Computational fluid mechanics

The governing equations of fluid flows are seldom analytically solved. Often, the analytical solutions involve restricted assumptions and approximations. Flence, many engineering problems involving fluid flows must be solved numerically. 

Software In view of the rigor involved in numerical solution, many commercial software packages have been developed that serve the purpose of computational fluid dynamics (CFD). Appendix 2. A gives a listing of sources for various commercial as well as free CFD codes. These CFD codes may be broadly categorized into either finite volume method based or finite element method based. For a detailed account of computational methods, see the books by Patankar (1980), Ferziger and Peric (2002), Ranade (2002), Chen (2005), Reddy (2005), and so forth. 

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Anderson, D.A., et al.. Computational fluid mechanics and heat transfer, Hemisphere, New York, 1984. 

Reduced mechanisms are used increasingly to describe chemical reactions in computational fluid mechanics. However, the development of a reduced mechanism often requires a thorough knowledge of the chemical kinetics of the system of interest, and the results obtained with the reduced mechanism are only valid in a limited domain of initial and operating conditions. Methods to automate the reduction procedure are currently being developed to facilitate the use of this modeling approach, for example, as discussed in Ref. [314],... 

The second contribution spans an even larger range of length and times scales. Two benchmark examples illustrate the design approach polymer electrolyte fuel cells and hard disk drive (HDD) systems. In the current HDDs, the read/write head flies about 6.5 nm above the surface via the air bearing design. Multi-scale modeling tools include quantum mechanical (i.e., density functional theory (DFT)), atomistic (i.e., Monte Carlo (MC) and molecular dynamics (MD)), mesoscopic (i.e., dissipative particle dynamics (DPD) and lattice Boltzmann method (LBM)), and macroscopic (i.e., LBM, computational fluid mechanics, and system optimization) levels. 

E.J. Kansa. Multiquadratics- a scattered data approximation scheme with applications to computational fluid mechanics, ii-solutions to parabolic, hyperbolic and elliptic partial differential equations. Computers Math. Applic., 19 147, 1990. 

J.C. Tannehill, D.A. Anderson, and R.H. Pletcher. Computational Fluid Mechanics and Heat Transfer. Taylor Francis, Washington, 2nd edition, 1997. 

PJ. Roache. Computational Fluid Mechanics. Hermosa Publishers, New Mexico, 1982. 

Flow, Springer-Verlag, Berlin, 1990), Canuto, Hussaini, Quarteroni, and Zang (Spectral Methods in Fluid Dynamics, Springer-Verlag, Berlin, 1988), Anderson, Tannehill, and Pletcher (Computational Fluid Mechanics and Heat Transfer Hemisphere, New York, 1984), and Patankar (Numerical Heat Transfer and Fluid Flow, Hemisphere, Washington, D.C., 1980). 

Performance of a stack of cells is limited by performance of the weakest cell in stack. It is therefore important to achieve high uniformity in performance of the individual cells in the stack, through stack design, mass production techniques, quality control, and automated stack assembly process. Optimum flow field design may be obtained by careful Computational Fluid Mechanic techniques and experimental validation including flow visualization techniques. 

With the great strides in computational fluid mechanics made over the past decades, the current trend is toward applying sophisticated finite element methods. These include both two- and three-dimensional (10-15) methods, which in principle allow the computation of two- or three-dimensional velocity and temperature fields with a variety of boundary... 

The availability of powerful three-dimensional flow computer simulation packages and personal computers capable of handling them is gradually transforming profile die design from an empirical trial-and-error process to one where design optimization benefits from computational results. Sebastian and Rakos (83) were the first to utilize realistic computational fluid-mechanical results in the design of profile dies. 

The FEM, which was originally developed for structural analysis of solids, has been very successfully applied in the past decades to viscous fluid flow as well. In fact, with the exponentially growing computer power, it has become a practical and indispensable tool for solving complex viscous and viscoelastic flows in polymer processing (20) and it is the core of the quickly developing discipline of computational fluid mechanics (cf. Section 7.5). 

The increasingly important role of computational fluid mechanics... 

Additional information on hydrodynamics of bubble columns and slurry bubble columns can be obtained from Deckwer (Bubble Column Reactors, Wiley, 1992), Nigam and Schumpe (Three-Phase Sparged Reactors, Gordon and Breach, 1996), Ramachandran and Chaudhari (Three-Phase Catalytic Reactors, Gordon and Breach, 1983), and Gianetto and Silveston (Multiphase Chemical Reactors, Hemisphere, 1986). Computational fluid mechanics approaches have also been recently used to estimate mixing and mass-transfer parameters [e.g., see Gupta et al., Chem. Eng. Sci. 56(3) 1117-1125 (2001)]. 

Anderson, D.. Tannehill, J.C.. and Pletcher, R.H., Computational Fluid Mechanics and Heat Transfer, Hemisphere Publ., Washington, D C., 1984. 

Chow, C.Y., Computational Fluid Mechanics, Wiley, New York. 1979. 

Empiricism—at one level little more than a synonym for reliance on experimental observation—need not be blind. In many cases, it is the crucial starting point for detailed analysis it is the source of the critical hypotheses which underlie successful theories, whose predictions are later verified by further experimental observation. Few scientists would contest this last statement. This has relevance to the comments made above about computer simulation, in that the latter can only be as good as the mathematical models on which it is based. In any new engineering situation, there may well be areas of uncertainty, say about the nature of the fluid in question, which mean that the relations required by a fully general computational fluid mechanics simulator program are not known. 

Baker, A. J., Finite Element Computational Fluid Mechanics. Hemisphere Publishing Corporation, New York, 1985. 

Materials in the macroscopic sense follow laws of continuum models in which the nanoscale phenomenon is accounted for by statistical averages. Continuum models and analysis separate materials into solids (structures) and fluids. Computational solid mechanics and structural mechanics emphasize the analysis of solid materials and its structural design. Computational fluid mechanics treats material behaviors that involve the equilibrium and motion of liquid and gases. A relative new area, called multiphysics, includes materials systems that contain interacting fluids and structures such as phase changes (solidification, melting), or interaction of control, mechanical and electromagnetic (MEMS, sensors, etc.). 

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