scholarly journals Numerical study of impeller-driven von Kármán flows via a volume penalization method

2014 ◽  
Vol 16 (10) ◽  
pp. 103001 ◽  
Author(s):  
S Kreuzahler ◽  
D Schulz ◽  
H Homann ◽  
Y Ponty ◽  
R Grauer
2021 ◽  
Author(s):  
Hui-li Xu ◽  
Marilena Greco ◽  
Claudio Lugni

Abstract Fishes are talented swimmers. Depending on the propulsion mechanisms many fishes can use flapping tails and/or fins to generate thrust, which seems to be connected to the formation of a reverse von Kármán wake. In the present work, the flow past a 2D flapping foil is simulated by solving the incompressible Navier-Stokes equations in the open-source OpenFOAM platform. A systematic study by varying the oscillating frequency, peak-to-peak amplitude and Reynolds number has been performed to analyze the transition of vorticity types in the wake as well as drag-thrust transition. The overset grid method is used herein to allow the pitching foil to move without restrictions. Spatial convergence tests have been carried out with respect to grid resolution and the size of overset mesh domain. Numerical results are compared with available experimental data and discussed. The results show that the adopted methodology can be well applied to simulate large amplitude motions of the flapping foil. The transitions in the types of wake are consistent with the benchmark experimental data, and the drag-thrust transition of the pitching foil does not coincide with von Kármán (vK)-reverse von Kármán (reverse-vK) wake transition and it is highly dependent on the Reynolds number.


In the first part of this paper opportunity has been taken to make some adjustments in certain general formulae of previous papers, the necessity for which appeared in discussions with other workers on this subject. The general results thus amended are then applied to a general discussion of the stability problem including the effect of the trailing wake which was deliberately excluded in the previous paper. The general conclusion is that to a first approximation the wake, as usually assumed, has little or no effect on the reality of the roots of the period equation, but that it may introduce instability of the oscillations, if the centre of gravity of the element is not sufficiently far forward. During the discussion contact is made with certain partial results recently obtained by von Karman and Sears, which are shown to be particular cases of the general formulae. An Appendix is also added containing certain results on the motion of a vortex behind a moving cylinder, which were obtained to justify certain of the assumptions underlying the trail theory.


Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 714
Author(s):  
Jiujiang Wang ◽  
Xin Liu ◽  
Yuanyu Yu ◽  
Yao Li ◽  
Ching-Hsiang Cheng ◽  
...  

Analytical modeling of capacitive micromachined ultrasonic transducer (CMUT) is one of the commonly used modeling methods and has the advantages of intuitive understanding of the physics of CMUTs and convergent when modeling of collapse mode CMUT. This review article summarizes analytical modeling of the collapse voltage and shows that the collapse voltage of a CMUT correlates with the effective gap height and the electrode area. There are analytical expressions for the collapse voltage. Modeling of the membrane deflections are characterized by governing equations from Timoshenko, von Kármán equations and the 2D plate equation, and solved by various methods such as Galerkin’s method and perturbation method. Analytical expressions from Timoshenko’s equation can be used for small deflections, while analytical expression from von Kármán equations can be used for both small and large deflections.


2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Mi Jin Lee ◽  
Jum-Ran Kang

Abstract In this paper, we consider the blow-up result of solution for a quasilinear von Karman equation of memory type with nonpositive initial energy as well as positive initial energy. For nonincreasing function $g>0$ g > 0 and nondecreasing function f, we prove a finite time blow-up result under suitable condition on the initial data.


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