Computational fluid dynamics experimental validation

Mao, Q., Pawliszyn, J., and Thormann, W., Dynamics of capillary isoelectric focnsing in the absence of fluid flow High-resolution computer simulation and experimental validation with whole column optical imaging, Anal. Chem., 72, 5493, 2000. 

Omran et al. have proposed a 3D, single phase steady-state model of a liquid feed DMFC [181]. Their model is implemented into the commercial computational fluid dynamics (CFD) software package FLUENT . The continuity, momentum, and species conservation equations are coupled with mathematical descriptions of the electrochemical kinetics in the anode and cathode channel and MEA. For electrochemical kinetics, the Tafel equation is used at both the anode and cathode sides. Results are validated against DMFC experimental data with reasonable agreement and used to explore the effects of cell temperature, channel depth, and channel width on polarization curve, power density and crossover rate. The results show that the power density peak and crossover increase as the operational temperature increases. It is also shown that the increasing of the channel width improves the cell performance at a methanol concentration below 1 M. 

Gas-liquid mixed tanks are used for various operations in industrial practise. The design of gas-liquid mixing units and reactors is still done by empirical correlations, which are usually valid for specific components, mixing conditions and geometries. Computational Fluid Dynamic (CFD) techniques have been used successfully for single-phase flow, but gas-liquid flow calculations are still tedious for computers. Therefore, simpler and more accurate multiphase models are needed. In order to verify multiphase CFD calculations and to fit unknown parameters in the multiphase models, experimental local bubble size distributions and flow patterns are needed. 

Although they are increasingly popular, computational fluid dynamic (CFD) calculations are notoriously difficult to validate Model equations may be available to the user, but the source code is typically proprietary, experimental data for comparison may be impossible to obtain, and the sheer volume of data available from the simulations makes complete and meaningful validations extremely difficult. Velocity measurements are difficult. Pressure drop measurements are easy but insensitive to the details of the flow. The RTD is a more sensitive test, but it is not unique since the RTD is derived from a flow-averaged velocity profile... 

Known scale-up correlations thus may allow scale-up when laboratory or pilot plant experience is minimal. The fundamental approach to process scaling involves mathematical modeling of the manufacturing process and experimental validation of the model at different scale-up ratios. In a paper on fluid dynamics in bubble column reactors, Lubbert and coworkers [52] noted Until very recently fluid dynamical models of multiphase reactors were considered intractable. This situation is rapidly changing with the development of high-perfonnance computers. Today s workstations allow new approaches to. .. modeling. ... 

These models require information about mean velocity and the turbulence field within the stirred vessels. Computational flow models can be developed to provide such fluid dynamic information required by the reactor models. Although in principle, it is possible to solve the population balance model equations within the CFM framework, a simplified compartment-mixing model may be adequate to simulate an industrial reactor. In this approach, a CFD model is developed to establish the relationship between reactor hardware and the resulting fluid dynamics. This information is used by a relatively simple, compartment-mixing model coupled with a population balance model (Vivaldo-Lima et al., 1998). The approach is shown schematically in Fig. 9.2. Detailed polymerization kinetics can be included. Vivaldo-Lima et a/. (1998) have successfully used such an approach to predict particle size distribution (PSD) of the product polymer. Their two-compartment model was able to capture the bi-modal behavior observed in the experimental PSD data. After adequate validation, such a computational model can be used to optimize reactor configuration and operation to enhance reactor performance. 

The basic fluid dynamics and heat- and mass-transfer processes for multicomponent condensation are poorly understood, and the computation is difficult available design methods are both heuristic and feasible only for computer solution. The basic model was developed by Silver [42] and put in more general form by Bell and Ghaly [43]. Computer-based design methods that have been validated against experimental data are commercially available. 

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