Wake-Induced Oscillatory Paths of Bodies Freely Rising or Falling in Fluids

Autor: Patricia Ern, Jacques Magnaudet, David Fabre, Frédéric Risso
Přispěvatelé: Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Institut de mécanique des fluides de Toulouse (IMFT), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées
Rok vydání: 2012
Předmět:
Zdroj: Annual Review of Fluid Mechanics
Annual Review of Fluid Mechanics, Annual Reviews, 2012, 44 (1), pp.97-121. ⟨10.1146/annurev-fluid-120710-101250⟩
ISSN: 1545-4479
0066-4189
DOI: 10.1146/annurev-fluid-120710-101250
Popis: International audience; Leaves falling in air and bubbles rising in water provide daily examples of nonstraight paths associated with the buoyancy-driven motion of a body in a fluid. Such paths are relevant to a large variety of applicative fields such as mechanical engineering, aerodynamics, meteorology, and the biomechanics of plants and insect flight. Although the problem has attracted attention for ages, it is only recently that the tremendous progress in the development of experimental and computational techniques and the emergence of new theoretical concepts have led to a better understanding of the underlying physical mechanisms. This review attempts to bring together the main recent experimental, computational, and theoretical advances obtained on this fascinating subject. To this end it describes the first steps of the transition in the wake of a fixed body and its connection with the onset and developmentof the path instability of moving bodies. Then it analyzes the kinematics and dynamics of various types of bodies along typical nonstraight paths and how the corresponding information can be used to build low-dimensional predictive models.
Databáze: OpenAIRE