Crash Area Estimation for Ground Risk of Small Unmanned Aerial Vehicles Due to Propulsion System Failures.

2022 ◽  
Author(s):  
Mohd Hasrizam Che Man ◽  
Hu Haoliang ◽  
Kin Huat Low
2020 ◽  
Vol 313 ◽  
pp. 00045 ◽  
Author(s):  
Jakub Hnidka ◽  
Dalibor Rozehnal ◽  
Karel Maňas

Small unmanned aerial vehicles (SUAVs) have found a widespread application in past decades. However, as the criticality of the missions for which they can be used increases, the demand for improvement of their efficiency increases as well. The paper focuses on a propeller driven SUAVs of a multirotor type, equipped with an electric motor, battery and propeller. The paper presents a simplified method of calculation of the SUAV maximal endurance, if the characteristics of all components of the propulsion system are known. To improve the overall efficiency of the propulsion system of an SUAV, the correct combination of all propulsion system components is critical. However, the largest impact on the maximal endurance is, arguably, caused by the propeller. The paper proposes a simple method of optimizing the propeller characteristics for hover and compares the proposed propeller design with conventional and commercially available propellers.


2021 ◽  
Vol MA2021-03 (1) ◽  
pp. 147-147
Author(s):  
Xin Tong ◽  
Yuqing Wang ◽  
Yixiang Shi

2021 ◽  
Vol 11 (11) ◽  
pp. 5209
Author(s):  
Cinzia Amici ◽  
Federico Ceresoli ◽  
Marco Pasetti ◽  
Matteo Saponi ◽  
Monica Tiboni ◽  
...  

The design of the propulsion system for Unmanned Aerial Vehicles (UAVs) demands an inclusive multidisciplinary approach from the earliest design phases, since every design choice strictly affects and is affected by the overall working conditions. This paper presents a review of the scientific literature focused on the design methods applied in defining and sizing the propulsion system of drones. The analysis, performed with a systematic approach, evaluated 123 papers according to two custom classification taxonomies, which investigated respectively the primary aim and specific content of the works. Finally, literature indications and hints were combined into an integrated framework for the functional design of the propulsion system of UAVs. The procedure aimed to support the designer in the preliminary selection of the propulsion candidates and the quick sizing of the supply system, during the first phases of the design process. According to the literature, design methods dramatically change depending on the expected applications and working conditions of UAVs, so that the detailed design of specific drone elements and propulsion components represents the focus of most of the papers in this field.


Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3969
Author(s):  
Massimo Cardone ◽  
Bonaventura Gargiulo ◽  
Enrico Fornaro

This article presents a numerical model of an aeronautical hybrid electric propulsion system (HEPS) based on an energy method. This model is designed for HEPS with a total power of 100 kW in a parallel configuration intended for ultralight aircraft and unmanned aerial vehicles (UAV). The model involves the interaction between the internal combustion engine (ICE), the electric motor (EM), the lithium battery and the aircraft propeller. This paper also describes an experimental setup that can reproduce some flight phases, or entire missions, for the reference aircraft class. The experimental data, obtained by reproducing two different take-offs, were used for model validation. The model can also simulate anomalous operating conditions. Therefore, the tests chosen for the model validation are characterized by the EM flux weakening (“de-fluxing”). This model is particularly suitable for preliminary stages of design when it is necessary to characterize the hybrid system architecture. Moreover, this model helps with the choice of the main components (e.g., ICE, EM, and transmission gear ratio). The results of the investigation conducted for different battery voltages and EM transmission ratios are shown for the same mission. Despite the highly simplified model, the average margin of error between the experimental and simulated results was generally under 5%.


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