Components Mass Validation

In this last section the sizing script created is used to validate the designed propulsion systems. The previous results obtained through the models, in term of power, masses, energy consumption and emissions are now compared with the one coming from the use of manufacturer data for the selected COTS. The fuel and battery mass are let as free variables in order to estimate the energy consumption of the units installed. In this way, the accuracy of the sizing process and components selection can be assessed. Lastly, a

trend of the propulsion mass as a function of the flight time is explored.

4.6.1 Designed Propulsion Mass Evaluation

The same study, iterated before with the support of the mass and power models for the components, is now repeated with the data coming from the market selection. Therefore, the baseline mission profile, shown in figure 4.2, is again simulated for the four propulsion system architectures, but now with the defined characteristics of the components from table 4.9. Still, conservative factors are kept in order to account for the mountings, inefficiencies and extra mass of the tilting mechanism.

Figure 4.6. Mass breakdown of the four de-sign- ed propulsion systems.

Aircraft Electric Gasoline Parallel Series

mbat [kg] 19.11 0.20 1.04 0.58

mf uel[kg] 0.00 3.78 1.79 1.61

mCO2 [kg] 2.43 11.90 5.76 5.14

mtot [kg] 21.58 8.19 7.30 7.61

mtot[%] -3.93 3.68 7.43 3.47

Table 4.10. Total mass of the four de-signed propulsion systems.

The results for the mass budget distribution portrayed in figure 4.6 resemble the ones obtained in the fully computational simulation analysis. For all the cases, a small under-prediction below 10% over the total mass of the propulsion systems is obtained from the tool, with the exception of the electric case that results slightly lighter thanks to the higher energy density of the battery pack selected. This overprediction can mainly be associated with an oversizing of the front motors, due to voltage compatibility and propeller size matching issues.

Other than this, the sizing script appears to be able of giving an accurate prediction of the propulsion system components mass. Therefore, the configuration warrants further researches and a deeper trade-off analysis through the HEPS simulation environment pre-sented in chapter 3.

Performance over cruise flight time

To conclude, one last analysis is performed on the designed propulsion systems. In this case, instead of changing the mass of the components or power parameters such as HF, the mission profile is the main variable. The goal is to evaluate the trend of the total propulsion mass with increasing cruise time. Specifically, the cruise conditions are swept from 0 to 6 hours of flight time, while the vertical take-off, transition and dash, both forward and backward, are kept as defined at the beginning of the chapter. Therefore, the results should

give an idea of the intrinsic efficiency of the different propulsion systems. In fact, since the mass of the components is now kept constant, what really affects the propulsion weight at take-off is the mass of the energy sources stored on board to cover that flight leg. The most efficiently the propulsion system can take advantage of them and the lightest it is going to be over time. It is important to mention that in the figure below the starting mass of 0h of cruise already takes into account the battery and fuel mass to perform the other segments of the mission.

Figure 4.7. Mass of the propulsion system as function of the flight time.

The results presented in figure 4.7 confirm what stated before. The electric configuration, even just after taking-off, dashing for 50 km and landing, overpasses all the other concepts and at 1h of cruise flight it is already over 10kg, which according to [44] corresponds to the upper limit of the mass of the propulsion system for VTOL architectures (which should not be higher than 30% the MTOM). The gasoline, as discussed before, results lighter at the beginning, but due to the higher fuel consumption related to the engine oversizing, after 2h of cruise the mass trend steeply reaches an intersection point from which this solution is not preferable anymore, compared to the hybrid configurations. Finally, the series and parallel architectures show a similar behaviour; although the series presents an initial mass penalty due to the additional generator mounted on board, it slowly reduces this gap thanks to the smarter control logic considered. Moreover, with increasing cruise time, also the recharge strategy of the battery becomes a relevant aspect, which is not taken into account here, and this is why a detailed simulation framework is needed.

To conclude, from this basic sizing analysis is already possible to understand that for an optimal design and correct sizing of the hybrid configuration, not only the UAV features play an important role, but also the mission performed and the strategy adopted to maximize the performance of interest, all together lead to the best component integration on the aircraft. Moreover, the novelty of accurately predict the mass of fuel burnt for an aircraft through an entire mission, results extremely advantageous when designing a UAV. Having the ability to precisely predict the required amount of fuel allows for correct engineering design decisions in the comparison study. Further, this also means that the additional payload capacity of the UAV can be exactly calculated, and the fuel reserves minimized. This would allow for the maximum amount of payload on board or equipment.

Mission Performance