A numerical simulation of wheel spray for simplified vehicle model based on discrete phase method

Hu, Xianliang; Liao, Lei; Lei, Yulong; Yang, Hanbo; Fan, Qingyin; Yang, Bo; Chang, Jing Hua; Wang, Jingyu

doi:10.1177/1687814015597190

Cited by 11 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This, along with evaporation, can affect the property of spray and its dynamics. One of the few CFD studies that considered both the break-up and coalescence of particles, as well as the back-coupling between the continuous and dispersed phases is the work by Hu et al [17]. However, it discussed neither the computational cost of these models, nor provided any statistical or analytical information about the influence of the extra physics on the overall contamination results.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Effect of Spray Breakup, Coalescence, and Evaporation on Vehicle Surface Contamination Dynamics

Kabanovs¹,

Garmory²,

Passmore³

et al. 2018

SAE Int. J. Passeng. Cars - Mech. Syst.

View full text Add to dashboard Cite

Vehicle surface contamination is an important design consideration as it affects drivers' vision and the performance of on board camera and sensor systems. Previous work has shown that eddy resolving methods are able to accurately capture the flow field and particle transport, leading to good agreement for vehicle soiling with experiments. What is less clear is whether the secondary break-up, coalescence and evaporation of liquid particles play an important role in spray dynamics. The work reported here attempts to answer this and also give an idea of the computational cost associated with these extra physics models. A quarter scale generic SUV model is used as a test case in which the continuous phase is solved using the Spalart-Allmaras IDDES model. The dispersed phase is computed concurrently with the continuous phase using the Lagrangian approach. The TAB secondary break-up and the stochastic O'Rourke coalescence models are used. The spray's rate of evaporation is calculated based on the relative humidity encountered on a typical October day in Britain. The secondary break-up model is found to be redundant, possibly due to the properties of spray. The coalescence model predicts high coalescence of particles close to the source and improves agreement with experiment, although at a high computational cost. Including evaporation removes small particles from the simulation and reduces overall contamination. When used along the coalescence model, evaporation is found to be negligible as it does not influence large particles to the same extent as it affects small particles. This suggests that droplet physics models need to be considered together as they can have a strong effect on each other as well as vehicle soiling. Here, we show that coalescence can be accounted for by using the time-averaged spray, obtained outside the region of high coalescence. This gives a very good agreement with experiment.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling the Effect of Spray Breakup, Coalescence, and Evaporation on Vehicle Surface Contamination Dynamics

Kabanovs¹,

Garmory²,

Passmore³

et al. 2018

SAE Int. J. Passeng. Cars - Mech. Syst.

View full text Add to dashboard Cite

show abstract

Robust Local and Cooperative Perception Under Varying Environmental Conditions

Gamerdinger,

Volk,

Teufel

et al. 2024

Cooperatively Interacting Vehicles

View full text Add to dashboard Cite

Robust perception of the environment under a variety of ambient conditions is crucial for autonomous driving. Convolutional Neural Networks (CNNs) achieve high accuracy for vision-based object detection, but are strongly affected by adverse weather conditions such as rain, snow, and fog, as well as soiled sensors. We propose physically correct simulations of these conditions for vision-based systems, since publicly available data sets lack scenarios with different environmental conditions. In addition, we provide a data set of real images containing adverse weather for evaluation. By training CNNs with augmented data, we achieve a significant improvement in robustness for object detection. Furthermore, we present the advantages of cooperative perception to compensate for limited sensor ranges of local perception. A key aspect of autonomous driving is safety; therefore, a robustness evaluation of the perception system is necessary, which requires an appropriate safety metric. In contrast to existing approaches, our safety metric focuses on scene semantics and the relevance of surrounding objects. The performance of our approaches is evaluated using real-world data as well as augmented and virtual reality scenarios.

show abstract

Control of water contamination on side window of road vehicles by A-pillar section parameter optimization

Wang

2020

J Hydrodyn

View full text Add to dashboard Cite

A numerical simulation of wheel spray for simplified vehicle model based on discrete phase method

Cited by 11 publications

References 7 publications

Modelling the Effect of Spray Breakup, Coalescence, and Evaporation on Vehicle Surface Contamination Dynamics

Modelling the Effect of Spray Breakup, Coalescence, and Evaporation on Vehicle Surface Contamination Dynamics

Robust Local and Cooperative Perception Under Varying Environmental Conditions

Control of water contamination on side window of road vehicles by A-pillar section parameter optimization

Contact Info

Product

Resources

About