Snow Contamination of Cars: Adhesive Particle Collisions with Exterior Surfaces
Doktorsavhandling, 2022

An ongoing challenge regarding autonomous vehicles is the obstruction of sensors by contaminants on exterior surfaces. This often occurs when driving in harsh weather conditions, where the contaminant can be, for example, water spray, dirt, or snow. Certain regions on a vehicle can have higher rates of deposition compared to others and it is therefore crucial when developing an autonomous vehicle to choose sensor locations that avoid contamination. The present research has aimed to increase the knowledge regarding snow deposition when a vehicle is driving on a snow-covered road.

Mathematical models for the cohesive properties of snow and ice have been developed to predict and understand snow deposition on exterior vehicles surfaces. The models were solved analytically or numerically for ice particle collisions with exterior surfaces. Multiple experimental studies were conducted ranging from small-scale experiments on millimeter-sized single ice particle collisions to large-scale climate wind tunnel experiments on bluff bodies. The cohesive properties of snow were measured using an experimental setup for the angle of repose of snow.

In summary, this thesis presents results for single ice particle collisions, the angle of repose of snow, and snow contamination on bluff bodies. A regime map for ice particle collisions was developed that predicts a nonlinear dependency between impact velocity and collisional damping. The angle of repose of snow was shown to strongly correlate with temperature, but also with particle size and fall height. Experimental results for the snow contamination of bluff bodies show that snow tends to deposit near aerodynamic wake regions and reattachment regions where the airflow velocities are expected to be low. A numerical model was proposed for the transport of ice particles in a turbulent flow. Simulations that replicate the experiments, show that the numerical model captures the main characteristics of the snow deposition obtained in the experiments.

particle resuspension

premelting

exterior soiling

snow physics

particle laden flow

icing

snow adhesion

sensor availability

turbulent dispersion

KA-salen, Kemigården 4, Chalmers.
Opponent: Dr. Henning Löwe, Institute for Snow and Avalanche Research SLF, Davos.

Författare

Tobias Eidevåg

Chalmers, Kemi och kemiteknik, Kemiteknik

Angle of repose of snow: An experimental study on cohesive properties

Cold Regions Science and Technology,; Vol. 194(2022)

Artikel i vetenskaplig tidskrift

Collisional damping of spherical ice particles

Powder Technology,; Vol. 383(2021)p. 318-327

Artikel i vetenskaplig tidskrift

Modeling of dry snow adhesion during normal impact with surfaces

Powder Technology,; Vol. 361(2020)p. 1081-1092

Artikel i vetenskaplig tidskrift

Självkörande bilar har potential att göra trafik säkrare och mer miljövänlig. Tekniken bygger på att bilar är utrustade med sensorer som detekterar bilens omgivning. Bilen gör egna bedömningar och beslut beroende på informationen från dessa sensorer. Det finns därför ett ökat behov i jämförelse med vanliga bilar att sensorerna som är på självkörande bilar ska vara mer tillgängliga och tillförlitliga.

En självkörande bil kan få sina exteriöra ytor nedsmutsade av partiklar vid körning. Dessa partiklar kan vara damm, grus, vatten eller is partiklar (”snö”). När partiklar fastnar på ytor kan dessa påverka prestandan av sensorer eller t.o.m. blockera sensorerna helt. Under vintersäsongen kan snö som faller på marken skapa snötäckta vägar på stora delar av norra halvklotet. När en bil kör på sådana vägar lyfts is partiklar upp från vägen som sedan kan fastna och ackumuleras på bilens ytor.

Avhandlingen studerar hur snö kan kontaminera exteriöra ytor på en bil. Målet för forskningen har varit att öka förståelsen för vidhäftningsförmågan för snö på exteriöra fordonsytor. Matematiska modeller har utvecklats som innefattar de huvudsakliga mekanismerna för snöns vidhäftningsförmåga och uppbyggnaden av snö på en yta. Från dessa modeller kan slutsatser dras som är användbara när framtidens bilar ska utvecklas. Experiment på förenklade geometrier visar att snö fastnar och byggs upp på områden där partiklar kolliderar med ytor i låg hastighet och där de aerodynamiska krafterna vid ytorna är låga. Simuleringar av dessa experiment genomfördes och en jämförelse visar att de huvudsakliga mekanismerna för snöuppbyggnad fångas av modellerna

Self-driving cars have the possibility to make traffic safer and more environmentally friendly. The technology relies on cars equipped with sensors, which monitor the surrounding environment. The car then makes its own driving decisions based on the information from these sensors. Therefore, there is an increased need for sensors to function properly for a self-driving car, compared to a conventional vehicle.

A self-driving car driven on a road can get contaminated by particles on the exterior vehicle surfaces. These particles can be dust, dirt, water droplets or ice particles. When particles stick on the surfaces, they can cause sensors to malfunction or even block the sensors. Snowfall during winter season causes roads to become snow-covered in large areas of the Northern Hemisphere. When a vehicle is driven on such a road, the vehicle can lift snow from the ground, which then accumulates on the vehicle surfaces.

This thesis investigates how snow can contaminate exterior surfaces on a car. The aim of the research has been to increase the knowledge regarding the adhesion of snow, and when snow is likely to stick and accumulate on a car. Mathematical models have been developed to capture the main mechanisms of the studied topic and from these further conclusions could be drawn, which are useful when designing future cars.  Experiments on simplified geometries show that snow tends to stick at regions where low velocities for the particle-wall collisions are expected, and when the forces from the airflow acting on surfaces are low. Model simulations replicating the experiments were carried out and the comparisons show that the main mechanisms for snow contamination were captured by the models.

CAE methodology for vehicle snow packing and sensor availability for active safety and autonomous vehicles

VINNOVA (2017-03029), 2018-01-01 -- 2021-12-31.

Ämneskategorier

Kemiteknik

Strömningsmekanik och akustik

ISBN

978-91-7905-666-7

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5132

Utgivare

Chalmers

KA-salen, Kemigården 4, Chalmers.

Online

Opponent: Dr. Henning Löwe, Institute for Snow and Avalanche Research SLF, Davos.

Mer information

Senast uppdaterat

2022-05-12