e

electric set (motor, battery, ESC) efficiency

η_prop

propeller efficiency

BWB

blended wing body

C_D

drag coefficient

C_L

lift coefficient

C_B

battery capacity [mAh]

C_min

minimum battery capacity [mAh]

E

endurance [min]

I_B

total current [A]

I_m

motor current [A]

I_m0

nominal no-load motor current [A]

K_V

voltage constant [RPM/V]

N_p

number of propellers

RC

rate of climb [m/s]

S

planform wing area [m²]

P_A

power available [W]

P_R

power required [W]

P_Rp

preliminary power required [W]

P_shaft

power shaft of the motor [W]

P_bat

power of battery [W]

Q

torque [Nm]

T

thrust [N]

UAV

unmanned aerial vehicle

U_B

nominal battery voltage [V]

U_m0

nominal no-load voltage of motor [V]

U_eo

equivalent battery voltage [V]

V_max

maximum flight speed [m/s]

V_c

cruise speed [m/s]

V_s

stall speed [m/s]

W/S

wing loading [N/m²]

W/P

weight to power ratio [N/W]

W_battery

battery weight [N]

W_ESC

electronic speed control weight [N]

W_fuselage

fuselage weight [N]

W_motor

motor weight [N]

W_payload

payload weight [N]

W_propeller

propeller weight [N]

W_TO

takeoff gross weight [N]

W_TOp

preliminary takeoff gross weight [N]