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Active flow control for drag reduction with reinforcement learning (RL) is performed in the wake of a 2D square bluff body at laminar regimes with vortex shedding. Controllers parameterised by neural networks are trained to drive two…

流体动力学 · 物理学 2024-01-17 Chengwei Xia , Junjie Zhang , Eric C. Kerrigan , Georgios Rigas

The present study tests the efficacy of the well-known viscous drag reduction strategy of imposing spanwise wall oscillations to reduce pressure drag contributions in a transitional- and fully-rough turbulent wall flow. This is achieved by…

流体动力学 · 物理学 2024-01-15 Rahul Deshpande , Aman G. Kidanemariam , Ivan Marusic

This study investigates the influence of aerofoil shape optimisation on blade aerodynamic performance under curvilinear and unsteady kinematics characteristic of vertical-axis turbines and cycloidal propellers. Using a cyclorotor in hover…

流体动力学 · 物理学 2026-03-10 Benjamin Irwin , David Toal , Swathi Krishna

We investigate the properties of magnetized stars in the propeller regime using axisymmetric numerical simulations. We modelled the propeller regime for stars with realistically large magnetospheres (5-7 stellar radii), so that our results…

太阳与恒星天体物理 · 物理学 2018-02-21 Marina M. Romanova , Alisa A. Blinova , Galina V. Ustyugova , Alexander V. Koldoba , Richard V. E. Lovelace

This work assesses the hydrodynamic efficiency of Underwater Unmanned Vehicles (UUVs) equipped with soft morphing wings compared to conventional rigid wings. Unlike rigid wings, deformable counterparts can alter their aerodynamic properties…

Emulating natural mechanisms in technology has become a very efficient technique to optimize and improve the current machinery. Riblets are such kinds of bio-inspired surface patterns which are seen on Sharks. Their geometric properties…

流体动力学 · 物理学 2021-09-22 Aakash Ghosh , Priyam Gupta , Jayant , Raj Kumar Singh

Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control…

机器人学 · 计算机科学 2024-03-27 H. Smith , A. Shankar , J. Gielis , J. Blumenkamp , A. Prorok

We perform particle-resolved simulations of subsonic and transonic flows past random arrays of spherical particles. The Reynolds number is held at $Re{\approx}300$ to ensure the flow remains in the continuum regime. At low volume fractions,…

流体动力学 · 物理学 2022-08-23 Mehdi Khalloufi , Jesse Capecelatro

Direct numerical simulations (DNS) of rotating pipe flows up to $Re_{\tau} \approx 3000$ are carried out to investigate drag reduction effects associated with axial rotation, extending previous studies carried out at a modest Reynolds…

流体动力学 · 物理学 2024-07-29 Maochao Xiao , Alessandro Ceci , Paolo Orlandi , Sergio Pirozzoli

Soft lubricated contacts exhibit complex interfacial behaviours governed by the coupled effects of multiscale surface roughness and non-linear fluid-solid interactions. Accurately capturing this interplay across thin-film flows is…

软凝聚态物质 · 物理学 2025-11-12 Qian Wang , Suhaib Ardah , Tom Reddyhoff , Daniele Dini

Conventional multi-rotors are under-actuated systems, hindering them from independently controlling attitude from position. In this study, we present several distinct configurations that incorporate additional control inputs for…

机器人学 · 计算机科学 2025-09-26 Ali Kafili Gavgani , Amin Talaeizadeh , Aria Alasty , Hossein Nejat Pishkenari , Esmaeil Najafi

Turbulence modeling has the potential to revolutionize high-speed vehicle design by serving as a co-equal partner to costly and challenging ground and flight testing. However, the fundamental assumptions that make turbulence modeling such…

流体动力学 · 物理学 2023-04-18 Chitrarth Prasad , Datta V. Gaitonde

Deep reinforcement learning (DRL) is employed to develop control strategies for drag reduction in direct numerical simulations (DNS) of turbulent channel flows at high Reynolds numbers. The DRL agent uses near-wall streamwise velocity…

流体动力学 · 物理学 2025-03-19 Zisong Zhou , Mengqi Zhang , Xiaojue Zhu

Turbofan engine performance depends highly on the characteristics and conditions of the inlet flow. Swirl distortions, caused by non-uniformities in flows arising from boundary layer or ground/fuselage vortex ingestion are of concern and…

流体动力学 · 物理学 2019-03-27 Tamara Guimarães , K. Todd Lowe , Walter F. O'Brien

In large eddy simulation of atmospheric boundary layer flows over wind farms, wall-layer models are generally imposed for the surface fluxes without considering the spatial variability of the surface roughness. In this study, we consider…

流体动力学 · 物理学 2022-05-03 Jagdeep Singh , Jahrul Alam

Direct Numerical Simulations are used to solve turbulent flow and heat transfer over a variety of rough walls in a channel. The wall geometries are exactly resolved in the simulations. The aim is to understand the effect of roughness…

流体动力学 · 物理学 2018-06-25 Pourya Forooghi , Matthias Stripf , Bettina Frohnapfel

Acquiring a comprehensive understanding of the interplay between foam friction and surface roughness is essential for achieving precise control over their flow dynamics. In particular, a major challenge is to reduce friction, which can be…

流体动力学 · 物理学 2025-01-16 Alexis Commereuc , Emmanuelle Rio , François Boulogne

The innovative bladeless design of the AirMultiplier, patented by Dyson in 2009, has generated significant interest due to its unique approach to air circulation that leverages the Coanda effect. In this study, we present a comprehensive…

Deep Reinforcement Learning is quickly becoming a popular method for training autonomous Unmanned Aerial Vehicles (UAVs). Our work analyzes the effects of measurement uncertainty on the performance of Deep Reinforcement Learning (DRL) based…

机器人学 · 计算机科学 2023-03-14 Bhaskar Joshi , Dhruv Kapur , Harikumar Kandath

We investigate the three-dimensional flow around and through a porous screen for various porosities at high Reynolds number Re = O(10000). Historically, the study of this problem was focused on two-dimensional cases and for a screen…

流体动力学 · 物理学 2024-05-22 Olivier C. Marchand , Sophie Ramananarivo , Camille Duprat , Christophe Josserand