Analysis of Sensor Locations in Drone Aided Environmental Monitoring System Using Computational Fluid Dynamics (CFD) Studies

Abstract

Recent advancements in unmanned aerial vehicle (UAV) technology have enabled flexible, high-resolution monitoring of atmospheric CO₂, particularly in complex or otherwise inaccessible environments. This study employs Computational Fluid Dynamics (CFD) to investigate the downwash flow field of a quadcopter UAV in hover condition with the objective of identifying low-disturbance regions suitable for accurate atmospheric sensor placement. A quadcopter model was simulated using the SST k-ω turbulence model. Simulations were performed at rotor speeds ranging from 1000 to 6000 rpm. Results show that the strongest downwash and turbulence occur directly beneath the rotors, while airflow above the central fuselage and regions laterally distant from the rotors remain significantly calmer. The findings strongly recommend placing gas sensors either above the drone body or sufficiently far horizontally from the rotor plane to minimize measurement errors caused by propeller-induced flow.

Keywords

UAV / Quadcopter / Computational fluid dynamics / Downwash / Sensor placement / Autodesk Fusion / ANSYS Fluent

Acknowledgements

G.K. and P.G. thank to Devicha Centre for Climate Change for travel and other support during study.

Author Contributions

Conceptualization: P.K., G.K.; Methodology, P.K.; Software: G.K., P.G.; Validation: A.M., P.K.; Formal Analysis: P.G.; Original Draft Preparation: P.K. & G.K.; Review & Editing: P.G. & A.M.

Ethics Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The data is available with CSIR-NEERI.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Jońca J, Pawnuk M, Bezyk Y, Arsen A, Sówka I. Drone-Assisted Monitoring of Atmospheric Pollution—A Comprehensive Review. Sustainability 2022, 14, 11516. DOI:10.3390/su141811516

[2] Li XB, Wang DS, Lu QC, Peng ZR, Wang ZY. Investigating vertical distribution patterns of lower tropospheric PM2.5 using unmanned aerial vehicle measurements. Atmos. Environ. 2018, 173, 62-71. DOI:10.1016/j.atmosenv.2017.11.009

[3] Osei-Ntansah K, Reliford A, Smith ST. Design and Implementation of 3D Printed Sensor Mounts for sUAS-Based Atmospheric Research. In Proceedings of the AIAA SCITECH 2025 Forum, Orlando, FL, USA, 6-10 January 2025.

[4] Li S. Wildfire early warning system based on wireless sensors and unmanned aerial vehicle. J. Unmanned Veh. Syst. 2018, 7, 76-91. DOI:10.1139/juvs-2018-0022

[5] Ventura Diaz P, Yoon S. High-fidelity simulations of a quadrotor vehicle for urban air mobility. In Proceedings of the AIAA SciTech 2022 Forum, San Diego, CA, USA, 3-7 January 2022. p. 0152. DOI:10.2514/6.2022-0152

[6] Ilyas M, Noack BR, Hu G, Xiong H, Gao N, Khan A, et al. Drone anemometry of atmospheric winds—A review. Phys. Fluids 2025, 37, 061302. DOI:10.1063/5.0259355

[7] Christodoulou K, Vozinidis M, Karanatsios A, Karipidis E, Katsanevakis F, Vlahostergios Z. Aerodynamic Analysis of a Quadcopter Drone Propeller with the Use of Computational Fluid Dynamics. Chem. Eng. Trans. 2019, 76, 181-186. DOI:10.3303/CET1976031

[8] Rangan S, Shanmugam Y, Valluvan G, Prakash T. A customized drone for ocean and atmospheric measurements and its performances. Marit. Technol. Res. 2024, 6, 267638. DOI:10.33175/mtr.2024.267638

[9] Yaakub MF, Wahab AA, Abdullah A, Mohd NN, Shamsuddin SS. Aerodynamic prediction of helicopter rotor in forward flight using blade element theory. J. Mech. Eng. Sci. 2017, 11, 2711-2722.

[10] Kokate P, Middey A, Sadistap S. Drone-Aided Particulate Monitoring System for Industrial Complex to Analyze the Dust Suppressing Capacity. J. Appl. Eng. Sci. 2023, 13, 237-242, DOI:10.2478/jaes-2023-0030

[11] Ventura Diaz P, Yoon S. High-Fidelity Computational Aerodynamics of Multi-Rotor Unmanned Aerial Vehicles. In Proceedings of the 2018 AIAA Aerospace Sciences Meeting, Kissimmee, FL, USA, 8-12 January 2018. DOI:10.2514/6.2018-1266

[12] Shamsudin SS, Madzni MZ. Aerodynamic Analysis of Quadrotor UAV Propeller using Computational Fluid Dynamic. J. Complex Flow 2021, 3, 28-32.

[13] Arafat M, Ishak IA, Mohd Maruai N, Muhammad Razif MR, Mohd Sakri F, Anugraha RA. CFD Assessment for Small UAV Propeller Aerodynamics. CFD Lett. 2024, 17, 81-92. DOI:10.37934/cfdl.17.6.8192

[14] Umirzakova S, Muksimova S, Shavkatovich Buriboev A, Primova H, Choi AJ. A Unified Transformer Model for Simultaneous Cotton Boll Detection, Pest Damage Segmentation, and Phenological Stage Classification from UAV Imagery. Drones 2025, 9, 555. DOI:10.3390/drones9080555

[15] Russell S, Carl R, Martin K. Comprehensive Analysis Modeling of Small-Scale UAS Rotors - NASA Technical Reports Server (NTRS). Available online: https://ntrs.nasa.gov/citations/20170011320 (accessed on 26 January 2026)

[16] İnan AT, Çetin B. CFD Analysis of Aerodynamic Characteristics in a Square-Shaped Swarm Formation of Four Quadcopter UAVs. Appl. Sci. 2024, 14, 6820. DOI:10.3390/app14156820