Sensors and exposition analyses for aerosol transport in dynamic situations (SENSAERO)
Motivation:
Aerosol particles play an essential role in the transmission of respiratory viruses such as SARS-CoV-2, which is why masks, ventilation and air purification methods are effective protective measures. Investigating the effectiveness of such measures requires fluid dynamics studies, whether using experimental or numerical methods, as flow conditions largely influence their effectiveness. In particular, the flow direction in rooms plays a special role. If the air circulation is low, for example, the air exchange can be very slow, and in the case of mobile air cleaners, the positioning in the room, the noise development and the flow around obstacles such as furniture, lamps, etc. are important.
Objectives:
The investigation of the flow conditions can be done numerically as well as by experiments. In particular, the project partners conduct experimental studies with Lagrangian particle tracking on aerosol dispersion in spaces and have extensive knowledge in experimental flow dynamics, optical flow measurement and data analysis for different flow situations such as boundary layer flows and rotating flows. LED arrays, light-sectioning techniques and density-neutral, submillimetre-sized, so-called helium-filled soap bubbles (HFSB) are used. The latter are used to simulate small aerosol particles that follow the flow, enabling Lagrangian tracking in large volumes using fast cameras and pulsed illumination. One disadvantage of this experimental methodology is that dynamic situations involving humans are difficult to reproduce. In particular, the intense light of the recording technique poses a danger. In addition, the light-sectioning technique relies on unobstructed optical transmission paths. The third shortcoming is that not only the distribution of the aerosol particles in the room, but also the probability of their inhalation by humans is of interest. In order to be able to carry out experimental studies with test persons, a portable sensor is being developed that is able to detect and count helium-filled micro-soap bubbles as surrogate particles in front of the airway openings of persons.
Methods and results:
The basis for the experiments carried out is a mobile counting device for HFSBs. It is a low-cost and robust sensor solution consisting of a measuring head, which in turn consists of an LED illumination and a camera with optics, with which images of the light reflection of the approximately 350 μm HFSBs are generated. This measuring head is connected via a CSI connection to a Raspberry-Pi single board computer running an application that helps to detect and count the submillimetre bubbles using image processing methods. The system can be worn by a test person in the form of a face mask, capturing HFSBs in the air intake area while also allowing them to be passively distributed in larger quantities throughout the room. All devices are controlled by a central computer via WLAN and a web based frontend, which starts and stops the measurement process and initiates the data transmission. Additionally, the system was expanded to integrate and manage other measurement systems, such as particulate matter sensors (Particulate Matter SPS30).
Publications:
S. Merbold, G. Hasanuzzaman, T. Buchwald, C. Schunk, D. Schmeling, A. Volkmann, et al.
Referenzexperiment zum Aerosolpartikel-Transport für dynamische Situationen
29. Fachtagung Experimentelle Strömungsmechanik, 06.-08.09.2022, Illmenau, Germany
S. Merbold, G. Hasanuzzaman, T. Buchwald, C. Schunk, D. Schmeling, A. Volkmann, A., et al.
Reference experiment on aerosol particle transport for dynamic situations
tm-Technisches Messen, 90, 5 (2020): 340-352
G. Hasanuzzaman , T. Buchwald, A. Schröder, C. Egbers, C. Schunk, U. Hampel
DATIV - Remote enhancement of smart aerosol measurement system using Raspberry Pi based distributed sensors
MDPI Sensors, 24, 13 (2024): 4314
C. Schunk
DATIV-Dynamic Aerosol Transport for Indoor Ventilation with Smart Array of Particulate MAtter Sensors (SAPMAS)
2023, https://www.hzdr.de/publications/Publ-38035, https://github.com/chsc/DATIV