Computational fluid dynamics temperature profiles

Computer Models, The actual residence time for waste destmction can be quite different from the superficial value calculated by dividing the chamber volume by the volumetric flow rate. The large activation energies for chemical reaction, and the sensitivity of reaction rates to oxidant concentration, mean that the presence of cold spots or oxidant deficient zones render such subvolumes ineffective. Poor flow patterns, ie, dead zones and bypassing, can also contribute to loss of effective volume. The tools of computational fluid dynamics (qv) are useful in assessing the extent to which the actual profiles of velocity, temperature, and oxidant concentration deviate from the ideal (40). 

Solving the full Navier-Stokes equations in the channels requires a rigorous computational fluid dynamics (CFD) simulation. During transient operation, such as start-up and shut-down, the flow fields can have a significant effect on the concentration and temperature profiles in the system. Under normal operation, it may be desirable to assume fuUy developed laminar flow to reduce the computational time and quickly estimate flow parameters based on fluid dynamics correlations. 

The National Energy Technology Laboratory (NETL) developed a 3-dimensional computational fluid dynamics (CFD) model to allow stack developers to reduce time-consuming build-and-test efforts. As opposed to systems models, 3-dimensional CFD models can address critical issues such as temperature profiles and fuel utilization important considerations in fuel cell development. 

The other form of mathematical model is the more rigorous computational fluid dynamics (CFD) approach that solves the complete three-dimensional conservation equations. These methods have been applied with encouraging results (Britter, 1995 Lee et al. 1995). CFD solves approximations to the fundamental equations, with the approximations being principally contained within the turbulence models—the usual approach is to use the K-e theory. The CFD model is typically used to predict the wind velocity fields, with the results coupled to a more traditional dense gas model to obtain the concentration profiles (Lee et al., 1995). The problem with this approach is that substantial definition of the problem is required in order to start the CFD computation. This includes detailed initial wind speeds, terrain heights, structures, temperatures, etc. in 3-D space. The method requires moderate computer resources. 

The preceding two chapters review briefly the fundamentals of computational fluid dynamics (CFD) and computational heat transfer (CHT) for predicting the fluid velocity and temperature profiles as well as the relevant parameters for a specified process such methodologies have been applied to the engineering and scientific areas with success. 

Over the last decades, the application of computational fluid dynamics (CFD) to study the velocity and temperature profiles in packed column has been frequently reported [1-5]. However, for the prediction of concentration profile, the method commonly employed is by guessing an empirical turbulent Schmidt number Sc, or by using experimentally determined turbulent mass diffusivity D, obtained by using the inert tracer technique under the condition of no mass transfer [6, 7]. Nevertheless, the use of such empirical methods of computation, as pointed out in Chap. 3, is unreliable and not always possible. To overcome these drawbacks, the development of rigorous mathematical model is the best choice. 

Subchannel analysis models have been investigated for CSRIOOOEA by using the experimental data available and the computational fluid dynamics (CFD) code (Du et al., 2013). The analysis results are used to improve a subchannel code. The steady-state subchannel analysis is conducted on the CSRIOOO FA to obtain the temperature distribution of coolant and cladding and pressure drop in the FA. The results show that smaller pitch will flatten the profile of the coolant temperature and reduce maximum cladding surface temperatures, but it increases the pressure drop in the assembly. 

The heart of any melter simulation will be an engineering model (Figure 21.1). This focuses on the fluid dynamics behavior of a vitrification melt. Simulations of the thermal and mass transport within the melter present a quantitative picture of the dynamic temperature profile of the glass. The boundary conditions arising in these computational fluid dynamics (CFD) simulations act as an input to finite element (FE) simulations of material stresses developed within the melter material— which in turn can be used to advise on how to input heat to the melt and melter to reduce the thermal gradients that cause stress. 

We are interested in computing the dynamic response of the axial temperature profile of the fluid flowing in the inner tube for a step change in the inlet temperature T z = 0). The steam shall maintain a constant wall temperature of T in the exchanger. The describing differential equation is obtained by writing an energy balance around the tube and yields... 

Numerical simulations allowed the reproduction of the reactor s dynamic behavior, mainly the thermal balance. Despite the differences between the models, both models reproduced almost in the same way in terms of the reactor and coolant fluid temperature dynamic profiles. Regarding the use of a specific model, the authors advise to take into account some points if the internal heat and mass-transfer coefficients of the catalyst particles are significant. Dynamic Model I is more suitable to represent the reactor dynamic behavior in case of difficulties in the measurement of such parameters. Dynamic Model II must be chosen. For design and simulation studies, where computational time and numeric difficulties for model solution are not limiting factors. Dynamic Model I is the most reliable however, if the same factors are limiting. Dynamic Model II should be the best alternative. 

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