Date of Award
3-23-2017
Document Type
Thesis
Degree Name
Master of Science in Industrial Hygiene
Department
Department of Systems Engineering and Management
First Advisor
Robert Eninger, PhD.
Abstract
Applicability of aerosol sampling on multi-rotor unmanned aerial systems (UAS) platform was investigated. Multi-rotor UAS have impacts of wind speed, turbulence, and orientation possibly contributing to sampling bias. The SKC IMPACT sampler, Tecora C.A.Th.I.A., and modified three-dimensionally printed Universal Inlet for Airborne-Particle Size-Selective Sampling were selected based on particle size-selectivity and operational independence to wind. Airflow visualizations concluded that below UAS fuselage was optimal sampler placement. Tests were conducted with Arizona Road Dust in a still-air chamber, and aerosolized sugar in a wind tunnel. Inlet mounting was evaluated in, upright, upside-down, and horizontal orientations. Horizontal orientations of all inlets resulted in negative sampling bias compared to upright/upside-down positions. Sampling bias of inlets mounted on the UAS were compared with and without motor employment. In wind tunnel tests, the IMPACT sampler averaged lowest count concentration bias while the 3D printed inlet resulted in the largest percent difference. Results suggests, UAS turbulence and low wind speed produced negative sampling bias. The 3D printed inlet was designed with Stokes’ scaling factor, and compared with the well-characterized IMPACT sampler. Three-dimensional printing bolstered a cost-effective and fast method of inlet design and construction. Iterative designs can optimize aerosol inlets suitable for mounting on multi-rotor UAS.
AFIT Designator
AFIT-ENV-MS-17-M-179
DTIC Accession Number
AD1051571
Recommended Citation
Chavez, Inna D., "Optimal Configurations for Aerosol Monitoring with Multi-Rotor Small Unmanned Aerial Systems" (2017). Theses and Dissertations. 809.
https://scholar.afit.edu/etd/809
Included in
Occupational Health and Industrial Hygiene Commons, Systems Engineering and Multidisciplinary Design Optimization Commons