The European Physical Journal Special Topics

, Volume 224, Issue 17–18, pp 3199–3210 | Cite as

Paramecia swimming in viscous flow

  • P. Zhang
  • S. Jana
  • M. Giarra
  • P.P. Vlachos
  • S. JungEmail author
Regular Article Physics of Locomotion
Part of the following topical collections:
  1. Dynamics of Animal Systems


Ciliates like Paramecia exhibit fore-aft asymmetry in their body shapes, and preferentially swim in the direction of the slender anterior rather than the wider posterior. However, the physical reasons for this preference are not well understood. In this work, we propose that specific features of the fluid flow around swimming Paramecia confer some energetic advantage to the preferred swimming direction. Therefore, we seek to understand the effects of body asymmetry and swimming direction on the efficiency of swimming and the flux of fluid into the cilia layer (and thus of food into the oral groove), which we assumed to be primary factors in the energy budgets of these organisms. To this end, we combined numerical techniques (the boundary element method) and laboratory experiments (micro particle image velocimetry) to develop a quantitative model of the flow around a Paramecium and investigate the effect of the body shape on the velocity fields, as well as on the swimming and feeding behaviors. Both simulation and experimental results show that velocity fields exhibit fore-aft asymmetry. Moreover, the shape asymmetry revealed an increase of the fluid flux into the cilia layer compared to symmetric body shapes. Under the assumption that cilia fluid intake and feeding efficiency are primary factors in the energy budgets of Paramecia, our model predicts that the anterior swimming direction is energetically favorable to the posterior swimming direction.


Particle Image Velocimetry Boundary Element Method European Physical Journal Special Topic Body Shape Surface Velocity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences and Springer 2015

Authors and Affiliations

  • P. Zhang
    • 1
  • S. Jana
    • 1
  • M. Giarra
    • 2
  • P.P. Vlachos
    • 2
    • 3
  • S. Jung
    • 1
    Email author
  1. 1.Department of Engineering Science and MechanicsVirginia TechBlacksburgUSA
  2. 2.Department of Mechanical EngineeringVirginia TechBlacksburgUSA
  3. 3.School of Mechanical Engineering, Purdue UniversityWest LafayetteUSA

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