• Internal Code :
  • Subject Code : SIT740
  • University : Deakin University
  • Subject Name : IT Computer Science

Identifying Requirements and Modellings 

Table of Contents

Considered requirements and rationale

Methods, models and roles

Performance measures

Findings based on the results of evaluation

References

Considered Requirements and Rationales

The drones require a new network of urban warehouses for support. The additional warehouses require energy for controlling the drones. However, the energy requirement of drones is much less than that of fossil fuel-powered vehicles (Stolaroff, 2018). The rationale behind introducing drones in the commercial transport system is to reshape the energy system by reducing the emission of greenhouse gases. Truck transport constitutes 24 % of the greenhouse gas emission in the transport system of the USA (Stolaroff, 2018). Therefore changes in the energy and environment system are necessary for the USA.

Methods, Models and Roles

The method of calculating the total required thrust that is the drone energy use model is used for understanding the consumption of greenhouse gas by commercial drones. The quadrotor test flights are also performed with nine batteries for assessing the greenhouse gas consumption. The role of the drone energy model is to calculate the total thrust required by the drones (Stolaroff, 2018). The role of the rotor model is to calculate the effective incident air velocity. For that, the component of air speed is taken perpendicular to the motor. So, both of the models are deployed to check the efficiency of the drones. The measurements were taken for different flight campaigns using a quadrotor drone.

Performance Measures

The performance of the drones is measured by the amount of energy it consumed during transportation. The amount of mass that the drones can carry is one of the important performance measures. The performance of carrying loads is directly proportional to the sizes of the quadcopters. For instance, small quadcopters can carry up to 0.5 kg and large octocopter can carry up to 8.1 kg (Stolaroff, 2018). With the help of an analytical model, the energy consumed by a drone is measured. Now that energy consumed reflects the performance efficiency of the drones. By analysing the energy consumption and the mass carrying capacity, it is found that drones have more performance efficiency than vehicles.

Findings Based on The Results of Evaluation

The automated and unmanned vehicles have imposed a shift in the freight sector. The energy has got reshaped with the introduction of flying drones, as they consume low energy and also reduce greenhouse gas consumption. Delivering commercial packages by drones has become a new industry in the freight sector. The multicopters and quadcopters can deliver packages up to 4 km distance. This means that they can travel long distances quenching the need for long-distance commercial packaged delivery needs. The long-distance traveled by the drones do have severe lifecycle impacts and they need an additional warehouse support system. However, the impact is quite less than that of commercial vehicles that are used for ground-based delivery (Stolaroff, 2018). Drone-based delivery can reduce the emission of greenhouse gases and the usage of energy in the freight sector. Now, to limit the environmental depletion the size of the drones should not be exceedingly large and must not be deployed for long-distance delivery, as the warehouse support systems will start consuming more energy. UPS reports 1628 GWh of electric energy consumption in the warehouses that controls the drones. Therefore the global impact due to drones is reduced by limiting investments in the warehouses.

References

Stolaroff, J. K., Samaras, C., O’Neill, E. R., Lubers, A., Mitchell, A. S., & Ceperley, D. (2018). Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nature communications, 9(1), 1-13.

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