Author : Robert F. Kunz
Publisher :
ISBN 13 :
Total Pages : 44 pages
Book Rating : 4.:/5 (228 download)
Book Synopsis Multiphase CFD Modeling of Developed and Supercavitating Flows by : Robert F. Kunz
Download or read book Multiphase CFD Modeling of Developed and Supercavitating Flows written by Robert F. Kunz and published by . This book was released on 2001 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Engineering interest in natural and ventilated cavities about submerged bodies and in turbomachinery has led researchers to study and attempt to model large scale cavitation for decades. Comparatively simple analytical methods have been used widely and successfully to model developed cavitation, since the hydrodynamics of these flows are often dominated by irrotational and rotational inviscid effects. However, a range of more complex physical phenomena are often associated with such cavities, including viscous effects, unsteadiness, mass transfer, three-dimensionality and compressibility. Though some of these complicating physics can be accommodated in simpler physical models, the ongoing maturation and increased generality of multiphase Computational Fluid Dynamic (CFD) methods has motivated recent research by a number of groups in the application of these methods for developed cavitation analysis. This paper focuses on the authors' recent research activities in this area. The authors have developed an implicit algorithm for the computation of viscous two-phase flows. The baseline differential equation system is the multi-phase Navier-Stokes equations, comprised of the mixture volume, mixture momentum and constituent volume fraction equations. Though further generalization is straightforward, a three-species formulation is pursued here, which separately accounts for the liquid and vapor (which exchange mass) as well as a non-condensable gas field. The implicit method developed employs a dual-time, preconditioned, three-dimensional algorithm, with muti-block and parallel execution capabilities. Time-derivative preconditioning is employed to ensure well-conditioned eigenvalues, which is important for the computational efficiency of the method.