Aeronautics project continues to thrive and leads to innovation!
Inspired by the vision of the previous generation of project members, Aeronautics presents the inception of the design of a pioneering Unmanned Aerial Vehicle (UAV). This particular UAV will be powered by solar radiation with the use of solar panels, with its primary objectives the surveillance of forest areas in case of wildfires, the prediction of the direction they are most likely to spread and the aerial observation of high at risk re-ignition areas. It is the first time Aeronautics proceeds to develop a UAV beyond the barriers of their knowledge and the guidelines of a competition. For this reason and in order to archive our goal, the project subsystems are required to expand into new design methods in an attempt to meet the challenges that will arise.
To meet the needs and requirements of such a design, a new subsystem was created in the project by the name of Solar Energy Management (SEM), which is responsible for the precise distribution of solar energy throughout the electrical components of the aircraft. SEM subsystem is currently in the process of choosing the right solar panels and the appropriate type of rechargeable battery considering our goal. In addition, they are required to develop the necessary circuit layout that charges the battery correctly by using only solar energy.
Avionics subsystem has begun the development procedure of an Automated Pilot system that is intended to control the aircraft, while aspires to engage in techniques of path optimization, thus the aircraft will fly autonomously in areas that we will define depending on the needs. Moreover, they intend to compare flight data and imaging with meteorological data to predict the direction of fire spread, but also archive the flight path of minimizing energy consumption.
Aerodynamics Design subsystem is responsible for the development of an innovative aircraft configuration that will not only have the necessary surface area to generate lift, but also include the necessary number of solar panels and electrical systems. Cooperation with the Avionics subsystem will be particularly important, as it will be needed to give extra attention to the stability and controllability of the aircraft in order to archive long flight endurance in steady level flight. The aerodynamic mission of the UAV will be set in a next part of the design.
Finally, Structural subsystem is required to conduct studies in the structures of solar powered UAV’s but also take into consideration that the aircraft has to be light in order to lift the demanding payload determined by the Avionics and SEM subsystems. In addition, they will be challenged to choose the materials and conduct the necessary structural analysis of various design assemblies of the aircraft to ensure its structural integrity.
Our payload reflects the mission of the UAV and the goals we are trying to achieve through it. What we consider as payload are all the Avionics systems that are not necessary to achieve flight but required in order to automatically control the UAV, monitor woodland areas and ensure wide communication range. More specifically, a Pixhawk flight controller is included to achieve automatic control of the aircraft, controlling the motor and servomotors connected to control surfaces (the latter are also part of the payload). As for the fire monitoring and recognition process, the camera system, comprising of two cameras and a gimbal, will provide feed to a Raspberry Pi computer running the recognition algorithm. Moreover, a satellite antenna will be mounted on the aircraft, enabling us to constantly monitor the flight through a Ground Station. As far as SEM systems are concerned, it is important to mention that solar panels are not considered part of the payload, as their mass depends on the main wing’s planform area, which in turn is determined through aerodynamic design parameters. Assuming the aforementioned payload, its total mass amounts to approximately 4.5Kg, which is an initial estimation, as some components have not been precisely sized yet (eg the antenna), while a safety margin has also been included in the calculation
Flight profile & altitude
One of the first objectives defined during the determination of a UAV’s mission, is the flight altitude. We know as a fact that altitude affects the surveillance ability of the UAV. As the continuous aerial surveillance is the main objective of our solar UAV, an operating altitude higher than usual was chosen to cover as much of the aeria under surveillance as possible. Specifically, the solar unmanned aerial vehicle was set to fly at 2.5 kilometers above sea level.
Regarding the flight profile of Phoenix, it is worth noting that most of it is occupied by the loiter and cruise phases, while the climb segment is estimated to be quite time consuming, due to the selected flight altitude and the low climb rate which is inevitable.
Daily Obtained Energy Profile
To calculate the solar radiation reaching the Earth’s surface, satellite data are provided for long-term averages, calculated from hourly values of total and diffuse radiation. Below we present a daily average radiation distribution for June for the urban area of Thessaloniki . The solar radiation data available here have been calculated from the operational solar radiation dataset provided by the Climate Monitoring Satellite Application Facility (CM SAF). The distribution of interest in our case is the global one. It is the sum of the direct normal irradiance (DNI) and the diffuse horizontal irradiance, as well as the radiation reflected from the ground.
To calculate the daily solar energy received, a simple mathematical model is initially used, consisting of two parameters: maximum irradiance and day length, which can be easily interpreted for the purposes of our mission. Many odds are added to the model as factors affecting the daily solar energy , introduced either due to weather phenomena or other important aircraft properties.
MGTOM & Mass fractions
The MGTOM(Maximum Gross Take Off Mass) and mass fractions were estimated by utilizing correlation equations and trade studies both for Solar UAVs but also for conventional aircrafts. Due to the large number of unknown variables the Structural sub–system developed a MatLab code–based tool that converges into various results regarding our initial requirements. The convergence was based on Newtons bisection law and correlation equations for the battery mass fraction, the MGTOM, the required payload, and the empty weight fraction. The beginning of the analysis started with the aircraft’s weight classification. We distinguished the aircraft into 3 main mass fractions as seen below:
- we empty weight: contains the mass of the airframe plus the mass of the propulsion system and the solar panels, its corresponding mass fraction is we/wo
- wb battery mass: contains the mass of the battery and its corresponding mass fraction is BMF
- wpl payload mass: The payload mass is the only known mass of the aircraft and it has been estimated previously. In our case it contains the mass of all the individual electronic systems that will allow us to carry out our mission, its corresponding mass fraction is wpl/wo
First step was to calculate the BMF for every flight segment. If we are trying to achieve the 24-hour flight the batteries will be needed for a total of 10 hours during insufficient sunlight. As for the energy consumption in every flight segment we know for a fact that the greatest demand in power exists during the climb segment. Moreover, to achieve the 24-hour flight we needed to take-off and climb while the motor is powered from solar radiation, otherwise we would need excessive amounts of battery capacity. The additional energy storage can be expressed in two ways, one , as an extra battery weight that would increase the overall MGTOM and secondly as a higher energy density that is a battery characteristic which depends on the supplier and the targeted market.
By narrowing down our BMF calculation into 2 flight segments loiter and cruise for a total of 10 hours we managed to acquire the battery mass fraction of the aircraft.
The empty weight fractionis correlated to the MGTOM thus the need to converge arithmetically in a result. Through consecutive runs we realized that the convergence of the result for MGTOM was affected greatly from the battery’s specific energy, thus we adjusted the code to converge for a variety of energy density values.
On the diagrams below you can observe the results of our analysis for powered–high aspect ratio aircrafts.
Constraint diagram & power consumption
Moving on with the design phase we proceeded into the performance of a constraint analysis in order to give shape to aerodynamic
mission parameters regarding performance and power consumption. Although, the results that came up from the MGTOM estimation made us study two different cases, one for MGTOM = 55.58 Kg & Esb = 260 Wh/Kg and a second one for MGTOM = 37.48 Kg & Esb = 325 Wh/Kg.
For the construction of an aircraft’s constraint diagram a tool has been created from the aerodynamics design sub-system through excel, that takes as inputs all the aircraft’s requirements (weight ratios, air density and flight altitude) and helps us estimate inputs such us stalling speed, maximum velocity, ROC, TOD and range. All these calculated inputs were then confirmed through xflr5 an open-source low fidelity CFD software. Note that from this point on in the following procedures we were looking into two different case studies for different MGTOM and battery energy densities.
The main constraint which determines the wing’s loading was the stall velocity and regarding the category of our design mindset (high AR aircrafts) it takes low values in the order of magnitude of 7 to 10 m/s. As a result, the weighted average power consumption was estimated to have a lower value for the second case, clarifying that this power requirement is only for loiter and cruise segments, which demand the least in terms of power consumption.
There are two proposed photovoltaic panels technologies. The first one is monocrystalline flexible solar cells , the ones our team decided to test with have an efficiency of 22-23%. The second one is flexible GaAs solar cells which are now one of the most advanced solar cell technologies in the world . Τheir efficiency is usually above 30% .
The results of our research showed that, both case studies for different battery energy densities the use of GaAs panels is the optimal option for the flight endurance part of our mission. GaAs panels require none or little excess area or mean power reduction, while the use of monocrystalline panels did introduce some serious extra requirements for mean power or excess area.
Taking into consideration the MGTOM & mass fractions analysis above as well as the power consumption estimation we decided to validate our results in order to proceed in the conceptual design requirements selection. Beginning from the weighted average power consumption in every flight segment and some specific battery energy density values, we calculated the BMF in order to compare it with the estimated one from the MGTOM & mass fractions analysis. Our trade studies showed that there was a slight error from each estimation method, while the optimal results were in the case that the BMF from the MGTOM is coincident with the BMF from the weighted average power consumption.
The difference in battery weight is not something we did not expect, moreover the results for weight and power emerged from correlation equations and sensitivity analyses and they cannot be considered as estimations but as first guesses. The goal at this point was to find solutions that satisfied both the required power and the weight fractions. We took into consideration some options in that matter, we could make compromises in the battery powered flight endurance by setting a limit to the motor operation and including some unpowered glide flight. Also, we could reduce the empty weight fraction which will consecutively allow an increase to the BMF, although reducing the empty weight fractions not a viable solution. As we have mentioned in a previous section the empty weight ensures the structural integrity of our aircraft as it resembles our airframe weight. Regarding our case where we tend towards a sailplane design is not something we would be able to compromise on a big scale.
The battery’s energy density value plays a big part in the appearance of this error as we have seen form the deviations in the MGTOM vs Esb diagram as well as from the later conducted validation studies. The MGTOM starts to converge for higher battery specific energies.
Relying on the fact that we could not compromise the structural integrity of our aircraft and neither reduce our endurance in flight we have decided to contact various enterprises that conduct research on such battery technologies while keeping our requirement for the energy density over 325Wh/Kg.
From an economic/ergonomic point of view
Beginning with the economic risk assessment it is proper to mention that bigger is not always better! We saw that the dimensions in every case are much more greater regarding the team’s standards, this results in an increase to the overall budget for experimentation, prototype construction and the construction of the final “product”. The raw materials needed to fabricate a UAV of such a scale would be much more expensive especially because we have settled on composites. As a result, it is required to scale down the UAV with dimensional analysis methods in order to save funds due to failure risks in test flights and optimization stages. Although a case where the UAV would be smaller due to high battery energy density batteries may not seem as imposing for the eye, the surfaces required to be covered with solar panels are still oversized and if in the future we settle on GaAs solar panels a failure would be catastrophic in economic terms.
Note that a scaled down UAV won’t be able to carry 4.5Kg of payload thus, all the avionics systems testing would require the function of a previous UAV and the scaled prototype would serve only as a way to optimize the energy retrieval and distribution systems.