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Carvalho M, Hangan H. Modelling Weather Precipitation Intensity on Surfaces in Motion with Application to Autonomous Vehicles. Sensors (Basel) 2023; 23:8034. [PMID: 37836864 PMCID: PMC10575205 DOI: 10.3390/s23198034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 10/15/2023]
Abstract
With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or cause water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modeling covers a wide range of parameters, such as varying the wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modeling results were partially validated against direct measurements carried out using a test vehicle. The model outputs showed a strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed, and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. A dimensional analysis of the problem highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface for the perceived precipitation level. The proposed model was used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.
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Affiliation(s)
- Mateus Carvalho
- Department of Mechanical Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada;
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Canepa F, Burlando M, Romanic D, Solari G, Hangan H. Downburst-like experimental impinging jet measurements at the WindEEE Dome. Sci Data 2022; 9:243. [PMID: 35624297 PMCID: PMC9142528 DOI: 10.1038/s41597-022-01342-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/04/2022] [Indexed: 11/09/2022] Open
Abstract
This paper describes the dataset of measurements collected and published in the context of the comprehensive experimental campaign on downburst-like outflows that was performed at the WindEEE Dome at Western University, Canada. Downbursts are strong downdrafts of air that originate from thunderstorm clouds and create vigorous radial outflows upon hitting the ground. Downbursts are here simulated as transient phenomena produced by large-scale impinging jet. Two jet velocities were adopted in the experiments. The three-component velocity measurements were recorded using 7 Cobra probes mounted on a vertical stiff mast and displaced at 10 radial positions in respect to the downdraft centerline. For every radial position, each experiment with the same initial condition was repeated 20 times to inspect the deterministic features of the signal. Overall, the total of 2800 tests (2 jet velocities × 20 repetitions × 10 radial positions × 7 heights) represent one of the largest experimental campaigns on downburst winds carried out in a wind tunnel facility thus far.
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Affiliation(s)
- Federico Canepa
- Department of Civil, Chemical and Environmental Engineering (DICCA), Polytechnic School, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy. .,Wind Engineering, Energy and Environment (WindEEE) Research Institute, Western University, 2535 Advanced Avenue, London, Ontario, N6M 0E2, Canada.
| | - Massimiliano Burlando
- Department of Civil, Chemical and Environmental Engineering (DICCA), Polytechnic School, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy
| | - Djordje Romanic
- Department of Civil, Chemical and Environmental Engineering (DICCA), Polytechnic School, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy.,Wind Engineering, Energy and Environment (WindEEE) Research Institute, Western University, 2535 Advanced Avenue, London, Ontario, N6M 0E2, Canada.,Department of Atmospheric and Oceanic Sciences, Faculty of Science, McGill University, Burnside Hall, 805 Sherbrook Street West, Montreal, Quebec, H3A 0B9, Canada
| | - Giovanni Solari
- Department of Civil, Chemical and Environmental Engineering (DICCA), Polytechnic School, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy
| | - Horia Hangan
- Wind Engineering, Energy and Environment (WindEEE) Research Institute, Western University, 2535 Advanced Avenue, London, Ontario, N6M 0E2, Canada.,Faculty of Engineering and Applied Science, Ontario Tech University, 2000 Simcoe Street North, Oshawa, Ontario, L1G 0C5, Canada
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Abstract
The flow and concentration fields for various medical oxygen delivery devices are numerically investigated. Simulations are performed for a classical Venturi mask and two new OxyArm portable devices. The velocity and oxygen concentration fields are investigated for: (i) a constant (steady-state) inhalation and (ii) a complete respiratory cycle (unsteady). The numerical results are qualitatively compared with clinical trials. It is found that the optimal functioning of these medical devices implies a balance between oxygen delivery by advection and the mixing process that allows for reliable CO2 monitoring (capnographic capability). Also, at the typical scales associated with these devices the flow is found to be Reynolds number dependent.
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Affiliation(s)
- H Hangan
- Boundary Layer Wind Tunnel Laboratory, University of Western Ontario, London, Ontario, Canada.
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