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The eVTOL industry is a rapidly growing mass market expected to start in 2024. eVTOL compete, caused by their predicted missions, with ground-based transportation modes, including mainly passenger cars. Therefore, the automotive and classical aircraft design process is reviewed and compared to highlight advantages for eVTOL development. A special focus is on ergonomic comfort and safety. The need for further investigation of eVTOL’s crashworthiness is outlined by, first, specifying the relevance of passive safety via accident statistics and customer perception analysis; second, comparing the current state of regulation and certification; and third, discussing the advantages of integral safety and applying the automotive safety approach for eVTOL development. Integral safety links active and passive safety, while the automotive safety approach means implementing standardized mandatory full-vehicle crash tests for future eVTOL. Subsequently, possible crash impact conditions are analyzed, and three full-vehicle crash load cases are presented.
Two- and three-dimensional avalanche dynamics models are being increasingly used in hazard-mitigation studies. These models can provide improved and more accurate results for hazard mapping than the simple one-dimensional models presently used in practice. However, two- and three-dimensional models generate an extensive amount of output data, making the interpretation of simulation results more difficult. To perform a simulation in three-dimensional terrain, numerical models require a digital elevation model, specification of avalanche release areas (spatial extent and volume), selection of solution methods, finding an adequate calculation resolution and, finally, the choice of friction parameters. In this paper, the importance and difficulty of correctly setting up and analysing the results of a numerical avalanche dynamics simulation is discussed. We apply the two-dimensional simulation program RAMMS to the 1968 extreme avalanche event In den Arelen. We show the effect of model input variations on simulation results and the dangers and complexities in their interpretation.
Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9% in city-like worlds and reduces energy consumption by 28%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.
Up in the clouds and above fuels and construction materials must be very carefully selected to ensure a smooth flight and touchdown. Out of around 38,000 single and dual-engined propeller aeroplanes, roughly a third are affected by a new trend in the fuel sector that may lead to operating troubles or even emergency landings: The admixture of bio-ethanol to conventional gasoline. Experiences with these fuels may be projected to alternative mixtures containing new components.
Numerical models have become an essential part of snow avalanche engineering. Recent
advances in understanding the rheology of flowing snow and the mechanics of entrainment and
deposition have made numerical models more reliable. Coupled with field observations and historical
records, they are especially helpful in understanding avalanche flow in complex terrain. However, the
application of numerical models poses several new challenges to avalanche engineers. A detailed
understanding of the avalanche phenomena is required to specify initial conditions (release zone
dimensions and snowcover entrainment rates) as well as the friction parameters, which are no longer
based on empirical back-calculations, rather terrain roughness, vegetation and snow properties. In this
paper we discuss these problems by presenting the computer model RAMMS, which was specially
designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged
equations governing avalanche flow with first and second-order numerical solution schemes. A
tremendous effort has been invested in the implementation of advanced input and output features.
Simulation results are therefore clearly and easily visualized to simplify their interpretation. More
importantly, RAMMS has been applied to a series of well-documented avalanches to gauge model
performance. In this paper we present the governing differential equations, highlight some of the input
and output features of RAMMS and then discuss the simulation of the Gatschiefer avalanche that
occurred in April 2008, near Klosters/Monbiel, Switzerland.