.png){kind=small}
The researchers from our partner, Delft University Faculty of Aerospace Engineering (TUDelft) participated in the psychoacoustic experiments we carried out in May 2024 in Athens by providing the drone equipment, which simultaneously recorded the air quality data, and performing the measurements of the drone noise, as shown in full in the below picture.
The DJI Mavic 3E drone was used for this experiment with the Sniffer 4D air quality sensor as a payload, shown as a close-up in picture 2. This was the same drone used in the February experiment, and the Sniffer sensor had the benefits of measuring the air quality and providing extra weight to create more drone noise for the measurements.
The microphone array used to measure the sound is the CAE Bionic M-112 array, with 112 microphones, an optical camera, and a tripod (Picture 3). The system has a sampling rate of 48 kHz, and it is placed at a 45-degree angle from the ground to best capture the flight trajectories. The measurements took place over the full one minute that the participants were listening. To synchronise the clocks from the University of Salford's recording equipment and those of TU Delft, a clap was performed, so the measurements could be aligned from there. The recorded data from these microphones is used in a process called beamforming to localise the acoustic source, creating the strength of a source map on top of the recorded optical image. It is also used in more traditional noise metrics such as frequency versus time and sound pressure levels. Combined with the responses from the participants, this noise data can provide a direct correlation between these metrics to human annoyance.
Additionally, a B&K Sound Level Meter (SLM) was used to capture the precise sound metrics necessary for psychoacoustic analysis, which are LAeq, LCeq, LAF95, and LAF5. These metrics were recorded across the full minute in which the participants were observing the noise.
Additionally, air quality measurements were carried out throughout the drone flights to record the local air quality across the park, creating a 3D map of the concentrations. The species include CO, CO2, VOC, SO2, NO, NO2, O3, PM10, PM2.5, and PM1, along with temperature and humidity. Due to the presence of a busy road nearby, higher air pollutant concentrations of for example CO were obtained near this busy road, as CO is directly emitted by traffic. Additionally, these measurements can be compared to the air quality measurements from February to see how the concentrations change throughout the year. Measuring air quality with the use of drones is one of the examples of how Urban Air Mobility can be used.
Related Articles
You can stay updated by subscribing to our newsletter and checking the news section on the project’s website. Don't forget to engage with the project results on LinkedIn and X.
Comments