Identification of wake vortices in a simplified car model during significant aerodynamic drag increase under crosswind conditions

Nakamura, Yusuke; Nakashima, Takuji; Yan, Chao; Shimizu, Keigo; Hiraoka, Takenori; Mutsuda, Hidemi; Kanehira, Taiga; Nouzawa, Takahide

doi:10.1007/s12650-022-00837-8

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…The preceding discussion of the fluid flow disturbances and their application in reservoir computing should also be applicable to cars [131,132], bicycles [133] and other road vehicles [134] that create turbulence and give rise to other physical effects that can be used in reservoir computing. However, the physical contact of road vehicles with the ground often results in unique nonlinear dynamical processes that can be employed in a physical reservoir computer, as schematically illustrated in Figure 1c.…”

Section: Physical Reservoir Computing For Ugvsmentioning

confidence: 99%

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

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Artificial intelligence (AI) systems of autonomous systems such as drones, robots and self-driving cars may consume up to 50% of total power available onboard, thereby limiting the vehicle’s range of functions and considerably reducing the distance the vehicle can travel on a single charge. Next-generation onboard AI systems need an even higher power since they collect and process even larger amounts of data in real time. This problem cannot be solved using the traditional computing devices since they become more and more power-consuming. In this review article, we discuss the perspectives of development of onboard neuromorphic computers that mimic the operation of a biological brain using nonlinear-dynamical properties of natural physical environments surrounding autonomous vehicles. Previous research also demonstrated that quantum neuromorphic processors (QNPs) can conduct computations with the efficiency of a standard computer while consuming less than 1% of the onboard battery power. Since QNPs is a semi-classical technology, their technical simplicity and low, compared with quantum computers, cost make them ideally suitable for application in autonomous AI system. Providing a perspective view on the future progress in unconventional physical reservoir computing and surveying the outcomes of more than 200 interdisciplinary research works, this article will be of interest to a broad readership, including both students and experts in the fields of physics, engineering, quantum technologies and computing.

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Section: Physical Reservoir Computing For Ugvsmentioning

confidence: 99%

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Abbas,

Abdel-Ghani,

Maksymov

2024

Preprint

View full text Add to dashboard Cite

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“…Here, the second invariant of the velocity gradient tensor Q (Hunt, 1987) and the improved sectional-pressure-minimum-and-swirl method (Nakamura et al, 2020), which are effective in identifying vortices around automobiles, were used. A study using the improved sectional-pressure-minimum-and-swirl method successfully identified vortices related to a significant change in the drag of a notchback car model at a certain yaw angle (Nakamura et al, 2022). Finally, the flows associated with these vortices were identified by visualizing the streamlines passing through the vortex core lines.…”

Section: Introductionmentioning

confidence: 99%

Identification of wake vortices using a simplified automobile model under parallel running and crosswind conditions

Nakamura

Nakashima

Shimizu

et al. 2023

JFST

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Previous studies have revealed that the change in aerodynamic drag on a test automobile under a parallel running condition is mainly because of the pressure effect, which is caused by the pressure field of the parallel running vehicle, and the effect of the change in wind direction caused by the parallel running vehicle. Apart from the drag change, which can be estimated by assuming a uniform crosswind, the change in wind direction has other effects. In this study, to clarify the other effects, the differences in the wake vortices associated with the change in drag between parallel running and uniform crosswind conditions were identified from the numerical simulation results. The surface pressure on the back excluding the pressure effect under the parallel running condition was lower on the opening side than that under the uniform crosswind condition. In addition, the wake vortices near the region of low surface pressure and their related vortices were focused. Visualizing the flows related to these vortices indicates that the non-uniformity of the flow along the upstream side and the strengthening of the entrainment at the rear end owing to the interference affect the change in the drag in the parallel running condition compared with the uniform crosswind condition. These results are useful for developing control methods to suppress the increase in drag in the parallel running condition.

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Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions

Rijns,

Teschner,

Blackburn

et al. 2024

Physics of Fluids

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This study investigates the impact of yaw conditions on the aerodynamic performance and flow field of three high-performance vehicle model configurations by means of wind tunnel testing and unsteady Reynolds-Averaged Navier–Stokes-based computational fluid dynamics simulations. While yaw effects on automotive vehicles have been explored, the effects on far more complex flow fields of high-performance vehicles remain insufficiently researched. This paper reveals that yaw conditions have a significant negative influence both downforce and drag performance. Spoiler and rear wing devices enhance downforce but increase the vehicle's sensitivity to yaw. Furthermore, yaw conditions significantly alter vortex structures and local flow velocities, affecting downstream flow behavior. Surface pressure measurements on the slant confirm these findings and highlight notable yaw effects and upstream effects from spoiler and rear wing devices. Wake analyses through total pressure measurements show that yaw induces a substantial deviation from straight-line wake characteristics, which become dominated by an inboard rotating vehicle body vortex. Overall, this research enhances the understanding of the effects of yaw conditions on high-performance vehicle aerodynamics and provides valuable data for future vehicle aerodynamics research in real-world operating conditions.

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Identification of wake vortices in a simplified car model during significant aerodynamic drag increase under crosswind conditions

Cited by 5 publications

References 35 publications

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Classical and Quantum Physical Reservoir Computing for Onboard Artificial Intelligence Systems: A Perspective

Identification of wake vortices using a simplified automobile model under parallel running and crosswind conditions

Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions

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