Representative-in-class vehicles for fleet-level aviation noise analysis

Global air traffic demand is projected to nearly double by 2035 (7.2 billion passengers) compared to the 3.8 billion passengers in 2016. At such a growth rate, the aviation sector might cause an important detriment of the welfare of those living around airports via a substantial increase in noise. For addressing such a concern, the aviation industry is required to assess a significant number of aviation scenarios, involving different technology platforms and operational procedures, in order to define the strategies that ensure the higher reduction in aircraft noise impact. A common approach to reduce the combinatorial nature of fleet-level studies and enable more flexibility for exploring multiple aviation scenarios, is to simplify the fleet into a number of representative-in-class vehicles that capture the noise performance of the various classes within the fleet. In this paper, a statistical classification process is implemented for reducing the UK commercial fleet into a number of representative-in-class vehicles based on aircraft noise characteristics. The optimal number of representative-in-class aircraft is analysed for three airports in the UK (London Gatwick, Heathrow and Stansted), with significant differences in aircraft movements and fleet composition, on the basis of the accuracy vs. computational time when calculating noise contour areas. Finally, it is discussed the use of these representative-in-class vehicles as baseline models for projecting the reduction in aviation noise impact with future technology implementation

Effects of a hovering Unmanned Aerial Vehicle on urban soundscapes perception

Several industry leaders and governmental agencies are currently investigating the use of Unmanned Aerial Vehicles (UAVs), or ‘drones’ as commonly known, for an ever-growing number of applications from blue light services to parcel delivery. For the specific case of the delivery sector, drones can alleviate road space usage and also lead to reductions in CO2 and air pollution emissions, compared to traditional diesel-powered vehicles. However, due to their unconventional acoustic characteristics and operational manoeuvres, it is uncertain how communities will respond to drone operations. Noise has been suggested as a major barrier to public acceptance of drone operations in urban areas. In this paper, a series of audio-visual scenarios were created to investigate the effects of drone noise on the reported loudness, annoyance and pleasantness of seven different types of urban soundscapes. In soundscapes highly impacted by road traffic noise, the presence of drone noise lead to small changes in the perceived loudness, annoyance and pleasantness. In soundscapes with reduced road traffic noise, the participants reported a significantly higher perceived loudness and annoyance and a lower pleasantness with the presence of the same drone noise. For instance, the reported annoyance increased from 2.3±0.8 (without drone noise) to 6.8±0.3 (with drone noise), in an 11-point scale (0-not at all, 10-extremely). Based on these results, the concentration of drone operations along flight paths through busy roads might aid in the mitigation of the overall community noise impact caused by drones

Facilitation of Ca 2+ -dependent exocytosis by Rac1-GTPase in bovine chromaffin cells

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66111/1/jphysiol.2003.039073.pd

Towards predicting noise-power-distance curves for propeller and rotor powered aircraft

Propeller and rotor based propulsion systems are the predominant choice of power delivery system in the upcoming Urban Air Mobility market. Fully electric air-taxis (car sized vehicles with Vertical Take-off and Landing, VTOL, capabilities) concepts are using the benefits of the scalable properties of electric motors to distribute propulsor units all over the airframe. The large variety of concepts and configurations of these vehicles poses a serious issue in predicting noise generated on the ground. The need for a high-level model to aid in acoustic decision making is evident. Through the demonstrated methodology of computationally deriving Noise – Power – Distance curves for conventional turbo fan aircraft, this paper delivers the capability of dealing with propeller propulsion systems and the associated propeller tonal noise sources to generate the NPDs and therefore noise exposure maps. The aims can be broken down into two objectives: a) demonstrate the capabilities of the proposed propeller harmonics noise scaling laws to calculate noise variation from a baseline scenario and b) incorporate the scaling components into the larger capability of producing noise exposure contours, by the means of computationally deriving Noise-Power-Distance curves for propeller power aircraft. Preliminary NPD curves for General Aviation sized propeller power aircraft are generated and discussed